Reviewerly/exHarmony huggingface dataset:
Performance portability study of epistasis detection using SYCL on NVIDIA GPU

We describe the experience of converting a CUDA implementation of a high-order epistasis detection algorithm to SYCL. The goals are for our work to be useful to application and compiler developers with a detailed description of migration paths between CUDA and SYCL. Evaluating the CUDA and SYCL applications on an NVIDIA V100 GPU, we find that the optimization of loop unrolling needs to be applied manually to the SYCL kernel for obtaining comparable performance. The performance of the SYCL group reduce function, an alternative to the CUDA warp-based reduction, depends on the problem and work group sizes. The 64-bit popcount operation implemented with tree of adders is slightly faster than the built-in popcount operation. When the number of OpenMP threads is four, the highest performance of the SYCL and CUDA applications are comparable.
A review of sentiment computation methods with R packages

Four packages in R are analyzed to carry out sentiment analysis. All packages allow to define custom dictionaries. Just one - Sentiment R - properly accounts for the presence of negators.
Identifying the Missing People using Deep Learning Method

Finding the face of objects is one of the major challenging tasks in many applications especially finding the thief. Every day, plenty of news comes related to the abovementioned issue to identify the face of a particular person in terms of doing the suspicious activity. Image classification is used to find similar images matches and discover the properties of the image based on the predefined pattern. Segmenting the specific region of an image and matching the portion of the image with the already present image. The similarity portion of the image is found using the pixels clustering based on the threshold value. The intention of the work is to find the missed people by applying the deep learning technique to provide the optimal solution. The process is performed by using matching the segmented image with the predefined image present in the database. The suggested technique act as a better solution for finding the missing people involved in various illegal activities.
When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations

Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent studies have suggested that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category–viewpoint combinations, that is, combinations not seen during training. Here we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and three-dimensional viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization. We show that increasing the number of in-distribution combinations (data diversity) substantially improves generalization to OOD combinations, even with the same amount of training data. We compare learning category and viewpoint in separate and shared network architectures, and observe starkly different trends on in-distribution and OOD combinations, that is, while shared networks are helpful in distribution, separate networks significantly outperform shared ones at OOD combinations. Finally, we demonstrate that such OOD generalization is facilitated by the neural mechanism of specialization, that is, the emergence of two types of neuron—neurons selective to category and invariant to viewpoint, and vice versa. The combination of object recognition and viewpoint estimation is essential for visual understanding. However, convolutional neural networks often fail to generalize to object category–viewpoint combinations that were not seen during training. The authors investigate the impact of data diversity and architectural choices on the capability of generalizing to out-of-distribution combinations.
An Enhanced RFID-Based Authentication Protocol using PUF for Vehicular Cloud Computing

RFID (radio frequency identification) is an Internet of Things (IoT) enabling technology. All physical devices can be connected to the Internet of Things thanks to RFID. When RFID is extensively utilized and fast increasing, security and privacy concerns are unavoidable. Interception, manipulation, and replay of the wireless broadcast channel between the tag and the reader are all possible security threats. Unverified tags or readers provide untrustworthy messages. IoT requires a safe and consistent RFID authentication system. PUFs are also physical one-way functions made up of the unique nanoscopic structure of physical things and their reactivity to random occurrences. PUF includes an unclonable feature that takes advantage of physical characteristics to boost security and resistance to physical attacks. We analyze the security of the RSEAP2 authentication protocol that has been recently proposed by Safkhani et al., a hash-based protocol, and elliptic curve cryptosystem-based protocol. Our security analysis clearly shows important security pitfalls in RSEAP2 such as mutual authentication, session key agreement, and denial-of-service attack. In our proposed work, we improved their scheme and enhanced their version using physically unclonable function (PUF), which are used by the proposed protocol in tags. This research proposes a cloud-based RFID authentication technique that is both efficient and trustworthy. To decrease the RFID tag’s overhead, the suggested authentication approach not only resists the aforementioned typical assaults and preserves the tag’s privacy, but also incorporates the cloud server into the RFID system. According to simulation results, our approach is efficient. Moreover, according to our security study, our protocol can withstand a variety of attacks, including tracking, replay, and desynchronization assaults. Our scheme withstands all the 18 security features and further consumes the computation cost as 14.7088 ms which is comparable with the other schemes. Similarly, our scheme consumes the communication cost as 672 bits during the sending mode and 512 bits during the receiving mode. Overall, the performance of our proposed method is equivalent to that of related schemes and provides additional security features than existing protocols. Mutual authentication, session key generation, and ephemeral session security are all achieved. Using the real-or-random concept, we formalize the security of the proposed protocol.
When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model

