Reviewerly/exHarmony huggingface dataset:
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.
The Value of Extended Reality Techniques to Improve Remote Collaborative Maintenance Operations: A User Study

In the Architecture, Engineering and Construction (AEC) sector, data extracted from building information modelling (BIM) can be used to create a digital twin (DT). The algorithms of a BIM-based DT can facilitate the retrieval of information, which can then be used to improve building operation and maintenance procedures. However, with the increased complexity and automation of the building, maintenance operations are likely to become more complex and may require expert intervention. Collaboration and interaction between the operator and the expert may be limited as the latter may not be on site or within the company. Recently, extended reality (XR) technologies have proven to be effective in improving collaboration during maintenance operations, through data display and shared interactions. This paper presents a new collaborative solution using these technologies to enhance collaboration during remote maintenance operations. The proposed approach consists of a mixed reality (MR) set-up for the operator, a virtual reality (VR) set-up for the remote expert and a shared Digital Model of a heat exchanger. The MR set-up is used for tracking and displaying specific information, provided by the VR module. A user study was carried out to compare the efficiency of our solution with a standard audio-video collaboration. Our approach demonstrated substantial enhancements in collaborative inspection, resulting in a significative reduction in both the overall completion time of the inspection and the frequency of errors committed by the operators
Student attitudes to the factors that determine the success of online teaching and the acquisition of professional skills in accounting subjects during the COVID-19 pandemic

Transition to online teaching is the best approach to coping and overcoming the difficult situation that universities all over the world are facing due to the COVID-19 pandemic, as it will have the least possible negative effects on students' education and the development of professional skills. Based on the above, the primary objective of the paper is to identify the factors that influence the effectiveness of acquiring professional skills in accounting subjects in online classes during the COVID-19 pandemic as well as factors that determine the success of online teaching based on students' views. The research was conducted on a sample of 373 students from universities in Kragujevac, Belgrade, Novi Sad and Niš. Students who attended online classes in at least one accounting subject were surveyed. In the methodological sense, the analysis of the collected data was based on the application of descriptive statistics, factor analysis, and multiple regression. The research results indicate that the factors determining the success of online teaching are the adequacy, availability, and autonomy of information and communication technologies (ICT); Internet connection, advantage and simplicity of ICT use; communication with the teacher; the readiness and competence of teachers; the impact of the pandemic; online teaching flexibility and student-perceived satisfaction. In addition, it was identified that student-perceived satisfaction has the greatest impact on the development of professional skills in the accounting subject, followed by the effects of the pandemic and the adaptability of online teaching
Thermal Image Super-Resolution Methods Using Neural Networks

Thermography has gained popularity in many fields.Since the human eye cannot see the thermal spectrum and in low light, thermal image analysis has become an integral part of medicine, manufacturing, construction, and other industries.Most thermal cameras produce low-resolution images when analyzing the temperature of objects, which complicates the process of analyzing original thermogram.Therefore, the problem of improving the quality of thermal images is relevant today.With the development of artificial intelligence and deep learning technologies, new super-resolution methods are emerging.Such methods can also be adapted for thermal image processing.This work examines the performance of modern superresolution methods in the thermal vision domain.
A supervised classification phenotyping approach using machine learning for patients diagnosed with primary breast cancer

This paper presents a methodology for the early detection and diagnosis of breast cancer using the Wisconsin dataset. The methodology involves four main steps, including data collection, preprocessing, feature selection, and classification. Fine needle aspiration technique is used to extract the ultrasound image features of breast cancer, and preprocessing is performed to eliminate outliers, null values, and noise. Redundant parameters are removed during the feature selection process to improve accuracy. Six machine learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Tree, and Gaussian Naive Bayes, are employed for the classification of the breast cancer dataset. Support Vector Machine and K-Nearest Neighbor achieved the highest accuracy, with Logistic Regression, Gaussian Naive Bayes, Random Forest, and Decision Tree having lower accuracy scores. The proposed methodology could aid in the timely detection and diagnosis of breast cancer, and help doctors in selecting the optimal clinical treatment plan for their patients. Further work will be carried out to investigate the effectiveness of additional preprocessing algorithms in improving the classification accuracy of the breast cancer dataset.
Secure authentication protocol for IoT applications based on blockchain technology

The lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.
Mobile visual search algorithm based on improved VGG-F and hash with application in IoT

This paper explores the use of deep learning based hash method in IoT to build a more powerful and real-time mobile visual search, and proposes a lightweight, low latency and high-precision mobile visual search algorithm based on deep hash method and its application in IoT. This paper discusses two aspects: image semantic feature extraction and fast retrieval. Based on the deep learning method and hash method, an image semantic feature extraction model for Library Digital Humanities is constructed, and the loss function applicable to the field is constructed. The model training and MVS retrieval process experiment are carried out through the Library Data set, and this method is applied to the library IoT system. In order to verify the effectiveness of our method, we compared the proposed method with VHB, SSFS, DLBH, SSDH and other methods. Experimental results show that the proposed method is more effective and robust than the existing methods.
Towards Understanding Students’ Sense-Making of Test Case Design

