Reviewerly/exHarmony huggingface dataset:
A Study on Blockchain Sandwich Attack Strategies Based on Mechanism Design Game Theory

The rapid progression of Decentralized Finance (DeFi) has established Decentralized Exchanges (DEX) as critical elements in the financial landscape. Nevertheless, the open and transparent nature of DEX makes them susceptible to strategic manipulations, especially the sandwich attack. During such maneuvers, ill-intentioned actors exploit price slippage by positioning their transactions strategically around a target’s order to reap unfair profits. This paper introduces a ground-breaking framework rooted in mechanism design game theory to lessen the impact of sandwich attacks. The framework delineates the precise strategy of the sandwich attack and its repercussions, shedding light on the tactical aspects and utility functions pertinent to both the attackers and the ordinary traders, subsequently referred to as workers. The discussion extends to defining utility functions for both the market and the workers, emphasizing the benefits of liquidity provision for the market and the potential profits and losses for the workers. The proposal encourages adopting a market-centric mechanism design grounded in game theory, wherein the market, operating as the designer, creates rules to maximize its utility while considering the workers’ utility. Through a meticulous analysis of this game-theoretic approach, the study identifies optimum strategies for all the involved parties, demonstrating that these strategies can reach a balanced state. Further, this study presents a comparative view against existing research, highlighting the limitations of contemporary solutions and asserting the effectiveness of the proposed model in protecting the interests of both the market and the workers. Ultimately, this research furnishes stakeholders with new perspectives and instruments to thwart sandwich attacks and lays a foundation for creating resilient and fair decentralized trading infrastructures.
A stream processing abstraction framework

Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value.
GlotLID: Language Identification for Low-Resource Languages

Several recent papers have published good solutions for language identification (LID) for about 300 high-resource and medium-resource languages. However, there is no LID available that (i) covers a wide range of low-resource languages, (ii) is rigorously evaluated and reliable and (iii) efficient and easy to use. Here, we publish GlotLID-M, an LID model that satisfies the desiderata of wide coverage, reliability and efficiency. It identifies 1665 languages, a large increase in coverage compared to prior work. In our experiments, GlotLID-M outperforms four baselines (CLD3, FT176, OpenLID and NLLB) when balancing F1 and false positive rate (FPR). We analyze the unique challenges that low-resource LID poses: incorrect corpus metadata, leakage from high-resource languages, difficulty separating closely related languages, handling of macrolanguage vs varieties and in general noisy data. We hope that integrating GlotLID-M into dataset creation pipelines will improve quality and enhance accessibility of NLP technology for low-resource languages and cultures. GlotLID-M model, code, and list of data sources are available: https://github.com/cisnlp/GlotLID.
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment

Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning. However, due to differences in the distribution and scale of visual-grounded datasets and language corpora, the language model tends to mix up the context of the tokens that occurred in the grounded data with those that do not. As a result, during representation learning, there is a mismatch between the visual information and the contextual meaning of the sentence. To overcome this limitation, we propose GroundedBERT - a grounded language learning method that enhances the BERT representation with visually grounded information. GroundedBERT comprises two components: (i) the original BERT which captures the contextual representation of words learned from the language corpora, and (ii) a visual grounding module which captures visual information learned from visual-grounded datasets. Moreover, we employ Optimal Transport (OT), specifically its partial variant, to solve the fractional alignment problem between the two modalities. Our proposed method significantly outperforms the baseline language models on various language tasks of the GLUE and SQuAD datasets.
Optimizing Adaptive Video Streaming with Human Feedback

Quality of Experience (QoE)-driven adaptive bitrate (ABR) algorithms are typically optimized using QoE models that are based on the mean opinion score (MOS), while such principles may not account for user heterogeneity on rating scales, resulting in unexpected behaviors. In this paper, we propose Jade, which leverages reinforcement learning with human feedback(RLHF) technologies to better align the users' opinion scores. Jade's rank-based QoE model considers relative values of user ratings to interpret the subjective perception of video sessions. We implement linear-based and Deep Neural Network (DNN)-based architectures for satisfying both accuracy and generalization ability. We further propose entropy-aware reinforced mechanisms for training policies with the integration of the proposed QoE models. Experimental results demonstrate that Jade performs favorably on conventional metrics, such as quality and stall ratio, and improves QoE by 8.09%-38.13% in different network conditions, emphasizing the importance of user heterogeneity in QoE modeling and the potential of combining linear-based and DNN-based models for performance improvement.
OccluBEV: Occlusion Aware Spatiotemporal Modeling for Multi-view 3D Object Detection

