Reviewerly/exHarmony huggingface dataset:
Improvement of Breast Density Classifier based on CNN Features Extraction and SVM in Mammogram Images

Screening mammography has been clinically practiced as a common method for monitoring any potential breast diseases, especially in denser breasts among women.The advancement in medical imaging technology proved that the integration of artificial intelligence had given a significant impact on the breast screening process.However, due to various demographic patient backgrounds in clinical profile and non-standardized configurations of the developed intelligence models, such application is incapable of being applied by a health practitioner.Commonly, the deep learning method trained on a single network classifier has resulted in lower performance accuracy.With the motivation to improve the performance of classifying the dense breast, this paper proposes an improved classification model using deep learning approach of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) employed on different sets of publicly established mammogram images of craniocaudal (CC) and medio-lateral (MLO) views.In this study, pre-trained CNN models, namely GoogleNet, ResNet50, ResNet101 and AlexNet, are used with SVM as a classifier.The proposed method for the classification of breast density region is superior to the existing methods as indicated from model performance quantitative results of accuracy, precision and area under the curve (AUC) of its receiver operating characteristic (ROC) curves.Significant improvement in model performance has been obtained using ResNet50 and GoogleNet with SVM classifier with > 94% accuracy and AUC > 0.95.Furthermore, the model is proved to have good feature extraction capabilities to work for various breast density images that can be further explored into detecting malignancy from screening mammogram images.
Graph structure learning based on feature and label consistency

Graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly. Most GNNs assume that the networks are homophilous, which is not always true in the real world, i.e., structure noise or disassortative graphs. Only a few works focus on generalizing graph neural networks to heterophilous or low homophilous networks, where connected nodes may have different labels. In this paper, we design a simple and effective Graph Structure Learning strategy based on Feature and Label consistency (GSLFL) to increase the homophilous level of networks for generalizing any existing GNNs to heterophilous networks. Specifically, we first introduce a method to learn graph structure based on node features and then modify the graph structure based on label consistency. Further, we combine the GSLFL with three existing GNNs to learn node representations and graph structure together. And we design a self-training method to iteratively train models and modify graph structure with pseudo-labels. Finally, our empirical results on 6 public networks with homophily or heterophily, and structure attacks show that our methods outperform the state-of-the-art methods in most cases.
Robotic Weld Image Enhancement Based on Improved Bilateral Filtering and CLAHE Algorithm

Robotic welding requires a higher weld image resolution for easy weld identification; however, the higher the resolution, the higher the cost. Therefore, this paper proposes an improved CLAHE algorithm, which can not only effectively denoise and retain edge information but also improve the contrast of images. First, an improved bilateral filtering algorithm is used to process high-resolution images to remove noise while preserving edge details. Then, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm and Gaussian masking algorithm are used to enhance the enhanced image, and then differential processing is used to reduce the noise in the two images, while preserving the details of the image, enhancing the image contrast, and obtaining the final enhanced image. Finally, the effectiveness of the algorithm is verified by comparing the peak signal-to-noise ratio and structural similarity with other algorithms.
Open science and Big Data in South Africa

With the Square Kilometer Array (SKA) project and the new Multi-Purpose Reactor (MPR) soon coming on-line, South Africa and other collaborating countries in Africa will need to make the management, analysis, publication, and curation of "Big Scientific Data" a priority. In addition, the recent draft Open Science policy from the South African Department of Science and Innovation (DSI) requires both Open Access to scholarly publications and research outputs, and an Open Data policy that facilitates equal opportunity of access to research data. The policy also endorses the deposit, discovery and dissemination of data and metadata in a manner consistent with the FAIR principles - making data Findable, Accessible, Interoperable and Re-usable (FAIR). The challenge to achieve Open Science in Africa starts with open access for research publications and the provision of persistent links to the supporting data. With the deluge of research data expected from the new experimental facilities in South Africa, the problem of how to make such data FAIR takes center stage. One promising approach to make such scientific datasets more "Findable" and "Interoperable" is to rely on the Dataset representation of the Schema.org vocabulary which has been endorsed by all the major search engines. The approach adds some semantic markup to Web pages and makes scientific datasets more "Findable" by search engines. This paper does not address all aspects of the Open Science agenda but instead is focused on the management and analysis challenges of the "Big Scientific Data" that will be produced by the SKA project. The paper summarizes the role of the SKA Regional Centers (SRCs) and then discusses the goal of ensuring reproducibility for the SKA data products. Experiments at the new MPR neutron source will also have to conform to the DSI's Open Science policy. The Open Science and FAIR data practices used at the ISIS Neutron source at the Rutherford Appleton Laboratory in the UK are then briefly described. The paper concludes with some remarks about the important role of interdisciplinary teams of research software engineers, data engineers and research librarians in research data management.
Information Extraction from Invoices by using a Graph Convolutional Neural Network: A Case Study of Vietnamese Stores

