Reviewerly/exHarmony huggingface dataset:
Empirical Analysis of Cheating Among Students on the Basis of Surveys

The article examines cheating among students as a special case of opportunism studied by the new institutional economic theory. The study is based on a survey in which more than 500 students from the Higher School of Economics in St. Petersburg took part from March to April 2023. This survey was complemented by an experiment in the form of a multi-question quiz designed to test students' knowledge of popular culture, economics, and technology, with both the format of the quiz and its content encouraging students to be opportunistic. As a result, in such an experimental way, it was possible to prove the connection between the use of a cheat sheet and cheating in everyday life. The authors built five logistic regressions - one for each variable explained in the form of one or another manifestation of cheating - double delivery of work, submission of other people's work, purchase of work, use of a cheat sheet, and fabrication of data. Among the factors influencing the likelihood of belonging to a group of unscrupulous students, one can highlight high income, studying in educational programs such as Management, Economics, Political Science, Public Policy, and Analytics, a focus on performance instead of mastery, being at the bottom of the rating, the presence of retakes, a negative attitude towards academic ratings, distance learning, deviant behavior in the form of theft, violation of prohibitions and stowaways, the external environment, and a short session. This study also made it possible to create a typical portrait of a fraudster at HSE using clustering associated with the identification of unobserved groups based on the presented observed categorical variables.
Contact tracing using a real-time location system in a tertiary-care hospital in Singapore

Background: Densely populated metropolitan cities like Singapore are susceptible to emerging infectious disease (EID) outbreaks. Singapore’s pandemic control measures include running biennial simulation exercises for all public hospitals on EID case management, in which a key assessment criterion is contact tracing. Current methods of contact tracing that involve retrospective review of the electronic medical record (EMR) are time-consuming and heavily manpower dependent, and they fail to capture a significant number of contacts. A real-time location system (RLTS) was accurate and effective in contact tracing. We compared the time taken to perform contact tracing and list of contacts identified for RTLS versus EMR, and we compared manpower and manpower hours required to perform contact tracing for RTLS versus EMR. Then we extrapolated the cost incurred by RTLS versus EMR. Methods: A prospective case study was conducted during a simulation exercise to determine and compare the list of contacts, time required, manpower required, and manpower hours required between RTLS and EMR. The costs of both methods were also compared. Results: RTLS identified almost 3 times more contacts than EMR (Fig. 2) with a 96.2% reduction in time taken, a 97.6% reduction in manpower, and a 97.5% reduction in manpower hours (Fig. 1). RTLS incurred significant equipment cost and therefore might require many contact-tracing episodes before providing economic benefit (Fig. 3). However, its speed and accuracy provided during contact tracing will allow the hospital to quickly isolate potentially exposed contacts, reducing the number of infected people during the spread of an infectious disease, particularly one like COVID-19. Conclusions: Albeit costly, RTLS is effective at contact tracing. RTLS has the potential to be the gold standard in contact-tracing methods of the future, particularly considering the current pandemic. Disclosures: None
Evaluation of Innovation and Entrepreneurship Education for College Students Based on BP Neural Network

With the steady advancement, innovation, and entrepreneurship education (IEE) in higher education institutions has gradually become a powerful measure and a vital approach for cultivating talents. Therefore, establishing a complete and feasible evaluation system for IEE is vital for the enhancement of education level and quality. On the basis of the principles for the evaluating system, the study selected the evaluation indicators for IEE. Then, the adaptive flower pollination algorithm was used to evolve the BP’s initial weight and threshold applied to the evaluation of the IEE of college students. The improved BP network had a faster speed in the first to fifth iteration, and the mean square error converged between 10-7 and 10-6. In the comparison of fitness values, the method converged only in the 16th generation, and the fitness value was stable at about 1.95. In practical application, the error rate of this method was about 13%, and it had a high accuracy rate. Its high accuracy indicated that this method could effectively evaluate the IEE in higher education institutions, and provided certain technical support for the cultivation of innovative talents.
Uniform Training and Marginal Decoding for Multi-Reference Question-Answer Generation

Question generation is an important task that helps to improve question answering performance and augment search interfaces with possible suggested questions. While multiple approaches have been proposed for this task, none addresses the goal of generating a diverse set of questions given the same input context. The main reason for this is the lack of multi-reference datasets for training such models. We propose to bridge this gap by seeding a baseline question generation model with named entities as candidate answers. This allows us to automatically synthesize an unlimited number of question-answer pairs. We then propose an approach designed to leverage such multi-reference annotations, and demonstrate its advantages over the standard training and decoding strategies used in question generation. An experimental evaluation on synthetic, as well as manually annotated data shows that our approach can be used in creating a single generative model that produces a diverse set of question-answer pairs per input sentence.
Lifted Successor Generation by Maximum Clique Enumeration

