Reviewerly/exHarmony huggingface dataset:
Unsupervised person re-identification guided by refinement feature in multi-label distribution sorting learning

Traditional person re-identification methods are mostly based on supervised state of manually annotated data sets. Although the recognition accuracy is high, the algorithm completely relies on effective annotation information and lacks practicability. Based on the above problems, an unsupervised person re-identification method is proposed, which refines features to guide multi-label distribution sorting learning. Firstly, a single and multi-label classification network structure is constructed to improve the matching accuracy of tags by learning the characteristics of each person and its k nearest neighbor and sharing the same class. Secondly, the refinement feature extraction module is designed and embedded into the original ResNet50 network, and the potential key areas in the person image are adaptively located. While weakening the appearance differences of the features, multi-information mining is carried out to obtain the refined features of the characters and assist the classification network to improve the performance. Thirdly, reliable multi-category labels are screened by punishing the sorting between positive and negative categories of labels in multi-category labels. Finally, the network is supervised by combining multi-label distribution sorting loss and multi-label classification loss. Experiments show that without any annotation information, the recognition accuracy of Market-1501 and DukeMTMC-reID datasets is 58.4% and 42.3%, respectively, and the mAP of Market-1501 dataset is increased by 12.9%.
Saraiki Language Word Prediction And Spell Correction Framework

Word prediction, spelling error correction and finding similarity between words are very useful features in any language. The Saraiki is one of the popular languages spoken in Pakistan. To the best of our knowledge, very little work has been done in the literature for word prediction, spell correction and finding similar words for the Saraiki language. In this paper we address these issues by presenting a novel approach for word prediction, finding similar words, and spell correction in the Saraiki language. To achieve this, we used CBOW and Skip-Gram for the vectorization of the Saraiki language. From our results, we achieved word prediction accuracy of 24 % in case of word2vec while 29 % in case of the fastText. In case of word similarity, we achieved similarity score equal to 0.35, and 0.39 for word2vec CBOW and word2vec Skip-Gram respectively and similarity score of 0.35 and 0.41 for the fastText CBOW and the fastText Skip-Gram respectively. Our spell correction results show that as we increase wrong characters in words, the accuracy gets decreased. For sentence-level word prediction, we achieved accuracy of 63% in case of RoBERTa and 58% for distilled respectively.
A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea.
Fairness-Based Multi-AP Coordination Using Federated Learning in Wi-Fi 7

Federated learning is a type of distributed machine learning in which models learn by using large-scale decentralized data between servers and devices. In a short-range wireless communication environment, it can be difficult to apply federated learning because the number of devices in one access point (AP) is small, which can be small enough to perform federated learning. Therefore, it means that the minimum number of devices required to perform federated learning cannot be matched by the devices included in one AP environment. To do this, we propose to obtain a uniform global model regardless of data distribution by considering the multi-AP coordination characteristics of IEEE 802.11be in a decentralized federated learning environment. The proposed method can solve the imbalance in data transmission due to the non-independent and identically distributed (non-IID) environment in a decentralized federated learning environment. In addition, we can also ensure the fairness of multi-APs and determine the update criteria for newly elected primary-APs by considering the learning training time of multi-APs and energy consumption of grouped devices performing federated learning. Thus, our proposed method can determine the primary-AP according to the number of devices participating in the federated learning in each AP during the initial federated learning to consider the communication efficiency. After the initial federated learning, fairness can be guaranteed by determining the primary-AP through the training time of each AP. As a result of performing decentralized federated learning using the MNIST and FMNIST dataset, the proposed method showed up to a 97.6% prediction accuracy. In other words, it can be seen that, even in a non-IID multi-AP environment, the update of the global model for federated learning is performed fairly.
‘Japa’ Phenomenon And Nigeria Students In The Mix Of A Proxy War In Ukraine