Sheep face recognition models deployed on edge devices require a good trade-off between model size and accuracy, but the existing recognition models cannot do so. To solve the above problems, this paper combines Mobilenetv2 with Vision Transformer to propose a balanced sheep face recognition model called MobileViTFace. MobileViTFace enhances the model’s ability to extract fine-grained features and suppress the interference of background information through Transformer to distinguish different sheep faces more effectively. Thus, it can distinguish different sheep faces more effectively. The recognition accuracy of 96.94% is obtained on a self-built dataset containing 5490 sheep face photos of 105 sheep, which is a 9.79% improvement compared with MobilenetV2, with only a small increase in Params (the number of parameters) and FLOPs (floating-point operations). Compared to models such as Swin-small, which currently performs SOTA, Params and FLOPs are reduced by nearly ten times, whereas recognition accuracy is only 0.64% lower. Deploying MobileViTFace on the Jetson Nano-based edge computing platform, real-time and accurate recognition results are obtained, which has implications for practical production.
A Bibliometric Analysis of Solar Energy Forecasting Studies in Africa

Solar energy forecasting is considered an essential scientific aspect in supporting efforts to integrate solar energy into power grids. Moreover, solar energy forecasting plays an essential role in mitigating greenhouse gas emissions and conserving energy for future use. This study conducted a bibliometric analysis to assess solar energy forecasting research studies evolution at the continental (Africa) and southern Africa levels. Key aspects of analysis included (i) scientific research trends, (ii) nature of collaboration networks, (iii) co-occurrence of keywords and (iv) emerging themes in solar energy forecasting over the last two decades, between the years 2000–2021. The results indicate that solar energy forecasting research has, on average, expanded by 6.4% and 3.3% in Africa and southern Africa, respectively. Based on the study context, solar energy forecasting research only gained momentum in 2015, peaking in 2019, but it is generally still subtle. The scientific mapping illustrated that only South Africa ranks among the leading countries that have produced high numbers of published documents and also leads in contributions to the research area in both Africa and southern Africa. Three emerging topics were identified from the thematic map analysis—namely, “solar irradiance”, “artificial intelligence” and “clear sky”, which implies that researchers are paying attention to solar irradiance, using modelling techniques that incorporate machine learning techniques. Overall, this study contributes to scientific information on the potential bankability of renewable energy projects that could assist power utilities, governments and policymakers in Africa to enforce the green economy through accelerated decarbonisation of the energy systems and building relationships with developed countries for support and better transitioning to solar energy. From a Water–Energy–Food nexus perspective, the results of this work could assist the scientific community in Africa to take advantage of the inherent interconnectedness of water, energy and food resources, whilst also advancing the use of integrated solutions to shape the focus of solar energy research into a more systems thinking and transdisciplinary approach involving the interconnected primary resources and stakeholders pursuit of the Sustainable Development Goals.
Multi-Image Encryption Algorithm Based on Cascaded Modulation Chaotic System and Block-Scrambling-Diffusion

