Reviewerly/exHarmony huggingface dataset:
A survey of machine learning based techniques for hate speech detection on Twitter

The use of the Internet and social networks for communication has significantly increased in recent years. Twitter is the third most popular worldwide Online Social Network (OSN), only after Facebook and Instagram. Compared to other OSNs, Twitter presents a simpler data model and more straightforward data access API, making it a valuable tool for studying and analyzing online behavior, including abusive patterns. This survey attempts to create a machine learning-based guide for automatic hate speech classification, including a description of Twitter's technology and terminology, social graphs, sentiment analysis concepts, and hate speech identification. This study also adopted a systematic literature review on the most advanced computing techniques involved with the subject, focusing on state-of-the-art machine learning and research directions.
Let’s Get Digital: ICT Training Needs in Pre-Service Language Teaching

During the last five years, language teaching in Europe has been heavily influenced by two major occurrences. On the one hand, the outbreak of COVID-19 forced teachers to extensively adapt many of their teaching practices to the digital world; this major paradigm shift is likely to have continued repercussions post-pandemic in terms of methodology and use of resources. At the same time, the publication of an updated version of the Common European Framework, commonly known as the Companion Volume focuses our attention on the real-life communicative needs of language users. The Companion Volume emphasizes digital and online communication processes throughout the development of language skills, and this focus inevitably translates into changes in national and regional curricula for language education. The present study investigates the degree to which future teachers are prepared for this new reality and explores emerging digital training needs among pre-service teachers. The investigation obtains quantitative and qualitative data from 30 pre-service teachers who have completed postgraduate studies in language education, which included a stage of school-based teaching practice. Results indicate that while pre-service language teachers have a positive view of ICT and moderate levels of general digital competence, they feel additional specific and in-depth preparation is required within their initial training.
Sonic placemaking—facilitating listening behaviour through design

In this article, we build on space and place theory to define sonic space and sonic place as fundamental concepts for designers who wish to incorporate audible elements into their work. Based on an interdisciplinary approach that brings together interaction design and sound studies, we propose a Sonic Placemaking Model to guide designers in exploring the sonic environment and address how design choices affect space and place. Based on the development of soundscape designs for three cultural history museums, we unfold the years-long design process and discuss how we applied methods from design and sound studies to understand users as listeners. Lastly, we present three areas of concern when designing for sonic placemaking: (1) going from sounds of objects to sounds of activities, (2) designing for informative and atmospheric experiences, and (3) facilitating spatial and temporal experiences.
Application of new technologies in narrow Fields

The agricultural sector, a pivotal player in shaping our future, is confronted with an array of challenges that necessitate innovative solutions. This article serves as a comprehensive exploration of the transformative role played by new technologies in agriculture, emerging as a beacon of hope in addressing the escalating issues facing the sector. In a landscape characterized by dynamic changes, be it technological advancements, evolving regulations, or the surging demands of a growing population, this work delves into the crux of the matter. Beginning with a succinct introduction to the topic, the narrative navigates through the complexities that mandate the implementation of new technologies in agriculture. The primary focus of this article lies in delineating the utilization of novel technologies and methods within the agricultural sector while concurrently shedding light on the intrinsic issues necessitating their adoption. Through a synergistic approach, the article aims to not only highlight the pressing challenges but also underscore the instrumental role of technology in paving the way for a more sustainable and efficient agricultural future.
Effective methods of improving the quality of education in biology lessons through computer technology

Improvement of school biological education is required at all levels of its organization. At the same time, the introduction of modern computer technologies into the learning process is of particular importance. One of the most natural and productive ways of introducing them into the educational system of the school is to directly link the learning process with the improvement of the content, methods and forms of teaching, orienting the entire program towards solving generally significant pedagogical problems. The article discusses the features of the use of modern computer technologies as one of the ways to increase the interest of schoolchildren in biology. The results of experimental work carried out on the basis of secondary school No 5 of the Nizhnekamsk region of the Republic of Tatarstan are presented. Based on the results of the analysis, conclusions were drawn about the effectiveness of the use of computer technologies in the educational process of the school.
Evaluation of effluent treatment efficiency by fractal analysis

The paper proposes a comparative evaluation method of effluent treatment efficiency for different effluent treatment facilities using fractal analysis. The smallest value of the fractal dimension of the field of compass-diagrams of the multiplicity of exceeding the maximum permissible concentration (hereinafter - MPC) by pollutants of effluent is taken as an integral criterion of the effluent purification degree. Pharmaceutical industry enterprises purification method of modeled values of treatment systems effluent indicators has been tested.
Information technologies in the educational process of ecology and environmental sciences