Information extraction automatically obtains structured information from unstructured or semi-structured machine-readable documents. The extraction steps consist mainly of classifying words (tagging). The output can be stored as key-value pairs in a computer-friendly file format, and then stored in a database for later reference. Information extraction from receipts or invoices is a difficult task because the tagging step should not be done solely on machine-readable words. Also, we obtain layout information or positions of words relative to other words in the invoices or receipts. This study deployed optical character recognition solutions for the Vietnamese language (VietOCR) combining a graph convolutional network (GCN) to extract information from 731 Vietnamese invoices issued by several stores. First, we collected invoice images captured with smartphones from supermarkets in Vietnam. Then, with those images we proceeded with text detection and recognition, then feature processing. The dataset was classified into two parts for training and testing, and we executed classification tasks with two GCNs. Experimental results revealed that our proposed method reached 99.50%, 98.52%, 98.52%, and 98.52% for accuracy, recall, precision, and F1-score, respectively. This work is expected to prove useful for information extraction from image-based documents.
A scheme of opinion search &amp; relevant product recommendation in social networks using stacked DenseNet121 classifier approach

Traditional methods have resulted in lower-quality search results with a lower accuracy rate. This problem is addressed and a recommended technique using deep learning methods is provided with the goal of improving prediction quality. Via this paper, a novel paradigm for pertinent product recommendations in social networks is provided. The major goal of this strategy is to let computers learn automatically without any assistance from humans, consequently controlling operations as needed. The social input data set in this proposed study is first pre-processed to remove noise. Following that, a Fisher discriminant method based on information is used for feature extraction. Then, using the Hierarchical Agglomerative and Attribute-based Clustering procedure, the features are chosen from the retrieved ones. Following that, such clusters are predicted using the stacked DenseNet121 method, and Attention-based MLP is used to propose the product. Finally, to verify the effectiveness of the suggested system, the expected output was evaluated, the performance measure was examined, and comparisons with conventional methods were made. Out of 2033 reviews, the suggested approach has a positive score percentage of 92.22%. The investigation demonstrates that the suggested system is more effective at providing improved results for pertinent product recommendations.
Reciprocal consistency graph learning by aligning multi-semantic spaces for multi-view clustering

Graph-based multi-view clustering can effectively reveal the latent cluster structure of multi-view data, however, it remains challenging to construct high-quality graphs by exploring the discriminant information from data. Most of the existing graph-based methods usually construct the graph in the original space or subspace, which may not take the differences of semantic information in different spaces into considerations. Moreover, existing approaches generally measure the relevance of the pair-wise views, which may result in insufficient information exploitation and limited graph representation. To remedy these issues, we proposed an innovative multi-view clustering algorithm called reciprocal consistency graph learning (RCGL), which learns consistency representation by aligning the semantic information from three spaces simultaneously to uncover latent complementarity and consistency among different views. Specifically, we construct the low-dimensional space based on linear projection to preserve essential semantic information. Furthermore, RCGL maps the low-dimension representation from linear space into nonlinear space via Hilbert–Schmidt independence criterion, to explore the nonlinear information hidden in data. Moreover, multiple graphs in kernel space are extended to tensor space for learning a high-order graph, which is helpful to better measure the correlation between multiple views. Finally, the objective function is solved by an alternating optimization scheme. Extensive experiments on five benchmark multi-view datasets demonstrate that RCGL outperforms state-of-the-art baselines markedly.
Towards Quantum-algorithms-as-a-service

Quantum computing is an emerging field of high interest. Many companies have started to work on developing more powerful and stable quantum computers. However, developers still struggle to master the art of programming with a quantum computer. One of the major challenges faced is the integration of quantum parts of a system with the classical one. This paper proposes a novel development model called Quantum-Algorithms-as-a-Service (QAaaS). This new model aims to allow developers to abstract the quantum components away from the design of the software they are building. The model leverages Software-as-a-Service and Function-as-a-Service to support multiple quantum cloud providers and run their algorithms regardless of the underlying hardware.
Industrial IoT with Secure Authentication Mechanism Through Blockchain Technology

