bluuebunny/crossref_metadata_2025 huggingface dataset:
Illusions of Confidence in Artificial Systems
Authors: Colombatto Clara , Fleming Stephen M
Published: September 2023

To effectively communicate and collaborate with others, we must monitor not only other people’s cognitive states (e.g., what someone thinks or believes), but also their metacognitive states (e.g., how confident they are in their beliefs). Confidence is however rarely communicated explicitly: instead, we often perceive others’ confidence via implicit signals such as speech prosody or movement dynamics. Recent advances in artificial intelligence (AI) have broadened the scope of these metacognitive inferences: artificial agents often perform similarly to humans yet rarely explicitly signal their confidence in their beliefs, raising the question as to how humans attribute confidence to AI. Here we report five pre-registered experiments in which participants observed human and artificial agents make perceptual choices, and reported how confident they thought the observed agent was in each choice. Overall, attributions of confidence were sensitive to observed variables such as task difficulty, accuracy, and response time. Strikingly, participants attributed higher confidence to AI agents compared to other humans, even though their behaviour was identical. An illusion of greater confidence in artificial agents’ decisions generalised across different behavioural profiles (Experiment 2), agent descriptions (Experiment 3), and choice domains (Experiment 4). Attributions of confidence also influenced advice-taking behaviour, as participants were more willing to accept the advice of artificial systems compared to matched humans (Experiment 5). Overall, our results uncover a systematic illusion of confidence in AI decisions, and highlight the importance of metacognition in guiding human-machine interactions.
Beyond Mediators: A Critical Review and Methodological Path Forward for Studying Mechanisms in Alcohol Use Treatment Research
Authors: Meisel Samuel N. , Boness Cassandra L. , Miranda Robert , Witkiewitz Katie
Published: September 2023

Background: Understanding how treatments for alcohol use disorder (AUD) facilitate behavior change has long been recognized as an important area of research for advancing clinical care. Even after decades of research, however, the specific mechanisms of change for most AUD treatments remain largely unknown because most prior work in the field has solely focused on statistical mediation. Statistical mediation is a necessary but not sufficient condition for establishing evidence for a mechanism of change. Mediators are intermediate variables that statistically account for the relationship between an independent and dependent variable whereas mechanisms provide more detailed explanations of how an intervention leads to a desired outcome. Thus, mediators and mechanisms are not equivalent. Methods: To advance mechanisms of behavior change research, this critical review provides an overview of methodological shortfalls of existing AUD treatment mechanisms research and introduces an etiologically informed precision medicine approach that facilitates testing mechanisms of behavior change rather than treatment mediators. Results: We propose a framework for studying mechanisms in alcohol treatment research that holds promise for facilitating our understanding of behavior change and precision medicine (i.e., for whom a given mechanisms of behavior change operates under what conditions). The framework forwarded in this review has several overarching goals including providing a methodological roadmap for testing AUD recovery mechanisms. We provide two examples of our framework, one pharmacological and one behavioral, to facilitate future efforts to implement this methodological approach to mechanisms research. Conclusions: The framework proposed in this critical review facilitates the alignment of AUD treatment mechanisms research with current theories of etiologic mechanisms, precision medicine efforts, and cross-disciplinary approaches to testing mechanisms. Although no framework can resolve all the challenges related to mechanisms research, our goal is to help facilitate a shift towards more rigorous and falsifiable behavior change research.
Micropolarization: performances of antagonism and struggles for recognition during the Covid-19 pandemic
Authors: Revers Matthias , Coleman Stephen
Published: September 2023

This article theorizes how political divisions permeate social interaction, examining how the political becomes personal in everyday life. Drawing on affective polarization research, we underscore the importance of identification and feelings toward political in-groups and out-groups. Beyond conventional measurements of cross-group resentments, however, we conceptualize polarization as the lived experience of political conflict. Our argument posits that polarization materializes in struggles for recognition, shaped by the perception and processing of political antagonism disseminated through public channels of communication. Adopting a performance theoretical lens, this study connects the symbolic language, emotions and forms of misrecognition in micropolarization to the public drama of political conflict. Through the analysis of interview data concerning family and friendship disputes stemming from COVID-19 vaccination denial in both the UK and Germany, we explore the intersection between public and interpersonal political conflict. The research agenda arising from this inquiry focuses on interactional dynamics across online/offline communication, diverse social groups, and differing levels of involvement and relational bonds to unravel the complexities of political antagonism. The insights from this agenda, we argue, will help mitigate political conflict, not only in personal but also public discourse.
Prison Activism on Trial: Canadian Feminists Mobilize against the Carceral State during the 1970s
Authors: Sangster Joan
Published: September 2023