To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algorithm with block-scrambling-diffusion is proposed using CMCS. The algorithm makes full use of the features of CMCS to achieve the effect of one encryption at a time for images. Firstly, the key-value associated with the plaintexts is generated using a secure hash algorithm-512 (SHA-512) operation and random sequence, and the three images are fully confused by the double scrambling mechanism. Secondly, the scrambled image is converted into a bit-level matrix, and the pixel values are evenly distributed using the bit-group diffusion. Finally, the non-sequence diffusion of hexadecimal addition and subtraction rules is used to improve the security of the encryption algorithm. Experimental results demonstrate that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.
The Impact of Internal Marketing Practices on Employees’ Job Satisfaction during the COVID-19 Pandemic: The Case of the Saudi Arabian Banking Sector

Based on the social exchange theory, the current study aimed to develop and test a conceptual model that integrates the relationships among internal marketing dimensions (i.e., supportive and participative leadership, training and development, information and communication, and selection and appointment) and job satisfaction in the banking sector of Saudi Arabia, particularly during the COVID-19 pandemic. We collected data from 329 employees working in different private and public banks in Riyadh, Saudi Arabia. Overall, the findings confirmed the significant and positive effects of supportive and participative leadership, training and development, information and communication, and selection and appointment on employees’ job satisfaction. The current research contributes to the understanding of the broad role played by internal marketing practices in maintaining the job satisfaction of banking sector employees, during and possibly after the COVID-19 pandemic.
Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care

AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video. One solution arising to overcome these data limitations in relation to medical records is the synthetic generation of tabular data based on real world data. Consequently, ML-assisted decision-support can be interpreted more conveniently, using more relevant patient data at hand. At a methodological level, several state-of-the-art ML algorithms generate and derive decisions from such data. However, there remain key issues that hinder a broad practical implementation in real-life clinical settings. In this review, we will give for the first time insights towards current perspectives and potential impacts of using synthetic data generation in palliative care screening because it is a challenging prime example of highly individualized, sparsely available patient information. Taken together, the reader will obtain initial starting points and suitable solutions relevant for generating and using synthetic data for ML-based screenings in palliative care and beyond.
Detection of Large Herbivores in UAV Images: A New Method for Small Target Recognition in Large-Scale Images

Algorithm design and implementation for the detection of large herbivores from low-altitude (200 m–350 m) UAV remote sensing images faces two key problems: (1) the size of a single image from the UAV is too large, and the mainstream algorithm cannot adapt to it, and (2) the number of animals in the image is very small and densely distributed, which makes the model prone to missed detection. This paper proposes the following solutions: For the problem of animal size, we optimized the Faster-RCNN algorithm in terms of three aspects: selecting a HRNet feature extraction network that is more suitable for small target detection, using K-means clustering to obtain the anchor frame size that matches the experimental object, and using NMS to eliminate detection frames that have sizes inconsistent with the size range of the detection target after the algorithm generates the target detection frames. For image size, bisection segmentation was used when training the model, and when using the model to detect the whole image, we propose the use of a new overlapping segmentation detection method. The experimental results obtained for detecting yaks, Tibetan sheep (Tibetana folia), and the Tibetan wild ass in remote sensing images of low-altitude UAV from Maduo County, the source region of the Yellow River, show that the mean average precision (mAP) and average recall (AR) of the optimized Faster-RCNN algorithm are 97.2% and 98.2%, respectively, which are 9.5% and 12.1% higher than the values obtained by the original Faster-RCNN. In addition, the results obtained from applying the new overlap segmentation method to the whole UAV image detection process also show that the new overlap segmentation method can effectively solve the problems of the detection frames not fitting the target, missing detection, and creating false alarms due to bisection segmentation.
Optimize Naïve Bayes Classifier Using Chi Square and Term Frequency Inverse Document Frequency For Amazon Review Sentiment Analysis

