Reviewerly/exHarmony huggingface dataset:
Efficient Computation of Answer Sets via SAT Modulo Acyclicity and Vertex Elimination

Abstract Answer set programming (ASP) is a declarative programming paradigm where the solutions of a search problem are captured by the answer sets of a logic program describing its solutions. Besides native algorithms implemented as answer-set solvers, the computation of answer sets can be realized (i) by translating the logic program into propositional logic or its extensions and (ii) by finding satisfying assignments with appropriate solvers. In this work, we recall the graph-based extension of propositional logic, viz. SAT modulo graphs, and the case of acyclicity constraint which keeps a digraph associated with each truth assignment acyclic. This particular extension lends itself very well for answer set computation, e.g., using extended SAT solvers, such as GraphSAT , as back-end solvers. The goal of this work, however, is to translate away the acyclicity extension altogether using a vertex elimination technique, giving rise to a translation from ASP into propositional clauses only. We use non-tight benchmarks and a state-of-the-art SAT solver, Kissat , to illustrate that performance obtained in this way can be competitive against GraphSAT and native ASP solvers such as Clasp and Wasp .
Battlefield Environment Design for Multi-agent Reinforcement Learning

In reinforcement learning, an agent interacts with an environment for learning its policy. Designing the environment is an important part of training the agent because the change of the environment can affect the agent&#x0027;s policy. In this paper, we introduce the new battlefield environment for multi-agent reinforcement learning. In our environment, two groups of agents compete for their contrasting goals with limited information. As a result, we define the map of the environment and design the state, action, reward, and transition of each agent.
Geochemical Study of Heavy Metal Contamination of Shalmanrud River Sediments

Background &amp; Aims: Due to chemical stability, low degradation, and high bioaccumulation power at different levels of the food chain, heavy metals pose many ecological hazards to living organisms. Therefore, the present study investigated the concentrations of heavy metals As, Cr, Cu, Ni, Pb, V, and Zn and evaluated the ecological, carcinogenic, and non-carcinogenic risks of the sediments of the Shalmanrud river. Materials and Methods: Nineteen samples were taken from 15 stations in summer 2021 as control and geochemical background to investigate the heavy metal contamination of the sediments of the Shalmanrud river. After preparation, the samples were chemically degraded using the ICPOES method with the digestion of four acids. Indicators of origin and contamination degree, including geoaccumulation index (Igeo), Enrichment Factor (EF), Pollution Load Index (PLI), Contamination Factor (CF), and Contamination degree (Cd), were calculated to assess the level of sediment contamination. Results: Statistical results showed that the mean concentration of heavy metals increased as As Ni&gt; Cu&gt; V&gt; Zn&gt; Cr&gt;As. The EF index for all studied metals except two samples of Pb metal was less than 1, which indicates the lack of human activity and geogenic concentration of heavy metals in the area due to the geological characteristics of the area. The Contamination Factor (CF) study showed that most samples are in the low to medium contamination class. The results of the Contamination degree (Cd) of most samples of Shalmanrud river showed a moderate contamination trend. Examination of the Pollution Load Index (PLI) showed that most samples were not polluted in the Shalmanrud river. The Ecological Risk (ER) index and Environmental Risk (ER) index of heavy metals indicated a low risk of heavy metals in the sediments of the Shalmanrud river. Conclusion: The results of this study showed that the potential risk for cancer and noncancerous diseases in children was higher than adults and by estimating the non-carcinogenic risk of all pathways (HI), Cr&gt; As&gt; Pb metals in the swallowing pathway and V&gt; Ni&gt; Cr metals were unauthorized in the study area and are hazardous to the health of residents around the Shalmanrud river. The study of contamination indices for Pb, Ni, and Cu metals showed a high level of contamination compared to other heavy metals in the area, which reflects the relatively heterogeneous distribution of these metals due to the geochemical diversity of geological units in the area and anthropogenic activities, such as agriculture, etc.
A Conception of Blockchain Platform for Milk and Dairy Products Supply Chain in an Indian Context

