In a 1955 lecture the physicist Richard Feynman reflected on the place of doubt within scientific practice. ‘Permit us to question, to doubt, to not be sure’, proposed Feynman, ‘it is possible to live and not to know’. In our contemporary world, the science of machine learning algorithms appears to transform the relations between science, knowledge and doubt, to make even the most doubtful event amenable to action. What might it mean to ‘leave room for doubt’ or ‘to live and (...) not to know’ in our contemporary culture, where the algorithm plays a major role in the calculability of doubts? I propose a posthuman mode of doubt that decentres the liberal humanist subject. In the science of machine learning algorithms the doubts of human and technological beings nonetheless dwell together, opening onto a future that is never fully reduced to the single output signal, to the optimised target. (shrink)

Using a mixed methods approach, we surveyed 780 adults and conducted follow-up interviews with 21 participants in China, to explore cognitive and behavioral factors shaping media trust. Results show that algorithm literacy and news literacy significantly predict trust in mainstream media, especially when considered interactively. However, these factors do not significantly influence trust in social media, suggesting the importance of platform-specific dynamics. Applying the Theory of Planned Behavior (TPB), we find that algorithm literacy enhances attitudes, subjective norms, and (...) perceived behavioral control, which subsequently relate to trust in mainstream media. Interview data further reveal that negative attitudes, norms, and perceived behavioral control surrounding social media undermine trust in them, even among news- and algorithm-literate individuals. This study advances TPB by illuminating how algorithm literacy and TPB jointly shape trust in the media. (shrink)

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground-truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call (...) the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm—suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources. (shrink)

Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use (...) of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility. (shrink)

Autonomous vehicles must be programmed with procedures for dealing with trolley-style dilemmas where actions result in harm to either pedestrians or passengers. This paper outlines a Rawlsian algorithm as an alternative to the Utilitarian solution. The algorithm will gather the vehicle’s estimation of probability of survival for each person in each action, then calculate which action a self-interested person would agree to if he or she were in an original bargaining position of fairness. I will employ Rawls’ assumption (...) that the Maximin procedure is what self-interested agents would use from an original position, and then show how the Maximin procedure can be operationalized to produce unique outputs over probabilities of survival. (shrink)

Simulation of complex biological systems with agent-based models is becoming more relevant with the increase in Graphics Processing Unit power. In those simulations, up to millions of virtual cells are individually computed, involving daunting processing times. An important part of computational models is the algorithm that manages how agents perceive their surroundings. This can be particularly problematic in three-dimensional environments where agents have deformable virtual membranes. This article presents a GPU algorithm that gives the possibility for agents to (...) integrate the signals scattered on their virtual membrane. It is detailed to be coded in languages like OpenCL or Cuda. Its performances are tested to show its speed with current GPU devices. Finally, it was implemented inside an existing software to test and illustrate the possibilities it offers. (shrink)

The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm. In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization. The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed (...) class='Hi'>algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test. (shrink)

I identify an undertheorized problem with decisions we make with the aid of algorithms: the problem of inauthenticity. When we make decisions with the aid of algorithms, we can make ones that go against our commitments and values in a normatively important way. In this paper, I present a framework for algorithm-aided decision-making that can lead to inauthenticity. I then construct a taxonomy of the features of the decision environment that make such outcomes likely, and I discuss three possible (...) solutions to the problem. (shrink)

Recent debates in political philosophy of science underscore the need for normative frameworks to determine which values should guide scientific inquiry (Douglas 2009). Philosophers increasingly draw on principles from political theory, such as democratic majoritarianism (Schroeder 2021), deliberative proceduralism (Lusk 2021), and Rawlsian public reason (Rawls 1999; Badano 2025), to inform scientific investigations. I argue these approaches fail in societies where basic institutions developed through historical racial domination—including chattel slavery, Jim Crow segregation, COINTELPRO’s targeting of Black movements, racially exclusionary housing (...) policies, and mass incarceration without reparative correction. Drawing on Mills’s (2017) critique of ideal theory, I contend that these frameworks abstract away from the racialized structures that organize contemporary scientific and technological practices, and that the normative constraints they propose are insufficient to secure legitimacy under such conditions. Using Artificial Intelligence as a case study, I introduce the concept of the Racial Algorithm to show how machine learning systems encode and amplify racial inequities through algorithmic decision-making. I conclude by sketching a framework for scientific legitimacy grounded in non-ideal theory. (shrink)

