Reviewerly/exHarmony huggingface dataset:
Soft-margin classification of object manifolds

A neural population responding to multiple appearances of a single object defines a manifold in the neural response space. The ability to classify such manifolds is of interest, as object recognition and other computational tasks require a response that is insensitive to variability within a manifold. Linear classification of object manifolds was previously studied for max-margin classifiers. Soft-margin classifiers are a larger class of algorithms and provide an additional regularization parameter used in applications to optimize performance outside the training set by balancing between making fewer training errors and learning more robust classifiers. Here we develop a mean-field theory describing the behavior of soft-margin classifiers applied to object manifolds. Analyzing manifolds with increasing complexity, from points through spheres to general manifolds, a mean-field theory describes the expected value of the linear classifier's norm, as well as the distribution of fields and slack variables. By analyzing the robustness of the learned classification to noise, we can predict the probability of classification errors and their dependence on regularization, demonstrating a finite optimal choice. The theory describes a previously unknown phase transition, corresponding to the disappearance of a nontrivial solution, thus providing a soft version of the well-known classification capacity of max-margin classifiers. Furthermore, for high-dimensional manifolds of any shape, the theory prescribes how to define manifold radius and dimension, two measurable geometric quantities that capture the aspects of manifold shape relevant to soft classification.
Self Assessment Tool to Bridge the Gap Between XR Technology, SMEs, and HEIs

Extended reality (XR) is a cutting-edge technology. With the introduction of low-cost head-mounted display (HMD) devices and affordable mobile devices with augmented features, this technology has become widely available. However, due to concerns about applicability of XR technology in business-related activities, it has not reached its full potential for small and medium size businesses (SMEs) yet. In this paper, we present our novel approach to fill the gap between the technology and target groups, with a focus on identifying specific difficulties and challenges to overcome through the use of a novel self-assessment tool. Besides that, SME representatives are able to validate their experience towards XR technology with the solution. The tool also includes a separate module for higher education institutions (HEIs) to improve the curriculum, allowing graduates to be better prepared for business needs and to promote this technology. The implementation results with a report that suggests the level of understanding of XR technology based on the responses. The level groups are divided to receive individual coaching programs developed as part of a European level project. Preliminary results suggest that the developed tool is sufficient for both groups to recognize the advantages of XR technology and identify the gaps. At the same time, the results will give a better understanding to XR developers and experts towards the expectations and interests of SMEs in different sectors.
Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding

Entity alignment is a crucial task in knowledge graph fusion. However, most entity alignment approaches have the scalability problem. Recent methods address this issue by dividing large KGs into small blocks for embedding and alignment learning in each. However, such a partitioning and learning process results in an excessive loss of structure and alignment. Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives. First, we propose a centrality-based subgraph generation algorithm to recall some landmark entities serving as the bridges between different subgraphs. Second, we introduce self-supervised entity reconstruction to recover entity representations from incomplete neighborhood subgraphs, and design cross-subgraph negative sampling to incorporate entities from other subgraphs in alignment learning. Third, during the inference process, we merge the embeddings of subgraphs to make a single space for alignment search. Experimental results on the benchmark OpenEA dataset and the proposed large DBpedia1M dataset verify the effectiveness of our approach.
Sleep Behavior Detection Based on Pseudo-3D Convolutional Neural Network and Attention Mechanism

Good sleep is very important for everyone to protect physical and mental health. People&#x2019;s sleep behavior at night reflects their sleep status. In this work, we propose a method to detect people&#x2019;s sleep behavior at night by adopting Pseudo-3D (P3D) convolution neural network with attention mechanism. In particular, we propose a new structure, which integrates Squeeze-and-Excitation (SE) blocks into P3D blocks, named P3D-Attention. For the input video, we use P3D blocks to extract spatial-temporal features, and use SE blocks to pay more attentions to the important channel features. The proposed network is tested on the Sleep Action (SA) dataset, which consists of five different actions, namely turn over, get up, fall off bed, play mobile phone, and normal sleep. Experimental results show that the proposed network achieves reasonably good detection results, and the accuracy rate on the test set can reach 90.67%. Compared with 3D convolutional neural networks (C3D), our proposed network can increase the accuracy by about 6% with only 1/6 model parameter size, and achieves an average prediction speed about 1.75 item/s. Compared with the residual spatiotemporal convolution network (R(2+1)D), our proposed network can increase the accuracy rate by about 1.5% with less than 1/2 model parameter size.
Smart Door for COVID Restricted Areas