This study examined the Nigerian mentality, educational system, in context to migration as against the challenges faced by Nigerians in the Russo- Ukraine war in 2022. It also explains the ‘japa’ phenomenon and how foreign education is supporting migration. The research also traced the history of tertiary education in Nigeria. A retrospect and review of the facts was made, in order that scholars would have an insight on perceptions as well as the strategies anddiplomatic challenges Nigeria passed through in the course of this war. The study relied on documentary data. The documentary data were sourced from newspapers, journal papers, internet sources and correspondence. The documentary was subjected to internal and external criticism for authentication, and then to textual and contextual analyses. Nigerian students found themselves in a war that was never their making but the emigration from their nation to Ukraine made them a part of this piece of History.
Exploring Experiential Patterns Depending on Time Lapses in Virtual Reality Spectatorship (VRS): The Role of Interruption in Reducing Satiation

Virtual reality spectatorship (VRS) is receiving the limelight as a new form of sports media consumption in the sports industry, but prolonged exposure to the virtual reality (VR) environment is likely to reduce the benefits of VR due to satiation or adaptation. Therefore, the purpose of the present study was to examine the experiential differences in telepresence, flow experience, and satisfaction between a two-dimensional (2D) screen, VRS without interruptions, and VRS with interruptions. For this purpose, 150 participants were recruited and randomly assigned to one of the three conditions. They watched a 12-min-long soccer game and then answered the measurement items. A total of 149 participants were used for the data analyses, including confirmatory factor analysis (CFA), multivariate analysis of variance (MANOVA), and repeated measures of analysis of variance (ANOVA). The results revealed that VRS offers more enhanced telepresence, flow experience, and satisfaction than a 2D screen. Furthermore, flow experience was enhanced in VRS with interruptions compared to VRS without interruptions. Lastly, satisfaction was found to increase depending on time lapses in VRS with interruptions. The experiential patterns in VRS depending on interruptions and time lapses imply that inserting interruptions such as commercials could be not only beneficial for viewing experiences but also effective practice for increasing revenue from advertising.
Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers

Embedded computing platforms such as neural network accelerators deploying neural network models need to quantize the values into low-bit integers through quantization operations. However, most current embedded computing platforms with a fixed-point architecture do not directly support performing the quantization operation for the LSTM layer. Meanwhile, the influence of sequential input data for LSTM has not been taken into account by quantization algorithms. Aiming at these two technical bottlenecks, a new sequential-characteristics-based operators disassembly quantization method for LSTM layers is proposed. Specifically, the calculation process of the LSTM layer is split into multiple regular layers supported by the neural network accelerator. The quantization-parameter-generation process is designed as a sequential-characteristics-based combination strategy for sequential and diverse image groups. Therefore, LSTM is converted into multiple mature operators for single-layer quantization and deployed on the neural network accelerator. Comparison experiments with the state of the art show that the proposed quantization method has comparable or even better performance than the full-precision baseline in the field of character-/word-level language prediction and image classification applications. The proposed method has strong application potential in the subsequent addition of novel operators for future neural network accelerators.
A New Mixture Differential Cryptanalysis on Round-Reduced AES

AES is the most widely used secret-key cryptosystem in industry, and determining the security of AES is a central problem in cryptanalysis. The mixture differential property proposed in Eurocrypt 2017 is an essential property to setup state-of-the-art key recovery attacks on some round-reduced versions of AES. In this paper, we exploit mixture differential properties that are automatically deduced from a mixed integer linear programming (MILP)-based model to extend key recovery attacks on AES. Specifically, we modify the MILP model toolkit to produce all mixture trails explicitly and test a 5-round secret-key mixture differential distinguisher on small-scale AES experimentally. Moreover, we utilize this distinguisher to do a key recovery attack on 6-round AES-128 that outperforms previous work in the same fashion. We also for the first time utilize a 6-round AES secret-key distinguisher to set up a key recovery attack on 7-round AES-192. This work is a new yet simple cryptanalysis on AES by exploiting mixture differential properties.
Accuracy-Time Efficient Hyperparameter Optimization Using Actor-Critic-based Reinforcement Learning and Early Stopping in OpenAI Gym Environment