This chapter uncovers the complex negotiations for authority in various representations about futures of life which have been advanced by different branches of the sciences, and have culminated in the emerging concept of life as algorithm. It charts the historical shifts in expertise and representations of life, from naturalists, to mathematical modellers, and specialists in computation, and argues that physicists, game theorists, and economists now take a leading role in explaining and projecting futures of life. The chapter identifies Richard (...) Dawkins and Max Tegmark at the forefront of the concept of life as algorithm: the first inspired by game theory as a means to study evolution; the second proposing that a materialist basis of life could place humans and artificial intelligence on an equal footing. Through close reading of their respective texts, the chapter demonstrates that both thinkers consider life as an algorithm programmed to achieve success in survival and reproduction. (shrink)

Any moral algorithm for autonomous vehicles must provide a practical solution to moral problems of the trolley type, in which all possible courses of action will result in damage, injury, or death. This article discusses a hitherto neglected variety of this type of problem, based on a recent psychological study whose results are reported here. It argues that the most adequate solution to this problem will be achieved by a moral algorithm that is based on Confucian ethics. In (...) addition to this philosophical and psychological discussion, the article outlines the mathematics, engineering, and legal implementation of a possible Confucian algorithm. The proposed Confucian algorithm is based on the idea of making it possible to set an autonomous vehicle to allow an increased level of protection for selected people. It is shown that the proposed algorithm can be implemented alongside other moral algorithms, using either the framework of personal ethics settings or that of mandatory ethics settings. (shrink)

We propose a probabilistic algorithm for multiple-choice exams with negative marking that combines deterministic knowledge and data-driven guessing. The algorithm leverages the portion of questions a student knows for certain, alongside historical exam data, to optimize guesses on unknown questions. Recursive iterations and goodness-of-fit analysis enhance the expected score, providing a principled method to maximize the probability of passing, even when only a fraction of questions are fully understood.

A vast number of real-world problems can be associated with multi-criteria decision-making (MCDM). This paper discusses MCDM in agricultural industry. Methodological hybrid analytical hierarchy processes, ELECTRE I and genetic algorithm method are proposed here, and it is shown how such a model can be used for complete ranking model. The proposed hybrid bio-inspired method is implemented on real-world data set collected from agricultural industry in Serbia.

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from binary data where an (...) item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

This article argues for the reestablishment of philosophy generalis as the structural framework of intelligibility, rather than as a subordinate or interpretative discourse. Against the backdrop of disciplinary fragmentation and scientific naturalism, it proposes a stratified ontological hierarchy in which philosophy, metaphysics, and physics occupy distinct but interrelated roles. Central to this argument is the concept of the ontological algorithm: not a computational mechanism, but a structural framework that defines the admissibility conditions for conceptual coherence across domains. Drawing from (...) recent developments in physics and metaphysics, the paper emphasizes the need for a philosophical architecture that accounts for the projective stabilization of intelligibility without collapsing into speculative absolutism. It critiques scientistic reductionism and positions philosophy not as a producer of positive content, but as the organizing grammar of ontological possibility. Ultimately, the article defends a conservative yet rigorous view of philosophy as the reflexive limit of rationality — the structural condition under which both metaphysical and empirical knowledge gain legibility. (shrink)