The rapid development of the internet has made information flow rapidly wich has an impact on the world of commerce. Some people who have bought a product will write their opinion on social media or other online site. Long-text buyer reviews need a machine to recognize opinions. Sentiment analysis applies the text mining method. One of the methods applied in sentiment analysis is classification. One of the classification algorithms is the naïve bayes classifier. Naïve bayes classifier is a classification method with good efficiency and performance. However, it is very sensitive with too many features, wich makes the accuracy low. To improve the accuracy of the naïve bayes classifier algorithm it can be done by selecting features. One of the feature selection is chi square. The selection of features with chi square calculation based on the top-K value that has been determined, namely 450. In addition, weighting features can also improve the accuracy of the naïve bayes classifier algorithm. One of the feature weighting techniques is term frequency inverse document frequency (TF-IDF). In this study, using sentiment labelled dataset (field amazon_labelled) obtained from UCI Machine Learning. This dataset has 500 positive reviews and 500 negative reviews. The accuracy of the naïve bayes classifier in the amazon review sentiment analysis was 82%. Meanwhile, the accuracy of the naïve bayes classifier by applying chi square and TF-IDF is 83%.
MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering

Many web platforms now include recommender systems. Network representation learning has been a successful approach for building these efficient recommender systems. However, learning the mutual influence of nodes in the network is challenging. Indeed, it carries collaborative signals accounting for complex user-item interactions on user decisions. For this purpose, in this paper, we develop a Mutual Interaction Graph Attention Network "MIGAN", a new algorithm based on self-supervised representation learning on a large-scale bipartite graph (BGNN). Experimental investigation with real-world data demonstrates that MIGAN compares favorably with the baselines in terms of prediction accuracy and recommendation efficiency.
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph

The generalizability to new databases is of vital importance to Text-to-SQL systems which aim to parse human utterances into SQL statements. Existing works achieve this goal by leveraging the exact matching method to identify the lexical matching between the question words and the schema items. However, these methods fail in other challenging scenarios, such as the synonym substitution in which the surface form differs between the corresponding question words and schema items. In this paper, we propose a framework named ISESL-SQL to iteratively build a semantic enhanced schema-linking graph between question tokens and database schemas. First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner. Then the schema linking graph is further optimized during the training process through a deep graph learning method. Meanwhile, we also design an auxiliary task called graph regularization to improve the schema information mentioned in the schema-linking graph. Extensive experiments on three benchmarks demonstrate that ISESL-SQL could consistently outperform the baselines and further investigations show its generalizability and robustness.
Utilizing YouTube to Enhance English Speaking Skill: EFL Tertiary Students’ Practices and Perceptions

In the 4.0 era, social media are becoming increasingly popular worldwide. For better learning, one way is to utilize these platforms, one of which is YouTube. This study aims to investigate the practices and perceptions of EFL university students who use the YouTube platform to boost their English-speaking skills. The study included a total of 106 EFL university students from various classes in different academic school years. The research used a questionnaire and a semi-structured interview as instruments to collect quantitative and qualitative data and determine the level of perceptions and practices. The findings reveal that EFL tertiary students can improve their English speaking skills when learning with the YouTube platform. The results also show that students recognized YouTube as a helpful tool in the learning process when they needed to practice and improve their speaking skills. Based on the study's findings, the researchers suggest that EFL students use the YouTube platform to improve their speaking skills. Following this, there are some recommendations for further research into YouTube platform utilization.
Local and Context-Attention Adaptive LCA-Net for Thyroid Nodule Segmentation in Ultrasound Images

The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with 7×1, 1×7 convolutions and the activation function Mish is designed, which enlarges the receptive field and extracts more feature details. Furthermore, a nodule adaptive convolution (NAC) module is introduced to adaptively deal with thyroid nodules of different sizes and positions, thereby improving the generalization performance of the model. Simultaneously, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. The proposed LCA-Net, validated on the public TN-SCUI2020 and TN3K datasets, achieves Dice scores of 90.26% and 82.08% and PA scores of 98.87% and 96.97%, respectively, which outperforms other state-of-the-art thyroid nodule segmentation models. This paper demonstrates the superiority of the proposed LCA-Net for thyroid nodule segmentation, which possesses strong generalization performance and promising segmentation accuracy. Consequently, the proposed model has wide application prospects for thyroid nodule diagnosis in clinics.