Reviewerly/exHarmony huggingface dataset:
VALIDITAS DAN RELIABILITAS ANGKET MOTIVASI BERPRESTASI

Achievement motivation has an important role in education, besides acting as an encouragement for students to achieve optimal achievement, achievement motivation also plays a role as a determinant of learning outcomes and student learning achievements. Therefore, it is necessary to measure achievement motivation in the form of a achievement motivation questionnaire that has gone through the validity test stage and the reliability test stage. The subjects in this study were students of SMK 01 Cipongkor class XII totaling 20 students. From the results of the validity test of 38 statements there are 30 questions that are declared valid and the validity test gets a value of 0.941 which means it is included in the very high category. Thus the achievement motivation questionnaire can be used as a tool to measure the level of achievement motivation of students. Keywords: Achievement Motivation, Validity and Reliability
Textual Analysis and Sentiment Analysis in Accounting

A pesar del relativamente escaso uso de técnicas de análisis textual y de análisis del sentimiento en finanzas y contabilidad, éstas tienen un gran potencial en contabilidad, tanto por el elevado volumen de documentos utilizados para la comunicación de información financiera como por el crecimiento en el uso de herramientas digitales y medios de comunicación social. En este sentido, estas técnicas de análisis pueden ayudar a los investigadores a analizar pistas ocultas o buscar información adicional a la observada a través de los estados financieros, incrementando la cantidad y calidad de la información tradicionalmente utilizada, y proporcionando una nueva perspectiva de análisis. Por ello, el objetivo de este estudio es realizar una revisión del uso del análisis textual y del análisis del sentimiento en contabilidad. Tras presentar los conceptos de análisis textual y análisis del sentimiento y justificar teóricamente su papel en la investigación en contabilidad, llevamos a cabo una revisión de la literatura previa en el uso de estas técnicas en finanzas y contabilidad y describimos las principales técnicas de análisis del sentimiento, así como el procedimiento a seguir para el uso de esta metodología. Finalmente, sugerimos tres líneas de investigación futura que pueden beneficiarse del uso del análisis textual y del análisis del sentimiento. In spite of the relatively scarce use of textual analysis and sentiment analysis techniques in finance and accounting, they have great potential in accounting, both because of the volume of documents used for the communication of information and due to the growth in the use of digital tools and social media. In that regard, these techniques of analysis may help researchers to analyse hidden clues or look for additional information to that one observed through financial information, increasing the quantity and quality of the information traditionally used, and providing a new perspective of analysis. The aim of this study is to review the use of textual analysis and sentiment analysis in accounting. After presenting the concepts of textual analysis and sentiment analysis and expose their interest in accounting, we perform a review of the previous literature on the use of these techniques in finance and accounting and describe the main techniques of sentiment analysis, as well as the procedure to be followed for the use of this methodology. Finally, we suggest three lines of future research that may benefit from the use of textual and sentiment analysis.
Potentials of Virtual Social Spaces for Construction Education

The reality of COVID-19 public health concerns and increasing demand for distance education have forced educators to move to online delivery of their courses. Particularly in construction education, the majority of physical location-based educational activities (e.g., labs, site visits, or field trips) have been canceled during the pandemic that results in reducing students’ engagement, learning motivation, and cognitive achievement. Virtual Social Spaces (VSS) with innovative interaction affordances and immersive experience are well poised to supplement current online construction education. This paper discusses the potentials of VSS for construction education while focusing on the common applications of VSS, the communication and collaboration affordances of VSS, and design principles of this technology based on 15 popular VSS platforms. Overall, VSS applications are mainly found in education, entertainment, and socializing. The main communication and collaboration affordances of VSS include avatars, multi-user support, asynchronous commenting, synchronous chat, and visual-sharing affordances. These technical features illustrate the potentials of VSS for improving online construction education quality, eliminating the challenges associated with geographical dispersion of students, and decreasing the students’ lack of engagement.
Development Of Students' Leadership Qualities In Research Activities

The article covers the main aspects of the influence of students' research activities on the development of leadership qualities in the implementation of uniform educational requirements.Pedagogical methods and technologies of involving modern school students in research activities in the course of cognition and social interactions deserve the most attention, since modern society needs the latest technological developments, creative ideas, effective leaders and progressive scientific discoveries that bring the economy and socio-political development of the state to the highest possible level.In this regard, in the learning process, it becomes necessary to create the same pedagogical conditions and a single educational space for the development of the personality of each school student and the formation of his/her leadership qualities.The conducted psychological and pedagogical research and detailed analysis of theoretical sources made it possible to establish that this type of educational activity contributes to the natural development of many personality traits, including the formation of leadership qualities.The formulations of leadership, its structure, factors of formation are considered as a theoretical basis.The article presents modern approaches to the methodology for the development of leadership qualities of modern school students in educational activities: scientific research, project activities, cooperative learning, etc.It also demonstrates the results of empirical research on the development of personal and leadership qualities of school students in secondary and basic schools in the course of research work.
Immersive Virtual Reality in an Industrial Design Education Context: What the Future Looks Like According to its Educators