Context: Software testing is the most used technique for quality assurance in industry. However, in computer science education software testing is still treated as a second-class citizen and students are unable to test their software well enough. One reason for this is that teaching the subject of software testing is difficult as it is a complex intellectual activity for which students need to allocate multiple cognitive resources at the same time. A myriad of primary and secondary studies have tried to solve this problem in education, however still with very limited results.Objective: Before we can design interventions to improve our pedagogical approaches, we need to gain more in-depth understanding and recognition of sensemaking as it is happening when students design test cases.Method: An initial exploratory study identified four different sensemaking approaches used by students while creating test models. In this paper we present a follow-up study with 50 students from a large university in Spain. The used methodology was based on the previous study with the improvements that originated from its evaluation. We asked the participants to create a test model based on a description of a test problem using a specialized web-based tool for modelling test cases. We measured how well these models fit the test problem, the sensemaking process that students went through when creating the models, and the students’ perception of the modelling task. The participants received no compensation for their efforts, and we scheduled the experiment during a regular class. Apart from the created models and their metadata, we also collected recordings of the students’ computer screens made during the experiment and used a questionnaire to study their perspectives on the assignment. All the collected textual, graphical, and video data was analyzed using an iterative inductive analysis process to allow new information about the different sensemaking approaches to emerge.Results: We gained better insights into the sensemaking processes of students while modelling test cases for a problem. The results enabled us to refine our previous findings, and we identified new sensemaking approaches.Conclusions: Based on these results, we can further investigate ways to influence the sensemaking process in education, the possible misconceptions that have a negative influence on it, and the desired mental model we want our students to have to design test cases.
Silicon Photonic Hopfield-like Electro-optical Recurrent Network for Time-series Data Processing and Recognition

We propose and experimentally demonstrate a Hopfield-like electro-optical recurrent network based on silicon photonic circuits for processing time-series data to extract feature vectors just by one-time sampling, which can offer robust and simple waveform recognition. © 2022 The Author(s)
Quantum Noise Secured Terahertz Communications

The quantum noise based terahertz signal encryption scheme is proposed, a 16 Gbits <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> secure terahertz communication system at 300 GHz with the optical communication realms is demonstrated, taking a significant step toward high-security wireless communications.
TRANSGNN: Towards Knowledge Enhanced Top-K Recommendation via Transformed Heterogeneous Graph Neural Network

The knowledge graph (KG) enhanced recommendation system has shown superior performance and attracted considerable attention. In recent years, researchers have attempted to apply graph neural networks (GNNs) and build end-to-end knowledge graph enhanced recommendation systems. Although several methods are proposed, it is still challenging to apply GNNs to solve the problem of KG enhanced recommendation due to the heterogeneous, redundant, and large-scale nature of KG versus the homogeneous nature of GNNs. To address the limitations of existing techniques, we propose a transformed heterogeneous graph neural network (TRANSGNN), an item-centric and GNN-powered framework that can better explore the heterogeneous, redundant, and large-scale relations among items in KG for an effective and efficient KG enhanced top- K recommendation. In particular, we propose an item-centric graph transformation module to transform the heterogeneous KG into a homogeneous item-item graph. This scheme allows us to distill the knowledge from the KG and encode the heterogeneous, redundant, and large-scale item-item relationship into a smaller, compact, homogeneous graph, better fitting the GNNs architecture that prefers homogeneous and small-scale graph input. We further introduce a collaborative graph fusion module to fuse the item-wise KG relational signals and the collaborative signals by augmenting the collaborative graph with the transformed item-item graph. Next, we propose to use a graph collaborative encoder over the augmented graph to propagate information along with high-order connectivity. The proposed framework is general and can be easily extended with advanced KG embedding and GNN models. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach in significantly (up to 23%) improving the KG enhanced recommendation performance over the state-of-the-art methods, such as KGIN, KGAT, KGNN-LS in both regular and cold-start settings. The proposed approach is also efficient with significantly (up to 31%) fewer parameters.
Deep learning acceleration at the resource-constrained tactical edge

This paper outlines how we modified the torch2trt library which allowed us to build a recursive framework that can quantize previously unsupported PyTorch models. The framework partitions the PyTorch model into supported and unsupported modules, and then rebuilds the PyTorch model by replacing the supported PyTorch modules with faster TensorRT modules. The framework allows us to optimize and deploy more advanced Deep Neural Network algorithms that are not natively supported by torch2trt.
Finger Gesture Recognition using Recurrent Neural Network and Convolution Neural Network