The potential for adulteration in the Indian dairy supply chain process is immense. The possibility of incorrect information recorded by middlemen cannot be ruled out and found to be rampant. The reality is that the data required to assess the safety and quality of milk produced is inadequate in the existing setup. The current set of checks and balances to fight adulteration of milk and dairy products in India is studied and articulated. An elaborate and daunting set of procedures marks these checks and is still significantly found wanting. To increase the product's safety and traceability of the product an alternate pathway to deploy Blockchain technology in the milk and dairy product supply chain has been proposed. Despite the proposal requiring drastic changes in the milk and dairy industry, the authors believe the benefits of implementing a Blockchain platform far outweigh the challenges involved.
A framework for effectively utilising human grading input in automated short answer grading

Short answer questions are effective for recall knowledge assessment.Grading a large amount of short answers is costly and time consuming.To apply short answer questions on MOOCs platforms, the issues of scalability and responsiveness must be addressed.Automated grading uses a computing process and a machine learning grading model to classify answers into correct, wrong, and other levels of correctness.The divide-and-grade approach is proven effective in reducing the annotation effort needed for the learning the grading model.This paper presents an improvement on the divideand-grade approach that is designed to increase the utility of human actions.A novel short answer grading framework is proposed that addresses the selection of impactful answers for grading, the injection of the ground-truth grades for steering towards purer final clusters, and the final grade assignments.Experiment results indicate the grading quality can be improved with the same level of human actions.
The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k -nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.
Dual Redundant UAV Path Planning and Mission Analysis Based on Dubins Curves

UAV cluster mission planning is an optimization problem with multiple objectives, strong coupling, and constraints. Aiming at the task planning problem of hybrid dynamic target cooperative search in UAV cluster detection, this paper introduces the tracking curves of Dubins to study the complex scenes of multiple target types. We study the Dubins tracking curve, and the Dubins tracking method is proposed to solve the search-tracking multi-task problem in UAV cluster detection. Experimental results show that ten UAV dynamics constraints and tracking distance limitations can be satisfied by adding Dubins tracking curves.
Pengembangan e-modul berbasis flipbook maker untuk meningkatkan hasil belajar peserta didik pada kelas XII SMK

This study aims to develop an e-module based on a flipbook maker in the subject of Public Relations and Protocol Governance Automation to improve learning outcomes for class XII students of the Office Governance Automation Expertise Program at SMK PGRI 6 Malang. The model used by the researcher is Research and Development (RnD) with the Borg and Gall development model. Research and development used in research can be in the form of conceptual models, theoretical models, and procedural models. This study uses two types of data, namely quantitative data and qualitative data. Quantitative data consists of two types, namely data that comes from the results of the validation assessment of the flipbook maker-based e-module product by the validator. Qualitative research data, namely comments and suggestions from the subject of research trials and product development of flipbook maker-based e-modules. the results of the validation of material experts are 88, 33% are very valid, module experts are 91, 25% are very valid and field trials are 87, 27% are very valid.
Track-to- Track Fusion for Elliptical Extended Targets Parameterized with Orientation and Semi-Axes Lengths

Track-to-track fusion considers the problem of fusing multiple tracks (e.g., from different sensor nodes) of the same target object. In case the spatial extent of the target object is estimated, unique challenges for the track-to-track fusion method arise, e.g., there may be ambiguities in the parameterization. In this work, we present an approach for distributed track-to-track fusion in case of elliptical extend targets, where correlations from common prior and process noise are explicitly incorporated. The approach is based on the previously introduced Random Ellipse Density (RED) framework, which deals with ambiguous ellipse representations and utilizes a Minimum Mean Gaussian Wasserstein (MMGW) estimator that is optimal with respect to the Gaussian Wasserstein (GW) distance. We provide simulated experiments in which measurements from different sensors are tracked by elliptic extended object trackers, fusing the trackers' estimates to improve the estimation.
Posterior linearisation filter for non-linear state transformation noises

This paper is concerned with discrete time Kalman-type filtering with state transition and measurement noises that may be non-additive or non-linearly transformed. More specifically, we extend the iterative estimation algorithm Posterior Linearization Filter (PLF) for estimation with this kind of noises. The approach solves the prediction and update step simultaneously, which allows to use the PLF iterations to improve the estimation in the non-linear state transition model. The proposed algorithm also produces single step fixed-lag smoothing estimates. We show in examples how the proposed approach can be used with non-Gaussian state transition noises and non-linearly transformed state transition noises.
Adaptation of Pedagogical Interaction between a Teacher and Students to the Realities of a Digital Language Educational Environment