High relief is a sculpture where more than half of 3D figure is attached onto a background plane.The main problem of high relief modeling from 3D object is how to transform the 3D geometry within limited depth range.This paper presents a novel method to generate high reliefs, which benefits from the technique of Laplacian-based mesh deformation.Given a 3D object as input, we first select a set of handle points on the input model and compute their offset distances to the background.Taking these handle points as constraints, we then optimize the depth field by solving a bi-Laplacian-based linear system.The deformed object is ensured to attach onto the background with preserved depth structure and geometrical details.Our method is effective in dealing with different types of input shapes, even the ones with topology-disconnected components.Experimental results and comparisons with previous method demonstrate the effectiveness of the proposed method.
Offline Pashto Characters Dataset for OCR Systems

In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.
Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection

We present the results and main findings of the shared task at WOAH 5 on hateful memes detection.The task include two subtasks relating to distinct challenges in the fine-grained detection of hateful memes: (1) the protected category attacked by the meme and (2) the attack type.3 teams submitted system description papers.This shared task builds on the hateful memes detection task created by Facebook AI Research in 2020.
ICDAR 2021 Competition on On-Line Signature Verification

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3).
Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data

Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems.As a result, they do not contain any of the richness and variety of natural-occurring utterances, where humans ask about data they need or are curious about.In this work, we release SEDE, a dataset with 12,023 pairs of utterances and SQL queries collected from real usage on the Stack Exchange website.We show that these pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset, propose an evaluation metric based on comparison of partial query clauses that is more suitable for realworld queries, and conduct experiments with strong baselines, showing a large gap between the performance on SEDE compared to other common datasets.
Deep Multi-loss Hashing Network for Palmprint Retrieval and Recognition

With the wide application of biometrics technology, the scale of biometrics databases is increasing rapidly. In this situation, fast retrieval technology is more and more necessary for large-scale biometrics retrieval and recognition. Palmprint recognition is one of the emerging biometrics technologies. However, the research on fast palmprint retrieval algorithm is still preliminary. Hashing is one of the most popular image retrieval technologies due to its fast speed and low storage cost. In this paper, we propose a new deep palmprint hashing method, which integrates classification loss, pairing loss and quantization loss in a unified deep learning framework. Experimental results show that the proposed deep multi-loss hashing method has better performance for palmprint recognition and retrieval than other existing classic hashing methods.
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model

In version control using Git, the commit message is a document that summarizes source code changes in natural language.A good commit message clearly shows the source code changes, so this enhances collaboration between developers.To write a good commit message, the message should briefly summarize the source code changes, which takes a lot of time and effort.Therefore, a lot of research has been studied to automatically generate a commit message when a code modification is given.However, in most of the studies so far, there was no curated dataset for code modifications (additions and deletions) and corresponding commit messages in various programming languages.The model also had difficulty learning the contextual representation between code modification and natural language.To solve these problems, we propose the following two methods: (1) We collect code modification and corresponding commit messages in Github for six languages (Python, PHP, Go, Java, JavaScript, and Ruby) and release a wellorganized 345K pair dataset.(2) In order to resolve the large gap in contextual representation between programming language (PL) and natural language (NL), we use CodeBERT (Feng et al., 2020), a pre-trained language model (PLM) for programming code, as an initial model.Using two methods leads to successful results in the commit message generation task.Also, this is the first research attempt in finetuning commit generation using various programming languages and code PLM.Training code, dataset, and pretrained weights are available at https://github.com/graykode/commitautosuggestions.
Context Sensitivity Estimation in Toxicity Detection

User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets.Hence, toxicity detectors trained on current datasets will also disregard context, making the detection of context-sensitive toxicity a lot harder when it occurs.We constructed and publicly release a dataset of 10k posts with two kinds of toxicity labels per post, obtained from annotators who considered (i) both the current post and the previous one as context, or (ii) only the current post.We introduce a new task, context sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered.Using the new dataset, we show that systems can be developed for this task.Such systems could be used to enhance toxicity detection datasets with more context-dependent posts, or to suggest when moderators should consider the parent posts, which may not always be necessary and may introduce an additional cost.
Toward agent-based LSB image steganography system

Abstract In a digital communication environment, information security is mandatory. Three essential parameters used in the design process of a steganography algorithm are Payload, security, and fidelity. However, several methods are implemented in information hiding, such as Least Significant Bit (LBS), Discrete Wavelet Transform, Masking, and Discrete Cosine Transform. The paper aims to investigate novel steganography techniques based on agent technology. It proposes a Framework of Steganography based on agent for secret communication using LSB. The most common image steganography databases are explored for training and testing. The methodology in this work is based on the statistical properties of the developed agent software using Matlab. The experiment design is based on six statistical feature measures, including Histogram, Mean, Standard deviation, Entropy, Variance and Energy. For steganography, an Ensemble classifier is used to test two scenarios: embedding a single language message and inserting bilingual messages. ROC Curve represents the evaluation metrics. The result shows that the designed agent-based system with 50% training/testing sample set and 0.2 Payload can pick out the best cover image for the provided hidden message size to avoid visual artifact.
Performance Evaluation of Bundle Adjustment with Population Based Optimization Algorithms Applied to Panoramic Image Stitching