This article uses the 1978–79 trial of Vancouver prison activists and feminists Betsy Wood and Gay Hoon as a means of exploring the changing nature of feminist prison activism in Canada during the 1970s. The trial is placed in its historical context, with a focus on contemporary discussions about reform, abolition, and the carceral state, particularly in the feminist press and feminist organizations. The Wood/Hoon trial is explored through an analysis of both media accounts and archival records, including the very few records of the trial that survived. Discussion surrounding the trial shows the changing nature of feminist thinking about prisons: older feminist traditions of “helping” reform were challenged with new, left-wing, more radical perspectives that argued that all incarceration sustained and perpetuated forms of gender, race, and class social control.
Peanut Dryer Noise Levels with and without Muffler
Authors: McIntyre J.S. , Cook H.J.
Published: September 2023

Harvested peanuts must be dried before being stored to prevent spoilage. Peanuts are dried using forced heated air dryers that produce a great deal of noise while operating. In an effort to abate dryer noise, a cylindrical muffler was designed, fabricated from sheet metal, and tested. The muffler was 1.17 m in diameter, 0.61 m in length and was lined with 0.55 m of R 30 fiberglass batting. Five configurations of the muffler were tested and the most effective was the two inline mufflers attached to the air intake of the dryer. The two inline mufflers were found to reduce noise 4.5m in front of the dryer air intake to the OSHA permissible level of 90 dBA for an eight-hour time period. The inline lined mufflers reduced noise levels at 4.5 m from the side of the dryer to just less than 85 dBA the level above which OSHA recommends employers initiate hearing conservation programs. Sound levels at 23 m in front of the dryer air intake were reduced to 69 dBA which is equivalent to classroom chatter. Though the muffler designed in the study reduced dyer noise to acceptable levels, study results and new technologies suggest that further noise reduction is possible by redesigning the muffler using natural harmonics of the dryer/muffler structure, new tuned noise damping materials, and designing for sound resonance effects.
Reduction of load amplitudes on the drive of a mine horizontal belt conveyor by transportation route adjusting
Authors:   State Institution of Higher Education «Donetsk National Technical University», Р. BELYTSKYI , O. LIUBIMENKO ,   State Institution of Higher Education «Donetsk National Technical University», O. SHTEPA ,   State Institution of Higher Education «Donetsk National Technical University»
Published: September 2023

The article examines the nature of the dependence of the amplitudes that arise on the drive of a mine belt conveyor from changes in the belt load, from the angle of transportation, as well as technological parameters of transportation and design parameters of the drive and the mechanical part of the conveyor. These amplitudes have a significantly affect to the resource of the mechanical and electrical parts of the conveyor, in particular: the conveyor belt, reducer, electric motor, etc. A mathematical model of the load amplitude formation on the elements of the mechanical and electrical parts of the belt conveyor drive due to the fluctuation of the belt load has been developed. The nonlinear nature of the amplitudes’dependence that arise on the mine belt conveyor drive from the variable load of the belt and from the angle of transportation has been established. Graphically and analytically, the characteristic angle of installation of the belt conveyor is determined, at which the moment of resistance does not depend on the belt load and will remain constant under the existing fluctuations in the amount of the mass of the cargo on the conveyor belt. Mining technical conditions that correspond to the transportation of bulk cargo by a belt conveyor at the installation angle described above are recommended as rational from the point of view of minimizing the amplitudes of loads on the conveyor drive, which is expected to increase the conveyor's resource and its reliability in general. The functioning of conveyors with minimum load amplitudes by the mechanical and electrical part of their drive corresponds to the operating conditions of horizontal and slightly inclined conveyors with the value of the angle transportation of the cargo down. The modulus of this angle is equal to the arctangent of the tape motion resistance coefficient.
Solving optimization problems in energy with genetic algorithm.
Authors:   State Institution of Higher Education «Donetsk National Technical University», D. KARDASH , O. KOLLAROV ,   State Institution of Higher Education «Donetsk National Technical University»
Published: September 2023