The study aims to determine the most effective forms and types of pedagogical interaction between a teacher and students that make it possible to adapt its significant functions to the new realities of digital foreign language education. The paper presents a theoretical basis and interpretation for an experience of adapting pedagogical interaction between a teacher and students to the conditions of a digital language educational environment, which can be taken into account in the professional training of future foreign language teachers. Scientific novelty of the study lies in providing a theoretical substantiation for the features of a digital language educational environment (DLEE) and describing its functions, which need to be taken into consideration for the effective adaptation of pedagogical interaction between a teacher and students to a new didactic context. As a result of the study, the researchers have identified the competence repertoire necessary for a teacher to implement digital pedagogical interaction and have proposed methodological recommendations for transforming the styles of interaction between a teacher and students for their implementation in the digital language educational space.
DOI Registrations for all Ghost Blogs

This blog since earlier this month is no longer using a JAMStack setup but a regular Ghost setup using Ghost Pro for hosting. The primary driver were the new native search and native comments, but I needed to do a little bit of work to keep the DOI registration working.
Fifth International Workshop on Languages for Modelling Variability (MODEVAR@SPLC 2022)

Feature models were invented in 1990 and have been recognised as one of the main contributions to the Software Product Line community. Although there have been several attempts to establish and study a sort of standard variability modelling language, there is still no consensus on a simple feature modelling language. There can be many motivations to have one but among others, there is one that is very important: information sharing among researchers, tools and developers. Following the spirit of the four previous editions, this workshop is an interactive event where all participants share knowledge, but also ongoing realizations about how to build up a feature modelling language that all the community can agree on.
We're not gonna break it!

When configuring a software product line, finding a good tradeoff between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions are based on evolutionary search. They encode configurations as bit vectors and use standard mutation and cross-over operators, typically using the IBEA algorithm as their implementation of evolutionary search. This encoding, together with the use of standard operators, means that the algorithms create invalid configurations in intermediate steps. As a result, they require additional repair actions, reducing the efficiency of the search.
Monadic Expressions and their Derivatives

We propose another interpretation of well-known derivatives computations from regular expressions, due to Brzozowski, Antimirov or Lombardy and Sakarovitch, in order to abstract the underlying data structures (e.g. sets or linear combinations) using the notion of monad. As an example of this generalization advantage, we introduce a new derivation technique based on the graded module monad. We also extend operators defining expressions to any n-ary functions over value sets, such as classical operations (like negation or intersection for Boolean weights) or more exotic ones (like algebraic mean for rational weights). Moreover, we present how to compute a (non-necessarily finite) automaton from such an extended expression, using the Colcombet and Petrisan categorical definition of automata. These category theory concepts allow us to perform this construction in a unified way, whatever the underlying monad. Finally, to illustrate our work, we present a Haskell implementation of these notions using advanced techniques of functional programming, and we provide a web interface to manipulate concrete examples.
Update on the World Wide Web Consortium (W3C)

The World Wide Web Consortium <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (W3C) continues its efforts to bring support for rich audio-visual experiences to the web platform. Below are recent highlights.
Continual Contrastive Learning for Image Classification

Recently, self-supervised representation learning gives further development in multimedia technology. Most existing self-supervised learning methods are applicable to packaged data. However, when it comes to streamed data, they are suffering from catastrophic forgetting problem, which is not studied extensively. In this paper, we make the first attempt to tackle the catastrophic forgetting problem in the mainstream self-supervised methods, i.e., contrastive learning methods. Specifically, we first develop a rehearsal-based framework combined with a novel sampling strategy and a self-supervised knowledge distillation to transfer information over time efficiently. Then, we propose an extra sample queue to help the network separate the feature representations of old and new data in the embedding space. Experimental results show that compared with the naive self-supervised baseline, which learns tasks one by one without taking any technique, we improve the image classification accuracy by 1.60% on CIFAR-100, 2.86% on ImageNet-Sub, and 1.29% on ImageNet-Full under 10 incremental steps setting. Our code will be available at https://github.com/VDIGPKU/ContinualContrastiveLearning.
The study of factors affecting student satisfaction with learning using distance learning technologies