The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg–Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg–Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.
BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains

State-of-the-art image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupervised translation network, BalaGAN, specifically designed to tackle the domain imbalance problem. We leverage the latent modalities of the richer domain to turn the image-to-image translation problem, between two imbalanced domains, into a multi-class translation problem, more resembling the style transfer setting. Specifically, we analyze the source domain and learn a decomposition of it into a set of latent modes or classes, without any supervision. This leaves us with a multitude of balanced cross-domain translation tasks, between all pairs of classes, including the target domain. During inference, the trained network takes as input a source image, as well as a reference style image from one of the modes as a condition, and produces an image which resembles the source on the pixel-wise level, but shares the same mode as the reference. We show that employing modalities within the dataset improves the quality of the translated images, and that BalaGAN outperforms strong baselines of both unconditioned and style-transfer-based image-to-image translation methods, in terms of image quality and diversity.
S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging

The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents.This paper represents the second-place method for the task, an ensemble of two approaches.While one approach relies on combining different embedding methods to extract diverse semantic and syntactic representations of words in context; the other utilizes extra data with a slightly customized Self-training, a semisupervised learning technique, for sequence tagging problems.Both of our architectures take advantage of a strong language model, which was fine-tuned on a toxic classification task.Although experimental evidence indicates higher effectiveness of the first approach than the second one, combining them leads to our best results of 70.77F1-score on the test dataset.
Promoting English learning from home to Indonesian families: an alternative approach to learning foreign languages at an early age

This paper will describe an alternative approach to promoting English as a foreign language from an early age among Indonesian families. We are aware that several approaches have been taken by researchers, curriculum writers, and school teachers, but in reality, the success has not been as satisfying as the expectations of all parties, especially the government. Therefore, we have collected data and analyzed it under a descriptive qualitative study approach. Data analysis starts from applying data coding, critical interpretation, evaluation, and data organization to become valid, finding data to answer questions as above. Based on research evidence from hundreds of works of literature, we can conclude from the results of this study, among other things, that the approach to promoting English language learning in families at an early age has proven to be very successful compared to the approach to learning in schools when they are teenagers. Thus, it is hoped that this finding will be beneficial for many parties; curriculum designers, teachers, and parents.
Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections

We propose Steadiness and Cohesiveness, two novel metrics to measure the inter-cluster reliability of multidimensional projection (MDP), specifically how well the inter-cluster structures are preserved between the original high-dimensional space and the low-dimensional projection space. Measuring inter-cluster reliability is crucial as it directly affects how well inter-cluster tasks (e.g., identifying cluster relationships in the original space from a projected view) can be conducted; however, despite the importance of inter-cluster tasks, we found that previous metrics, such as Trustworthiness and Continuity, fail to measure inter-cluster reliability. Our metrics consider two aspects of the inter-cluster reliability: Steadiness measures the extent to which clusters in the projected space form clusters in the original space, and Cohesiveness measures the opposite. They extract random clusters with arbitrary shapes and positions in one space and evaluate how much the clusters are stretched or dispersed in the other space. Furthermore, our metrics can quantify pointwise distortions, allowing for the visualization of inter-cluster reliability in a projection, which we call a reliability map. Through quantitative experiments, we verify that our metrics precisely capture the distortions that harm inter-cluster reliability while previous metrics have difficulty capturing the distortions. A case study also demonstrates that our metrics and the reliability map 1) support users in selecting the proper projection techniques or hyperparameters and 2) prevent misinterpretation while performing inter-cluster tasks, thus allow an adequate identification of inter-cluster structure.
Practical quantum multiparty signatures using quantum-key-distribution networks

Digital signatures are widely used for providing security of communications. At the same time, the security of currently deployed digital signature protocols is based on unproven computational assumptions. An efficient way to ensure an unconditional (information-theoretic) security of communication is to use quantum key distribution (QKD), whose security is based on laws of quantum mechanics. In this work we develop an unconditionally secure signature scheme that guarantees authenticity and transferability of arbitrary length messages in a QKD network. In the proposed setup, the QKD network consists of two subnetworks: (i) an internal network that includes the signer and with limitation on the number of malicious nodes and (ii) an external network that has no assumptions on the number of malicious nodes. A consequence of the absence of the trust assumption in the external subnetwork is the necessity of assistance from internal subnetwork recipients for the verification of message-signature pairs by external subnetwork recipients. We provide a comprehensive security analysis of the developed scheme, perform an optimization of the scheme parameters with respect to the secret key consumption, and demonstrate that the developed scheme is compatible with the capabilities of currently available QKD devices.