Reviewerly/exHarmony huggingface dataset:
Continuous Sign Language Recognition and Translation Using Hybrid Transformer-Based Neural Network

The speech-impaired community uses a system of visual motions and gestures called sign language for basic communication. There is no universal sign language; only a few individuals can understand and comprehend it, which makes it much more difficult for the speech-impaired community and the majority of the population with normal hearing to have basic communication. Sign language recognition, a breakthrough for assisting those with speech and hearing impairments, has been studied for many years to address this communication barrier. A unique hybrid Transformer-based Sign-Spot Deep Neural Network for sign spotting and continuous sign language translation has been proposed in this paper to address the existing research gap. The spatiotemporal characterizations are learned from the video clips by applying 3D Convolutional Neural Networks (CNNs). Multiple queries have been built from each sign video employing multiple frame-level strides to address the temporal scale differences between the continuous sentence videos and the isolated word videos. The search clips are subjected to self-attention to generate a continuous temporal scale space. To acquire the context within the sequence, a self-attention mechanism is employed in the continuous sequence video. Finally, the search clip is located inside the continuous sequence by correlating the temporal scales using mutual attention. Then the gloss is generated, and the sign gesture recognition is done using the Connectionist Temporal Classification and Recurrent Neural Network encoder-decoder techniques. The proposed framework outperforms cutting-edge approaches to sign spotting and recognition by leveraging the attention mechanism and the adaptive characteristics.
Competition and Speculation in Cryptocurrencies

This paper uses mutual fund manager data to examine how managers' performance incentives generated speculative demand during the 2020-2022 cryptocurrency boom and bust. We find that managers with strong relative performance incentives began investing in crypto after their competitors began investing in it, consistent with a model of rational performance hedging. In contrast, managers who invest their personal wealth in the funds that they manage, who have strong direct performance incentives, were significantly less responsive to their competitors' investment decisions. Our findings suggest that relative performance incentives can encourage managers to mimic their competitors instead of trading on their beliefs. In equilibrium, this competitive hedging motive can magnify the scope of speculative demand.
TRAINING AS A TOOL FOR ACQUIRING SKILLS FOR DIGITALIZATION OF THE HUMAN RESOURCE MANAGEMENT IN THE REPUBLIC OF NORTH MACEDONIA

The digital transformation process includes people, tools, and processes, and it enables the use of digital evaluation, monitoring, and testing systems to automatically document and track the progress of every employee in the business.The need for educated and trained staff with modern knowledge and skills for digitization is dictated by new trends.Those skills are typically acquired through an informal education process, primarily through training.As a systematic and continuous process, in the shorter term, the digitization training improves the skills of the employees for the application of new sophisticated ways and opportunities for better and more efficient performance of the work at a specific workplace.The objective of this paper is to convey the significance and requirement of digitization in human resource management, with a focus on the need to conduct training for businesses in Republic North Macedonia.The paper analyses empirical data from surveys conducted on Macedonian businesses.The findings are sublimated in the concluding observations about the need to implement training for digitization of human resource management processes and their relevance for greater competitiveness of Macedonian businesses on the global market.
Uniform-in-phase-space data selection with iterative normalizing flows

Abstract Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.
Software Code Smells and Defects: An Empirical Investigation

Code smells indicate weaknesses in software design that may slow down development or increase the risk of bugs or failures in the future.This paper aims to investigate the correlation of code smells with defects within classes.The method used uses a tool to automatically detect code smells in selected projects and then assesses the correlation of these to the number of defects found in the code.Most existing articles determine that software modules/classes with more smells tend to have more defects.However, while the experiments in this paper covering a range of languages agreed with this, the correlation was found to be weak.There remains a need for further investigation of the types of code smells that tend to indicate or predict defects occurring.Future work will perform more detailed experiments by investigating a larger quantity and variety of software systems as well as more granular studies into types of code smell and defects arising.
Extracting Queryable Knowledge Graphs from User Stories: An Empirical Evaluation