The article deals with the advantages of using information technology in the process of education for ecology and environmental science. Information technologies significantly expand the possibilities of presenting educational information. The use of color, graphics, sound, and all modern video equipment allows you to recreate the real environment of the activity. The use of ICT in the educational process increases the possibilities of setting educational tasks and managing the process of their solution. Computers allow you to build and analyze models of various objects, situations, phenomena. The use of information technology makes the learning process interesting, productive, carries out a differentiated approach to learning, makes it possible to objectively and timely monitor and summarize. Also, modern information technologies allow teachers to qualitatively change the content, methods and organizational forms of training.
Digital activity and literacy of youth in Tatarstan: Digitalisation prospects the regional socio-economic development

The study of digital activity and literacy of young people in the Republic of Tatarstan is based on the sociological analysis of Internet activity. The results revealed a high level of digital activity, but also noted a gap between this activity and self-assessed literacy. Young people show interest in various online spheres, with high competence in digital communication and content creation. However, the importance of improving digital security knowledge is emphasised. The findings could be key for the development of educational programmes, promoting the safe use of digital technologies in everyday life and relevant to interests in environment, energy and land sciences.
Decomposition method of synthesis of autonomous control system of multidimensional dynamic object

The work deals with issues of synthesis of multidimensional system of automatic control of dynamic object, having interconnected adjustable variables. In order to solve the task, a dynamic decomposition method is proposed, which provides autonomous control of output variables of the object. The method allows analytically synthesizing a multidimensional regulator with minimal complexity and allows ensuring stability of the control system, required dynamic properties. The structure of the multi-dimensional regulator is determined on the basis of models of the multi-dimensional control object in state variables without introducing additional stabilization elements, which will significantly simplify the structure of the synthesized system. The effectiveness of the proposed method is shown in the example of synthesis of a multi-dimensional control system of a two-dimensional object.
A mathematical modification of the WNTM associative algorithm for cognitive analysis and efficient detection of dualisms in text and video streaming data

We report on a modification of the WNTM (Word Network Topic Model) algorithm for efficient modelling of bitermes – pairs of words that frequently occur together in texts of different topics. The modified algorithm is an extension of the classical topic model and allows efficient detection and extraction of semantic relations between pairs of words. The paper presents formalized mathematical equations describing the process of modelling biterms, and also presents the results of experiments on real text data, confirming the effectiveness of the proposed approach.
Weight distributions and weight hierarchies of a class of binary linear codes with a few weights

Linear codes can be applied in consumer electronic, communication and data storage system. In addition, the weight distribution and weight hierarchy of a linear code are two significant research topics in coding theory. In this paper, by extending Li et al.'s construction in [Finite Fields Appl. 73(2021), 101865], a class of binary linear codes with a few weights is constructed by selecting the defining set properly, then the weight distributions and weight hierarchies are determined by calculating the corresponding exponential sums. In particular, the codes constructed in this paper are $ \mathfrak{m} $-MDS codes and minimal and thus can be used in secret sharing schemes.
Research Catalog of Inland Seismicity in the Southern Korean Peninsula from 2012 to 2021 Using Deep Learning Techniques

Abstract A seismicity catalog spanning 2012–2021 is proposed for the inland and near-coastal areas of the southern Korean Peninsula (SKP). Using deep learning (DL) techniques combined with conventional methods, we developed an integrated framework for compiling a comprehensive seismicity catalog. The proposed DL-based framework allowed us to process, within a week, a large volume of data (spanning 10 yr) collected from more than 300 seismic stations. To improve the framework’s performance, a DL picker (i.e., EQTransformer) was retrained using the local datasets from the SKP combined with globally obtained data. A total of 66,858 events were detected by phase association using a machine learning algorithm, and a DL-based event discrimination model classified 29,371 events as natural earthquakes. We estimate source information more precisely using newly updated parameters for locations (a 1D velocity model and station corrections related to the location process) and magnitudes (a local magnitude equation) based on data derived from the application of the DL picker. Compared with a previous catalog, the proposed catalog exhibited improved statistical completeness, detecting 21,475 additional earthquakes. With the newly detected and located earthquakes, we observed the relative low seismicity in the northern SKP, and the linear trends of earthquakes striking northeast–southwest (NE–SW) and northwest–southeast (NW–SE) with a near-right angle between them. In particular, the NE–SW trend corresponds to boundaries of major tectonic regions in the SKP that potentially indicates the development of fault structures along the boundaries. The two predominant trends slightly differ to the suggested optimal fault orientations, implying more complex processes of preexisting geological structures. This study demonstrates the effectiveness of the DL-based framework in analyzing large datasets and detecting many microearthquakes in seismically inactive regions, which will advance our understanding of seismotectonics and seismic hazards in stable continental regions.
Examining Voice Community Use