In this paper, we present accuracy-time efficient hyperparameter optimization (HPO) using advantage actor-critic (A2C)-based reinforcement learning (RL) and early stopping in OpenAI Gym environment. The A2C RL can improve the hyperparameter selection such that the resulting accuracy of machine learning (ML) algorithms including XGBoost, support vector classifier (SVC), random forest shows comparable. According to the specified accuracy of the ML algorithms, the early stopping scheme can save the computation cost. Ten standard datasets are used to valid the accuracy-time efficient HPO. Experimental results show that the presented accuracy-efficient HPO architecture can improve 0.77% accuracy on average compared with default hyperparameter for random forest. The early stopping can save 64% computation cost on average compared to without early stopping for random forest.
A Machine Learning based Approach to Identify User Interests from Social Data

Social media platforms like Twitter, Facebook, Instagram, etc., are considered a common source of extracting information about individuals, such as their needs, interests, and opinions. Our major contribution in this paper is to identify user interests and desires related to the fashion industry in Pakistan. Since people in Pakistan mostly write tweets and reviews in Roman Urdu, the dataset we focused on in this research was comprised of Roman Urdu Tweets and Google Map reviews. From the literature, we observed that not much effort has been done on Roman Urdu tweets and reviews because of its being a low resource language. In terms of methodology, we applied LDA, LSA, and BERT for topic modeling; Vadar combined with TextBlob and DistilBert for sentiment analysis; and K-Means for identifying user clusters with similar interests. In our experiments, we used 15000 tweets and 6000 Google reviews. We were able to create five distinct clusters for each brand. These clusters were further used to track the users based on their interests. We evaluated the performance of our approach and validated it empirically based on Cohen's Kappa score, and achieved a score of 0.45 that shows moderate agreement between human and machine.
Blockchain Technologies for Renewable Energy Resources with Case Study: SHA-256, 384, and 512

Blockchain is a new technology that has captured the attention of energy companies, supply companies, entrepreneurs, software companies, investment firms, government bodies, and academics. This chapter analyzes the performance of the hardware used to simulate the peer-to-peer energy trading between distributed generations with proof-of-work (PoW) consensus mechanism especially in the case of SHA-256, 384, and 512. The PoW mechanism creates a blockchain environment to trade between parties without authorization, and it establishes a systematic methodology for the trading of energy with an advanced contract system. Performance is measured by the CPU and GPU of the device during the complete period of simulation. Linear trendline and percentage evaluation demonstrate different scenarios of three secure hash algorithms (SHAs). The result shows that SHA-512 has the highest performance efficiency in terms of hardware usage and transaction duration in peer-to-peer energy trading systems. It means that SHA-512 takes more memory size in creating a block than other SHAs; however, it has sustainable development in the processing of the blockchain demonstration.
DTM-Based Comparative Geomorphometric Analysis of Four Scoria Cone Areas—Suggestions for Additional Approaches

Morphometric studies of scoria cones have a long history in research. Their geometry and shape are believed to be related to evolution by erosion after their formation, and hence the morphometric parameters are supposed to be related with age. We analysed 501 scoria cones of four volcanic fields: San Francisco Volcanic Field (Arizona, USA), Chaîne des Puys (France), Sierra Chichinautzin (Mexico), and Kula Volcanic Field (Turkey). All morphometric parameters (cone height, cone width, crater width, slope angles, ellipticity) were derived using DTMs. As new parameters, we calculated Polar Coordinate Transformed maps, Spatial Elliptical Fourier Descriptors to study the asymmetries. The age groups of the four volcanic fields were created and their slope distributions were analysed. The age groups of individual volcanic fields show a statistically significant decreasing tendency of slope angles tested by Mann–Whitney tests. By mixing the age groups of the volcanic fields and sorting them by age interval, we can also observe a general, statistically significant decrease. The interquartile ranges of the distributions also tend to decrease with time. These observations support the hypothesis that whereas the geometry of individual scoria cones differs initially (just after formation), general trends may exist for their morphological evolution with time in the various volcanic fields.
Deep learning based masked face recognition in the era of the COVID-19 pandemic