Bird's-Eye-View (BEV) based 3D visual perception, which formulates a unified space for multi-view representation, has received wide attention in autonomous driving due to its scalability for downstream tasks. However, view transform in transformer-based BEV methods is agnostic of 3D occlusion relationships, resulting in model degradation. To construct a higher-quality BEV space, this paper analyzes the mutual occlusion problems in the view transform process and proposes a new transformer-based method named OccluBEV. OccluBEV alleviates the occlusion issue via point cloud information distillation in both the image and BEV space. Specifically, in the image space, we perform depth estimation for each pixel and utilize it to guide image feature mapping. Further, since predicting depth directly from monocular image is ill-posed, ignoring stereo information such as multi-view and temporal cues, this paper introduces a voxel visibility segmentation task in 3D BEV space. The task explicitly predicts whether each voxel in the 3D BEV grid is occupied or not. In addition, to alleviate the overfitting problem in BEV feature learning under a single task, we design a multi-head learning framework which jointly models multiple strongly-correlated tasks in a unified BEV space. The effectiveness of the proposed method is fully validated on the nuScenes dataset, achieving a competetive NDS/mAP score of 57.5/47.9 on the nuScenes test leaderboard using ResNet101 backbone, which is superior to state-of-the-art camera-based solutions.
Benchmarking Computer-Vision-Based Facial Emotion Classification Algorithms While Wearing Surgical Masks

Effective human communication relies heavily on emotions, making them a crucial aspect of interaction. As technology progresses, the desire for machines to exhibit more human-like characteristics, including emotion recognition, grows. DeepFace has emerged as a widely adopted library for facial emotion recognition. However, the widespread use of surgical masks after the COVID-19 pandemic presents a considerable obstacle to its performance. To assess this issue, we conducted a benchmark using the FER2013 dataset. The results revealed a substantial performance decline when individuals wore surgical masks. “Disgust” suffers a 22.6% F1-score reduction, while “Surprise” is least affected with a 48.7% reduction. Addressing these issues improves human–machine interfaces and paves the way for more natural machine communication.
Training Future Citizens through Audiovisual Resources

This approach is positioned as a vital resource to stimulate learning from different perspectives, generating a major social impact, the particularity of this research was to promote social responsibility and socio-educational training in students of an educational institution in northern Peru. Framed in a descriptive and explanatory analysis, a questionnaire was elaborated in order to know the perception of the students, taking a sample of 120 students, who revealed the metacognitive processes in their construction of knowledge, the reflection on their cognitive abilities, the control and planning of their performance, evidencing an active and significant learning in the development of vital competences.
On the Relevance of the Study of Professional Competence of Specialists: Analysis of Approaches

. The article considers modern approaches to the research of professional competence of specialists. Five main approaches are identified, within the framework of which professional competence is understood as a set of acquired knowledge, skills and abilities of a person, a specialist; a personal construct that includes a set of characteristics of a specialist’s personality; the method of performing labor functions, applicable to a single activity, including tactics and strategies for implementation in labor; readiness and ability of a specialist to im-plement the already acquired knowledge, skills and abilities in professional activities; a certain level of profes-sional training of the subject of professional activity and the ability to perform professional tasks. Promising directions of professional competence research, based on the consideration of individual competence re-sources, application of modern educational practices for its formation, development of strategic competence as part of professional interaction, which can serve as a basis for the creation and implementation of a systematic approach, a model of psychological support of professional activity of specialists, are highlighted.
INFORMATION SYSTEM FOR ADMISSION OF STUDENT INTERNSHIP AT RRI MEDAN

The Internship Acceptance Information System at RRI Medan is a platform designed to facilitate the process of accepting internship students at Radio Republik Indonesia (RRI) Medan. The purpose of this system is to increase efficiency and effectiveness in the process of accepting apprentice students and to strengthen the relationship between RRI Medan and the surrounding universities. This system offers several important features, including online registration, submission of files, and tracking of application status. Students who are interested in doing an internship at RRI Medan can access this system via a website or an easy-to-use mobile application. They can fill out the online registration form and upload the required files, such as CVs, grade transcripts, and letters of recommendation. After registration is complete, the system will process the application and provide notifications to students regarding the status of their application. Students can track the progress of their applications through this system, including information regarding interview schedules or other selection tests. If accepted, students will receive confirmation through this system and can continue with the internship preparation stage. This information system is built on a web basis.
THE EFFECTS OF A WEB-BASED DIDACTIC DESIGN ON TEACHERS’ DIGITAL COMPETENCE AND THE USE OF TECHNOLOGICAL TOOLS IN THE ENGLISH CLASSROOM

