Reviewerly/exHarmony huggingface dataset:
Access to Electricity in Ghanaian Basic Schools and ICT in Education Policy Rhetoric: Empirical Quantitative Analysis and Access Theory Approach

The attempt to integrate ICTs into the education system in Ghana has existed close to two decades following the adoption of the ICT for Accelerated Development (ICTAD) policy in 2003. However, empirical quantitative studies to analyse the access of basic schools to electricity, which is the major power of technology devices, over the period appear non-existent. This article is a descriptive quantitative analysis of basic school access to electricity for the academic years 2010 / 2011 to 2017/2018 using secondary data obtained from the Educational Management and Information System (EMIS) through the lens of Access Theory. The study was undertaken using secondary educational statistical data and document reviews as data sources. The results of the data analysis indicate a low electricity access rate in basic schools in the Northern Region of the country. The study concludes that the gap between the ICT in education policy rhetoric and the reality in ICT for education (ICT4E) at the Ghanaian basic school level appears unhealthy and requires stakeholders’ immediate attention to realize the desired impact of ICT in education policy if the goal of the country to achieve the Sustainable Development Goals 4 and 10 need to be success. The findings of these studies provide valuable insights for policymakers and education stakeholders in designing effective policies and interventions to improve access to electricity and promote the integration of ICT in basic education.
Integrated Artificial Intelligence in Data Science

Artificial Intelligence (AI) is increasingly pervading everyday life since it can be used to solve high-complexity problems, as well as determine optimal solutions, in various domains and for numerous applications [...]
THE MAIN DIRECTIONS OF RESEARCH CONDUCTED IN THE SHARED RESEARCH FACILITIES «HIGH PERFORMANCE COMPUTING AND BIG DATA» (CKP «INFORMATICS»)

The paper presents the main directions of fundamental and applied research carried out using high-performance computing resources of the computing environment of the CKP «Informatics».
Improving education through implementation of information technologies into the educational process

The ways of improving the educational space through the introduction of information technologies into the educational process are clarified and the decisive factors of the qualitative application of information technologies in the educational process are singled out. The main professional tasks of using information technologies in the educational process are listed, and the positive factors of information technologies that contribute to increasing the effectiveness of student education are highlighted. The main ways of qualitative informatization of education are shown. The didactic possibilities of ways to improve education through the introduction of information technologies into the educational process are highlighted. Groups of conditions for the effective use of information technologies for the improvement of the educational space are defined. The advantages of using information technologies to improve the educational space, which are manifested in the content, organizational, and functional aspects of the learning process, are emphasized. Features of virtual reality and augmented reality are shown. The necessity of distance education and multimedia technologies in the process of professional training of competitive specialists in institutions of higher education is proven and the principles that are effective when using multimedia in educational classes are described.
Laser Point Cloud Target Detection Based on Improved PointPillars

Laser point cloud object detection plays a key role in autonomous driving and intelligent transportation systems, therefore, particularly important is the development of reliable algorithms for 3D object recognition. The accuracy of PointPillars algorithm in identifying small targets is low, this paper proposes an improved laser point cloud object detection method. Three key improvements were introduced based on PointPillars: Softplus activation function, attention mechanism SOPA (pillar second order of point attention) and increased positive sample weight. First, the activation function in PointPillars is replaced by Softplus. As a smooth nonlinear activation function, the Softplus function improves the detection ability of the target by enhancing the nonlinear modeling ability. Second, The attention mechanism SOPA is introduced, which improves the detection ability of important objects by adaptively weighting the key information in the point cloud. Finally, by increasing the weight of positive samples, the model places more emphasis on the accuracy of positive sample classification, which may improve the detection ability of positive samples. The extensive experimental evaluation of the improved PointPillars algorithms are conducted on the publicly available KITTI dataset. Compared with the traditional PointPillars algorithm, our method has made significant improvements in the 3D AP index. The average detection accuracy of car, Pedestrian, and Cyclist categories has increased by 0.99, 2.03, and 2.37, respectively, compared to the original algorithm. From this, it can be seen that the improvement of the algorithm is effective.
The Use of a Morphological Analyzer in Assessing the Achievements of Learning Outcomes