Classical planning instances are often represented using first-order logic; however, the initial step for most classical planners is to transform the given instance into a propositional representation. For example, action schemas are converted into ground actions, aiming to generate as few ground actions as possible without eliminating any viable solutions to the problem. This step can become a bottleneck in some domains due to the exponential blowup caused by the grounding process. A recent approach to alleviate this issue involves using the lifted (first-order) representation of the instance and generating all applicable ground actions on-the-fly during the search for each expanded state. In this paper, we propose a method that addresses this problem by enumerating all maximum cliques of a graph encoding the state and the action schema’s preconditions. We compare our method with state-of-the-art across 47 domains, showcasing improved performance in 23 domains. In some cases, simply changing the maximum clique enumeration algorithm results in a significant speedup compared to the state-of-the-art.
Enhanced Machine Reading Comprehension Method for Aspect Sentiment Quadruplet Extraction

In the NLP domain, Aspect-Based Sentiment Analysis (ABSA) has gained significant attention in recent years due to its ability to perform fine-grained sentiment analysis. A challenging task in ABSA is Aspect Sentiment Quadruplet Extraction (ASQE), which involves the extraction of aspect terms and their associated opinion terms, sentiment polarities, and categories in the form of quadruplets. However, existing studies have ignored the strong dependence among the multiple subtasks involved in ASQE. In this paper, we propose a novel Enhanced Machine Reading Comprehension (EMRC) method and formalize ASQE task as a multi-turn MRC task. Our EMRC effectively learns and utilizes the relationships among different subtasks by incorporating previously generated query answers into the current queries. We design a hierarchical category classification strategy to perform the category prediction in a structured manner, enabling the model to tackle intricate categories with ease. Furthermore, we employ the bi-directional attention mechanism, i.e., context-to-query and query-to-context attentions, to map the context into a task-aware representation. We conduct extensive experiments on two benchmark datasets. The results demonstrate that EMRC outperforms the state-of-art baselines. The source code is publicly available at https://github.com/Little-Yeah/EMCR.
Uncertainty-Driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning

Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can not be guaranteed that the generated samples from the trained dynamics model are reliable (e.g., some synthetic samples may lie outside of the support region of the static dataset). To address this issue, we propose Trajectory Truncation with Uncertainty (TATU), which adaptively truncates the synthetic trajectory if the accumulated uncertainty along the trajectory is too large. We theoretically show the performance bound of TATU to justify its benefits. To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO. Furthermore, we integrate TATU with several off-the-shelf model-free offline RL algorithms, e.g., BCQ. Experimental results on the D4RL benchmark show that TATU significantly improves their performance, often by a large margin. Code is available here.
ESSL: Enhanced Spatio-Temporal Self-Selective Learning Framework for Unsupervised Video Anomaly Detection

Unsupervised Video Anomaly Detection (UVAD) utilizes completely unlabeled videos for training without any human intervention. Due to the existence of unlabeled abnormal videos in the training data, the performance of UVAD has a large gap compared with semi-supervised VAD, which only uses normal videos for training. To address the problem of insufficient ability of the existing UVAD methods to learn normality and reduce the negative impact of abnormal events, this paper proposes a novel Enhanced Spatio-temporal Self-selective Learning (ESSL) framework for UVAD. This framework is designed for capturing both the appearance and motion features through effective network structures by solving the spatial and temporal jigsaw puzzles. Specially, we develop a Self-selective Learning Module (SLM) for UVAD, which prevents the model learning abnormal features and enhances the model by selecting normal features. Experimental results on three benchmark datasets show that the proposed method not only surpasses the state-of-the-art UVAD works, but also achieves the performance comparable to the classic semi-supervised methods for video anomaly detection that needs normal videos selected manually. Code is available at: https://github.com/xusuger/ESSL.
Preserving Semantics in Textual Adversarial Attacks

