Reviewerly/exHarmony huggingface dataset:
CHANGING PRIORITIES IN STAFF MOTIVATION DURING THE CRISIS AND POST-CRISIS PERIOD

The article examines the problems of changing priorities in staff motivation during the crisis and post-crisis period, as a complex socio-psychological phenomenon that can be explained with the help of behavioral economics. On the basis of a theoretical analysis, the main “side” effect, the transformation of motivational priorities, was investigated, which consists in a decrease in the level of job satisfaction with an increase in the level of remuneration, as noted in empirical studies conducted over a very long period of time. PANAS-C was chosen as the tools for empirical study of this phenomenon – a test of an active life position, a survey regarding the degree of satisfaction and significance of the WAMI work, as well as a test to identify the motivational type of Gerchikov. On a representative sample of employees of enterprises belonging to various fields of activity, having different sizes and forms of ownership, we tested and verified the proposed approach to explain the main effect of the transformation of motivational priorities, which consists in the fact that a decrease in the level of employee satisfaction with the growth of material incentives can be compensate with a positive attitude. A hypothesis has been formulated and confirmed regarding the desire of the staff to hide the true, purely material motivation, trying to give “socially justified” answers to questions regarding the motives of activity. The identified feature of behavior manifests itself almost regardless of the form of ownership, size and scope of the company. The authors proposed a phenomenological model that describes the dynamics of the process of changing priorities between external and internal motivation, which manifests itself through a significant decrease in the rate of growth in the efficiency of personnel activities while a significant increase in the level of material incentives continues. A simple approach is proposed to determine the moment of changing priorities between external and internal motivation, which can significantly increase the efficiency of the use of material resources when paying for labor. A kind of “conservation law” for material and non-material incentives (intrinsic and extrinsic motivation) was formulated in the following form: the cumulative result of the influence of material and non-material incentives on the efficiency of the enterprise is a constant value that is preserved for this enterprise or its individual division while ensuring a negligible staff turnover. Using the mathematical interpretation of the most probable law describing the relationship between job satisfaction and efficiency, it was possible to explain many empirical effects that had been identified over a fairly long period. At the same time, the assumption about the existence of a stratification of respondents’ reactions according to different hierarchical levels of the organization did not find adequate confirmation.
ERDERP: Entity and Relation Double Embedding on Relation Hyperplanes and Relation Projection Hyperplanes

Since data are gradually enriched over time, knowledge graphs are inherently imperfect. Thus, knowledge graph completion is proposed to perfect knowledge graph by completing triples. Currently, a family of translation models has become the most effective method for knowledge graph completion. These translation models are modeled to solve the complexity and diversity of entities, such as one-to-many, many-to-one, and many-to-many, which ignores the diversity of relations themselves, such as multiple relations between a pair of entities. As a result, with current translation models, it is difficult to effectively extract the semantic information of entities and relations. To effectively extract the semantic information of the knowledge graph, this paper fundamentally analyzes the complex relationships of the knowledge graph. Then, considering the diversity of relations themselves, the complex relationships are refined as one-to-one-to-many, many-to-one-to-one, one-to-many-to-one, many-to-one-to-many, many-to-many-to-one, one-to-many-to-many, and many-to-many-to-many. By analyzing the complex relationships, a novel knowledge graph completion model, entity and relation double embedding on relation hyperplanes and relation projection hyperplanes (ERDERP), is proposed to extract the semantic information of entities and relations. First, ERDERP establishes a relation hyperplane for each relation and projects the relation embedding into the relation hyperplane. Thus, the semantic information of the relations is extracted effectively. Second, ERDERP establishes a relation projection hyperplane for each relation projection and projects entities into relation projection hyperplane. Thus, the semantic information of the entities is extracted effectively. Moreover, it is theoretically proved that ERDERP can solve antisymmetric problems. Finally, the proposed ERDERP are compared with several typical knowledge graph completion models. The experimental results show that ERDERP is significantly effective in link prediction, especially in relation prediction. For instance, on FB15k and FB15k-237, Hits@1 of ERDERP outperforms TransH at least 30%.
Virtual Big Heads in Extended Reality: Estimation of Ideal Head Scales and Perceptual Thresholds for Comfort and Facial Cues

Extended reality (XR) technologies, such as virtual reality (VR) and augmented reality (AR), provide users, their avatars, and embodied agents a shared platform to collaborate in a spatial context. Although traditional face-to-face communication is limited by users’ proximity, meaning that another human’s non-verbal embodied cues become more difficult to perceive the farther one is away from that person, researchers and practitioners have started to look into ways to accentuate or amplify such embodied cues and signals to counteract the effects of distance with XR technologies. In this article, we describe and evaluate the Big Head technique, in which a human’s head in VR/AR is scaled up relative to their distance from the observer as a mechanism for enhancing the visibility of non-verbal facial cues, such as facial expressions or eye gaze. To better understand and explore this technique, we present two complimentary human-subject experiments in this article. In our first experiment, we conducted a VR study with a head-mounted display to understand the impact of increased or decreased head scales on participants’ ability to perceive facial expressions as well as their sense of comfort and feeling of “uncannniness” over distances of up to 10 m. We explored two different scaling methods and compared perceptual thresholds and user preferences. Our second experiment was performed in an outdoor AR environment with an optical see-through head-mounted display. Participants were asked to estimate facial expressions and eye gaze, and identify a virtual human over large distances of 30, 60, and 90 m. In both experiments, our results show significant differences in minimum, maximum, and ideal head scales for different distances and tasks related to perceiving faces, facial expressions, and eye gaze, and we also found that participants were more comfortable with slightly bigger heads at larger distances. We discuss our findings with respect to the technologies used, and we discuss implications and guidelines for practical applications that aim to leverage XR-enhanced facial cues.
SimTune: bridging the simulator reality gap for resource management in edge-cloud computing

Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively.
Is mobile game-based learning effective for international adults learning Maltese?

Abstract The EULALIA (Enhancing University Language courses with an App powered by game-based learning and tangible user interface activities) project aimed to enhance the learning methodologies of four university language courses for Erasmus students in Italy, Malta, Poland and Spain by developing innovative and effective learning tools based on mobile and game-based learning paradigms and the use of tangible user interfaces. This study focuses on Malta by providing an in-depth view of the impact of game-based applications on enhancing international adult learning of Maltese as a second language (ML2). The findings encourage international adult students to learn ML2 through a game-based application to aid in increasing cultural awareness and better communication with locals. As part of the methodology, pre-surveys and post-surveys were used on a test group comprising 28 pre-surveyed and 9 post-surveyed ML2 adult learners who used the app and a reference group of 24 pre-surveyed and 23 post-surveyed ML2 learners who did not use the app. The results revealed that according to the participants, game-based learning did not improve cognitive function even though the learners were more engaged in language activities, and thus could process and absorb a wider range of information. The research found that game-based learning did not have a statistically significant effect on adult learners’ language proficiency and digital skills.
Uncertain Disturbance Attenuation and Rejection for Interval Type-2 Fuzzy Systems via Disturbance Observer

This study is concerned with the composite anti-disturbance issue for nonlinear systems subject to state saturation and parameter uncertainties, including the unknown harmonic disturbance. First, an interval type-2 (IT2) Takagi–Sugeno fuzzy model is proposed herein to describe the nonlinear system with state saturation and parametric uncertainties. Next, a novel disturbance observer framework is designed to eliminate the unknown harmonic disturbance. By introducing the new framework, the expression of the unknown harmonic disturbance is transformed into a new expression having only an uncertain parameter, which is then estimated. The corresponding composite anti-disturbance control scheme is then put forward and the sufficient conditions guaranteeing the stochastically stable of the composite system are established. Finally, the simulations are conducted to confirm the availability of the this control scheme.
Explainable Artificial Intelligence (XAI): Understanding and Future Perspectives

Currently, we are seeing a rapid and broad acceptance of artificial intelligence (AI) in our everyday lives, which is contributing to the acceleration of the transition towards a more algorithmic society. The availability of enormous datasets, as well as recent advancements in deep learning methods, are allowing AI systems to perform at or even beyond the level of human performance on an expanding variety of challenging tasks. However, because of their layered non-linear structure, these strong models have traditionally been regarded as “black boxes,” since they provide no information about how they arrive at their predictions and hence cannot be used trustily to generate predictions. They are often opaque, but they are capable of making accurate predictions that cannot be explained in any other way. All of these choices are having an increasing amount of effect on the lives of individuals. The distrust of totally non-human, autonomous artificial intelligence systems is established as the cornerstone of the movement. The root of distrust lies in a lack of knowledge as to why intelligent systems make certain decisions in certain situations. As a result, this problem has sparked a fresh discussion over explainable artificial intelligence (XAI). Several lines of research work have since picked up on the definition, understanding, and implementation of explainability, including expert systems, machine learning, recommender systems, and approaches to neural-symbolic learning and reasoning, all of which have occurred primarily during different periods in the history of artificial intelligence. This chapter serves as an introduction for academics and practitioners who are interested in learning about essential parts of the new and quickly increasing field of research on XAI. The first section, titled “Introduction,” provides an overall summary of the Explainable Artificial Intelligence. Section 2 describes the need of trust and transparency in AI, which is what led to the development of the idea of XAI. Section 3 discusses the many approaches that contribute to the functioning of XAI. The principal applications domain of Explainable Artificial Intelligence is the topic of discussion in Sect. 4. In Sect. 5, the upcoming difficulties and potential directions that this developing technology may take are discussed.
Evidential Support and Contraposition

Abstract The concept of an evidential conditional If A then C that can be defined by the conjunction of $$A&gt;C$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>A</mml:mi><mml:mo>&gt;</mml:mo><mml:mi>C</mml:mi></mml:mrow></mml:math> and $$\lnot C &gt; \lnot A$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>¬</mml:mo><mml:mi>C</mml:mi><mml:mo>&gt;</mml:mo><mml:mo>¬</mml:mo><mml:mi>A</mml:mi></mml:mrow></mml:math> , where &gt; is a conditional of the kind introduced by Stalnaker and Lewis, has recently been studied in a series of papers by Vincenzo Crupi and Andrea Iacona. In this paper I argue that Crupi and Iacona’s central idea that contraposition captures the idea of evidential support cannot be maintained. I give examples showing that contraposition is neither necessary nor sufficient for a conditional’s antecedent supporting its consequent. Crupi and Iacona’s alternative account of evidential conditionals that is based on a probabilistic measure of evidential support cannot add to the credentials of their modal account, because both the theoretical role of contraposition and the resulting logic are different in this account.
Can decentralized science help tackle the deterioration in working conditions in academia?

