Reviewerly/exHarmony huggingface dataset:
Ship detection using SAR images based on YOLO (you only look once)

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar (SAR) Images using YOLO deep learning framework. YOLO (You Only Look Once) is an object detection algorithm Object detection algorithms using region proposal includes RCNN, Fast RCNN, and Faster RCNN, etc. Region based Convolutional Neural Networks (RCNN) algorithm uses a group of boxes for the image and then analyses in each box if either of the boxes holds a target. It employs the method of selective search to pick those sections from the picture. YOLO can be used to assist in making safety checks for ships and mariners. We train the YOLO model on our dataset in this paper for our detector to learn to detect objects in SAR images such as ships. The preliminary YOLO test results showed an increase in the accuracy of ship detection at Cyprus’s Coast and can be applied in the field of ship detection.
Ranking for Learning: Studying Users’ Perceptions of Relevance, Understandability, and Engagement

General-purpose search engines are frequently used to retrieve content for learning. However, their ranking strategies are typically optimised for relevance, which means that they do not take into account other criteria important in the learning context, such as the understandability and the degree of engagement of the retrieved resources. We have conducted a user study to assess the extent to which ranking algorithms used by a popular search engine satisfy the expectations of users who are learning by searching. We study the relationships between users' perceptions of topical relevance, engagement, and understandability for retrieved documents with respect to their ranks. While we observe that the perceived user-assigned rank is strongly associated with all dimensions of relevance under study, specifically engagement ( $$\rho =0.89$$ ), understandability ( $$\rho =0.58$$ ) and topical relevance ( $$\rho =0.88$$ ), the relationship between SERP ranks and user-assigned ranks appears unstable, indicating that learners are not necessarily always served well by general-purpose search engines.
Multi-view Graph-Based Text Representations for Imbalanced Classification

Text classification is a fundamental task in natural language processing, notably in the context of digital libraries, where it is essential for organizing and retrieving large numbers of documents in diverse collections, especially when tackling issues with inherent class imbalance. Sequence-based models can successfully capture semantics in local consecutive text sequences. On the other hand, graph-based models can preserve global co-occurrences that capture non-consecutive and long-distance semantics. A text representation approach that combines local and global information can enhance performance in practical class imbalance text classification scenarios. Yet, multi-view graph-based text representations have received limited attention. In this work, we introduce Multi-view Minority Class Text Graph Convolutional Network (MMCT-GCN), a transductive multi-view text classification model that captures textual graph representations for the minority class, along with sequence-based text representations. Experiments show that MMCT-GCN variants outperform baseline models on multiple text collections.
CNNTuner: Image Classification with A Novel CNN Model Optimized Hyperparameters

Today, the impact of deep learning in computer vision applications is growing every day. Deep learning techniques apply in many areas such as clothing search, automatic product recommendation. The main task in these applications is to perform the classification process automatically. But, high similarities between multiple apparel objects make classification difficult. In this paper, a new deep learning model based on convolutional neural networks (CNNs) is proposed to solve the classification problem. These networks can extract features from images using convolutional layers, unlike traditional machine learning algorithms. As the extracted features are highly discriminative, good results can be obtained in terms of classification performance. Performance results vary according to the number of filters and window sizes in the convolution layers that extract the features. Considering that there is more than one parameter that influences the performance result, the parameter that gives the best result can be determined after many experimental studies. The specified parameterization process is a difficult and laborious process. To address this issue, the parameters of a newly proposed CNN-based deep learning model were optimized using the Keras Tuner tool on the Fashion MNIST (F-MNIST) dataset containing multi-class fashion images. The performance results of the model were obtained using the data separated according to the cross-validation technique 5. At the same time, to measure the impact of the optimized parameters on classification, the performance results of the proposed model, called CNNTuner, are compared with state-of-the-art (SOTA) studies.
IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety

Numerous elderly folks reside alone in their homes. Seniors may find it difficult to ask for assistance if they fall. As the elderly population keeps growing, elderly fall incidents are becoming a critical public health concern. Creating a fall detection system for the elderly using IoT and blockchain is the aim of this study. Data collection, pre-processing, feature extraction, feature selection, fall detection, and emergency response and assistance are the six fundamental aspects of the proposed model. The sensor data is collected from wearable devices using elderly such as accelerometers and gyroscopes. The collected data is pre-processed using missing value removal, null value handling. The features are extracted after pre-processed data using statistical features, autocorrelation, and Principal Component Analysis The proposed approach utilizes a novel hybrid HSSTL combines Teaching-Learning-Based Optimization and Spring Search Algorithm to select the optimal features. The proposed approach employs TriNet, including Long Short-Term Memory, optimized Convolutional Neural Network (CNN), and Recurrent Neural Network for accurate fall detection. To enhance fall detection accuracy, use the optimized Convolutional Neural Network obtained through the hybrid optimization model HSSTL. Securely store fall detection information in the Blockchain network when a fall occurs. Alert neighbours, family members, or those providing immediate assistance about the fall occurrence using Blockchain network. The proposed model is implemented in Python. The effectiveness of the suggested model is evaluated using metrics for accuracy, precision, recall, sensitivity, specificity, f-measure, NPV, FPR, FNR, and MCC. The proposed model outperformed with the maximum accuracy of 0.974015 at an 80% learning rate, whereas the suggested model had the best accuracy score of 0.955679 at a 70% learning rate.
Types of Personal Changes of Future Specialists, Psychologists-Psychotherapists

Background. Socio-political processes exacerbated the problems of psychological assistance to citizens; training of qualified psychologists-therapists. One of the factors of a specialist's personality is the ability for personal change. The goal is to reveal the content of the main factors of personal changes of future psychologists-therapists Methods. The research used the "Big Five" methods; Herzberg's test; "Diagnostics of socio-psychological personality attitudes" (O. Potemkina); "Value questionnaire" (Sh. Schwartz); "Research of volitional self-regulation" (A. Zverkov, E. Eidman); "Diagnostics of motivational orientations in interpersonal communications" (I. Ladanov, V. Urazaeva); "Motivation of professional activity" (K. Zamfir); "Test of social intelligence" (J. Gilford); "Self-Assessment Questionnaire" (V. Stolin, S. Pantileev); "Scale of psychological well-being" (K. Riff) - separate scales from which factors of personal changes were included. Results. As a result of factor analysis using the method of principal components, four factors of personal changes of future psychologists- therapists were identified: 1) "pattern" - tendency to change, voluntary self-control, responsibility, ability to achieve personal success, openness to experience, orientation to the process, independence, universalism, inner type motivation and general self-regulation, social thinking, psychological well-being; 2) "personal-purpose" - social recognition, achievement of personal success, content of work, orientation to results, power, compromise; hedonism, self-control, internal and external positive motivation, self-acceptance; 3) "professional-purpose" - pleasantness, cooperation, work content, process orientation, money; selfishness, kindness, external positive and negative motivation; 4) "pragmatic-humanistic" - extraversion, neuroticism, cooperation, altruism, independence, security, orientation towards acceptance of others, positive relationships. Сonclusion. The process of unfolding individual personal states and traits is the development of the personality, therefore personal changes are the development of the personality. The concept of personal change (personal changes) is generic to the concept of personality development, since the latter involves the specification of personal formations according to the criteria of their complexity and specification, therefore the concepts of change and personality change are different.
Determination of heavy metals in soils using diffuse reflectance spectroscopy: a case study in Northern Greece

Industrialization and intense human activity affect soil ecosystem, and as a result of heavy metal contamination, its physicochemical and biological activities are disturbed. To safeguard soil capital, prevention and monitoring strategies need to be introduced, but taking into consideration the induced costs, in terms of time, effort and money. Diffuse reflectance spectroscopy is proven to be a rapid and inexpensive method for the determination of various chemical compounds found in soil, including heavy metals. We collected 64 soil samples from an area of 10km2 at the western part of Thessaloniki, Greece, which is known to be degraded due to heavy industrial activity. The samples were analyzed at the accredited chemical laboratory of the InterBalkan Environment Center for the determination of two (Cadmium, Lead,) heavy metals (DTPA extraction with the use of Atomic Absorption Spectroscopy AAS). In parallel the soil samples were also spectroscopically analyzed, aiming to construct a regional soil spectral library that can be used as a dataset for fitting Machine Learning models for the quantification of land degradation through the determination of heavy metal concentration. Aspiring to reduce the laboratory procedure, we resampled the spectrum to the bands captured by a recently launched hyperspectral mission, providing a proof of concept regarding the monitoring of soil contamination by heavy metals by analyzing spaceborne hyperspectral data.
Benden sosyal medya uzmanı olur mu? Türkiye’de yayınlanan iş ilanlarındaki gerekli beceri ve nitelikler üzerine betimsel bir içerik analizi