AbstractData privacy and security are the challenge in the real world in Industrial Internet of things (IIoT) applications. It depends on the user’s reliability to expose the security and privacy of IIoT applications to ensure the user’s authentication. In the real world, industrial applications need a reliable data interface that makes exposed security and privacy as data leakage, DoS attack, eavesdropping, and replay attack. The existing authentication approach suffers from the single authentication, poor flexibility, user privacy upon increasing the number of users. To eliminate these issues, the proposed mechanism has proposed a hybrid authentication scheme, that is, the combination of elliptic curve cryptography and public key infrastructure based on blockchain technology. In addition, the proposed approach enhances the privacy of users and trust using private blockchain mechanism in industrial applications. The proposed mechanism is validated and verified by depicting the results by showing the authentication of users in the IIoT applications.KeywordsBlockchainSolidity remix (smart contract)Public key infrastructureElliptic curve cryptographyGanacheIPFS
ActivityPoser: Activity driven Full-Body Pose Estimation from Sparse IMU Configurations

On-body IMU-based pose tracking systems have gained prevalence over their external tracking counterparts due to their mobility, ease of installation and use. However, even in these systems, an IMU sensor placed on a particular joint can only estimate the pose of that particular limb. In contrast, activity recognition systems contain insights into the whole body’s motion dynamics. In this work, we present ActivityPoser, which uses the activity context as a conditional input to estimate the pose of limbs for which we do not have any direct sensor data. ActivityPoser compensates for impoverished sensing paradigms by reducing the overall pose error by up to 17%, compared to a model bereft of activity context. This highlights a pathway to high-fidelity full-body digitization with minimal user instrumentation.
Measuring the impact of practicum schedule and mental workload on stress levels in hybrid learning: A case study in Physics practicum

The Physics Laboratory of the Faculty of Engineering, University of Sultan Ageng Tirtayasa, has implemented hybrid learning-based learning after the Covid-19 pandemic abated. Students admitted to online practicum the previous semester said they felt depressed and overthinking while participating in the physics practicum. The assignments and report must be handwritten after they watch a video explanation of the experimental procedure. The internet network condition is also an obstacle for online practicum. Based on these problems, examining the impact of hybrid learning-based learning in shift practicum and mental workload on stress levels was necessary. This study aims to identify the mental workload experienced by online and offline participants in the Physics Laboratory practicum. The advantage of this study is to review two practicum methods, online and offline practicum. The research instruments used to measure the mental workload and stress level experienced by students are NASA-TLX and DASS-21, respectively. The results showed a simultaneous influence between shift practicum and mental workload on stress levels. The mental workload on the stress level of online and offline practice is significantly different. However, the shift of practicum to the stress level of online and offline practice is similar—the mental workload of online and offline practice in the dominant medium and heavy categories. Heavy and normal categories dominate stress levels. The dominant mental workload factors are effort, frustration, and physical demand.
Stereo camera pair calibration for traffic surveillance applications

We proposed an innovative stereo camera pair calibration method suitable for traffic surveillance applications. In the method, we first split the sought parameters into two groups. The first group contains parameters that can be computed prior to the device being installed in the desired location, and we determine their values using already established methods. The second group contains parameters that need to be evaluated after the device is installed on-site, and we utilize calibration vehicles for their evaluation. We first localize the calibration vehicles by detecting and tracking their license plates in a series of stereo images; afterward, we exploit the known information about their speed and acceleration to compute the distances that they traveled between two frames; finally, we determine the internal and external stereo camera pair parameters by minimizing the difference between the computed and estimated distances while preserving the epipolar geometry. We evaluated the presented method on a task of measuring the distances that vehicles traveled between two consecutive frames. For this evaluation, we recorded a dataset containing almost 700 vehicles with trajectories that were recorded using prototype hardware and ground truth distance measurements that were obtained from a pair of single-beam LIDARs. The evaluation results were compared with the current state-of-the-art methods for long-distance stereo camera pair calibration and used in the application of the proposed method on the vehicle speed measurement task.
Organizational Pedagogical Conditions Of Professional Development Of Pedagogical Staff

The article deals with the issues of teacher training and retraining, improving the quality of teachers in schools and theircontinuous training, the introduction of a system of continuous methodological assistance, the transformation of the system of teachertraining into a system of continuous professional development, hybrid (mixed) training issues such as its widespread introduction intoeducational practice are covered in detail.
Post-Quantum Cryptography: A Snapshot of Standardization Efforts