The article discusses the application of genetic algorithms in the field of energy optimization. Linear programming is commonly used for optimization problems in energy systems. Linear programming is a mathematical optimization method that seeks the optimal solution under constraints, where all constraints and the objective function are linear functions. In the realm of artificial intelligence,genetic algorithms are employed for optimization tasks. genetic algorithms mimic natural evolution processes, including selection, crossover, mutation, and adaptation, to solve optimization and search problems. The article outlines the process of a genetic algorithm, starting with the formation of an initial population and proceeding through selection, crossover, mutation, and evaluation. This cycle repeats until an optimal solution is achieved. Advantages of genetic algorithms include their ability to handle complex solution spaces, find global optima, adapt to changing conditions, optimize multi-objective functions, and work with non-linear and non-differentiable objective functions. However, they may require significant computational resources and parameter tuning. The article then presents a case study of applying a genetic algorithm to optimize the allocation of a power load in an energy system. The mathematical model is developed, and the simplex method is initially used for solution. Subsequently, a Python program for genetic algorithm implementation is provided. The algorithm's efficiency and convergence are demonstrated through a graphical representation of the optimization process. In conclusion, the article highlights the effectiveness of genetic algorithms in energy optimization, showcasing their rapid convergence and ability to find near-optimal solutions in complex scenarios.
Development of autonomous energy supply of a mobile surface degassing plant.
Authors:   State Institution of Higher Education «Donetsk National Technical University», L. V. SERHIIENKO
Published: September 2023

Currently, in connection with the difficult situation with energy supply, issues related to the application of new measures to conserve energy resources as well as the introduction of new promising technologies using alternative energy sources, such as mined methane, have gained great importance. The problem of methane from coal deposits is of particular importance. This problem primarily concerns the safety of miners' work, methane production as an alternative source of energy resources, and solving environmental problems related to methane emissions into the atmosphere of coal mining enterprises. The problem of reducing the efficiency of degassing is caused by the long length of the degassing pond and the depth of cleaning operations. The use of a mobile surface degassing unit makes it possible to effectively carry out degassing and achieve significant volumes of extracted gas with a methane content of up to 80%, but high costs of electricity complicate its operation. Therefore, the paper proposes an autonomous power supply for a mobile surface degassing plant. For the autonomous power supply of a mobile surface degassing plant, it is advisable to use an electric generator since an uninterrupted supply of electricity is important. Gas power plants are widely used in industrial enterprises. In accordance with the power consumption of the electrical equipment of the mobile surface degassing plant, the work justified and selected a gas power plant and electric power equipment for the autonomous power supply of the mobile surface degassing plant. The gas-piston power plant is designed for the production of electrical energy and can be both the main and backup source. As a fuel, mainline natural gas with an average supply pressure or methane mine gas is used. The basic configuration of the power plant is a liquid-cooled gas-piston engine based on a diesel unit. A technological scheme for connecting the gas power plant to a mobile vacuum pumping unit and its power supply scheme has been developed, which will increase the safety of cleaning works, solve the issue of methane utilization, significantly reduce electricity costs, and improve the environmental situation in the industrial region.
Efficiency of application of maximum current protection in underground networks.
Authors:   State Institution of Higher Education «Donetsk National Technical University», V. KALYNYCHENKO
Published: September 2023

The article considers the use of maximum current protection in underground mine electrical networks, which disables short-circuit currents with an absolute value greater than the starting currents of powerful electric motors. It was determined that these protections have a number of disadvantages. The article reveals that the protection with the current-time characteristic is more perfect than the maximum current protection, but in the case of arcing short circuits, the values of which can be less than the starting ones in absolute value, the protection activation time significantly exceeds the permissible value. Protection with filters of symmetrical components of currents and voltages reduces the sensitivity to two-phase short circuits with a connected load, protection at the level of the third harmonic in the event of a short circuit has a limited range of action and requires duplication. It was investigated that with the use of valve converters and thyristor schemes for controlling the drives of lifting installations and conveyor transport in underground networks, the level of higher harmonic currents increased. In the practice of work of domestic and foreign enterprises, it has been proven that capacitor banks, which are installed to compensate for reactive power, have protection against overcurrents and shortcircuit currents, but with non-sinusoidal voltage, in a number of cases, they quickly fail because they are overloaded with higher harmonics. The article shows that with the increase in the power of the engines of the underground district networks and the manifestation of the asymmetry of the air gap, the higher harmonics given exceed the permissible values. Therefore, the article presents the most promising directions for the construction of systems for the protection of mine distribution underground networks and electrical installations
Exploring the Pain Attenuating Potential of Imatinib in Chronic Constriction Injury Model of Neuropathic Pain
Authors: Bali Anjana
Published: September 2023