With the rise of artificial intelligence and automation, moral decisions that were formerly the preserve of humans are being put into the hands of algorithms. In autonomous driving, a variety of such decisions with ethical implications are made by algorithms for behaviour and trajectory planning. Therefore, here we present an ethical trajectory planning algorithm with a framework that aims at a fair distribution of risk among road users. Our implementation incorporates a combination of five ethical principles: minimization of the (...) overall risk, priority for the worst-off, equal treatment of people, responsibility and maximum acceptable risk. To the best of our knowledge, this is the first ethical algorithm for trajectory planning of autonomous vehicles in line with the 20 recommendations from the European Union Commission expert group and with general applicability to various traffic situations. We showcase the ethical behaviour of our algorithm in selected scenarios and provide an empirical analysis of the ethical principles in 2,000 scenarios. The code used in this research is available as open-source software. (shrink)

: Lately there is more attention paid to technological development in intelligent tutoring systems. This field is becoming an interesting topic to many researchers. In this paper, we are presenting an intelligent tutoring system for teaching DES Information Security Algorithm called DES-Tutor. The DES-Tutor target the students enrolled in cryptography course in the department Information Technology in Al-Azhar University in Gaza. Through DES-Tutor the student will be able to study course material and try the exercises of each lesson. An (...) evaluation of the DES-Tutor was carried out and the results were promising. (shrink)

Today, a consistent part of our everyday interaction with art and aesthetic artefacts occurs through digital media, and our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented, and then, we are fed back through tailored information. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamics of creative processes. All are (...) involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. With regard to this topic, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, and showing how aesthetics can contribute to the ongoing debate about the impact of today’s “algorithmic culture”. (shrink)

This document presents a non-rigid registration algorithm for the use of brain magnetic resonance images comparison. More precisely, we want to compare pre-operative and post-operative MR images in order to assess the deformation due to a surgical removal. The proposed algorithm has been studied in Chesseboeuf et al., following ideas of Trouvé, in which the author introduces the algorithm within a very general framework. Here we recalled this theory from a practical point of view. The emphasis is (...) on illustrations and description of the numerical procedure. Our version of the algorithm is associated with a particular matching criterion. Then, a section is devoted to the description of this object. In the last section we focus on the construction of a statistical method of evaluation. (shrink)

We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion to (...) algorithms that rigidly apply exogenously given human-created fairness principles to specific cases. In the second study, we found that people do not prefer humans to algorithms because they appreciate flesh-and-blood decision-makers per se, but because they appreciate humans’ freedom to transcend fairness principles at will. Our results contribute to a deeper understanding of algorithm aversion. They indicate that emphasizing the transparency of algorithms that clearly follow fairness principles might not be the only element for fostering societal algorithm acceptance and suggest reconsidering certain features of the decision-making process. (shrink)

According to Ernst Cassirer, the transition from the concept of substance to that of mathematical function as a guide of knowledge coincided with the end of ancient and the beginning of modern theoretical thought. In the first part of this article we argue that a similar transition has also taken place in the practical sphere, where mathematical function occurs in one of its specific forms, which is that of the algorithm concept. In the second part we argue that with (...) the rise of modernity the idea of substance and the related concepts of category and classification, which are deeply embedded in western culture, have not been totally supplanted by that of function. The intertwining of the concepts of substance and function has generated contradictory hybrids. These hybrids are used as a key for the understanding of the different repercussions of algorithmic logic on society in terms of social integration. (shrink)

English grammar error correction algorithm refers to the use of computer programming technology to automatically recognize and correct the grammar errors contained in English text written by nonnative language learners. Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of English grammar (...) error correction algorithm, elaborated the development background, current status, and future challenges of the classification model, introduced the methods and principles of feature extraction method and dynamic residual structure, constructed a basic model for English grammar error correction based on the classification model, analyzed the classification model and translation model of English grammar error correction, proposed the English grammar error correction algorithm based on the classification model, performed the analyses of the model architecture and model optimizer of the grammar error correction algorithm, and finally conducted a simulation experiment and its result analysis. The study results show that, with the continuous increase of training samples and the continuous progress of learning process, the proposed English grammar error correction algorithm based on the classification model will continue to increase its classification accuracy, further refine its recognition rules, and gradually improve correction efficiency, thereby reducing processing time, saving storage space, and streamlining processing flow. The study results of this paper provide a certain reference for the further research on English grammar error correction algorithm based on the classification model. (shrink)

