Reviewerly/exHarmony huggingface dataset:
Analysis of the Use of Online Learning Media on Students’ Achieved Learning Outcomes During the COVID-19 pandemic at SMP PGRI 363 Pondok Petir

This research analyses the use of online learning media on the students’ achieved learning outcomes during the COVID-19 pandemic for 8th-grade students at PGRI 363 Pondok Petir. This research applies the descriptive method, which describes and interprets objects as what they are. The data in this research were collected by using observation, interview, and document. The findings of this research show that Google Classroom was used for online media learning for 8th-grade students at SMP PGRI 363. Using Google Classroom resulted in decreased and increased student learning outcomes. The outcome decreased in learning materials; not all students could absorb them. However, there was increasing skill. There is an increase in the skill aspect related to students’ skills in using technology in the field of learning, namely Google Classroom and according to information from teachers, it is stated that students have become more responsible for their learning, where students try to complete the tasks given even without direct supervision from the teacher in class. The results of teacher discussions with student parents during the learning review process using Google Classroom also show that students are becoming increasingly trained to be more independent in managing their learning at home.
Recall-Driven Precision Refinement: Unveiling Accurate Fall Detection Using LSTM

This paper presents an innovative approach to address the pressing concern of fall incidents among the elderly by developing an accurate fall detection system. Our proposed system combines state-of-the-art technologies, including accelerometer and gyroscope sensors, with deep learning models, specifically Long Short-Term Memory (LSTM) networks. Real-time execution capabilities are achieved through the integration of Raspberry Pi hardware. We introduce pruning techniques that strategically fine-tune the LSTM model’s architecture and parameters to optimize the system’s performance. We prioritize recall over precision, aiming to accurately identify falls and minimize false negatives for timely intervention. Extensive experimentation and meticulous evaluation demonstrate remarkable performance metrics, emphasizing a high recall rate while maintaining a specificity of 96%. Our research culminates in a state-of-the-art fall detection system that promptly sends notifications, ensuring vulnerable individuals receive timely assistance and improve their overall well-being. Applying LSTM models and incorporating pruning techniques represent a significant advancement in fall detection technology, offering an effective and reliable fall prevention and intervention solution.
COMPOSING ACTIVITY OF V.I. SKURATOVSKYI IN THE CONTEXT OF HIS UNIVERSALISM

The article introduces to the oeuvre of the modern Ukrainian composer V.I. Skuratovskyi (1963–2016) and is dedicated to the 60th anniversary of his birth. A brief overview of the musicological studies of the II All-Ukrainian scientific and practical conference “Creative Personality – Hope for the Future” (March 28–29, 2023) is given. The research object is the composing oeuvre in combination with multi-vector activity of V. Skuratovskyi. For the first time, it is proposed using the concept of “cluster method” in relation to the artist’s creative career. Composing activity by V.I. Skuratovskyi in interaction with other forms of creativity is likened to the “cluster” phenomenon in its universal sense (namely, in connection with music, astronomy, biotechnology and economics). Characteristic features of V. Skuratovskyi’s composing style are emphasized – retrospective and dialogue nature, dichotomy of musical language – in connection with the artist’s performing, lecturing and research activities. The role of mentors and the influence of creative connections with fellow performers on composer impulses of V. Skuratovskyi are noted. One of his most famous contributions, “Album for Children” with 12 pieces for piano, is presented. It is highlighted the role of the suite genre in the composer’s oeuvre, which most characterizes the type of his creative journey. The focus is on the Suite for chamber orchestra “Looking into the Past”. The interaction of music and poetry in the pieces of V.I. Skuratovskyi and the features of considering his composing and literary gift from the standpoint of synesthesia are outlined. In this context, the importance of the Cycle of Choral Poems based on Lesya Ukrainka’s lyrics “Five Strings of Ukraine” is emphasized. The unity of composing, poetic, pedagogical and educational, and performing activities of V.I. Skuratovskyi is noted, and the ratio of different areas of activities is marked. The sources of intonation material and further research perspectives are indicated.
Deep Learning Based Framework for Forecasting Solar Panel Output Power

Energy Harvesting from diverse renewable energy sources has experienced rapid growth due to the adverse environmental impacts of using fossil fuels. Solar energy is a significant energy source frequently used for power generation in various applications. Due to the variable nature of solar irradiation, temperature, and other metrological parameters, Photovoltaic (PV) power generation is highly fluctuating. This unstable nature of output power has been evolved as a considerable issue in various applications of solar energy prediction system. In this work, a hybrid Deep Learning (DL) model based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Attention mechanism to forecast solar cell output power has been proposed. The proposed model is implemented, and its performance is compared with other DL models, including CNN, LSTM, and LSTM with an attention mechanism. The proposed model has been trained and evaluated with a publicly available dataset which contains 20 parameters on which solar panel output power is relatively dependent. The model yields maximum coefficient of determination (R2) up to 84.5%. A lightweight model has also been developed using the pruning technique to implement the DL model into a low-end hardware.
EHR Security and Privacy Aspects: A Systematic Review