Visual online communities can present accessibility challenges to older adults or people with vision and motor disabilities. Motivated by this challenge, accessibility and HCI researchers have called for voice-based communities to support aging and disability. This paper extends prior work on voice community design and short-term use by providing empirical data on how people interact with voice communities over time and intentional instances of non-use. We conducted a one-year study with 43 blind and low vision older adults, of whom 21 used a voice-based community. We use vignettes to unpack five different voice community member roles - the obligatory poster, routine poster, cross-platform lurker, busy socialite, and visual expertise seeker - and discuss community interactions over time. Findings show how participation varied based on engagement in other communities and ways that participants sought interaction. We discuss (1) how to design voice communities for member roles and (2) the implications of synchronous and asynchronous voice community interaction in voice-only communities.
Factors Influencing Blockchain Technologies Adoption in Supply Chain Management and Logistic Sectors: Cultural Compatibility of Blockchain Solutions as Moderator

The rapid advancements in blockchain technology have heralded a new era for various industries, including the supply chain and logistics. However, despite the promising opportunities, the adoption of blockchain within supply chain and logistics still needs to be explored, especially in Saudi Arabia. This study investigated the impact of technological, organizational, and environmental factors on blockchain adoption among Saudi supply chain and logistics companies. The study also tested cultural compatibility’s moderating role in solving blockchain issues. A quantitative method approach was employed, utilizing a survey questionnaire. Data were gathered from various stakeholders across the industry, including managers, IT experts, and logistics professionals. The findings indicate that relative advantage, top management support, and competitors influence blockchain technology adoption significantly and positively. However, openness to innovation use has an insignificant effect on adoption. Government support demonstrates the strongest positive influence on blockchain adoption, whereas vendor orientation and support showed an insignificant impact. Both blockchain technology adoption and the cultural compatibility of blockchain solutions significantly enhance operational excellence, but the moderating effect of cultural compatibility of blockchain solutions shows no effect on operational excellence. This study stands out as one of the first explorations into blockchain adoption in the supply chain and logistics sector within the Saudi context, identifying the factors contributing to blockchain’s successful implementation for operational excellence.
A Virtual Reality Scene Taxonomy: Identifying and Designing Accessible Scene-Viewing Techniques

Virtual environments (VEs) afford similar interactions to those in physical environments: individuals can navigate and manipulate objects. Yet, a prerequisite for these interactions is being able to view the environment. Despite the existence of numerous scene-viewing techniques (i.e., interaction techniques that facilitate the visual perception of virtual scenes), there is no guidance to help designers choose which techniques to implement. We propose a scene taxonomy based on the visual structure and task within a VE by drawing on literature from cognitive psychology and computer vision, as well as virtual reality (VR) applications. We demonstrate how the taxonomy can be used by applying it to an accessibility problem, namely limited head mobility. We used the taxonomy to classify existing scene-viewing techniques and generate three new techniques that do not require head movement. In our evaluation of the techniques with 16 participants, we discovered that participants identified trade-offs in design considerations such as accessibility, realism, and spatial awareness, that would influence whether they would use the new techniques. Our results demonstrate the potential of the scene taxonomy to help designers reason about the relationships between VR interactions, tasks, and environments.
Principal Component Approximation Network for Image Compression

In this work, we propose a novel principal component approximation network (PCANet) for image compression. The proposed network is based on the assumption that a set of images can be decomposed into several shared feature matrices, and an image can be reconstructed by the weighted sum of these matrices. The proposed PCANet is specifically devised to learn and approximate these feature matrices and weight vectors, which are used to encode images for compression. Unlike previous deep learning-based methods, a distinctive aspect of our approach is its consideration of network size in the bit-rate computation. Despite this inclusion, our proposed method yields promising results. Through extensive experiments conducted on standard datasets, we demonstrate the effectiveness of our approach in comparison to state-of-the-art techniques. To the best of our knowledge, this is the first machine learning approach that includes the size of networks during bitrate computation in image compression.
Incomplete Cross-Modal Retrieval with Deep Correlation Transfer