The growth of hateful online content, or hate speech, has been associated with a global increase in violent crimes against minorities [23]. Harmful online content can be produced easily, automatically and anonymously. Even though, some form of auto-detection is already achieved through text classifiers in NLP, they can be fooled by adversarial attacks. To strengthen existing systems and stay ahead of attackers, we need better adversarial attacks. In this paper, we show that up to 70% of adversarial examples generated by adversarial attacks should be discarded because they do not preserve semantics. We address this core weakness and propose a new, fully supervised sentence embedding technique called Semantics-Preserving-Encoder (SPE). Our method outperforms existing sentence encoders used in adversarial attacks by achieving 1.2× ∼ 5.1× better real attack success rate. We release our code as a plugin that can be used in any existing adversarial attack to improve its quality and speed up its execution. (The code, datasets and test examples are available at https://github.com/DavidHerel/semantics-preserving-encoder.)
Graduality in Probabilistic Argumentation Frameworks

Gradual semantics are methods that evaluate overall strengths of individual arguments in graphs. In this paper, we investigate gradual semantics for extended frameworks in which probabilities are used to quantify the uncertainty about arguments and attacks belonging to the graph. We define the likelihoods of an argument’s possible strengths when facing uncertainty about the topology of the argumentation framework. We also define an approach to compare the strengths of arguments in this probabilistic setting. Finally, we propose a method to calculate the overall strength of each argument in the framework, and we evaluate this method against a set of principles.
Reducing Oversmoothing in Graph Neural Networks by Changing the Activation Function

The performance of Graph Neural Networks (GNNs) deteriorates as the depth of the network increases. That performance drop is mainly attributed to oversmoothing, which leads to similar node representations through repeated graph convolutions. We show that in deep GNNs the activation function plays a crucial role in oversmoothing. We explain theoretically why this is the case and propose a simple modification to the slope of ReLU to reduce oversmoothing. The proposed approach enables deep networks without the need to change the network architecture or to add residual connections. We verify the theoretical results experimentally and further show that deep networks, which do not suffer from oversmoothing, are beneficial in the presence of the “cold start” problem, i.e. when there is no feature information about unlabeled nodes.
Gaifman Graphs in Lifted Planning

We introduce the metric induced by Gaifman graphs into lifted planning. We analyze what kind of information this metric carries and how it can be utilized for constructing lifted delete-free relaxation heuristics. In particular, we prove how the action dynamics influence the distances between objects. As a corollary, we derive a lower bound on the length of any plan. Finally, we apply our theoretical findings on the Gaifman graphs to improve the delete-free relaxation heuristics induced by PDDL homomorphisms.
Exploiting Epistemic Uncertainty at Inference Time for Early-Exit Power Saving

Distinguishing epistemic from aleatoric uncertainty is a central idea to out-of-distribution (OOD) detection. By interpreting adversarial and OOD inputs from this perspective, we can collect them into a single unclassifiable group. Rejecting such inputs mid-inference will reduce resource usage. To achieve this, we apply k-nearest neighbour (KNN) classifiers to the embedding space of branched neural networks. This introduces a novel means of additional power savings, through an early-exit reject. Our technique works out-of-the-box on any branched neural network and can be competitive on OOD benchmarks, achieving an area under receiver operator characteristic (AUROC) of over 0.9 in most datasets, and scores of 0.95+ when identifying perturbed inputs. A mixed input test set is introduced, we show how OOD inputs can be identified up to 50% of the time, and adversarial inputs up to 85% of the time. In a balanced test environment, this equates to power savings of up to 18% in the OOD scenario and 40% in the adversarial scenario. This allows a more stringent in-distribution (ID) classification policy, leading to accuracy improvements of 15% and 20% on the OOD and adversarial tests, respectively, when compared to conventional exit policies operating under the same conditions.
Diverse, Top-k, and Top-Quality Planning Over Simulators

Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.
Causal Discovery and Knowledge Injection for Contestable Neural Networks

Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them. We propose a novel method overcoming these issues by allowing a two-way interaction whereby neural-network-empowered machines can expose the underpinning learnt causal graphs and humans can contest the machines by modifying the causal graphs before re-injecting them into the machines. The learnt models are guaranteed to conform to the graphs and adhere to expert knowledge, some of which can also be given up-front. By building a window into the model behaviour and enabling knowledge injection, our method allows practitioners to debug networks based on the causal structure discovered from the data and underpinning the predictions. Experiments with real and synthetic tabular data show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7x smaller in the input layer, compared to SOTA regularised networks.
Omega-Regular Reward Machines

Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success. However, in many cases, the complexity of the learning objectives goes beyond the capabilities of the Markovian assumption, necessitating a more sophisticated reward mechanism. Reward machines and ω-regular languages are two formalisms used to express non-Markovian rewards for quantitative and qualitative objectives, respectively. This paper introduces ω-regular reward machines, which integrate reward machines with ω-regular languages to enable an expressive and effective reward mechanism for RL. We present a model-free RL algorithm to compute ε-optimal strategies against ω-regular reward machines and evaluate the effectiveness of the proposed algorithm through experiments.
Accelerating SAT-Based HTN Plan Verification by Exploiting Data Structures from HTN Planning

Plan verification is the task of deciding whether a given plan is a solution to a planning problem. In this paper, we study the plan verification problem in the context of Hierarchical Task Network (HTN) planning, which has been proved to be NP-complete when partial order (PO) is involved. We will develop a novel SAT-based approach exploiting the data structures solution order graphs and path decomposition trees which encodes an HTN plan verification problem as a SAT one. We show in our experiments that this new approach outperforms the current state-of-the-art (SOTA) planning-based approach for verifying plans for POHTN problems.
The Use of Grade-Level Readers in the Foundation Phase

Grade-Level Readers in the form of Reading Anthologies are provided in South African Foundation Phase classrooms (Grades 1-3) for home language learners in order to promote their reading interest and make teaching reading easier. However, evidence shows that teachers lack an understanding of teaching reading in line with methodologies prescribed in the curriculum. This study
Online Voter’s Education, Change of Voting Perceptions, Awareness and Social Media Use of Fake News and Fact-Checking Behaviors Among Tertiary Students

Voter’s education is an avenue which engages student-voters into political socialization. However, their exposure to social media and lack of keenness in verifying information can make them vulnerable to spread of fake news which in the long run may affect voting perceptions and behavior. Others may not be conducting fact checking behaviors that are necessary
Bridging the Gap Between Research and Education: The μNet Project and High School Science Education in Greece

Many educators believe that hands-on experience is more effective than classroom learning for science education. However, incorporating rapidly-evolving branches of science, such as Astronomy and Astroparticle Physics, into secondary education can be challenging due to the complexity and cost of equipment and the need for advanced scientific methodologies. To address this issue, the Physics Laboratory
The Value of Leisure Reading Among the Youth in A Higher Education Institution in South Africa

Some young adults like reading, but others struggle. They may not have learned the value of reading or how to read throughout their early schooling. Many children may grow up without local or worldwide information if they don't read. Due to their bad social group, not reading may keep them busy and out of trouble.
Exploring the Dynamics of Ice Hockey Strategies Using YOLOv8 and Gephi in Sports Education

In modern fixed-field team sports, computer vision techniques have been commonly applied in analyzing team strategies and tactics. This research presents a combination approach using object detection, multi-object tracking, and social network analysis (SNA) to investigate the dynamics of ice hockey strategies. Specifically, we utilize YOLOv8 object detection algorithm to detect players and ByteTrack to
TIC – TAC and Digital Competences in Military Higher Education

In the digital age, education with excellence must be supported by teachers with advanced levels in digital competencies, therefore, the research identifies the competency areas and their available levels by the different actors in military higher education, applying the scientific method and its progress is descriptive; through a survey to 124 people among Management staff,
Introducing Mobile Learning Into the Primary School Curriculum: A Case Study of the Continuous Pedagogical Use of Mobile Devices

The present paper is a brief overview of an innovative educational program in Primary Education regarding the pedagogical use of mobile devices in teaching. The program adopted Puentedura's (2010) SAMR model, which is based on Bloom's Revised Taxonomy, and proposes four levels of integration of digital applications into teaching practice. The program, which was implemented
Collaborative Lessons in a Cross-Border Space: Learning Each Other’s Language, Literature and History on the Basis of the CoBLaLT Model

In the paper we propose a new paradigm for teaching literature, language and history in cross-border territories from the point of view of cross-border didactics and cross-curricular collaboration at the higher elementary school level. Based on the CoBLaLT model, we propose interdisciplinar activities for students of two cross-border classes on both sides of the Slovenian-Italian
Enhance Your Student’s Perceived Learning, Attention and Motivation With Immersive Activities and Metaverse – Based Learning

This research was designed to test the effectiveness that immersive tools and activities, specifically metaverse-based activities, has on student learning. For this research, an experiment was designed with a control group and an experimental group in the course EM1009 - Business Model Innovation that is an Online Course lasting 5 weeks and was imparted in