Electronic Health Records (EHRs) have become increasingly popular in recent years, providing a convenient way to store, manage and share relevant information among healthcare providers. However, as EHRs contain sensitive personal information, ensuring their security and privacy is most important. This paper reviews the key aspects of EHR security and privacy, including authentication, access control, data encryption, auditing, and risk management. Additionally, the paper discusses the legal and ethical issues surrounding EHRs, such as patient consent, data ownership, and breaches of confidentiality. Effective implementation of security and privacy measures in EHR systems requires a multi-disciplinary approach involving healthcare providers, IT specialists, and regulatory bodies. Ultimately, the goal is to come upon a balance between protecting patient privacy and ensuring timely access to critical medical information for feature healthcare delivery.
A NEW APPROACH FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS

A new method for solving ordinary differential equations is suggested. As it is shown the differential equation can be solved not only by means of integration its differentiation as well. The developed method is applied for consideration of corresponding equations for the exponential and trigonometric functions. In the framework of the developed method the simple approach for derivation of Taylor series of a function is demonstrated. Based on the geometric definition the connections between trigonometric functions and their derivatives are obtained as well. The well-known Euler formula is also produced, which establishes a connection between trigonometric and exponential functions.
BACKPROPAGATION ARTIFICIAL NEURAL NETWORKS PREDICTION OF ELEMENTARY SCHOOL STUDENT GRADUATION WITH EXAMINATION PRACTICE SCORE

Backpropagation method is a computer technique to help predict and sort data. This method is usually used to change the connection between parts of the computer's brain in the hidden layer. Meanwhile, the Nervous System Network (ANN) is an information processing system that is very similar to the function of human brain cells. Value is a benchmark for a student's graduation, if the student'sscore is getting better, the more opportunities for the student's graduation. In predicting this pass using the method of Artificial Neural Networks (ANN), namely Backpropagation and using Matlab software with the MSE (Mean Square Error) result of 0.099512. Keywords: Graduation Prediction, Backpropagation, Examination Practice Value, Artificial Neural Networks, Matlab
BENCHMARKING DATA OF GROMACS: GPU AND GROMACS VERSION

The parallel scaling of GROMACS package has been studied in detail by estimation the performance of machine in presence of new generation graphical cards. This paper aims to estimate the speedup and performance issues depending on GROMACS network parametrics and as well as the version. The obtained results show that depending on threads/MPI ratio the new version of GROMACS plays rather good than previous one, meanwhile there are cases when the performance values are nearly same. The presence of GPU is significantly increasing the performance scaling up to ten time acceleration. Pinning threads to cores guaranties up to 15-35% increase.
NAMED ENTITY RECOGNITION USING OPENAI GPT SERIES MODELS

The amount of information grew dramatically over all available sources. One of the most important parts of it is textual data, so Natural Language Processing is one of the most important areas of research. A growing amount of information demands more sophisticated and effective models and approaches to be introduced. Named entity recognition is a key part of text processing and plays an important role in text understanding, automatic text summarization, translation, etc. A wide range of different approaches were used for named entity recognition, however, the introduction of the transformers architecture with self-attention mechanism made a significant impact on current approaches to Natural Language Processing tasks in general. Most tasks are currently leveraging transformers as a state-of-the-art approach. Meanwhile, simpler transformer architecture in comparison with others grants the possibility of large language models with a huge number of parameters like GPT-3. The main purpose of this article is to investigate how effectively OpenAI GPT series models could recognize named entities in English and Ukrainian texts. The research was based on the CoNLL 2003 dataset one of the most used for such kind of research and the lang-uk team labeled dataset. Due to known possibilities for GPT series models to be more effective with few-shot learning examples, experiments were built with zero, one, and three shots. Moreover, experiments were performed for whole articles and sentence by sentence from the same article to compare results. Different prompts were investigated, and one was chosen for the whole experiment. The estimation of the results was based on the F1 score and specifics of the results. Results demonstrate the overall great performance of the most recent models and the increase in performance from older to newer models. Furthermore, our findings indicate that there is still room for improvement and investigation. Keywords : Named entity recognition, natural language processing, GPT, OpenAI
LexVEN