During the Covid pandemic, the world has faced inevitable changes. In Kazakhstan, people receive color statuses depending on the vaccination status, which can be checked through mobile phones. Depending on this colour, different restrictions are imposed. This status, along with the presence of a facemask, is checked in public places. However, security workers may overlook or ignore visitors without facemasks or dangerous status. To address these issues, the following paper presents a method for automating the checking procedure of the visitors at the entrance to any building. The automatic door opener is programmed using Python and OpenCV image processing library. This door opener can identify the green color from the display of the mobile phone placed in front of the camera as well as the presence of the facemask on the human face. The door is opener sends signals to the Arduino microcontroller, which actuates the motor to open the door if all the above-mentioned conditions are satisfied. The main feature of this project is that a build-in face recognizer was used to classify the human faces on masked and non-masked. Despite this, such a method shows good accuracy of 99% in identifying non-masked faces and 77% if a masked face test dataset is used. This project has the potential benefit of automating the checkup process, avoiding the crowds’ formation in front of the door, and facilitating the work of security guards.
Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search

Users' search tasks have become increasingly complicated, requiring multiple queries and interactions with the results. Recent studies have demonstrated that modeling the historical user behaviors in a session can help understand the current search intent. Existing context-aware ranking models primarily encode the current session sequence (from the first behavior to the current query) and compute the ranking score using the high-level representations. However, there is usually some noise in the current session sequence (useless behaviors for inferring the search intent) that may affect the quality of the encoded representations. To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query. Specifically, we design three generative tasks that can help the encoder to infer the actual search intent: (1) predicting future queries, (2) predicting future clicked documents, and (3) predicting a supplemental query. We jointly learn the ranking task with these generative tasks using an encoder-decoder structured approach. Extensive experiments on two public search logs demonstrate that our model outperforms all existing baselines, and the designed generative tasks can actually help the ranking task. Besides, additional experiments also show that our approach can be easily applied to various Transformer-based encoder-decoder models and improve their performance.
Near Lossless Compression for 3D Radiological Images Using Optimal Multilinear Singular Value Decomposition (3D-VOI-OMLSVD)

Storage and transmission of high-compression 3D radiological images that create high-quality reconstruction upon decompression are critical necessities for effective and efficient teleradiology. To cater to this need, we propose a near lossless 3D image volume compression method based on optimal multilinear singular value decomposition called "3D-VOI-OMLSVD." The proposed strategy first eliminates any blank 2D image slices from the 3D image volume and uses the selective bounding volume (SBV) to identify and extract the volume of Interest (VOI). Following this, the VOI is decomposed with an optimal multilinear singular value decomposition (OMLSVD) to obtain the corresponding core tensor, factor matrices, and singular values that are compressed with adaptive binary range coder (ABRC), integrated as an entropy encoder. The compressed file can be transferred or transmitted and then decompressed in order to reconstruct the original image. The resultant decompressed VOI is acquired by reversing the above process and then fusing it with the background, using the bound volume coordinates associated with the compressed 3D image. The proposed method performance was tested on a variety of 3D radiological images with different imaging modalities and dimensions using quantitative evaluation metrics such as the compression rate (CR), bit rate (BR), peak signal to noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, we also investigate the impact of VOI extraction on the model performance, before comparing it with two popular compression methods, namely JPEG and JPEG2000. Our proposed method, 3D-VOI-OMLSVD, displayed a high CR value, with a maximum of 37.31, and a low BR, with the lowest reported to be 0.21. The SSIM score was consistently high, with an average performance of 0.9868, while using < 1 second for decoding the image. We observe that with VOI extraction, the compression rate increases manifold, and bit rate drops significantly, and thus reduces the encoding and decoding time to a great extent. Compared to JPEG and JPEG2000, our method consistently performs better in terms of higher CR and lower BR. The results indicate that the proposed compression methodology performs consistently to create high-quality image compressions, and overall gives a better outcome when compared against two state-of-the-art and widely used methods, JPEG and JPEG2000.
Logistics Information Traceability Mechanism of Fresh E-Commerce Based on Image Recognition Technology