In the era of rapid development of educational technologies, informatisation of the learning process has become one of the most popular areas of educational activity thus providing particular importance to study the conditions allowing the construction of an electronic educational space that would successfully integrate into the educational process and ensure its effectiveness. The article describes the results of an empirical study of the relationship between the characteristics of the information and educational environment and student satisfaction with learning activities. The study involved 217 students of Witte Moscow University, studying with the use of distance learning technologies. Mathematical processing of the obtained data showed the presence of a statistically significant relationship between students' satisfaction with educational activities with the position of the pedagogue, the correspondence of teaching to students' expectations, as well as the quality of feedback. The author suggests pedagogic techniques that allow to organise training centred on the interests and capabilities of students, as well as to increase the level of satisfaction with learning in an information and educational environment. The results of the study can be used to improve training organised with the use of distance learning technologies.
Two-Layered Security Mechanism for Enhanced Biometric Security of IoT-Based Digital Wallet

Biometric Security System has been incorporated to edge detection technique to boost performance while safeguarding Digital Wallet in IoT System. As we all know that the usage of digital wallets is expanding day by day, so there is a need to build such sorts of effective model to protect distinct digital wallets. There have been numerous approaches or mechanisms accessible which are giving security of digital wallets but these present ways or strategies have their limits. Others of the present security techniques offer modest security while some are functioning miserably. Therefore iris-based biometric procedures with the incorporation of edge detection have been suggested for the security of digital wallets. The incorporation of canny based edge detection is done to lower the space consumption of the biometric model. Moreover, it is also minimizing the time consumption during a comparison process. In the future, this iris-based digital wallet security system would be employed as a secure method than other biometric approaches. The present research has focused on improving the performance during biometric comparison operations, more over the size of file are also reduced in order to save storage space. The proposed research has provided efficient, space and time saving approach. The comparison of proposed work time and space consumption has been simulated in Matlab environment.
Paving the way for enriched metadata of linguistic linked data

The need for reusable, interoperable, and interlinked linguistic resources in Natural Language Processing downstream tasks has been proved by the increasing efforts to develop standards and metadata suitable to represent several layers of information. Nevertheless, despite these efforts, the achievement of full compatibility for metadata in linguistic resource production is still far from being reached. Access to resources observing these standards is hindered either by (i) lack of or incomplete information, (ii) inconsistent ways of coding their metadata, and (iii) lack of maintenance. In this paper, we offer a quantitative and qualitative analysis of descriptive metadata and resources availability of two main metadata repositories: LOD Cloud and Annohub. Furthermore, we introduce a metadata enrichment, which aims at improving resource information, and a metadata alignment to META-SHARE ontology, suitable for easing the accessibility and interoperability of such resources.
Valuing Research Data: Blockchain-Based Management Methods

Research data sets are not just considered highly valuable for scientific purposes; these data sets could be sold and traded for economic value. Data sets could also be regarded as intangible assets, which do not have physical properties but could provide future economic benefits. With consideration that life sciences organizations possess thousands of siloed data sets, these could be sold to support additional research and could add value when life sciences organizations are appraised. Blockchain-based technologies are increasingly used to manage the control and auditability of both data and asset transactions in ways not possible with traditional databases. This chapter encourages life sciences organizations to view their data silos differently and consider the potential value these can create for the organization. This chapter describes methods to value and monetize health-oriented life sciences research data using common accounting principles. The chapter also describes the assetization of data sets and when data sets could be classified and traded using blockchain as non-fungible tokens. Last, the authors share ethical, legal, and regulatory constraints that should be considered before implementation.
A Hierarchical Approach for Spanish Sign Language Recognition: From Weak Classification to Robust Recognition System

Approximately 5% of the world’s population has hearing impairments and this number is expected to grow in the coming years due to demographic aging and the amount of noise we are exposed to. A significant fraction of this population has to endure severe impairments even since their childhood and sign languages are an effective mean of overcoming this barrier. Although sign languages are quite widespread among the deaf community, there are still situations in which the interaction with hearing people is difficult. This paper presents the sign language recognition module from an ongoing effort to develop a real-time Spanish sign language recognition system that could also work as a tutor. The proposed approach focuses on the definitions of the signs, first performing the classification of their constituents to end up recognizing full signs. Although the performance of the classification of the constituents can be quite weak, good user-independent sign recognition results are obtained.