The increase in the demand for digital skills has attracted attention in job postings published in the communication sector in recent years. The aim of this study is to reveal the frequently demanded skills and qualifications for the position of social media specialist, which is directly related to digital skills, in the context of Türkiye. For this purpose, descriptive content analysis was employed on 244 job postings published on Bigumigu.com and Mediacat.com, which publish the job postings of agencies in Türkiye. As a result of the analysis, it is possible to say that the most dominant demand of the job postings is experience in terms of years. Having agency experience, being fluent in English, being able to work in a team and having high communication skills are among the most demanded qualifications. Digital content management, digital advertising management, data analytics, design, reporting, and presentation skills are also among the most frequently requested skills. The most surprising result of the research is that agencies do not require a university education. It is important for universities to revise their curricula on the basis of these demands to respond to the needs of the sector and to train qualified graduates.
Using version control to document genuine effort in written assignments: a protocol with examples for universities

This conceptual report describes a novel assessment technique to evaluate written assignments in universities, such as literature reviews, essays or research proposals, over the course of the academic semester in multiple milestones. The method can be adapted to undergraduate and graduate courses in disciplines with a writing requirement. Key features of this method include encouraging student self-regulation and spacing of learning, rapid scoring using quantitative elements, improving the authenticity and transparency of the written work, and guiding students towards better writing and thinking skills. The method involves the implementation of a system called version control , which is a class of software to track and manage changes to textual data, often used by programmers to track code. This report describes a use case in two psychology courses describing the logistics and marking dynamics surrounding the assessment. This protocol has been seeded on a public repository on GitHub where educators can contribute and develop the technique further.
Clock-G: Temporal Graph Management System

Graphs are a ubiquitous data model for capturing entities and their relationships. Since most graphs that model real-world networks evolve over time, efficiently managing temporal graphs is an important problem from both a theoretical and practical perspective. Querying the history of temporal graphs can lead to new applications such as object tracking, anomaly detection, and predicting future behavior. However, existing commercial graph databases lack native temporal support, hindering their usefulness in these use cases. This paper introduces Clock-G, a temporal graph management system designed to handle the history temporal graphs. What differentiates Clock-G from other temporal graph management systems is its comprehensive approach, covering query language, query processing, and physical storage. We define T-Cypher, a temporal extension of Cypher query language, enabling user-friendly and concise querying of the graph’s history. Additionally, we propose a query processor that utilizes temporal statistics collected from underlying temporal graphs to offer a good evaluation plan for T-Cypher queries. We also propose a novel storage technique that balances space usage and query evaluation time.
Efficient sequencing data compression and FPGA acceleration based on a two-step framework

With the increasing throughput of modern sequencing instruments, the cost of storing and transmitting sequencing data has also increased dramatically. Although many tools have been developed to compress sequencing data, there is still a need to develop a compressor with a higher compression ratio. We present a two-step framework for compressing sequencing data in this paper. The first step is to repack original data into a binary stream, while the second step is to compress the stream with a LZMA encoder. We develop a new strategy to encode the original file into a LZMA highly compressed stream. In addition an FPGA-accelerated of LZMA was implemented to speedup the second step. As a demonstration, we present repaq as a lossless non-reference compressor of FASTQ format files. We introduced a multifile redundancy elimination method, which is very useful for compressing paired-end sequencing data. According to our test results, the compression ratio of repaq is much higher than other FASTQ compressors. For some deep sequencing data, the compression ratio of repaq can be higher than 25, almost four times of Gzip. The framework presented in this paper can also be applied to develop new tools for compressing other sequencing data. The open-source code of repaq is available at: https://github.com/OpenGene/repaq.
Taijiquan Auxiliary Training and Scoring Based on Motion Capture Technology and DTW Algorithm

Learning Tai Chi requires long-term practice and guidance, which is difficult for beginners. This article proposes a Tai Chi-assisted training and scoring method based on motion capture technology and a dynamic time warping (DTW) algorithm. Firstly, by using motion capture technology, the key point data of Tai Chi movements can be accurately captured. Then, using the DTW algorithm, the learner's action sequence is compared and matched with the standard Tai Chi action sequence in order to evaluate the learner's action accuracy and fluency. Learners can promptly correct incorrect actions and improve the accuracy and fluency of their actions. This method has significant advantages in accuracy and reliability. In summary, the Tai Chi-assisted training and scoring method based on motion capture technology and DTW algorithm provides an effective auxiliary tool for Tai Chi learners, which can help them better master the techniques and essence of Tai Chi. This study is of great significance for promoting the popularization of Tai Chi and improving the learning effectiveness of Tai Chi.
Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