The article presents the algorithm of the generator of the educational program, which allows you to establish a connection between the goals of the curriculum, learning outcomes, competence and disciplines. Comprehension questions with open-ended questions are used to assess the achievement of learning outcomes. To extract meaning from the student's answer, models and methods are used, which are based on morphological, syntactic, semantic analyzers.
Finding the Best Reference Signal Pattern for Different Channel Models in 5G

One of the challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) systems is to efficiently estimate the Channel State Information (CSI) at the receiver to guarantee reliable signal detection and reception. The most commonly used channel estimation method is to insert a few known reference symbols into the time-frequency grid called the pilots. The accuracy of channel estimation depends on the pilots' density and location. In this paper, the best pilot position for 5G NR is found by using Deep learning techniques and Convolutional Neural Networks (CNN). The neural network is trained by large set of training data to learn the channel structure. Different pilot positions and patterns are tried with Demodulation-Reference Signal (DM-RS); and the pattern with less Mean Square Error (MSE) is determined by CNN. Firstly, the DM-RS symbols and indices for a given carrier and resource configuration are generated. It is then modulated and passed through different nrTDL delay profiles with different delay spread and Doppler shifts. Various patterns of conventional DM-RS resource grid with different configuration types and additional positions are simulated and the results are compared with the new Hybrid H-Type (a combination of parallel and block patterns) DM-RS pattern. It is observed from the results that the Hybrid H -Type pattern gives a 7% lesser channel estimation error compared to the conventional patterns.
Security Risk Assessment of Image Classification Model Based on ANP-TOPSIS

Recently, machine learning algorithms have been widely used in the fields of image processing, network security and natural language processing, etc., profoundly affecting human life. However, machine learning algorithms have the characteristics of uncertain output, vulnerability to adversarial attacks, and unexplained decision-making processes, which seriously threaten the security of machine learning-based face recognition, Malware detection, and autonomous driving. Hence, it is imperative for the security practitioners to evaluate algorithm security to ensure that security needs are met. In this article, the authors propose a set of security assessment index systems and methods for machine learning algorithms for image classification scenarios: Refer to the security specification of machine learning algorithms and requirements to construct the security index system of image classification model. Furthermore, The Analytic Network Process(ANP) is applied to quantify the index weights and the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) is applied to screen the optimal model, and finally the sensitivity analysis is applied to prove the stability of the proposed method. Experimental results show that this method has certain value and effect in assessing the security and model screening of image classification models.
Mitigating IT Risk of Bank Syariah Indonesia: A Study of Cyber Attack on May 8, 2023

The rapid development of information technology in Indonesia cannot be overlooked. Technological advancements in all fields must be implemented, including in the world of Islamic banking. Customers need convenience, smoothness, security, and flexibility in digital banking services. Bank Syariah Indonesia (BSI) is the largest Islamic bank in Indonesia. The hashtag "Bank Syariah Indonesia (BSI)" became a trending topic a few days after May 8, 2023. This happened due to the disruption of BSI's information system, causing customers to be unable to conduct banking transactions. Customers were greatly affected by this incident. The purpose of this research is to identify the information technology mitigation efforts carried out by BSI in relation to the incident. This research utilizes a qualitative approach and primary data sources. The primary data used is derived from Twitter, collected from May 4 to May 11, 2023, and reliable websites. The data from Twitter is processed and analyzed using Maxqda 2020 and Gephi version 0.9.6 software for Windows 10 64-bit. The conclusion of this research is that BSI's information technology mitigation efforts are still slow.
Construction of a three-parameter encryption scheme on Hermitian groups in the MST3 cryptosystem