Most cross-modal retrieval methods assume the multi-modal training data is complete and has a one-to-one correspondence. However, in the real world, multi-modal data generally suffers from missing modality information due to the uncertainty of data collection and storage processes, which limits the practical application of existing cross-modal retrieval methods. Although some solutions have been proposed to generate the missing modality data using a single pseudo sample, this may lead to incomplete semantic restoration and sub-optimal retrieval results due to the limited semantic information it provides. To address this challenge, this paper proposes an Incomplete Cross-Modal Retrieval with Deep Correlation Transfer (ICMR-DCT) method that can robustly model incomplete multi-modal data and dynamically capture the adjacency semantic correlation for cross-modal retrieval. Specifically, we construct intra-modal graph attention-based autoencoder to learn modality-invariant representations by performing semantic reconstruction through intra-modality adjacency correlation mining. Then, we design dual cross-modal alignment constraints to project multi-modal representations into a common semantic space, thus bridging the heterogeneous modality gap and enhancing the discriminability of the common representation. We further introduce semantic preservation to enhance adjacency semantic information and achieve cross-modal semantic correlation. Moreover, we propose a nearest-neighbor weighting integration strategy with cross-modal correlation transfer to generate the missing modality data according to inter-modality mapping relations and adjacency correlations between each sample and its neighbors, which improves the robustness of our method against incomplete multi-modal training data. Extensive experiments on three widely-tested benchmark datasets demonstrate the superior performance of our method in cross-modal retrieval tasks under both complete and incomplete retrieval scenarios. Our used datasets and source codes are available at https://github.com/shidan0122/DCT.git.
Enhancing Item-level Bundle Representation for Bundle Recommendation

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to the inherent difference between the bundle-level and item-level preferences, the item-level representations may not receive sufficient information from the bundle affiliations to make accurate predictions. In this paper, we propose a novel approach EBRec , short of E nhanced B undle Rec ommendation, which incorporates two enhanced modules to explore inherent item-level bundle representations. First, we propose to incorporate the bundle-user-item (B-U-I) high-order correlations to explore more collaborative information, thus to enhance the previous bundle representation that solely relies on the bundle-item affiliation information. Second, we further enhance the B-U-I correlations by augmenting the observed user-item interactions with interactions generated from pre-trained models, thus improving the item-level bundle representations. We conduct extensive experiments on three public datasets, and the results justify the effectiveness of our approach as well as the two core modules. Codes and datasets are available at https://github.com/answermycode/EBRec.
Learning Offset Probability Distribution for Accurate Object Detection

Object detection combines object classification and object localization problems. Current object detection methods heavily depend on regression networks to locate objects, which are optimized with various regression loss functions to predict offsets between candidate boxes and objects. However, these regression losses are difficult to assign the appropriate penalties for samples with large offset errors, resulting in suboptimal regression networks and inaccurate object offsets. In this paper, we consider object location as offset bin classification problem, and propose a distance-aware offset bin classification network optimized with multiple binary cross entropy losses to learn various offset probability distribution, including single label distribution and distance-aware label distribution. On the one hand, it provides gradient contributions for different samples based on the bounded probability instead of previous incalculable offset error. On the other hand, it explores the distance correlations between discrete offset bins to facilitate network learning. Specifically, we discretize the continuous offset into a number of bins, and predict the probability of each offset bin, in which the probability should be higher for the offset bin closer to the target offsets, and vice versa. Furthermore, we propose an expectation-based offset prediction and a hierarchical focusing method to improve the precision of prediction. We conduct extensive experiments to evaluate the effectiveness of our method. In addition, our method can be conveniently and flexibly inserted into existing object detection methods, which consistently achieves a large gain based on popular anchor-based and anchor-free methods on the PASCAL VOC, MS-COCO, KITTI and CrowdHuman datasets. Code will be released at: https://github.com/QiuHeqian/DBC.
JSON Web Token (JWT) Authentication

This chapter explores REST API and JWT authentication and authorization using Spring Boot 3 and Spring Security 6.
Web Security

This chapter shows how to build a Java web application using Spring Security 6 in Spring Boot 3. You see the inner workings of the security filter chain and the different metadata options at your disposal to define security constraints in your application.