The research study intends to assess the effectiveness of a Web-Based Learning Design to train English Teachers in incorporating technology into language teaching. The research process follows the stages of the ADDIE instructional design. This study is a mixed-methods quasi-experimental (intervention) design since the researcher collects and analyzes quantitative and qualitative data and integrates the information within an intervention to capture the participants' personal experiences (Creswell &amp;amp; Creswell, 2018). The target population comprises English teachers in Colombia who voluntarily accept to be part of the study.
The Lexico-grammatical Ecology of Media Arabic Genre: A Multi-dimensional Analysis

Media Arabic is a linguistically independent genre of Modern Standard Arabic. There are a number of explanatory reasons accounting for a characterized, integrally structured media Arabic form. Much within our scientific grasp is the overall combination of lexico-grammatical traits, constituting a major part of the internal textual ecology. Within our reach, again, there is parsimonious literature hardly informing, or predicting, what situationally and linguistically a media Arabic text is. The vast body of research is intuition-governed and prescriptive. This paper is a concise exposition of a work endeavoring to fill out such a gap in research. It summarizes the findings of a large-scale project in Arabic corpus linguistics that empirically investigates media Arabic genre from a functional lexico-grammatical perspective, by using Multi-Dimensional framework. The model uncovers the parameters of variation in media Arabic and explores underlying co-occurring functions. In documenting MSA textual similarities and differences among ten written media subgenres: Politics, Economics, Society, Sports, Science, Culture &amp; Arts, Technology, Women, Health &amp; Medicine, and Religion, Exploratory Factor Analysis was run and resolved within a six-factorial structure, the raw output to be subsequently interpreted as six-dimensional basis for the variable use of Arabic across the ten subgenres.
Application of the 'English Grammar' Application in improving student Grammar

In this research, researchers make grammar and applications as the focus of research to improve students' abilities in learning grammar. This study used an experimental method with a quasi-experimental design. Participants in this study were divided into two groups, namely the control group and the experimental group. Based on the data processing, it can be concluded that improving the grammar skills of this English grammar application has a positive impact on improving students' English grammar skills.
MICROBIOLOGICAL ASSESSMENT OF FECAL CONTAMINATION IN THE RIVER WATERS OF THE VOGHJI RIVER CATCHMENT BASIN

Quantitative parameters of total and fecal coliform, as well as Escherichia coli bacteria in the river waters of the Voghji River catchment basin were investigated to characterize fecal contamination in the investigated watershed basin. Water samples for microbial analyses were collected from the Voghji River and its Geghi, Norashenik and Vachagan tributaries in 2016, 2017 and 2020. The results of the study revealed the distribution, potential sources and environmental risks of fecal contamination in the Voghji River catchment basin.
Chandelier: Interaction Design With Surrounding Mid-Air Tangible Interface

Chandelier is a mid-air tangible interface where a user is surrounded in the center by 120 pendants that levitate independently and orbit in 5 concentric circumferences, where each pendant is touch-enabled and color-changeable by default. We explore interactions with Chandelier such as change blindness and repurposing formations from immersive experiences to mitigate the limitation of the hardware systems. We discuss the extent of Surrounding Mid-Air interactions in tangible interfaces and the design factors that could be brought into experiences of future levitation interfaces.
Conductive,Ferromagnetic and Bendable 3D Printed Hair for Designing Interactive Objects: Conductive,Ferromagnetic and Bendable 3D Printed Hair

The hair structure, a characteristic object that can be formed using 3D printers, is used to enrich the expressivity and haptic sensation of a printed object. However, in conventional 3D printing techniques, the hair structure is printed using a uniform and general plastic such as Poly-Lactic Acid (PLA). In this study, we attempt to print the hair structure using conductive and magnetic iron filaments, commonly used to allow an Fused Deposition Modeling (FDM) printer to create a functional object, to extend the possibility of the 3D printed hair technique. Furthermore, we planted the printed hair in the soft resin used for resin crafts. We provide detailed material information and validate the printability of each filament. With these methods, we demonstrate applications, such as hairy devices that can detect when a human touches hair, brushes attracted by magnet arrays, and flexible attachment of hair structures to the human body.
JudgeLM: Fine-tuned Large Language Models are Scalable Judges