Executives in business and government seek to leverage artificial intelligence (AI), the biggest driver of technological change, to inform decision-making. The intelligence behind AI comes from machine learning (ML) algorithms applied to large datasets. The goal of this research is to investigate the adage that while humans are fallible, computers are impartial with no implicit bias. Toward this purpose, the author used the Delphi research technique to achieve these three objectives: (1) identify and categorize the sources of flaws in (...) class='Hi'>algorithm design; (2) validate a framework for auditing AI-powered systems; and (3) propose strategies for resolving the sources and mitigating the flaws in algorithm design. The paper begins with a concise theoretical framework for algorithm design to familiarize the readers with the science of ML algorithms. Next, the author describes the research methodology including population and sample of the study. The findings of the Delphi study are presented in three sections that correspond with the three objectives of this investigation. A discussion of the findings is enhanced with supporting evidence from existing literature of unintended undesirable societal impacts of flaws in algorithm design in fields like education, healthcare, criminal justice, human resource management, and financial services. The expected outcome is cautious use of AI by decision-makers—employing AI while being aware of its limitations and shortcomings. (shrink)

This paper discusses an algorithmic tool introduced in the Basque Country (Spain) to assess the risk of intimate partner violence. The algorithm was introduced to address the lack of human experts by automatically calculating the level of violence based on psychometric features such as controlling or violent behaviour. Given that critical literature on risk assessment tools for domestic violence mainly focuses on English-speaking countries, this paper offers an algorithmic accountability analysis in a non-English speaking region. It investigates the algorithmic (...) risks, harms, and limitations associated with the Basque tool. We propose a transdisciplinary approach from a critical statistical and legal perspective. This approach unveils issues and limitations that could lead to unexpected consequences for individuals suffering from partner violence. Moreover, our analysis suggests that the algorithmic tool has a high error rate on severe cases, i.e., cases where the aggressor could murder his partner—5 out of 10 high-risk cases are misclassified as low risk—and that there is a lack of appropriate legal guidelines for judges, the end users of this tool. The paper concludes that this risk assessment tool needs to be urgently evaluated by independent and transdisciplinary experts to better mitigate algorithmic harms in the context of intimate partner violence. (shrink)

As algorithms pervade numerous facets of daily life, they are incorporated into systems for increasingly diverse purposes. These systems’ results are often interpreted differently by the designers who created them than by the lay persons who interact with them. This paper offers a proposal for human-centered algorithm design, which incorporates human and social interpretations into the design process for algorithmically based systems. It articulates three specific strategies for doing so: theoretical, participatory, and speculative. Drawing on the author’s work designing (...) and deploying multiple related systems, the paper provides a detailed example of using a theoretical approach. It also discusses findings pertinent to participatory and speculative design approaches. The paper addresses both strengths and challenges for each strategy in helping to center the process of designing algorithmically based systems around humans. (shrink)

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this (...) study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery. (shrink)

Despite the rapid adoption of technology in human resource departments, there is little empirical work that examines the potential challenges of algorithmic decision-making in the recruitment process. In this paper, we take the perspective of job applicants and examine how they perceive the use of algorithms in selection and recruitment. Across four studies on Amazon Mechanical Turk, we show that people in the role of a job applicant perceive algorithm-driven recruitment processes as less fair compared to human only or (...) algorithm-assisted human processes. This effect persists regardless of whether the outcome is favorable to the applicant or not. A potential mechanism underlying algorithm resistance is the belief that algorithms will not be able to recognize their uniqueness as a candidate. Although the use of algorithms has several benefits for organizations such as improved efficiency and bias reduction, our results highlight a potential cost of using them to screen potential employees during recruitment. (shrink)