Communication is essential for people to interact with one another. Sadly, not everyone can communicate verbally, so they resort to Sign language to engage in conversations and express themselves using various hand gestures. The deaf community mostly uses American sign language (ASL). However, only a few people can interpret ASL, and various translation systems have been developed. We aim to implement various Deep Learning techniques to translate ASL and read the sign out loud to find and develop an efficient and accurate sign language translation to make communication more accessible for hard-of-hearing individuals by building Recurrent Neural Networks and Convolution Neural Networks. On evaluating both these models highlighting the strengths and weaknesses of each approach. This is conducted based on various hyperparameters such as the Number of Epochs, Accuracy, Time, and Space Complexity. The study's findings offer exciting prospects for improving hand gesture recognition technology.
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation

Personalized recommender systems aim to predict users’ preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form groups based on their common interests. The users’ group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems. Users join different groups out of different interests. In this paper, we generate group representation from the user’s interests and propose IGRec (Interest-based Group enhanced Recommendation) to utilize the group information accurately. It consists of four modules. (1) Interest disentangler via self-gating that disentangles users’ interests from their initial embedding representation. (2) Interest aggregator that generates the interest-based group representation by Gumbel-Softmax aggregation on the group members’ interests. (3) Interest-based group aggregation that fuses user’s representation with the participated group representation. (4) A dual-trained rating prediction module to utilize both user-item and group-item interactions. We conduct extensive experiments on three publicly available datasets. Results show IGRec can effectively alleviate the data sparsity problem and enhance the recommender system with interest-based group representation. Experiments on the group recommendation task further show the informativeness of interest-based group representation.
Versatile Bengali OCR: Document Analysis Technique for Varied Document Styles and Content

In our research paper, we introduce a distinctive Bengali OCR system that boasts impressive capabilities. This system excels in reconstructing document layouts while maintaining the integrity of structure, alignment, and even images. It integrates advanced image and signature detection for precise extraction. Specifically, tailored models for word segmentation accommodate various document types, such as computer-compose, letterpress, typewritten, and handwritten documents. Notably, the system handles static and dynamic handwritten inputs, recognizing diverse writing styles. Additionally, it achieves remarkable recognition of compound characters in the Bengali language. The comprehensive data collection contributes to a diverse corpus, and sophisticated technical components enhance character and word recognition. Other notable features include image, logo, signature recognition, table recognition, perspective correction, layout reconstruction, and a queuing module for efficient and scalable processing. The system showcases exceptional performance in the efficient and accurate extraction and analysis of text.
BUILD-KG: Integrating Heterogeneous Data Into Analytics-Enabling Knowledge Graphs

Knowledge graphs (KGs), with their flexible encoding of heterogeneous data, have been increasingly used in a variety of applications. At the same time, domain data are routinely stored in formats such as spreadsheets, text, or figures. Storing such data in KGs can open the door to more complex types of analytics, which might not be supported by the data sources taken in isolation. Giving domain experts the option to use a predefined automated workflow for integrating heterogeneous data from multiple sources into a single unified KG could significantly alleviate their data-integration time and resource burden, while potentially resulting in higher-quality KG data capable of enabling meaningful rule mining and machine learning.In this paper we introduce a domain-agnostic workflow called BUILD-KG for integrating heterogeneous scientific and experimental data from multiple sources into a single unified KG potentially enabling richer analytics. BUILD-KG is broadly applicable, accepting input data in popular structured and unstructured formats. BUILD-KG is also designed to be carried out with end users as humans-in-the-loop, which makes it domain aware. We present the workflow, report on our experiences with applying it to scientific and experimental data in the materials science domain, and provide suggestions for involving domain scientists in BUILD-KG as humans-in-the-loop.
Game Based Virtual Reality Platform for Geometry

Teaching with virtual reality (VR) has transformed the traditional educational environment by providing immersive and engaging learning experiences. Students may explore realistic simulations, control items, and study hands-on in a virtual reality classroom. Using virtual reality technology Mathematics education may be taken to the next level by utilizing this immersive technology, which improves how students learn and engage with mathematical topics. 3D geometry and trigonometry issues are typically difficult for students to grasp when presented in 2D planes. Using VR Geometry students, in particular, may enter a three-dimensional mathematical environment where abstract concepts come to life. They are able to visualize geometric forms, manipulate things, and investigate mathematical concepts in ways that typical schools do not allow. In this article, a Geometrical classroom was created utilizing a virtual reality environment. There are two sections to the classroom. In the first section, students may study about various 3D geometrical forms such as cubes, cuboids, cones, cylinders, spheres, and pyramids. In the second section, a game-based quiz app was designed in which students could engage and earn reward points and also able to understand the answer through 3D visualization. For the scientific evaluation of the VR platform, a three-phase research study was conducted. A total of 24 students took part in the study, and they were separated into two groups based on their understanding of the themes presented on the platform. In the last step, technical evaluation of the system as well as learning improvement were measured using a questionnaire after going through the VR game-based platform. The study yields excellent findings in that students’ understanding increases, and 83% of students found that they can readily perceive diverse geometrical forms using the VR game-based platform.