Reviewerly/exHarmony huggingface dataset:
Pervasive Augmented Reality to support logistics operators in industrial scenarios: a shop floor user study on kit assembly

Abstract Augmented Reality (AR) is a pillar of the transition to Industry 4.0 and smart manufacturing. It can facilitate training, maintenance, assembly, quality control, remote collaboration and other tasks. AR has the potential to revolutionize the way information is accessed, used and exchanged, extending user’s perception and improving their performance. This work proposes a Pervasive AR tool, created with partners from the industry sector, to support the training of logistics operators on industrial shop floors. A Human-Centered Design (HCD) methodology was used to identify operators difficulties, challenges, and define requirements. After initial meetings with stakeholders, two distinct methods were considered to configure and visualize AR content on the shop floor: Head-Mounted Display (HMD) and Handheld Device (HHD). A first (preliminary) user study with 26 participants was conducted to collect qualitative data regarding the use of AR in logistics, from individuals with different levels of expertise. The feedback obtained was used to improve the proposed AR application. A second user study was realized, in which 10 participants used different conditions to fulfill distinct logistics tasks: C1 — paper; C2 — HMD; C3 — HHD. Results emphasize the potential of Pervasive AR in the operators’ workspace, in particular for training of operators not familiar with the tasks. Condition C2 was preferred by all participants and considered more useful and efficient in supporting the operators activities on the shop floor.
Implementation of STEM learning technology in the process of calibrating an NTC thermistor and developing an electronic thermometer based on it

The rapid development of information technology, robotics, nanotechnology, and biotechnology requires modern education to train highly qualified specialists who can support it, preparing students and students for producing creative work. The need to reform education to modern challenges is an urgent problem today. It is predicted that the most popular professions soon will be programmers, engineers, roboticists, nanotechnologists, biotechnologists, IT specialists, etc. STEM education can combine these areas into a complex, which can be implemented in different age groups. One example of the use of STEM technologies is the development and implementation of scientific and technical projects using the Arduino hardware and software complex. With the help of STEM technologies, a method for calibrating an NTC thermistor in the operating temperature range is proposed and a working model of an electronic thermometer is presented using the example of an NTC thermistor and an Arduino microcontroller.
Effectiveness analysis of e-learning implementation models and resource support in higher education institutions: case studies and insights amidst the COVID-19 pandemic

The COVID-19 pandemic has accentuated the need for comprehensive analysis of the effectiveness of e-learning implementation models and the supporting resources in higher education institutions. This article provides an overview of solutions and case studies, focusing on the selection and evaluation of individual services and learning management platforms. To gauge the effectiveness of electronic resources in meeting students’ educational needs, the study recommends employing quantitative indicators in conjunction with students’ performance descriptions. This includes leveraging educational analytics to gather data on students’ frequency and duration of engagement with specific e-resources. The functionality modules “Course Comparison” of the Moodle Learning Management System (LMS) and “Statistics”, along with the optional Analytics module, are reviewed. The article presents the results of applying these modules to analyze e-learning courses at the National University of Life and Environmental Sciences of Ukraine and Boris Grinchenko Kyiv University. Furthermore, the study investigates the factors contributing to students’ limited usage of individual e-courses. By examining these case studies and shedding light on the reasons behind students’ reduced engagement, this article contributes valuable insights to enhance the efficacy of e-learning implementation and resource support in higher education institutions.
Distance learning as a relevant educational technology in higher education institutions

The relevance of the subject is conditioned upon the fact that the current conditions, namely the COVID-19 epidemic and the introduction of martial law in Ukraine, cause dynamic changes in various spheres of human activity, including education. Despite internal and external obstacles, the educational process does not stop, and there is a need to adapt to large-scale changes in the country. The educational sector has transformed from the conventional form of the educational process, namely face-to-face, to online distance learning. Considering this, the purpose of the study is to analyse the peculiarities of implementing the distance learning process in a higher education institution in Ukraine, namely, the Higher Educational Institution of the Ukoopspilka “Poltava University of Economics and Trade”. For a broad understanding of the problem and outlining the prospects for a quality educational process in the context of global processes, the study used several methods, including analysis, synthesis, deduction and surveys. The study outlines the main changes implemented by the management of higher education institutions in response to the social challenges posed by the pandemic and war. The effectiveness of using special programmes during training sessions in the distance education process is assessed. The flexibility of the educational sphere is substantiated, and the advantages and disadvantages of distance learning in terms of the quality of education are identified. The results of a student survey reflecting the real attitude of students to online learning are presented. The practical value of the work is that its results can be used by teachers to select high-quality tools designed to implement learning tasks in a distance educational format.
Randomly-based Stepwise Multi-Level Distributed Medical Image Steganography