The article proposes a method for constructing a three-parameter encryption scheme based on Hermitian groups, which improves the security parameters of the existing MST3 cryptosystem. The challenge of improving existing approaches to building cryptosystems is driven by successes in building a quantum computer with sufficient computing power to render many public-key cryptosystems insecure. In particular, we are talking about those cryptosystems based on the complexity of factorization or the discrete logarithm problem, such as RSA, ECC, etc. There are several proposals that have become classic over the past almost 20 years for using non-commutative groups to build quantum-resistant cryptosystems The unsolvable word problem is an interesting area of research for cryptosystem construction. It was formulated by Wagner and Magyarik and lies in the plane of application of permutation groups. Logarithmic signatures (LS) were proposed by Magliveras. In this context, the logarithmic signature is a special type of factorization, it is applied to finite groups. The latest version of this implementation is known as MST3 and is based on the Suzuki group.&#x0D; In 2008, Magliveras demonstrated a transitive limit of LS for the MST3 cryptosystem. Svaba later proposed the eMST3 cryptosystem with improved security options. A secret homomorphic cover was added for this improvement. Then, in 2018, T. van Trung proposed an MST3 approach using strong aperiodic LS for abelian p-groups. Kong and colleagues conducted an extensive analysis of MST3 and noted that since there are no publications yet on the quantum vulnerability of the algorithm, it can be considered a candidate for the post-quantum era.&#x0D; One valuable idea is to improve encryption efficiency by optimizing the computational overhead. This is done while reducing the large size of the key space. This approach can be applied to LS calculations outside the center of the group. And this was done over the final fields of the small dimensions using groups with high order.
Effect of Industrial Activities Vis-à-Vis Ecological Imbalance–Sustainability Perspective

Industrial mining operations and associated activities cause adverse and sometimes irrevocable harm to ecosystems. Uncontrolled quarrying impacts on the local geology and vegetation, thus it is important to examine local tree varieties, also known as alpha diversity, and species turnover over landscape gradients, known as beta diversity. This has prompted a review of three important issues: (i) How have mining affected the region's alpha and beta diversity? (ii) How can floral changes along altitudinal gradients impact the composition of tree communities? and (iii) Factors able to forecast changes in the environmental sustainability? A case study analysis was conducted to address these questions with the estimation of the significance value index for pioneer and successional tree species, utilizing certain phytosociological characteristics. The findings of the study indicated a limited range of species variety and carbon storage across three distinct elevation gradients, specifically those exceeding 450 meters, those at precisely 450 meters, and below 450 meters. The results of this study provide a theoretical basis for the implementation of management strategies aiming to achieve sustainable practices in industrial operations. Additionally, it will contribute to multidisciplinary environmental sustainability science by employing scientific ecosystem analysis as a framework.
Wizualization: A “Hard Magic” Visualization System for Immersive and Ubiquitous Analytics

What if magic could be used as an effective metaphor to perform data visualization and analysis using speech and gestures while mobile and on-the-go? In this paper, we introduce WIZUALIZATION, a visual analytics system for eXtended Reality (XR) that enables an analyst to author and interact with visualizations using such a magic system through gestures, speech commands, and touch interaction. Wizualization is a rendering system for current XR headsets that comprises several components: a cross-device (or ARCANE FOCUSES) infrastructure for signalling and view control (WEAVE), a code notebook (SPELLBOOK), and a grammar of graphics for XR (OPTOMANCY). The system offers users three modes of input: gestures, spoken commands, and materials. We demonstrate Wizualization and its components using a motivating scenario on collaborative data analysis of pandemic data across time and space.
Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration

In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profles, and non-periodic patterns such as fuctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modifed horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://osf.io/34t8c/</uri> .
Hierarchical Machine Unlearning

In recent years, deep neural networks have enjoyed tremendous success in industry and academia, especially for their applications in visual recognition and natural language processing. While large-scale deep models bring incredible performance, their massive data requirements pose a huge threat to data privacy protection. With the growing emphasis on data security, the study of data privacy leakage in machine learning, such as machine unlearning, has become increasingly important. There have been many works on machine unlearning, and other research has proposed training several submodels to speed up the retraining process, by dividing the training data into several disjoint fragments. When the impact of a particular data point in the model is to be removed, the model owner simply retrains the sub-model containing this data point. Nevertheless, current learning methods for machine unlearning are still not widely used due to model applicability, usage overhead, etc. Based on this situation, we propose a novel hierarchical learning method, Hierarchical Machine Unlearning (HMU), with the known distribution of unlearning requests. Compared with previous methods, ours has better efficiency. Using the known distribution, the data can be partitioned and sorted, thus reducing the overhead in the data deletion process. We propose to train the model using the hierarchical data set after partitioning, which further reduces the loss of prediction accuracy of the existing methods. It is also combined with incremental learning methods to speed up the training process. Finally, the effectiveness and efficiency of the method proposed in this paper are verified by multiple experiments.
Computer Vision Mouse for Tetraplegic Individuals to Access Computers Using Facial Expressions