User stories are brief descriptions of a system feature told from a user's point of view.During requirements elicitation, users and analysts co-specify these stories using natural language.A number of approaches have tried to use Natural Language Processing (NLP) techniques to extract different artefacts, such as domain models and conceptual models, and reason about software requirements, including user stories.However, large collections of user story models can be hard to navigate once specified.We extracted different components of user story data, including actors, entities and processes, using NLP techniques and modelled them with graphs.This allows us to organise and link the structures and information in user stories for better analysis by different stakeholders.Our NLP-based automated approach further allows the stakeholders to query the model to view the parts of multiple user stories of interest.This facilitates project development discussions between technical team members, domain experts and users.We evaluated our tool on user story datasets and through a user study.The evaluation of our approach shows an overall precision above 96% and a recall of 100%.The user study with eight participants showed that our querying approach is beneficial in practical contexts.
What's in a Persona? A Preliminary Taxonomy from Persona Use in Requirements Engineering

Personas have been widely used during requirements engineering-related tasks.However, the presentation, composition, level of details and other characteristics varies greatly by domain of use.To better understand these, we formed a curated set of nearly 100 personas from 41 academic papers and analysed their similarities and differences.We then used our analysis to formulate a preliminary taxonomy of personas used for Requirements Engineering-related tasks.We describe our key findings from our analysis with examples, our preliminary taxonomy, and discuss ways the taxonomy can be used and further improved.
Efficacy of Kumkum as a Surrogate for Eosin in Routine Histological Sections: An Observational Study

Introduction: Stains are a crucial component of laboratory procedure. They help in highlighting the different tissues, both normal and abnormal, which plays an important role in diagnostic histopathology. Synthetic dyes are utilised for majority of the stains in histology. Among the natural surrogates studied, Kumkum is less researched. Aim: To compare the staining characteristics of Kumkum, which imparts red colour as an alternative to eosin. Materials and Methods: This observational study was conducted at Histopathology section, Pathology department at Chettinad Hospital and Research Institute for a period of six months from January 2022 to June 2022. In 50 tissue blocks, each was made into two sections and one was stained with routine Haematoxylin and Eosin (H&amp;E), while the other was stained with Haematoxylin and Kumkum solution (H&amp;K). The Cellular Architecture (CA) (based on distinct or indistinct nucleus, cytoplasm) and quality of staining (poor, satisfactory or good) of H&amp;E and H&amp;K solution was compared and a scoring system was given. The scores were analysed with Chi-square test using SPSS software (version 25). Results: Out of 100 slides, 45 (90%) stained with H&amp;E and 48 (96%) with H&amp;K showed distinct CA. A total of 47 slides (94%) stained with H&amp;K and 41 (82%) with H&amp;E showed good quality of staining. Conclusion: Kumkum solution appears to be an efficient counterstain in place of eosin in highlighting the normal structures in histopathological sections. It highlighted the RBCs in the arteries and cytoplasm of the cells in glandular and squamous epithelium better than or equally good as eosin.
Insights into Groundwater Treatment and Mechanism: Efficient Cd(Ii) Removal Using Mixed Metal Oxide-Graphitic Carbon Nitride Composite

Graphitic carbon nitride (GCN) and its mixed metal oxide (MMO) composites are synthesized via co-precipitation followed by the pyrolysis method using various ratios of calcium aluminium (Ca:Al) with GCN, to enhance the adsorption ability towards Cd(II) present in wastewater. Synthesized composite materials were characterized using X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) surface analysis, Fourier transforms infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) analyses. Due to its higher specific surface area (218.65 m2/g), the produced GCN and CaAl mixed metal oxide composite (GCN-MMO) exhibits improved adsorption ability towards Cd(II) ions. Approximately > 99% were adsorbed with Langmuir qmax of 131.57 mg/g. An overall pseudo-second-order (PSO) model fitted the kinetics data well. The possible mechanisms were precipitation, surface complexation, and acid-base mechanism. Further, the groundwater application was studied, and it achieved adsorption below the maximum permissible limit of USEPA with excellent cycling stability. The findings of this study showed that GCN-MMO composite material may be useful for the adsorption of Cd(II) heavy metal from an aqueous solution.
Nsga2 Algorithm for Improving Elite Individual Optimization Strategy