Steganography deals with concealing sensitive information that can either be communicated across a network or stored in a secured location. The concealment of information is accomplished through the carrier, making data access by an unauthorized person more difficult. There are many stenographic techniques that have been used. Unfortunately, the hybrid-multi-level approach was ignored. For this reason, the current research utilized image steganography on a hybrid-multi level involving encryption, data compression, and two-stage high data concealment. The proposed technique can be used to conceal information in medical images without any distortion, allowing flexible and secure transfer capability. After using the Trible DES algorithm to encrypt the secret text at the beginning of the process, the next step involves embedding the secret encrypted cipher message into the host image while keeping the image intact. The findings indicate that the value of PSNR and NCC are satisfactory when compared to the sensitivity of the human eye. As a direct impact, the confidential message is hidden from the adversary. It can be seen that the PSNR value is quite high. Therefore, this indicates that the image after the stenographic process is relatively similar to the original image.
A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.
The Efficiency of Small Financial Institutions

This study considers how financial institutions' local topology impacts how much systemic risk they contribute.Financial institutions are essential players in the financial ecosystem because they provide specialized and tailored services to individuals, small businesses, and communities.These organizations, including community banks, credit unions, and microfinance organizations, function on a smaller scale than their bigger counterparts.The dynamics of financial institution contributions to systemic risk, together with financial results and ideal customer service standards, are then examined.Finally, we find that financial institutions perform better with high-quality customer services and contribute more to systemic risk.
Revitalizing education through the integration of cloud technologies

Objective: This research aims to identify the strategic direction for modernizing the education system in Ukraine through the integration of cloud computing technology.Methods: The study utilizes analysis and forecasting to examine the current trends in education system modernization and the role of information and communication technologies. It focuses on identifying strategic areas for implementing cloud computing technology in Ukrainian education.Research focus: The research explores the theoretical and methodological foundations of using cloud computing technology as a means to modernize the education system in Ukraine.Results: The analysis reveals that the successful implementation of a modern educational paradigm, based on the principles of open education, requires comprehensive informatization of the national education system. The study highlights key tools of open education that significantly enhance teaching effectiveness. It also examines the evolution of information and communication technology networks in education, emphasizing the potential for radical improvement in content and technology through ICT integration. The role of cloud computing technology and ICT outsourcing in educational organization is discussed, along with strategic areas for education informatization in Ukraine.Conclusions and recommendations: Technological and organizational solutions are crucial for addressing important societal challenges and fostering progressive development. Practical application of these solutions will accelerate scientific and technological progress and contribute to the transition towards a knowledge-based society, advancing humanity's future.
An Efficient Approach to Face Emotion Recognition with Convolutional Neural Networks

Solutions for emotion recognition are becoming more popular every year, especially with the growth of computer vision. In this paper, classification of emotions is conducted based on images processed with convolutional neural networks (CNNs). Several models are proposed, both custom and transfer learning types. Furthermore, combinations of them as ensembles, alongside various methods of dataset modification, are presented. In the beginning, the models were tested on the original FER2013 dataset. Then, dataset filtering and augmentation were introduced, and the models were retrained accordingly. Two methods of emotion classification were examined: a multi-class classification, and a binary classification. In the former approach, the model returns the probability for each class. In the latter, separate models for each single class are prepared, together with an adequate dataset based on FER2013. Each model recognizes a single emotion from the others. The obtained results and a comparison of the applied methods across different models is presented and discussed.
HPO-RRT*: a sampling-based algorithm for UAV real-time path planning in a dynamic environment

Abstract The real-time path planning of unmanned aerial vehicles (UAVs) in dynamic environments with moving threats is a difficult problem. To solve this problem, this paper proposes a time-based rapidly exploring random tree (time-based RRT*) algorithm, called the hierarchical rapidly exploring random tree algorithm based on potential function lazy planning and low-cost optimization (HPO-RRT*). The HPO-RRT* algorithm can guarantee path homotopy optimality and high planning efficiency. This algorithm uses a hierarchical architecture comprising a UAV perception system, path planner, and path optimizer. After the UAV perception system predicts moving threats and updates world information, the path planner obtains the heuristic path. First, the path planner uses the bias sampling method based on the artificial potential field function proposed in this paper to guide sampling to improve the efficiency and quality of sampling. Then, the tree is efficiently extended by the improved time-based lazy collision checking RRT* algorithm to obtain the heuristic path. Finally, a low-cost path optimizer quickly optimizes the heuristic path directly to optimize the path while avoiding additional calculations. Simulation results show that the proposed algorithm outperforms the three existing advanced algorithms in terms of addressing the real-time path-planning problem of UAVs in a dynamic environment.
Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way

Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a hurdle. In this work, we propose an optical diffractive convolutional neural network (ODCNN) that is capable of performing image processing tasks in computer vision at the speed of light. We explore the application of the 4f system and the diffractive deep neural network (D2NN) in neural networks. ODCNN is then simulated by combining the 4f system as an optical convolutional layer and the diffractive networks. We also examine the potential impact of nonlinear optical materials on this network. Numerical simulation results show that the addition of convolutional layers and nonlinear functions improves the classification accuracy of the network. We believe that the proposed ODCNN model can be the basic architecture for building optical convolutional networks.
A framework for smart construction contracts using BIM and blockchain