Background: Preclinical studies have identified three members of tyrosine kinase receptor family (Trk), TrkA, TrkB, and TrkC in dorsal root of ganglia (DRG), which carry sensory neural signals to the central nervous system from the peripheral nervous system in neuropathic pain. Imatinib is a selective tyrosine kinase inhibitor and widely employed as anti-cancer drug in myeloid leukemia and gastrointestinal stromal tumor. The present study was designed to investigate the potential of imatinib in neuropathic pain. Method: Neuropathic pain was induced by chronic constriction injury in rats by putting four loose ligatures around sciatic nerve. The extent of neuropathic pain was assessed by noting paw withdrawal threshold (mechanical hyperalgesia) in pin prick test, paw withdrawal latency in hot plate test (heat hyperalgesia) and paw withdrawal duration in acetone drop test (cold allodynia) before surgery and on 14th day post surgery. Result: There was a significant development of cold allodynia, mechanical and heat hyperalgesia on 14th day in CCI-subjected rats. Administration of imatinib (25 mg/kg and 50 mg/kg) significantly abolished the behavioural deficits (hyperalgesia and allodynia) induced by CCI in a dose-dependent manner. Conclusion: The observed beneficial effects of imatinib in reduction of behavioral deficits in CCI-subjected rats may be possibly attributed to tyrosine kinase inhibition in dorsal root ganglia neurons associated with the neuropathic pain.
Rare Primary Neuroendocrine Neoplasm (NENs) Brain Tumor, Case Report and Literature Review
Authors: Faraji-Rad Mohammad
Published: September 2023

Rare Neuroendocrine neoplasms (NENs) are malignancies rarely involving the central nervous system. A 42-year-old man was referred to our clinic with chronic intermittent frontal headache and left hemiparesis. After an initial assessment with CTs and MRIs, proper basal ganglia involvement with a heterogeneous brain tumor was found. The patient underwent brain tumor resection, and pathology showed Neuroendocrine neoplasms. Surprisingly, tumor manifestation was similar to metastatic NENs. It is essential that the prognosis between primary NENs and metastatic NENs to the brain is utterly different, and treatment options are not identical. The patient was discharged concession with left hemiparesis and referred to a neuro-oncologist for further treatment.
Worldwide Productivity and Research Trend of Publications Concerning Spontaneous Intracranial Hypotension: a Bibliometric Analysis
Authors: Liu Jianmin
Published: September 2023

Background and Aim: Spontaneous intracranial hypotension (SIH) is a highly misdiagnosed disease. The main reason is the lack of awareness. This study sought to summarize the current status of research in this field via a bibliometric analysis. Materials and Methods: The publications were searched in the Web of Science Core Collection (WoSCC) database. Data analysis and visualization were carried out using the cooperative network and cluster analysis of R and Cite Space software. Results: A total of 618 publications from 2008 to 2021 were included in this study. The number of publications has a growth trend. The United States contributed the most articles, and Schievink WI is the most proliﬁc author. Mayo Clinical ranked the ﬁrst compared with other institutions. American Journal of Neuroradiology has published the most articles in this field. The top 3 clusters of reference co-citation are “delayed recurrence”, “cerebrospinal fluid-venous fistula”, and “floating dural sac sign”. The top 3 clusters of keyword co-appearance are “spinal fluid leak headache”, “symptom duration”, and “cerebral venous thrombosis”. The strongest citation burst of keywords is “CSF-venous fistula” and “paraspinal vein sign”. Conclusions: This study analyzes the research progress of SIH, and the current research mainly focuses on CSF-venous fistula. The bibliometric analysis of SIH is expected to help researchers effectively explore the disease.
What are the Causes of Variation in Vaccination Rates Between Countries? Empirical Evidence from a Set of Developed and Developing Countries
Authors: Ghannouchi Imen
Published: September 2023

Over two years, the Coronavirus pandemic radically changed the worldwide people's lifestyles and widely affected the socioeconomic environment. However, the development of new vaccines against covid-19 seems to be the first and only hope to mitigate this pandemic and go back to normal life. Several mass vaccination campaigns have been planned to vaccinate as many people as possible. Nonetheless, a wide range of people did not take their vaccines for hesitancy reason of the inequality of disturbance of vaccines. Hence, focusing on the macroeconomic factors, this study outlines the primary causes of the gap in vaccination rates between countries, specifically between developed and developing countries. The results provide interested insights: GDP has a positive and significant effect on the vaccination rate. However, the rural population rate negatively affects the vaccination rate. It is noteworthy that a more equitable disturbance of vaccines is required. Moreover, mass media campaign should be omnipresent to encourage people, particularly in rural zones, to obtain the vaccine.
Pediatric Oro-Pharyngeal Dysphagia Secondary to Post Diptheric Bulbar Palsy with Dilated Cardiomyopathy
Authors: S Narayanan Shalini
Published: September 2023