While there is growing awareness that knowledge of regional minority languages must be protected, assessing the vitality of such varieties is challenging due to the lack of assessment materials. Our contribution documents the creation and validation of LexVEN, a bimodal yes/no vocabulary task, with the purpose of assessing the proficiency of users of Venetan with receptive and/or productive skills. The materials will be freely available. The vocabulary items were validated in a survey with 161 speakers of Italian, of which 136 came from the Veneto area. We have relied on Classical Test Theory (CTT) and Item Response Theory (IRT) in validating test items, and we demonstrate correlations with self-reported measures of proficiency and language use. Given the absence of any other assessment tools of Venetan for cross-validation, we further collected speech samples from a subset of the Venetan participants with active proficiency whose proficiency was rated by native speakers of Venetan. We discuss the procedures within a validation framework, concluding that LexVEN is reliable as a proficiency assessment tool of Venetan.
Research on intelligent water valve fault classification method based on machine learning

Water valves are crucial components for controlling fluid flow in smart city pipeline networks. Identifying the types of faults in water valves, which are widely distributed and numerous, and timely judging and warning their operational status, will greatly improve the efficiency of water systems and significantly reduce the risk of accidents. Based on various datasets of water valve parameters, we simulated the normal operation data of the valve, as well as valve clogging, external leakage, and positioner supply pressure drop using DAMADICS. We then compared classification algorithms such as KNN, BP, SVM, etc. The KNN classification algorithm, which is suitable for online monitoring of valves, has been selected.
Transformer-based image restoration with divergent self-attentive mechanism

When convolution is used to process corrupted inputs, each filter of the convolution shares convolution kernel parameters spatially. For a single image containing both normal and abnormal regions, assigning the same kernel to operations on features that are valid, invalid, or a mix of both can easily lead to structural distortion, texture blurring and artifacts. This is especially true when the pattern is complex or the corrupted region is too large. Secondly, convolution is inefficient at modeling long-range image dependencies in CNNs with only local receptive fields. Accordingly, this paper proposes a novel Transformer-based split-flow multi-headed self-attentive mechanism image restoration method. The main components of the restoration network include a generator and a discriminator, alongside a divergent self-attentive mechanism and a detailed feedforward network that aim to extract contextual information in hierarchical features to generate appropriate feature maps for reconstructing images. The method presented in this paper effectively utilizes hierarchical features to extract relevant information from corrupted images. Experiments on the CelebA and Places datasets show that our proposed method have excellent performance.
<i>IT Professional</i> Special Issue on Security and Data Protection During the COVID-19 Pandemic and Beyond

The global landscape has been reshaped by the COVID-19 pandemic, causing unprecedented health, economic, and societal disruptions. In this transformative period, technology emerged as a vital lifeline, serving as both a shield against the virus and a catalyst for adapting to new challenges. Digital contact tracing frameworks rapidly emerged and became popular worldwide, thus enabling swift identification of potential exposure and containment of infections. In addition, the subsequent development of digital COVID certificates for vaccinations, immunity, and testing facilitated the safe resumption of daily activities and cross-border travel.
SAFEJ: An Efficient Infrastructure for Securing Julia Programs

Julia is an emerging programming language designed for high-performance and parallel computing. However, its simple-designed foreign function interface, ccall, lacks necessary security checks, making multilingual Julia applications vulnerable to exploitation. This paper introduces SAFEJ, an infrastructure developed to enhance Julia FFI security. SAFEJ includes two key components: 1) privilege separation to protect Julia by isolating the memory of Julia and native code and 2) pointer sanitization to filter and check memory accesses from unsafe native code to provide more flexible protection. We conducted extensive experiments to evaluate the effectiveness and performance of SAFEJ. The results demonstrate that SAFEJ effectively protects Julia from common memory attacks, and its efficiency tests indicate that pointer sanitization is on par with prior studies, while privilege separation achieves a maximum reduction of 85% in runtime performance overhead.
Bounding Box Vectorization for Oriented Object Detection with Tanimoto Coefficient Regression

Current oriented object detection methods mainly utilize a vanilla coordinate-angle representation for bounding box regression, which usually suffers from inconsistency between the bounding box regression losses and prediction errors induced with respect to different rotation angles, aspect ratios, and scales. Therefore, although the existing oriented object detectors have achieved very good performances under coarse evaluation metrics such as AP50, their performance significantly degrades when using stricter evaluation metric such as AP75. To address the abovementioned issues, we propose a new regression method with bounding box vectorization that implicitly represents the shape and orientation of an object with a set of orthogonal vectors. By doing this, the proposed method delicately avoids the inconsistency issues encountered in oriented bounding box regression. During training, we introduce the Tanimoto coefficient to evaluate the similarity of the bounding box vector in a shape- and orientation-aware manner, and we refer to the proposed box-to-vector loss as the B2V loss. In addition to 2D object detection, the proposed method can be easily generalized to 3D scenarios involving orientation estimation, such as autonomous driving. We evaluate the proposed method through extensive experiments conducted on four popular oriented object detection datasets, including both 2D and 3D datasets, where the proposed method significantly outperforms the recently developed state-of-the-art methods when using a more accurate evaluation metric.
Multi-semantics Aggregation Network based on the Dynamic-attention Mechanism for 3D Human Motion Prediction