The intensive use of the internet comes with negative and positive effects. Cyberbullying is one of the negative effects of using the internet. Cyberbullying has a negative effect on the victims emotionally, academically, and psychologically. Cyberbullying detection tools can help in reducing or eliminating cyberbullying on social media platforms. The aim of the study was to identify the elements that drive cyberbullying and build classification models to determine whether social media textual information contains cyberbullying text or not. The research aim was achieved through a mixed methods research design, containing qualitative and quantitative elements. The drivers of cyberbullying were identified through a literature review. These included age, gender, family structure, parental education, race, technology, anonymity, academic achievement, and awareness of cyber safety. The support vector machines and naïve Bayes models were used to classify the text dataset (Formspring.me dataset), with a 72.81% and a 99.87% classification accuracy, respectively.
Interaction Between Agile Methods and Organizational Culture – A Qualitative Study

Background: Agile practices have gained popularity in the 21st century. There is also a growing body of research on agile methods. While some earlier research on agile practices and organizational culture exists with the assumption that the optimal combination of agile practices and organizational culture exists, we examine how agile methods and organizational culture interact and are mutually adjusted. Method: To find out how agile methods and organizational culture are related in practice, we conducted 50 semi-structured interviews with software and embedded software developers from five Japanese companies and three software teams from Finland. Results: Adopting agile methods does not necessarily cause convergence of organizational culture toward democratic which seems to be most compatible with archetypal agile practices. Agile methods can be adapted to organizational cultures that are sometimes challenging to modify. We demonstrate that companies tried to fit agile practices into hierarchical organizational cultures, demonstrating that organizational culture can occasionally be viewed as an exogenous variable influenced firm-, product-, and industry-specific features. Also, it is possible to transform organizational cultures to democratic. The examples demonstrate how companies alter their culture in part through HRM techniques. It should be noted, however, that these efforts to conform to culture can have some restrictions. Conclusion: There is not a single ideal combination of agile methods and organizational culture, despite what some earlier studies suggested. The results show how organizational culture and agile methods interact and adapt to one another in different ways. According to several earlier studies, different organizational cultures may support different facets of agile methods. They do not presuppose organizational culture diversity; in that it goes beyond the notion of a one-to-one relationship between agile methods and democratic organizational culture. While implementing agile practices in various organizational cultural contexts, practitioners must consider how agile methods and organizational culture are interconnected.
Identification of Trajectory Anomalies on Video Surveillance Systems

Recently, CCTV surveillance applications have remarkably developed for public welfare. However, the investigation of different techniques for online implementation is always significantly restricted. Numerous implementations propose for detecting irregularities of moving objects in the videotape. Performance of fuzzy in trajectory's anomaly is one of the most robust detection procedures. In this paper, the authors propose a fuzzy implemented trajectory anomalies detection technique with the help of some parameters such as velocity, path deviation, and size of the moving objects. The critical aspect of the framework is a compact set of highly descriptive features extracted from a novel cell structure that helps us define support regions in a coarse-to-fine fashion. This paper also illustrates a small outline of different detection techniques. The authors also exhibit the outcome of experiments on the Queen Mary University of London junction dataset (QMUL).
Integrated Approach for Providing Data Security Verification Over Encrypted Cloud Data

The cloud has recently attracted a lot of user attention from both small and large businesses, including those in software, BPO, healthcare, schools, colleges, and many other industries. For data storage and access from remote places linked to one another from a central server with the aid of the internet, all of these organisations attempt to adopt this centralised cloud server. Because all data is stored remotely and only occasionally accessed locally, the confidentiality of data is crucial to cloud service providers. As is common knowledge, no cloud service provider currently offers data privacy through encryption and message digests to enable data authorisation. almost all businesses An untrusted user can query data files of interest by sending encrypted keywords as a search query to the cloud server. Try to search the data in a secure manner over encrypted cloud data. In this dishonest cloud environment, the returned query results may occasionally be correct, incorrect, or partial. As everyone is aware, cloud servers today nearly actively withhold some qualified results in order to conserve computational resources and communication overhead. In this research, we presented and analysed a safe, practical, and fine-grained query results verification mechanism, by which the query user, given an encrypted query results set, not only can confirm the integrity of each data file, but also determines the overall number of qualifying data files, which are not returned if the set is not finished before the decryption procedure. This served as our primary inspiration for creating a brand-new secure verification object for encrypted cloud storage. Here, the short signature key is created using the message digest method MD5, which is also used to confirm the data's authenticity. We tested our proposed model in a variety of ways, and the results demonstrate that it is a useful and effective system