Diphtheria happens to be a serious bacterial infection which adversely affects the mucous membranes of the nose and throat. It is caused by Corynebacteriumdiptheriae which multiplies on or near the surface of the throat. It can also multiply on the skin and this usually spreads through airborne droplets or contaminated household and personnel belongings.
Mathematics is required for treatment
Authors: Jana Prasenjit
Published: September 2023

A doctor must treat a patient who is a student, by giving the student a mathematical problem to solve. At first when a student comes to a doctor for illness, the doctor must have to give a mathematical problem to the student to know the condition of the student's brain if the student is not seriously injured in other parts of his or her body
2 Case Reports of Uterine Artery Pseudoaneurysm Diagnosed in Pregnancy and Postpartum – Successfully Embolised
Authors: Navakumar Nina
Published: September 2023

Pseudoaneurysm is an extraluminal collection of blood with a turbulent flow that communicates with flowing arterial blood through a defect in the arterial wall. Uterine artery pseudoaneurysm (UAP) is considered a rare case that occurs in 2–3/1,000 deliveries. Here we discuss 2 cases – first case is UAP diagnosed antenatally in a woman with acute abdomen and hydronephrosis, second case presented as secondary delayed postpartum haemorrhage. Sonography revealed UAP, and the diagnosis was confirmed by angiography. The defects were. treated by uterine artery embolization. UAE is to be considered a life-saving procedure obviating need for hysterectomy and improves maternal outcome.
Medicinal Plants for The Treatment of Obesity
Authors: M. Hamouda Ibrahim
Published: September 2023

A persistent increase in morbidity indicators has been connected to the global epidemic of obesity; it is regarded as a societal issue & poses significant health dangers. Synthetic medications and surgery are examples of therapeutic approaches; both have high prices and potentially dangerous side effects. Plant-based drugs provide an alternative strategy. Traditional uses of medicinal plants are one of the options for treating obesity, and they help with the development and research of obesity phytotherapy. In this review, we provided information regarding plant species along with their mode of action used for the effective treatment of obesity. It was revealed that the species in the catalogue had anti-obesity properties, including actions to slow down the absorption of fat, reduce enzymatic activity, mediate lipid levels, and boost the effects of lipolysis, which were primarily linked to phenolic compounds. For species with phenolic compounds in their chemical components, more thorough botanical, chemical, pre-clinical, and clinical research are especially required. The safe therapy for obesity may involve taking standardized medicinal plant extracts. The effectiveness of some plant combinations for medical purposes, however, may be reduced, and they may also have unanticipated negative effects.
The Master-Masquerader: Massive Pulmonary Embolism
Authors: R. Makhija Rakhee
Published: September 2023

A 69-year-old woman was brought to the emergency department (ED) by ambulance with symptoms of severe crushing chest pressure and dyspnea. Her history was limited at the time of evaluation due to the acuity of her symptoms and absence of family.
Changing words with AI helps us mentally
Authors: Jana Prasenjit
Published: September 2023

The words of a person can be positive or negative for another person. If we create the negative words to positive with respect to others then we can live a happy life. The doctor must take the help of a relative of a patient to treat the patient with words, where the close relative will act as a mediator between doctor and patient to convert negative words to positive to the patients and also negative words to positive to the doctor as required. If we use artificial intelligence (AI) then our problem will be solved.
The effect of digital literacy levels and e-learning attitudes on screen reading self-efficacy: A structural equation modelling
Authors: YILDIRIM Mesut , KESİK Cengiz , CİĞERCİ Fatih Mehmet
Published: September 2023

In this study, it was aimed to determine the relationships between digital literacy levels, e-learning attitudes and screen reading self-efficacy of primary school teacher candidates through structural equation modeling, and to examine to what extent digital literacy levels and e-learning attitudes predict screen reading self-efficacy perceptions. The study group, which was formed by simple random sampling in the research carried out with the relational screening model, consists of 677 primary school teacher candidates. "Digital Literacy Scale", "Attitude Scale Towards E-learning" and "Screen Reading Self-Efficacy Perception Scale" were used to collect data. The data were analyzed by structural equation modeling and LISREL and MPLUS package programs were used in the analysis of the data. As a result of the research, it has been determined that hypothesis models were valid and that the digital literacy levels and e-learning attitudes of the primary school teacher candidates significantly predict their screen reading self-efficacy perceptions both separately and together, and there are positive relations between the variables. In addition, while e-learning attitudes play a mediating role in the effects of digital literacy on screen reading self-efficacy perceptions of primary school teacher candidates, it is a result that digital literacy levels significantly predict screen reading self-efficacy perceptions and the relationship is positive if e-learning attitudes are considered as mediating variable.
IMPROVING ORGANIZATIONAL PERFORMANCE WITH ORGANIZATIONAL CULTURE AND TRANSFORMATIONAL LEADERSHIP THROUGH INTERVENING ORGANIZATIONAL COMMITMENT VARIABLES
Authors: Rahmatullah Asep , David Ramdansyah Agus , Kambara Roni , Haryadi Didit
Published: September 2023