Logistics migration and movement require precise information updates for traceability and visibility of goods through E-commerce platforms. Computer vision and digital image processing techniques are used for visual identification and tracking through different warehouses and delivery points. In this article, an incessant visualized tracking scheme (IVTS) is designed for identifying and tracking E-commerce logistics throughout the migration points. This scheme endorsed computer vision technology for logistics recognition and labelled data detection. In this scheme, the labelled logistics data is verified for its similarity in different migrating locations and to the endpoint. Based on the dimensional features and regional-pixel similarity factor, it is verified using the deep neural network. This learning process identifies dimensional variations due to logistics displacement and position suppressing the similarity variations. It is performed based on the migration and information available to prevent tracking errors. For the varying locations and logistics displacement, the error pixel regions are identified and trained for possible similarity detection. The proposed scheme effectively improves visual accuracy, tracking maximization, and logistics detection by reducing dimensional errors.
Effective nonlocal parity-dependent couplings in qubit chains

For the efficient implementation of quantum algorithms, practical ways to generate many-body entanglement are a basic requirement. Specifically, coupling multiple qubit pairs at once can be advantageous and may provide multiqubit operations useful in the construction of hardware-tailored algorithms. Here we extend the theory of fractional state transfer and harness the simultaneous coupling of qubits on a chain to engineer a set of nonlocal parity-dependent quantum operations suitable for a wide range of applications. The resulting effective long-range couplings directly implement a parametrizable Trotter-step for Jordan-Wigner fermions, and they can be used for simulations of quantum dynamics, efficient state generation in variational quantum eigensolvers, parity measurements for error-correction schemes, and the generation of efficient multiqubit gates. Moreover, we present numerical simulations of the gate operation in a superconducting quantum circuit architecture, which show a high gate fidelity for realistic experimental parameters.
Clustering Methods Comparison for Optimization of Adaptive Neural Fuzzy Inference System

Different methods have been developed to optimize the Adaptive Neural Fuzzy Inference System, which is used in many fields due to its flexible structure and trainability. Within the scope of this study, three different models were produced using two different datasets, using only the first clustering method, only the second clustering method, and both the first and second clustering methods. In this study, the Fuzzy C-Mean Clustering algorithm, which is one of the most efficient methods used to reduce the number of rules in the rule base of the hybrid intelligent system is compared with the Highly Connected Subgraphs algorithm. The models were compared over the square root of the mean square error, the number of nodes, the number of fuzzy rules, and the mean training time. As a result of the study, the second clustering method formed the most efficient result in terms of error rate with 0.084 and 0,008. It has been observed that the average training time of this method is approximately 31 times longer than the first clustering method mentioned above, and approximately 52 times longer than the model in which the first and second clustering methods are used together. In this study, it has been seen that the first clustering method is more successful in reducing the rule base by optimizing the second method by determining more suitable cluster centers. Based on the experimental results obtained in our study, these two different clustering methods were compared over three different models. Discussion and scientific results are included in our study.
Continuous-variable quantum key distribution: security analysis with trusted hardware noise against general attacks

In this paper, using the full security framework for continuous variable quantum key distribution (CV-QKD), we provide a composable security proof for the CV-QKD system in a realistic implementation. We take into account equipment losses and contributions from various components of excess noise and evaluate performance against collective and coherent attacks assuming trusted hardware noise. The calculation showed that the system remains operable at channel losses up to 10.2 dB in the presence of collective attacks and up to 7.5 dB in the presence of coherent ones.
Analysis of solutions for a blockchain compliance with GDPR

Abstract The aim of this paper was to perform an analysis of the state-of-the-art solutions of the permissioned blockchain compliance with the General Data Protection Regulation (GDPR), including the implementation of one of the analyzed methods and the own solution. This paper covers the subject of GDPR and its impact on already existing blockchain databases to determine the domain of the problem, including the necessity to introduce mutability in the data structure to comply with the ”right to be forgotten”. The performed analysis made it possible to discuss current research in technical terms as well as in the regulation itself. In the experimental part, attempts were made to research and implement the Reference-based Tree Structure (RBTS), including the performance tests. The proposed solution is efficient and easily reproducible. The deletion of unwanted content is quick and requires consent only from the owner of personal data; therefore, eliminating the dependency on the other blockchain network participants.
Determinants of Information Security Awareness and Behaviour Strategies in Public Sector Organizations among Employees