Graph convolutional network-based methods have recently shown promising performance in skeleton-based data processing. However, these methods have two critical issues in skeleton-based motion prediction tasks: First, graph modeling of motion poses is based on the fixed graph according to the physical connection of human joints and ignores the exploration of deep implicit information based on human dynamic kinetics. Second, existing methods usually use motion information in a single semantic space to model the whole motion sequences, underestimating diverse semantic patterns for improving the modeling ability. To address the first issue, we propose the Attention-based Dynamic Graph Convolution method, which tries to capture implicit semantic information dynamically. To address the second issue, we propose the Kinematic-based Semantics Aggregation Block (KSAB), which combines various semantic features from four semantic perspectives to rich motion representation. Integrating the above two designs, we propose a novel Multi-Semantics Aggregation Network (MANet), resulting in more comprehensive feature extraction in dynamic implicit semantics learning to enhance motion prediction. Extensive experiments are conducted to validate the effectiveness of MANet, which outperforms state-of-the-art methods by 10.9%, 6.6%, and 19.6% in terms of MPJPE for motion prediction on Human3.6M, CMU Mocap, and 3DPW datasets, respectively.
Privacy-Preserving Queries Using Multisource Private Data Counting on Real Numbers in IoT

In this paper, our primary focus is on the current lack of privacy-preserving queries tailored to real-number fields rather than integers. We take multisource Private Data Counting on Real numbers (R-PDC) within IoT architecture as a breakthrough point to enable diverse query services without revealing sensitive data. The advantage of this is that the parameters required for queries remain stable and minimal in the scenario of wide numerical domain and dynamic changed dataset. At first, we present a general R-PDC method based on curve approximation, in which an Approximate Query Function (AQF) is established to approximate the constructed ideal counting curve for element queries on target set. We also demonstrate curve construction process of AQF and provide the feasibility theorem of AQF for counting a target element within an allowable error. By integrating the R-PDC method with fixed-point fully homomorphic encryption, an efficient R-PDC scheme is presented to perform multiparty collaborative queries in IoT. In security aspect, the R-PDC scheme on (m,)-AQF is proved to be statistically secure against Chosen Element Attack (CEA) for cumulative error and dataset size m. Moreover, the scheme achieves O(nmγ) computation and O(n2mγ) communication complexities for n servers and γ-length fraction. Finally, as an extension of AQF over single attribute, multi-dimensional R-PDC method is applied into privacy-preserving Naive Bayes Classifier and Apriori algorithm over multiple attributes. Our work provides substantial support and insights for the advancement of privacy computation.
Offline Writer Identification Based on Diagonal Gradient Angle of Small Fragments

Writer Recognition constitutes an interesting field of research in the forensic examination of handwriting. As a general rule, an automatic writer recognition system is based on two phases relating to the extraction of the descriminative features and the classification based on the matching of the extracted descriptors and the results certainly depend on the quality of the features extracted during the extraction phase. In this paper, we propose a texture descriptor based on the histogram of the angles made by the ascending and descending diagonal pixels. The adopted approach combines between codebook-based systems and textural features. Thus, the proposed process starts with the detection of FAST key points and the extraction of small fragments centered around the detected points. Then, we calculate the histogram of diagonal gradient angle (HDGA) which will then be encoded via the VLAD method and classified with the KNN technique. The experimental study carried out on the three datasets CVL, IAM and CERUG-EN achieved the respective rates of 100, 97.6 and 99.1%.
Least-squares method constrained by phase smoothness for correcting illumination fluctuation errors in phase-shifting profilometry: erratum

This erratum corrects an error in Eq. (29) of the original paper, Appl. Opt. 62 , 8451 ( 2023 ) APOPAI 0003-6935 10.1364/AO.505327 .
Optoelectronic Devices for Quantum Information Processing

The recent developments of optoelectronics do promote the progress in many other fields. For quantum information processing, we made efforts in manufacturing quantum devices by using optoelectronic techniques. We designed quantum dot embedded nanocavities to serve as efficient quantum emitters; using spectral multiplexing technique, we fabricated a heralded single-photon source, emitting highly pure and speedy single photons; and defects in GaN were observed serving as room temperature quantum random number generators. An entangled photon emitter with visibility of 97% was developed using cascaded second-order nonlinear optical process in PPLN waveguides; and Si3N4 microrings were effectively applied to establish photon entanglers. Readout circuits were optimized to fabricate specific single-photon avalanche detectors, and telecomm-band single-photon avalanche detectors have been improved to 128 × 32 arrays for quantum imaging. A multiplexed quantum memory was explored to simultaneously store 1650 single photons. Opto-electro-mechanical devices were studied or fabricated in order to measure minor quantities in quantum level. These works may shed light on quantum information technology for the future.