This study aims to examine and analyze the influence of organizational culture and transformational leadership on organizational performance through organizational commitment. This study uses a descriptive quantitative method with a causal approach. The object of this research is the regeneration of the Indonesian Democratic Party of Banten Province using a sample of 158 respondents. Questionnaire answers were measured using an interval scale of 1 – 10. Data were analyzed using the SmartPLS version 3.29 software. The results of this study stated that: 1). Organizational culture has a positive and significant effect on organizational performance. 2). Transformational leadership has a positive and significant effect on organizational performance. 3). Organizational culture has a positive and significant effect on organizational commitment. 4). Transformational leadership has a positive and significant effect on organizational commitment. 5). Organizational commitment has a positive and significant effect on organizational performance. Organizational commitment can mediate between organizational culture and transformational leadership in partial mediation.
FACTORS AFFECTING CONTINUOUS INTEREST IN USING THE MOBILE APPLICATION BPJSTKU (SURVEY: WORKERS OF PT MITSUBISHI MOTORS KRAMA YUDHA INDONESIA, CIKARANG)
Authors: Malada Lazuardi Putra
Published: September 2023

This study aims to determine the factors that influence the continuing interest in using the BPJSTKU mobile application, at the workforce of PT Mitsubishi Motors Krama Yudha Indonesia, Cikarang. The research design uses a quantitative approach and the type of research is causal research. The research method uses a survey. The research population at PT Mitsubishi Motors Krama Yudha Indonesia is 3,107 workers. The sampling technique was purposive sampling with a total sample of 210 people. Collecting data in the form of a questionnaire. The research data were analyzed using the SEM method with data processing through the SmartPLS 3.0 application. The results of the study indicate that the factors that influence continued interest in using the BPJSTKU mobile application are system quality, information quality, social norms, which are mediated by the variables of satisfaction and perceived usefulness. System quality and information quality partially have a positive and significant effect on satisfaction and perceived usefulness. Then, social norms, satisfaction, and perceived usefulness partially have a positive and significant effect on continued interest in using the BPJSTKU mobile application.
On the distribution of Born transmission eigenvalues in the complex plane
Authors: Hovsepyan Narek
Published: September 2023

We analyze an approximate interior transmission eigenvalue problem in [Formula: see text] for [Formula: see text] or [Formula: see text], motivated by the transmission problem of a transformation optics-based cloaking scheme and obtained by replacing the refractive index with its first order approximation, which is an unbounded function. Using the radial symmetry we show the existence of (infinitely many) complex transmission eigenvalues and prove their discreteness. Moreover, it is shown that there exists a horizontal strip in the complex plane around the real axis, that does not contain any transmission eigenvalues.
Learning Task Automata for Reinforcement Learning Using Hidden Markov Models
Authors: Abate Alessandro , Almulla Yousif , Fox James , Hyland David , Wooldridge Michael
Published: September 2023

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification. We learn non-Markovian finite task specifications as finite-state ‘task automata’ from episodes of agent experience within environments with unknown dynamics. First, we learn a product MDP, a model composed of the specification’s automaton and the environment’s MDP (both initially unknown), by treating it as a partially observable MDP and employing a hidden Markov model learning algorithm. Second, we efficiently distil the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our automaton enables a task to be decomposed into sub-tasks, so an RL agent can later synthesise an optimal policy more efficiently. It is also an interpretable encoding of high-level task features, so a human can verify that the agent’s learnt tasks have no misspecifications. Finally, we also take steps towards ensuring that the automaton is environment-agnostic, making it well-suited for use in transfer learning.
Fair Rent Division on a Budget Revisited
Authors: Airiau Stéphane , Gilbert Hugo , Grandi Umberto , Lang Jérôme , Wilczynski Anaëlle
Published: September 2023