&lt;span lang="EN-US"&gt;During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models.&lt;/span&gt;
Manifold Regularized Principal Component Analysis Method Using L2,p-Norm

The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on l2,p-norm PCA and manifold learning method. To improve robustness, the model method adopts l2,p-norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when p=0.5. Finally, the experimental results show that the method proposed in this paper is robust and effective.
Review of Typical Vehicle Detection Algorithms Based on Deep Learning

Object detection is the crucial task in the field of computer vision. In recent years, intelligent driving technology and intelligent transportation system have set off a boom. Therefore, vehicle object detection has also become a hot research task in the field of computer vision and deep learning. With the rapid development of deep learning, the current mainstream vehicle detection algorithms are Convolutional Neural Networks (CNN)-based two-stage and one-stage object detection algorithms. Because of the local nature of the image presented by CNN, the global receptive field of the network is limited. At the same time, Transformer shows a strong long-distance dependence characteristic, and opens up a new idea of combining images with Transformer. Therefore, the research of object detection algorithm based on Transformer gradually causes a boom. This paper mainly introduces the advantages and disadvantages of several representative algorithm models, and makes a summary and prospect.
Examining the Relationship between Aspects of Human Capital Management and Employee Job Performance: Mediating Role of Employee Engagement and Moderating Role of Perceived Organizational Support

This paper examined the effect of the selected aspects of human capital management on employee job performance and the mediating role of employee engagement.Likewise, this study tested the moderation role of perceived organizational support on the HCMemployee performance link.The research sample is the responses of 426 participants from twelve banking organizations operating in Addis Ababa, Ethiopia, utilizing the social exchange theory and the theory of the resource-based view.This paper is structured quantitatively, with stratified and simple random sampling techniques.The research model analysis method applies structural equation modeling with AMOS to test the hypothesized relationships.The results showed that the Human Capital Management (HCM) practices, namely knowledge accessibility, learning capacity, workforce optimization, leadership practice, and career advancement, positively related to employee job performance.Moreover, the bias-corrected bootstrapping iteration revealed that employee engagement partially mediates this relationship between aspects of human capital management and employee job performance.The findings also show that perceived organizational support positively moderates the association between knowledge accessibility, learning capacity, leadership practice, career advancement, and employee job performance.Conversely, it has an insignificant moderation effect on the association between workforce optimization and employee job performance.Finally, the limitations and future research implications are discussed.
Convolutional neural network -based phantom image scoring for mammography quality control

Abstract Background Visual evaluation of phantom images is an important, but time-consuming part of mammography quality control (QC). Consistent scoring of phantom images over the device’s lifetime is highly desirable. Recently, convolutional neural networks (CNNs) have been applied to a wide range of image classification problems, performing with a high accuracy. The purpose of this study was to automate mammography QC phantom scoring task by training CNN models to mimic a human reviewer. Methods Eight CNN variations consisting of three to ten convolutional layers were trained for detecting targets (fibres, microcalcifications and masses) in American College of Radiology (ACR) accreditation phantom images and the results were compared with human scoring. Regular and artificially degraded/improved QC phantom images from eight mammography devices were visually evaluated by one reviewer. These images were used in training the CNN models. A separate test set consisted of daily QC images from the eight devices and separately acquired images with varying dose levels. These were scored by four reviewers and considered the ground truth for CNN performance testing. Results Although hyper-parameter search space was limited, an optimal network depth after which additional layers resulted in decreased accuracy was identified. The highest scoring accuracy (95%) was achieved with the CNN consisting of six convolutional layers. The highest deviation between the CNN and the reviewers was found at lowest dose levels. No significant difference emerged between the visual reviews and CNN results except in case of smallest masses. Conclusion A CNN-based automatic mammography QC phantom scoring system can score phantom images in a good agreement with human reviewers, and can therefore be of benefit in mammography QC.