Abstract Poor payment practices are perceived as one of the biggest challenges facing the construction industry. Since payments are issued according to project contract terms, the project’s cash flow is inherently affected by the contract and how parties fulfill their obligations. This research proposes a framework for payment automation in construction projects to achieve smart construction contracts. Payments are automatically issued upon satisfying contract conditions using blockchain. Cryptocurrency is proposed to be utilized in the framework to execute the contract terms with no need for a third party to process project payments. 5D BIM is used to model the geometry of buildings and visualize project progress together with payment status using Autodesk Revit, Navisworks, and Primavera P6. The developed framework has the potential to reduce the consequences of poor payments. An actual case study for a construction project in Cairo, Egypt is worked out to demonstrate the main features of the proposed framework. The results of the case study reveal that project cash flow is secured and payments are instantly issued. Moreover, electronic records of payments are kept on the blockchain.
Avocodo: Generative Adversarial Network for Artifact-Free Vocoder

Neural vocoders based on the generative adversarial neural network (GAN) have been widely used due to their fast inference speed and lightweight networks while generating high-quality speech waveforms. Since the perceptually important speech components are primarily concentrated in the low-frequency bands, most GAN-based vocoders perform multi-scale analysis that evaluates downsampled speech waveforms. This multi-scale analysis helps the generator improve speech intelligibility. However, in preliminary experiments, we discovered that the multi-scale analysis which focuses on the low-frequency bands causes unintended artifacts, e.g., aliasing and imaging artifacts, which degrade the synthesized speech waveform quality. Therefore, in this paper, we investigate the relationship between these artifacts and GAN-based vocoders and propose a GAN-based vocoder, called Avocodo, that allows the synthesis of high-fidelity speech with reduced artifacts. We introduce two kinds of discriminators to evaluate speech waveforms in various perspectives: a collaborative multi-band discriminator and a sub-band discriminator. We also utilize a pseudo quadrature mirror filter bank to obtain downsampled multi-band speech waveforms while avoiding aliasing. According to experimental results, Avocodo outperforms baseline GAN-based vocoders, both objectively and subjectively, while reproducing speech with fewer artifacts.
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective

Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenario, namely Single Domain Generalization (Single-DG), where only a single source domain is available for training. To tackle this challenge, we first try to understand when neural networks fail to generalize? We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as "model sensitivity". Based on our analysis, we propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies. Models trained with these hard-to-learn samples can effectively suppress the sensitivity in the frequency space, which leads to improved generalization performance. Extensive experiments on multiple public datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 2.55%. The source code is available at https://github.com/DIAL-RPI/Spectral-Adversarial-Data-Augmentation.
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation

Cross-domain recommendation has attracted increasing attention from industry and academia recently. However, most existing methods do not exploit the interest invariance between domains, which would yield sub-optimal solutions. In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning. Specifically, we perform two levels of cross-domain contrastive learning: 1) instance-to-instance contrastive learning, 2) instance-to-cluster contrastive learning. Not only that, we also take into account users' multi-granularity and multi-view interests. With this paradigm, SITN can explicitly learn the invariant knowledge of interest clusters between domains and accurately capture users' intents and preferences. We conducted extensive experiments on a public dataset and a large-scale industrial dataset collected from one of the world's leading e-commerce corporations. The experimental results indicate that SITN achieves significant improvements over state-of-the-art recommendation methods. Additionally, SITN has been deployed on a micro-video recommendation platform, and the online A/B testing results further demonstrate its practical value. Supplement is available at: https://github.com/fanqieCoffee/SITN-Supplement.
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models against Adversarial Examples

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause the performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.
DC-Former: Diverse and Compact Transformer for Person Re-identification

In person re-identification (ReID) task, it is still challenging to learn discriminative representation by deep learning, due to limited data. Generally speaking, the model will get better performance when increasing the amount of data. The addition of similar classes strengthens the ability of the classifier to identify similar identities, thereby improving the discrimination of representation. In this paper, we propose a Diverse and Compact Transformer (DC-Former) that can achieve a similar effect by splitting embedding space into multiple diverse and compact subspaces. Compact embedding subspace helps model learn more robust and discriminative embedding to identify similar classes. And the fusion of these diverse embeddings containing more fine-grained information can further improve the effect of ReID. Specifically, multiple class tokens are used in vision transformer to represent multiple embedding spaces. Then, a self-diverse constraint (SDC) is applied to these spaces to push them away from each other, which makes each embedding space diverse and compact. Further, a dynamic weight controller (DWC) is further designed for balancing the relative importance among them during training. The experimental results of our method are promising, which surpass previous state-of-the-art methods on several commonly used person ReID benchmarks. Our code is available at https://github.com/ant-research/Diverse-and-Compact-Transformer.