Academic staff’s working conditions have been deteriorating for years. In particular, the reduced availability of both research funding and permanent research positions has continuously led to insidious competition and intense stress among academics. Whereas governing bodies have made significant attempts to narrow pervasive social inequalities in the distribution of research funding within the scientific community, they have not truly taken into account the importance of the academics’ overall well-being in the development of more sustainable financing of academic researchers. This originates not only from the complexity to develop comprehensive models reflecting staff’s overall well-being in the academic environment, but also from the limited access to reliable and immutable data that transparently account for the staff’s direct experience. In this context, blockchain technology can push further the use of more transparent survey data collection and record-keeping that can help mitigate the systematic bias inherent to the centralized nature of traditional auditing. We discuss how research institutions and governing bodies can build on blockchain technology and the early momentum generated by the decentralized science (DeSci) movement to implement the future-proof research funding chain that values overall well-being across academia in a transparent and coordinated way.
Probabilistic interpretations of argumentative attacks: Logical and experimental results1

We present an interdisciplinary approach to argumentation combining logical, probabilistic, and psychological perspectives. We investigate logical attack principles which relate attacks among claims with logical form. For example, we consider the principle that an argument that attacks another argument claiming A triggers the existence of an attack on an argument featuring the stronger claim A ∧ B. We formulate a number of such principles pertaining to conjunctive, disjunctive, negated, and implicational claims. Some of these attack principles seem to be prima facie more plausible than others. To support this intuition, we suggest an interpretation of these principles in terms of coherent conditional probabilities. This interpretation is naturally generalized from qualitative to quantitative principles. Specifically, we use our probabilistic semantics to evaluate the rationality of principles which govern the strength of argumentative attacks. In order to complement our theoretical analysis with an empirical perspective, we present an experiment with students of the TU Vienna ( n = 139) which explores the psychological plausibility of selected attack principles. We also discuss how our qualitative attack principles relate to well-known types of logical argumentation frameworks. Finally, we briefly discuss how our approach relates to the computational argumentation literature.
Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model

Abstract In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt &amp; pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets.
Towards Sustainable Compressive Population Health: A GAN-based Year-By-Year Imputation Method

Population health monitoring is a fundamental component of the public health system. Due to the high-cost nature of traditional population-wise health-data collection methods, a class of sparse-sampling-completion algorithms are proposed to exploit the spatio-temporal correlation buried under the observed examples. However, for the population health data, a huge challenge for the state-of-the-art completion methods is the unstationary environment. Specifically, the underlying temporal correlation of the population health data are evolving from year to year. To this end, we propose a GAN-based year-by-year completion framework: uncertainty-aware augmented generative adversarial imputation nets (UAA-GAIN) , to address the problem of unstationary environment. To further restrain the error accumulation, we develop a stronger generator as well as a stronger discriminator in the min-max equilibrium. A by-product of the augmented GAIN model allows weighting the difficulty of examples. Inspired by the idea of curriculum learning, a better training schedule is implemented in the proposed framework. We evaluate the proposed method on three real-world chronic disease datasets and the results show that UAA-GAIN outperforms other baseline methods in various settings.
Assessing the Practices of Online Counterfeit Currency Vendors

The development of the Internet and e-commerce tools have simplified the process of commercial exchange for both legal and illicit goods. The rise of online markets for illicit goods enable access to facilitatory resources for crime, such as firearms and counterfeit identity documents. Few have considered how these online markets facilitate the sale of counterfeit currencies for use in offline environments. The current study attempted to address this gap in the literature through a qualitative crime script analysis of counterfeit currency vendors operating on both the Open and Dark Web to understand the ways vendors advertise, actualize, and exit these transactions. The implications of this analysis for our understanding of the acquisition processes of facilitatory products are explored in detail.
Bolt loosening angle detection based on binocular vision

Abstract Bolt looseness detection is critical in preventing bolt connection failure. Compared to traditional sensor-based bolt looseness detection, image-based methods are low-cost and contactless and have thus become the highlight of research. However, current monocular vision-based detection methods are prone to error scaused by the camera perspective . In this paper, we present a novel bolt loosening angle detection method based on binocular vision. Key points on the bolt are detected and matched by SuperPoint Gauss network for 3D coordinates reconstruction and motion tracking. The bolt loosening angle is solved by fitting the rotation equation using random sample consensus. Experiments verify the proposed method performs well under different perspectives of camera and illumination conditions with an average error of 1.5°. Comparative test shows our method is superior to the monocular vision-based method in terms of accuracy when there is a large perspective angle. The proposed method is mark-free and robust to various working conditions, which makes it of great value for engineering application.