When solving high-dimensional and nonlinear multi-objective optimization problems, NSGA2 algorithm has the defects of reduced computational efficiency, poor convergence of solution set, and uneven distribution at the pareto front. In order to further improve the performance of the algorithm, an improved NSGA2 algorithm is proposed. This method first introduces dominance strength calculation to evaluate the merits of individuals with non-dominance strength, and then introduces sparsity calculation to find sparse solutions according to sparsity, And carry out local search around it, and finally introduce the adaptive elitist individual optimization strategy to gradually improve the selection probability of the better solution during the evolution process, and dynamically adjust the size of the elitist reservation through the adaptive elitist reservation strategy, which can maintain the diversity of the population at the initial stage of evolutionary computation. With the progress of evolutionary computation, the selection probability gradually increases and only accepts the better solution, ensuring the stability of the algorithm convergence. The experimental results show that the pareto solution set obtained by the improved algorithm has better distribution and good convergence.
Disclosing the Challenges of Online Modality of Learning of Intermediate Students in Doña Remedios Trinidad

This study focused on how the eight intermediate students from Pulong Sampaloc Elementary School in Doña Remedios Trinidad, Bulacan, perceived their experiences using the online learning mode in terms of technological, pedagogical, and social challenges. The qualitative phenomenological research design was used in this study to understand and describe the universal essence of a phenomenon, herein the lived experiences related to the challenges they met in the new modality of instruction. The findings were taken from their responses to guided interview questions personally conducted and administered by the researcher. Observations and recordings of their experiences and challenges were taken, analyzed, and presented. The intermediate students of Pulong Sampaloc Elementary School faced technological, pedagogical, and social challenges. However, because they were determined to succeed and complete their education, they could deal with it and achieve the results of their efforts in the best available and possible way.
Galaxy Classification Upgraded: Boosting Machine Learning with the Fourier Transform

Classifying images is a critical task in various scientific and technological disciplines. However, the ever-increasing size of data sets and the need for more human resources have posed a significant challenge in the 21st century. Convolutional neural networks have been a promising solution to classify images based on their structural morphology. Despite this, their accuracy may be constrained when dealing with multiple categories. In this study, we propose using the Fast Fourier transform as a pre-processing step, which significantly improves the accuracy of classifying galaxies into several categories. Interestingly, using only the real part of the Fast Fourier transform enhances image classification by 10% compared to models without pre-processing. Moreover, this approach can be readily extended to solve classification problems in many other fields.
Frequent Itemset Mining by Fuzzification of Purchase Quantity

Association rules and frequent itemsets are generated based upon merely the existence of item in a transaction. Information about the quantities purchased is ignored. Both the patterns do not contain any information about the quantity of items purchased. The study in this paper is an attempt to generate frequent sets of items of different quantities and mine associations between them using the concept of fuzzy sets. The patterns generated not only contain information about the items but also contain purchase quantity-wise item information.
Up- and Down-Correlations in Normal Variance Mixture Models

We study conditional correlations between pairs of risks in normal variance mixture models. For several important families, we determine explicit formulas for up- and down-correlations defined as the conditional correlation between the sum of risks and an individual component, conditionally on the sum being large or small, respectively.
Synchronizing Data from Multiple Optical Sensors for Precise Hand Tracking

We present a concept that provides hand tracking for virtual and extended reality only with optical sensors. In this article, we compare two algorithms for calibrating such a system -- Iterative Closest Point and the Kabsch algorithm -- and propose a novel algorithm for computing the confidence of the tracking data. This allows us to fuse the tracking data from multiple sources to make hand tracking more precise. We conducted a series of experiments that proved that our new approach yields better results than algorithms from our previous research and the current algorithms from the leading manufacturer of optical sensors. Comparing the Iterative Closest Point and the Kabsch algorithm showed that Iterative Closest Point yields more precise results, but the Kabsch algorithm is faster.
Artificial Intelligence-Based Model for Detecting Inappropriate Content on the Fly