Abstract: Controlling the mouse by a physically challenged person is really a tough one. To find a solution for the people who cannot use the Mouse physically, we have proposed this mouse cursor control using Eye Movements. Eye gaze is an alternative way of accessing a computer using eye movements to control the mouse. For someone who fine touchscreens, mouse inaccessible, eye gaze is an alternative method to allow a user to operate their computer, using the movement of their eyes. Eye movement can be regarded as a pivotal real-time input medium for human-computer communication, which is especially important for people with physical disability. In order to improve the reliability, mobility, and usability of eye tracking technique in user-computer dialogue, a novel eye control system is proposed in this system using Webcam and without using any extra hardware. The proposed system focuses on providing a simple and convenient interactive mode by only using user’s eye. The usage flow of the proposed system is designed to perfectly follow human natural habits. The proposed system describes the implementation of both iris and movement of cursor according to iris position which can be used to control the cursor on the screen using webcam and implemented using Python.
Multilevel Motion Planning: A Fiber Bundle Formulation

High-dimensional motion planning problems can often be solved significantly faster by using multilevel abstractions. While there are various ways to formally capture multilevel abstractions, we formulate them in terms of fiber bundles. Fiber bundles essentially describe lower-dimensional projections of the state space using local product spaces, which allows us to concisely describe and derive novel algorithms in terms of bundle restrictions and bundle sections. Given such a structure and a corresponding admissible constraint function, we develop highly efficient and asymptotically-optimal sampling-based motion planning methods for high-dimensional state spaces. Those methods exploit the structure of fiber bundles through the use of bundle primitives. Those primitives are used to create novel bundle planners, the rapidly-exploring quotient-space trees (QRRT*), and the quotient-space roadmap planner (QMP*). Both planners are shown to be probabilistically complete and almost-surely asymptotically optimal. To evaluate our bundle planners, we compare them against classical sampling-based planners on benchmarks of four low-dimensional scenarios, and eight high-dimensional scenarios, ranging from 21 to 100 degrees of freedom, including multiple robots and nonholonomic constraints. Our findings show improvements up to 2 to 6 orders of magnitude and underline the efficiency of multilevel motion planners and the benefit of exploiting multilevel abstractions using the terminology of fiber bundles.
Making Differential Privacy Easier to Use for Data Controllers and Data Analysts using a Privacy Risk Indicator and an Escrow-Based Platform

Differential privacy (DP) enables private data analysis but is hard to use in practice. For data controllers who decide what output to release, choosing the amount of noise to add to the output is a non-trivial task because of the difficulty of interpreting the privacy parameter $\epsilon$. For data analysts who submit queries, it is hard to understand the impact of the noise introduced by DP on their tasks. To address these two challenges: 1) we define a privacy risk indicator that indicates the impact of choosing $\epsilon$ on individuals' privacy and use that to design an algorithm to choose $\epsilon$ and release output based on controllers' privacy preferences; 2) we introduce a utility signaling protocol that helps analysts interpret the impact of DP on their downstream tasks. We implement the algorithm and the protocol inside a new platform built on top of a data escrow, which allows controllers to control dataflows while maintaining high performance. We demonstrate our contributions through an IRB-approved user study, extensive experimental evaluations, and comparison with other DP platforms. All in all, our work contributes to making DP easier to use by lowering adoption barriers.
Simulating Scattering of Composite Particles

We develop a non-perturbative approach to simulating scattering on classical and quantum computers, in which the initial and final states contain a fixed number of composite particles. The construction is designed to mimic a particle collision, wherein two composite particles are brought in contact. The initial states are assembled via consecutive application of operators creating eigenstates of the interacting theory from vacuum. These operators are defined with the aid of the M{\o}ller wave operator, which can be constructed using such methods as adiabatic state preparation or double commutator flow equation. The approach is well-suited for studying strongly coupled systems in both relativistic and non-relativistic settings. For relativistic systems, we employ the language of light-front quantization, which has been previously used for studying the properties of individual bound states, as well as for simulating their scattering in external fields, and is now adopted to the studies of scattering of bound state systems. For simulations on classical computers, we describe an algorithm for calculating exact (in the sense of a given discretized theory) scattering probabilities, which has cost (memory and time) exponential in momentum grid size. Such calculations may be interesting in their own right and can be used for benchmarking results of a quantum simulation algorithm, which is the main application of the developed framework. We illustrate our ideas with an application to the $\phi^4$ theory in $1+1\rm D$.