In this digital era, protecting an organisation's sensitive information system assets against cyberattacks is challenging.Globally, organisations spend heavily on information security (InfoSec) technological countermeasures.Public and private sectors often fail to secure their information assets because they depend primarily on technical solutions.Human components create the bulk of cybersecurity incidents directly or indirectly, causing many organisational information security breaches.Employees' information security awareness (ISA) is crucial to preventing poor information security behaviours.Until recently, there was little combined information on how to improve ISA and how investigated factors influencing employees' ISA levels were.This paper proposed a comprehensive theoretical model based on the Protection Motivation Theory, the Theory of Planned Behaviour, the General Deterrence Theory, and Facilitating Conditions for assessing public sector employees' ISA intentions for information security behaviour.Using a survey and the structural equation modelling (SEM) method, this research reveals that the utilised factors are positively associated with actual information security behaviour adoption, except for perceived sanction certainty.The findings suggest that the three theories and facilitating conditions provide the most influential theoretical framework for explaining public sector employees' information security adoption behaviour.These findings support previous empirical research on why employees' information on security behaviours vary.Consistent with earlier research, these psychological factors are just as critical as facilitating conditions in ensuring more significant behavioural intention to engage in ISA activities, ensuring information security behaviour.The study recommends that public-sector organisations invest in their employees' applied information security training.
Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long–short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease.
Utilizing activity sensors to identify the behavioural activity patterns of elderly home care clients

Purpose The behavioural activity pattern is a behavioural and biological 24-hour rhythm. Ageing, diseases and memory disorders can change this pattern. Home care staff can utilize knowledge about the behavioural activity pattern of elderly home care clients in many ways. The purpose of this study was to evaluate whether home care staff could identify the behavioural activity pattern of elderly home care clients using activity sensors, namely, actigraphs and motion sensors, could identify the behavioural activity rhythms.Materials and methods A total of four elderly home care clients and one elderly home rehabilitation client took part in the study. The participants wore actigraphs on their wrist and motion sensors were installed in their apartment. In addition to sensor data, home care staff answered one open-ended question during each home care visit. The data collection period was two weeks. Both quantitative and qualitative methods were used in the analysis.Results The behavioural activity pattern was easy to identify from the motion sensor data, whereas actigraph data were difficult to interpret. The home care staff members' answers to open-ended questions reinforced the reliability of motion sensor data.Conclusions Motion sensors are relatively cheap, unobtrusive and reliable way to identify and detect changes in the behavioural activity patterns of elderly home care clients.Implications for rehabilitationMotion sensors are cheap, user-friendly and highly accepted technology for identifying and monitoring behavioural activity rhythm.Home care staff members can use the data about elderly home care client's behavioural activity rhythm to monitor deviations to the rhythm and detect changes in client's health.The information about behavioural activity rhythm can also be utilized in planning home care visits and interventions
Are Source Code Metrics “Good Enough” in Predicting Security Vulnerabilities?

Modern systems produce and handle a large volume of sensitive enterprise data. Therefore, security vulnerabilities in the software systems must be identified and resolved early to prevent security breaches and failures. Predicting security vulnerabilities is an alternative to identifying them as developers write code. In this study, we studied the ability of several machine learning algorithms to predict security vulnerabilities. We created two datasets containing security vulnerability information from two open-source systems: (1) Apache Tomcat (versions 4.x and five 2.5.x minor versions). We also computed source code metrics for these versions of both systems. We examined four classifiers, including Naive Bayes, Decision Tree, XGBoost Classifier, and Logistic Regression, to show their ability to predict security vulnerabilities. Moreover, an ensemble learner was introduced using a stacking classifier to see whether the prediction performance could be improved. We performed cross-version and cross-project predictions to assess the effectiveness of the best-performing model. Our results showed that the XGBoost classifier performed best compared to other learners, i.e., with an average accuracy of 97% in both datasets. The stacking classifier performed with an average accuracy of 92% in Struts and 71% in Tomcat. Our best-performing model—XGBoost—could predict with an average accuracy of 87% in Tomcat and 99% in Struts in a cross-version setup.