Evaluating Large Language Models (LLMs) in open-ended scenarios is challenging because existing benchmarks and metrics can not measure them comprehensively. To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks. We first propose a comprehensive, large-scale, high-quality dataset containing task seeds, LLMs-generated answers, and GPT-4-generated judgments for fine-tuning high-performance judges, as well as a new benchmark for evaluating the judges. We train JudgeLM at different scales from 7B, 13B, to 33B parameters, and conduct a systematic analysis of its capabilities and behaviors. We then analyze the key biases in fine-tuning LLM as a judge and consider them as position bias, knowledge bias, and format bias. To address these issues, JudgeLM introduces a bag of techniques including swap augmentation, reference support, and reference drop, which clearly enhance the judge's performance. JudgeLM obtains the state-of-the-art judge performance on both the existing PandaLM benchmark and our proposed new benchmark. Our JudgeLM is efficient and the JudgeLM-7B only needs 3 minutes to judge 5K samples with 8 A100 GPUs. JudgeLM obtains high agreement with the teacher judge, achieving an agreement exceeding 90% that even surpasses human-to-human agreement. JudgeLM also demonstrates extended capabilities in being judges of the single answer, multimodal models, multiple answers, and multi-turn chat.
Unraveling Feature Extraction Mechanisms in Neural Networks

The underlying mechanism of neural networks in capturing precise knowledge has been the subject of consistent research efforts. In this work, we propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms. Specifically, considering the infinite network width, we hypothesize the learning dynamics of target models may intuitively unravel the features they acquire from training data, deepening our insights into their internal mechanisms. We apply our approach to several fundamental models and reveal how these models leverage statistical features during gradient descent and how they are integrated into final decisions. We also discovered that the choice of activation function can affect feature extraction. For instance, the use of the \textit{ReLU} activation function could potentially introduce a bias in features, providing a plausible explanation for its replacement with alternative functions in recent pre-trained language models. Additionally, we find that while self-attention and CNN models may exhibit limitations in learning n-grams, multiplication-based models seem to excel in this area. We verify these theoretical findings through experiments and find that they can be applied to analyze language modeling tasks, which can be regarded as a special variant of classification. Our contributions offer insights into the roles and capacities of fundamental components within large language models, thereby aiding the broader understanding of these complex systems.
The use and abuse of artificial intelligence-enabled machine translation in the EFL classroom: An exploratory study

The integration of machine translation (MT) and artificial intelligence (AI) has significantly enhanced the precision of MT systems producing translations that rival the quality of skilled human translators. This innovation has broadened the applicability of AI-enabled MT systems attracting users for a variety of purposes. This paper explores the following areas: the application of AI-enabled MT, specifically Google Translate (GT) in English as a Foreign Language (EFL) classroom. Empirical findings and recent research indicate that students are increasingly reliant on MT particularly in higher educational settings where English is not their first language. The purpose of this research is to provide insight on the practices, beliefs and goals of EFL learners who use GT in the classroom. Data was gathered from 234 university students through a questionnaire along with pre- and post-tests to compare the quality of writing drafts created with and without the aid of GT. The findings are consistent with those of previous research conducted worldwide highlighting the immense potential of AI-enabled MT in not only boosting students' learning experiences but also cultivating more independent learners. The study also indicates that students have positive attitudes towards GT and employ diverse search strategies to address various language-related challenges.
Centroid Tuplet Loss for Person Re-Identification

Person Re-Identification (Person Re-ID) is an important computer vision task in area surveillance, in which the goal is to match a person’s identity across different cameras or locations in videos or image sequences. To solve this task, Deep Metric Learning with the combination of different neural networks and metric losses such as Triplet Loss has become a common framework and achieved several remarkable results on benchmark datasets. However, Deep Metric Learning loss functions often depend on delicately sampling strategies for faster convergence and effective learning. These common sampling strategies usually rely on calculating embedding distances between samples in training datasets and selecting the most useful triplets or tuplets of images to consider, which makes these methods computationally expensive and may incur the risk of causing sample bias. Additionally, Triplet Loss also appears fragile to outliers and noisy labels. In this paper, we designed a centroid-based metric loss function, Centroid Tuplet Loss, which uses randomly selected mean centroid representations of classes in each mini-batch to achieve better retrieval performance. Experiments on two widely used Person Re-ID datasets, Market-1501 and CUHK03 dataset, demonstrates the effectiveness of our method over existing state-of-the-art methods.