Le concept d’« agriculture intelligente», évolution de l’« agriculture de précision», a récemment émergé comme un nouveau paradigme en agriculture. Cette approche s’appuie sur des technologies numériques – telles que l’intelligence artificielle, les Big Data et l’Internet des objets – pour relever un défi majeur auquel l’agriculture moderne est confrontée : la nécessité d’augmenter la productivité tout en réduisant son impact écologique. L’agriculture intelligente vise ainsi à passer d’une agriculture axée sur les processus à une agriculture guidée par les données. (...) Cependant, le volume considérable de données générées et la dépendance croissante aux traitements algorithmiques, notamment les algorithmes d’apprentissage automatique, soulèvent des inquiétudes quant à une possible perte du pouvoir de décision des agriculteurs. Les outils d’aide à la décision risquent en effet de se transformer en décideurs autonomes. Pour les agronomes, ce déluge de données menace non seulement de rendre les approches scientifiques traditionnelles obsolètes, mais aussi de fragiliser les fondements mêmes de la science agronomique. Ainsi, cet article pose une question centrale : qui est réellement « intelligent» dans l’agriculture intelligente? Est-ce l’agriculteur, l’agronome ou les technologies numériques qui sont à l’origine de cette transformation? En explorant cette question, l’article examine les enjeux épistémiques soulevés par l’agriculture intelligente. (shrink)

Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. This paper presents a new neutrosophic clustering algorithm with the help of regularization. Firstly, the regularization term is introduced into the FC-PFS algorithm to generate sparsity, which can reduce the complexity of the algorithm on large data sets. Secondly, we propose a method to simplify the process (...) of determining regularization parameters. Finally, experiments show that the clustering results of this algorithm on artificial data sets and real data sets are mostly better than other clustering algorithms. Our clustering algorithm is effective in most cases. (shrink)

This paper presents a novel iterative algorithm for determining contact-rate parameters in individual-based propagation models. Conventional mathematical epidemiological approaches frequently utilize compartmental models with uniform epidemiological coefficients, which are unable to account for the heterogeneity of individual behaviors and interactions. This research employs Physics Informed Neural Networks (PINNs), which have demonstrated efficacy in addressing differential equations and parameter estimation in both direct and inverse problems. PINNs are applied to individual-based models, which consider unique epidemiological characteristics for each individual or (...) device, thus addressing the limitations of global models. The proposed method is particularly pertinent to the modeling of malware propagation in Internet of Things (IoT) networks, where each device exhibits distinct interaction patterns. The study employs a cellular automaton framework to simulate malware spread, thereby demonstrating the potential of PINNs in accurately estimating time-dependent contact rates. The methodology incorporates specific infection probabilities and recovery rates, thereby demonstrating its applicability to complex, heterogeneous systems. This approach represents a significant advancement in our understanding and prediction of the dynamics of disease and malware propagation, offering a robust tool for both biological and digital epidemiology. The findings indicate that individual-based models, enhanced with PINN-driven parameter estimation, can more accurately reflect real-world scenarios, thereby facilitating the development of more effective strategies for controlling epidemics and mitigating cybersecurity threats. (shrink)