Social media made it convenient for users to express, communicate, discuss, and exchange their opinions on various issues in recent years. For example, Twitter, YouTube, Facebook, and News portals allow users to express themselves through comments. However, such platforms are being misused in the name of freedom of speech. Numerous improper messages towards specific persons or communities can be found in them that use abusive, vulgar, hostile, or harsh words. Moreover, bots are also involved in exchanging such messages nowadays. As a result, user experiences are sometimes ruined on social media. Therefore, automatic identification and filtering of such offensive messages is a significant issue for improving user experience. This paper proposes a heterogeneous ensemble-based machine learning (ML) model powered by artificial intelligence (AI) that can classify messages into Threat, Obscenity, Insult, Identity Hate, Toxic, and Severe Toxic categories. The experimental evaluation of the proposed model on a standard dataset demonstrates the accuracy and adaptability of the proposed model.
Mixed-Curvature Knowledge-Enhanced Graph Contrastive Learning for Recommendation

Contrastive learning has recently inspired a series of beneficial research in the recommendation field, owing to its capability of extracting supervised information from large-scale unsupervised data, which aligns well with the recommendation system's needs in addressing the data sparsity issue. However, the randomness of the supervised data will inevitably affect the robustness of the recommendation algorithm based on contrastive learning. Although knowledge graphs can provide fruitful facts, thereby enriching supervisory signals and achieving the effect of data augmentation, knowledge graph embeddings cause the graph structure to be disturbed as the information of the graph nodes changes, and Euclidean space is not well suited to accommodate varying degrees of graph structure expansion. To fill this research gap, we propose a mixed-curvature knowledge-enhanced graph contrastive learning for recommendation (MKGCL). Specifically, we employ a knowledge enhancement method to augment the contrastive learning of graphs and improve the robustness of self-supervised recommender systems. In particular, according to the characteristics of the curvature space, we combine the product manifolds of multiple single-curvature spaces to construct a mixed-curvature space to explore the most suitable non-Euclidean space for heterogeneous structures. Extensive experiments on four benchmark datasets demonstrate that MKGCL achieves significant improvements over other state-of-the-art algorithms.
Top-Down Generation of Low-Resolution Representations Improves Visual Perception and Imagination

Perception or imagination requires top-down signals from high-level cortex to primary visual cortex (V1) to reconstruct or simulate the representations bottom-up stimulated by the seen images. Interestingly, top-down signals in V1 have lower spatial resolution than bottom-up representations. It is unclear why the brain uses low-resolution signals to reconstruct or simulate high-resolution representations. By modeling the top-down pathway of the visual system using the decoder of variational auto-encoder (VAE), we reveal that low-resolution top-down signals can better reconstruct or simulate the information contained in the sparse activities of V1 simple cells, which facilitates perception and imagination. This advantage of low-resolution generation is related to facilitating high-level cortex to form geometry-respecting representations observed in experiments. Moreover, our finding inspires a simple artificial-intelligence (AI) technique to significantly improve the generation quality and diversity of sketches, a style of drawings made of thin lines. Specifically, instead of directly using original sketches, we use blurred sketches to train VAE or GAN (generative adversarial network), and then infer the thin-line sketches from the VAE- or GAN-generated blurred sketches. Collectively, our work suggests that low-resolution top-down generation is a strategy the brain uses to improve visual perception and imagination, and advances sketch-generation AI techniques.
An Approach for Predicting Election Results with Trending Twitter Hashtag Information Using Graph Techniques and Sentiment Analysis

India is one of the largest democracies in the world where the Lok Sabha and the Rajya Sabha elections are held every five years. Nowadays, social media acts as an important and inexpensive platform for propagating messages of the political parties. In the present study, a methodology is proposed by combining sentiment analysis and graph techniques to look into the trending hashtag networks propagated by the political parties using Twitter. The demonstration of the proposed methodology is done on the trending hashtag’s information collected from Twitter on the Uttar Pradesh (U.P) state elections, 2022.
Students’ Perception and Attitude toward Mandatory Reserves Officer Training Corps of Senior High School Students in Matanao National High School

This study investigates the students' perception and attitude towards the Mandatory Reserve Officers’ Training Corps (ROTC) of the Senior High School students in Matanao National High School. This study uses a descriptive-quantitative, non-experimental design using a correlational technique. A total of 1494 Senior High School students are studying at Matanao National High School. Using quota sampling, data were gathered using the survey questionnaires distributed to 100 respondents who were willing to participate in the survey. The study revealed that the level of perception and attitude of the Senior High School students is high. Furthermore, significant relationships between the variables were measured, and the data have shown a positive correlation between the perception and attitude of Senior high school students toward mandatory ROTC