The production of quantum computers with fast processing power and practical use will adversely affect the foundations of secure communication, especially for public-key cryptosystems (PKCs). Potential problems and solution scenarios are created early to design quantum-resistant PKCs. In this paper, the emergence of the concept of post-quantum cryptography (PQC) and early precautionary actions are explained. In addition, cryptosystem families that are known/believed to be secure in the presence of quantum computers are discussed. Based on these classes, the selected encryption/key encapsulation (KEM) and signature schemes of the NIST PQC process are expressed. Finally, some open problems for the post-quantum era are summarized.
Virtual Reality for Enhancing Customer Value in Sri Lankan Hospitality and Tourism Industry: Perspective of Industry Experts

With the heat of emerging the Metaverse concept, the application of Virtual Reality (VR) technology has been elevated. VR’s importance is continuously growing due to its distinctive core traits, especially in the tourism and hospitality sectors. It is imperative to explore how VR adds value to its users. Therefore, the purpose of this study is to explore how tourism and hospitality industry service providers effectively engage with VR technology to enhance customer value. The study used qualitative research methodology and the phenomenological approach to extract the essence of this specific phenomenon. By unifying the noema, noesis, and intentionality concepts, the researchers captured the eight respondents' experiences regarding VR. Data collection was undertaken using in-depth interviews with respondents and analysis of data was completed using the generic qualitative data analysis procedure. As per the findings, from the service provider’s perspective, VR technology adds value to the customers of the hospitality and tourism industries in two ways; addressing customer issues through utility and by developing pleasant sensations. The result of this research study allows hoteliers to comprehend the importance of utilising VR to gain a sustainable competitive advantage by enhancing customer value. This research query will shine a new light on modern tourism and hospitality industry. VR technology is evolving rapidly, therefore future research should be constantly re-validated through continuous investigation of how VR can be used in the hospitality and tourism industry.
Theoretical analysis of quantum key distribution systems when integrated with a DWDM optical transport network

Theoretical research and numerical simulation of the noise influence caused by spontaneous Raman scattering, four-wave mixing, and linear channel cross talk on the performances of quantum key distribution (QKD) systems were conducted. Three types of QKD systems were considered: a coherent one-way QKD protocol, subcarrier-wave QKD system, and continuous-variable QKD integrated with classical dense wavelength division multiplexing (DWDM) channels. We calculate the secure key generation rate for the systems mentioned addressing different channel allocation schemes (i.e., configurations). A uniform DWDM grid is considered with a quantum channel located in the C-band and O-band (at 1310 nm) of a telecommunication window. The systems’ performances are analyzed in terms of the maximal achievable distance values. Configurations for further analysis and investigation are chosen optimally, i.e., their maximal achievable distances are the best.
Why algorithmic speed can be more important than algorithmic accuracy

Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.
Eliciting User-Defined Touch and Mid-air Gestures for Co-located Mobile Gaming

Many interaction techniques have been developed to best support mobile gaming – but developed gestures and techniques might not always match user behaviour or preferences. To inform this design space of gesture input for co-located mobile gaming, we present insights from a gesture elicitation user study for touch and mid-air input, specifically focusing on board and card games due to the materiality of game artefacts and rich interaction between players. We obtained touch and mid-air gesture proposals for 11 game tasks with 12 dyads and gained insights into user preferences. We contribute our classification and analysis of 622 elicited gestures (showing more collaborative gestures in the mid-air modality), resulting in a consensus gesture set, and agreement rates showing higher consensus for touch gestures. Furthermore, we identified interaction patterns – such as benefits of situational awareness, social etiquette, gestures fostering interaction between players, and roles of gestures providing fun, excitement, and suspense to the game – which can inform future games and gesture design.
Improved Traffic Sign Recognition Algorithm Based on YOLOV4-Tiny

We propose an improved YOLOV4-tiny traffic sign recognition algorithm for easy deployment on mobile or embedded devices to address the problems of a large number of parameters, low recognition accuracy in complex scenarios. The model uses the YOLOV4-tiny network as the basic framework. First, Octave Convolution is introduced into the backbone network to reduce the redundancy of low-frequency features. Second, the convolutional block attention module is used to strengthen the weights of traffic sign regions and reduce the weights of invalid features. Finally, the Feature Pyramid Network structure is replaced by the Simplified Path Aggregation Network structure in the feature fusion stage to enhance the feature information and further reduce the miss detection rate. The experiment proves that our method outperforms YOLOV4-tiny in recognition accuracy and detection speed on the TT100[Formula: see text]K dataset, and can easily meet the requirements of traffic sign recognition.