Rent division consists in simultaneously computing an allocation of rooms to agents and a payment, starting from an individual valuation of each room by each agent. When agents have budget limits, it is known that envy-free solutions do not necessarily exist. We propose two solutions to overcome this problem. In the first one, we relax envy-freeness to account for budget disparities. In the second one, we allow fractional allocations, in which agents may change rooms during the duration of the lease.
Causally Disentangled Generative Variational AutoEncoder
Authors: An SeungHwan , Song Kyungwoo , Jeon Jong-June
Published: September 2023

We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings.
SpArX: Sparse Argumentative Explanations for Neural Networks
Authors: Ayoobi Hamed , Potyka Nico , Toni Francesca
Published: September 2023

Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs’ outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original structure as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insights into the actual reasoning process of MLPs.
Representative Answer Sets: Collecting Something of Everything
Authors: Böhl Elisa , Gaggl Sarah Alice , Rusovac Dominik
Published: September 2023

Answer set programming (ASP) is a popular problem solving paradigm with applications in planning and configuration. In practice, the number of answer sets can be overwhelmingly high, which naturally causes interest in a concise characterisation of the solution space in terms of representative answer sets. We establish a notion of representativeness that refers to the entropy of specified target atoms within a collection of answer sets. Accordingly, we propose different approaches for collecting such representative answer sets, based on answer set navigation. Finally, we conduct experiments using our prototypical implementation, which reveals promising results.
Diffusion Model for Camouflaged Object Detection
Authors: Chen Zhennan , Gao Rongrong , Xiang Tian-Zhu , Lin Fan
Published: September 2023

Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks. Specifically, the object mask diffuses from the ground-truth masks to a random distribution, and the designed model learns to reverse this noising process. To strengthen the denoising learning, the input image prior is encoded and integrated into the denoising diffusion model to guide the diffusion process. Furthermore, we design an injection attention module (IAM) to interact conditional semantic features extracted from the image with the diffusion noise embedding via the cross-attention mechanism to enhance denoising learning. Extensive experiments on four widely used COD benchmark datasets demonstrate that the proposed method achieves favorable performance compared to the existing 11 state-of-the-art methods, especially in the detailed texture segmentation of camouflaged objects. Our code will be made publicly available at: https://github.com/ZNan-Chen/diffCOD.
TAOTF: A Two-Stage Approximately Orthogonal Training Framework in Deep Neural Networks
Authors: Cui Taoyong , Li Jianze , Dong Yuhan , Liu Li
Published: September 2023

The orthogonality constraints, including the hard and soft ones, have been used to normalize the weight matrices of Deep Neural Network (DNN) models, especially the Convolutional Neural Network (CNN) and Vision Transformer (ViT), to reduce model parameter redundancy and improve training stability. However, the robustness to noisy data of these models with constraints is not always satisfactory. In this work, we propose a novel two-stage approximately orthogonal training framework (TAOTF) to find a trade-off between the orthogonal solution space and the main task solution space to solve this problem in noisy data scenarios. In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization. In the second stage, unlike other existing methods, we apply soft orthogonal constraints for all layers of DNN model. We evaluate the proposed model-agnostic framework both on the natural image and medical image datasets, which show that our method achieves stable and superior performances to existing methods. Supplementary materials can be found in https://github.com/nonameinformation/anonymous/tree/main.
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection
Authors: Fan Lei , Ding Yiwen , Fan Dongdong , Wu Yong , Pagnucco Maurice , Song Yang
Published: September 2023

Cereal grain plays a crucial role in the human diet as a major source of essential nutrients. Grain Appearance Inspection (GAI) serves as an essential process to determine grain quality and facilitate grain circulation and processing. However, GAI is routinely performed manually by inspectors with cumbersome procedures, which poses a significant bottleneck in smart agriculture. In this paper, we endeavor to develop an automated GAI system: AI4GrainInsp. By analyzing the distinctive characteristics of grain kernels, we formulate GAI as a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible kernels are considered normal samples while damaged grains or unknown objects are regarded as anomalies. We further propose an AD model, called AD-GAI, which is trained using only normal samples yet can identify anomalies during inference. Moreover, we customize a prototype device for data acquisition and create a large-scale dataset including 220K high-quality images of wheat and maize kernels. Through extensive experiments, AD-GAI achieves considerable performance in comparison with advanced AD methods, and AI4GrainInsp has highly consistent performance compared to human experts and excels at inspection efficiency over 20× speedup. The dataset, code and models will be released at https://github.com/hellodfan/AI4GrainInsp.
On Solution Discovery via Reconfiguration
Authors: Fellows Michael R. , Grobler Mario , Megow Nicole , Mouawad Amer E. , Ramamoorthi Vijayaragunathan , Rosamond Frances A. , Schmand Daniel , Siebertz Sebastian
Published: September 2023