It is now a near-given that AI systems will be used in a variety of healthcare scenarios. Yet, there is also an upswell of concern that such “black box medicine” may damage both patient needs as well as trust in care providers. Trust and trustworthiness have been stressed as important factors of justifiable deployment of AI when we consider these needs. Recently, there has been a tremendous amount of attention being paid to the notion of trust regarding technology, and in (...) particular autonomous systems, with a focus on trustworthiness as a metric for justifiable deployment (Shneiderman, ACM Transactions on Interactive Intelligent Systems (TiiS) 10(4): 1–31, 2020; Kaur et al., ACM Computing Surveys (CSUR) 55(2): 1–38, 2022). In particular, the recent publication of the National Institute of Standards & Technology (NIST)’s Artificial Intelligence Risk Management Framework (AI RMF), as well as Newman’s Taxonomy of Trustworthiness (Newman, A Taxonomy of Trustworthiness for Artificial Intelligence. CLTC: North Charleston, 2023), underscores this concern. The AI RMF is to serve as a “living document” robust enough to be used for a variety of case instances, and intended to help AI experts assess trustworthiness of systems at different phases of an AI’s life cycle. This document establishes seven key characteristics of trustworthiness, without ever fully articulating what trust is. I hold we should take a step back from trustworthiness, and revisit trust itself. We ought to reflect on the trust involved when deploying Healthcare Artificial Intelligence systems. (shrink)

This paper presents an algorithm that, given a finite set E of pairs of second-order monadic terms, returns a finite set U of ‘substitution schemata’ such that a substitution unifies E iff it is an instance of some member of U . Moreover, E is unifiable precisely if U is not empty. The algorithm terminates on all inputs, unlike the unification algorithms for second-order monadic terms developed by G. Huet and G. Winterstein. The substitution schemata in U use (...) expressions which represent sets of terms that differ only in how many times designated strings of function constants follow themselves. The substitution schemata may contain unresolved ‘identity restrictions’; consequently, the members of U generally do not characterize all the unifiers of E in a completely explicit way. The algorithm is particularly useful for investigating the complexity of formal proofs. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns (...) out that the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing (...) new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints. (shrink)

Recent debates in political philosophy of science underscore the need for normative frameworks to determine which values should guide scientific inquiry.1 Philosophers of science increasingly draw on principles from political philosophy, such as democratic majoritarianism, deliberative proceduralism, and Rawlsian public reason, to inform scientific investigations.2 The article argues these approaches fail in societies where basic institutions developed through historical racial domination, including chattel slavery, Jim Crow segregation, COINTELPRO’s targeting of Black movements, racially exclusionary housing policies, and mass incarceration without reparative (...) correction. Drawing on Charles Mills’s (2017) critique of ideal theory, the article contends that these frameworks abstract away from the racialized causal structures that shape the data, models, and decisions of contemporary scientific and technological practices, and that the normative constraints they propose are insufficient to secure democratic legitimacy under conditions of unrepaired racial domination.3 Using Artificial Intelligence (AI) as a case study, the article introduces the concept of the Racial Algorithm to show how machine-learning systems fitted to racially structured training distributions can reproduce and compound racial inequities through algorithmic training and deployment. The article concludes by sketching a framework for scientific legitimacy grounded in nonideal theory. (shrink)

Abnormal behaviour detection algorithm needs to conduct behaviour analysis on the basis of continuous video inclination tracking, and the robustness of the algorithm is reduced for the occlusion of moving targets, the occlusion of the environment, and the movement of targets with the same colour. For this reason, the optical flow information between RGB images and video frames is used as the input of the network in view of group behaviour. Then, the direction, velocity, acceleration, and energy of (...) the crowd were weighted and fused into a global optical flow descriptor. At the same time, the crowd trajectory map is extracted from the original image of a single frame. Following, in order to realize the detection of large displacement moving target and solve the problem that the traditional optical flow algorithm is only suitable for the detection of displacement moving target, a video abnormal behaviour detection algorithm based on the double-flow convolutional neural network is proposed. The network uses two network branches to learn spatial dimension information and temporal dimension information, respectively, and uses short- and long-time neural network to model the dependency relationship between long-time video frames, so as to obtain the final behaviour classification results. Simulation test results show that the proposed method can achieve good recognition effect on multiple datasets, and the performance of abnormal behaviour detection can be significantly improved by using interframe motion information. (shrink)

The whale optimization algorithm is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the (...) local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model, double diode model, and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules. (shrink)

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based (...) on short-sequence enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art. (shrink)