The dynamics of real-world applications and systems require efficient methods for improving infeasible solutions or restoring corrupted ones by making modifications to the current state of a system in a restricted way. We propose a new framework of solution discovery via reconfiguration for constructing a feasible solution for a given problem by executing a sequence of small modifications starting from a given state. Our framework integrates different aspects of classical local search, reoptimization, and combinatorial reconfiguration. We exemplify our framework on a multitude of fundamental combinatorial problems, namely VERTEX COVER, INDEPENDENT SET, DOMINATING SET, and COLORING. We study the classical as well as the parameterized complexity of the solution discovery variants of those problems and explore the boundary between tractable and intractable instances.
A Semantics-Driven Methodology for High-Quality Image Annotation
Authors: Giunchiglia Fausto , Bagchi Mayukh , Diao Xiaolei
Published: September 2023

Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets. Our basic tenet is that these flaws are rooted in the many-to-many mappings which exist between the visual information encoded in images and the intended semantics of the labels annotating them. The net consequence is that the current annotation process is largely under-specified, thus leaving too much freedom to the subjective judgment of annotators. In this paper, we propose vTelos, an integrated Natural Language Processing, Knowledge Representation, and Computer Vision methodology whose main goal is to make explicit the (otherwise implicit) intended annotation semantics, thus minimizing the number and role of subjective choices. A key element of vTelos is the exploitation of the WordNet lexico-semantic hierarchy as the main means for providing the meaning of natural language labels and, as a consequence, for driving the annotation of images based on the objects and the visual properties they depict. The methodology is validated on images populating a subset of the ImageNet hierarchy.
Bridging the Transparency Gap: What Can Explainable AI Learn from the AI Act?
Authors: Gyevnar Balint , Ferguson Nick , Schafer Burkhard
Published: September 2023

The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed by the field of explainable AI (XAI), however, there is a fundamental difference between XAI and the Act regarding what transparency is. The Act views transparency as a means that supports wider values, such as accountability, human rights, and sustainable innovation. In contrast, XAI views transparency narrowly as an end in itself, focusing on explaining complex algorithmic properties without considering the socio-technical context. We call this difference the “transparency gap”. Failing to address the transparency gap, XAI risks leaving a range of transparency issues unaddressed. To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation – the Act and the related General Data Protection Regulation (GDPR) – view basic definitions of transparency. By comparing the disparate views of XAI and regulation, we arrive at four axes where practical work could bridge the transparency gap: defining the scope of transparency, clarifying the legal status of XAI, addressing issues with conformity assessment, and building explainability for datasets.
Transferring Procedural Knowledge Across Commonsense Tasks
Authors: Jiang Yifan , Ilievski Filip , Ma Kaixin
Published: September 2023

Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI fails to generalize its procedural reasoning to unseen stories. This paper is based on the hypothesis that the generalization can be improved by associating downstream prediction with fine-grained modeling and the abstraction of procedural knowledge in stories. To test this hypothesis, we design LEAP: a comprehensive framework that reasons over stories by jointly considering their (1) overall plausibility, (2) conflict sentence pairs, and (3) participant physical states. LEAP integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies based on natural and synthetic stories. To address the lack of densely annotated training data on participants and their physical states, we devise a robust automatic labeler based on semantic parsing and few-shot prompting with large language models. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of architectures, training regimes, and augmentation strategies. LEAP’s labeler consistently improves performance on out-of-domain datasets, while our case studies show that the dense annotation supports explainability.
Language Models as Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering
Authors: Lehmann Jens , Gattogi Preetam , Bhandiwad Dhananjay , Ferré Sébastien , Vahdati Sahar
Published: September 2023

We propose the use of controlled natural language as a target for knowledge graph question answering (KGQA) semantic parsing via language models as opposed to using formal query languages directly. Controlled natural languages are close to (human) natural languages, but can be unambiguously translated into a formal language such as SPARQL. Our research hypothesis is that the pre-training of large language models (LLMs) on vast amounts of textual data leads to the ability to parse into controlled natural language for KGQA with limited training data requirements. We devise an LLM-specific approach for semantic parsing to study this hypothesis. To conduct our study, we created a dataset that allows the comparison of one formal and two different controlled natural languages. Our analysis shows that training data requirements are indeed substantially reduced when using controlled natural languages, which is relevant since collecting and maintaining high-quality KGQA semantic parsing training data is very expensive and time-consuming.