Reviewerly/exHarmony huggingface dataset:
A Novel Whale Optimization Algorithm with Sparrow algorithm and Golden Sine Leading Strategy

Whale optimization algorithm (WOA) is a recently proposed optimization algorithm. In view of the slow convergence velocity, low precision and hard to get away from local optimum of WOA algorithm, this paper puts forward a whale optimization algorithm with sparrow algorithm and golden sine leading strategy (SGSWOA). First, the location update rule of the producer in the sparrow algorithm is integrated into the Encircling prey stage of WOA to increase the search space of the algorithm and escape from the local optimum. Then combined with the golden sine leading strategy, it can balance exploration and development capabilities and enhance the performance of WOA algorithm. Finally, by optimizing 16 benchmark functions and applying the SGSWOA algorithm to practical engineering optimization problems, the experimental results display the SGSWOA algorithm has better convergence accuracy, convergence speed, and robustness.
Randomness Testing with Neural Networks

Testing the quality of data produced by Random and Pseudo-Random Number Generators (RNG-s and PRNG-s) is necessary for their safe use in cryptographic applications. Randomness is a probabilistic property and various statistical tests can be applied to evaluate the generated data sequences.This paper presents a novel approach for applying such tests, by training Artificial Neural Networks (ANN) to replicate the behaviour of both stand-alone and combined statistical tests. The process includes the development of various augmentation techniques used for creating a synthetic data set, the development and training of different ANN architectures and also the evaluation of the final classification models. The trained models can detect the presence or absence of diverse statistical characteristics in the generated data sequences.The proposed solution reaches over 0.95 accuracy for both stand-alone and combined application of the following statistical tests from the NIST Statistical Test Suit [1]: Frequency Test, Frequency Test within a Block, Runs Test, Tests for the Longest-Run-of-Ones in a Block.
Risk Analysis, Security Surveys and Insurance

The area of risk analysis, as with law, can be complicated. Security managers who assume major responsibility for risk management should consider enrolling in courses directed toward risk management, cyber security, and insurance. When threats and vulnerabilities are identified, through the use of security surveys or security audits, and their impact on assets analyzed, the risk to those assets can be properly understood. To overlook the role of this important area of security is to move forward without a carefully developed plan. Without a thorough risk analysis, dollars may be spent on security measures that have little impact on the actual protection of company assets while other more critical assets remain vulnerable to destruction, theft, or other vulnerabilities. Insurance has become an integral part of the overall loss-prevention plan. Insurance allows for the transfer of risks. Security planning in turn has an impact on the cost associated with insurance. The more comprehensive the security plan, the lower the costs.
JavaScript API Deprecation Landscape: A Survey and Mining Study

JavaScript provides no native deprecation mechanisms. To assess how developers deprecate JavaScript application programming interfaces (APIs), we report the results of a survey and a mining study on JavaScript developers and projects. The results suggest several solutions but no standard solution to deprecate JavaScript APIs.
Hybrid Solution (ECDHE + NewHope) for PQ Transition

It is assumed that certain mathematical or computational problems which are used in traditional cryptographic schemes are hard to solve for an attacker using today’s computers. But, lots of companies are trying to build quantum computer and in coming few years commercial quantum computer will be in reality. Security of traditional asymmetric cryptographic algorithms can be broken using quantum computers. So, researchers all over the world are planning for transition to post-quantum cryptography. One solution is to build hybrid solution combining both traditional and post-quantum primitives which will provide traditional cryptographic guarantees as well as quantum resistance [1].The best and feasible hybrid solution can be used in the protocols like SSL/TLS, SSH and PGP.
Plasma cell identification based on evidential segmentation and supervised learning

Myeloma disease is among the most common type of cancer, it is characterised by proliferation of plasma cells, kind of white blood cell (WBC). Early diagnosis of the disease can improve the patient's survival rate. The manual diagnosis involves clinicians to visually examine microscopic bone marrow images for any signs of cells proliferation. This step is often laborious and can be highly subjective due to clinician's expertise. Automatic system based on WBC identification and counting provides more accurate result than manual method. This system is mainly based on three major steps: cell's segmentation, cell's characterisation and cell's classification. In the proposed system, microscopic images of bone marrow blood are segmented by the combination of watershed transform and the evidence theory, the segmented cells are characterised with shape and colour texture features, and then classified into plasma cells or not plasma cells with three supervised classifiers; support vector machines, K-nearest neighbour and decision tree. Experimental results show that the recognition of plasma cells with the K-nearest neighbour achieved 97% of correct rate with 100% of specificity.
MySQL

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Autoencoders

Autoencoder is an unsupervised learning algorithm that uses neural networks to reconstruct the features of a dataset. Just like the unsupervised algorithms that we earlier discussed in the chapter on machine learning, autoencoders can be used to reduce the dimensionality of a dataset and to extract relevant features. Moreso, peculiar to autoencoders is the ability to generate more examples of the dataset after learning an internal representation (also called coding) that reconstructs the features of the inputs to the neural network.
Intelligent lecture recording system based on coordination of face-detection and pedestrian dead reckoning

Automatic lecture recording is an appealing alternative approach to manually recording lectures in the process of online course making as it can to a large extent save labor cost. The key of the automatic recording system is lecturer tracking, and the existing automatic tracking methods tend to lose the target in the case of lecturer's rapid movement. This article proposes a lecturer tracking system based on MobileNet-SSD face detection and Pedestrian Dead Reckoning (PDR) technology to solve this problem. First, the particle filter algorithm is used to fuse the PDR information with the rotation angle information of the Pan-Tilt camera, which can improve the accuracy of detection under the tracking process. In addition, to improve face detection performance on the edge side, we utilize the OpenVINO toolkit to optimize the inference speed of the Convolutional Neural Networks (CNNs) before deploying the model. Further, when the lecturer is beyond the camera's field of view, the PDR auxiliary module is enabled to capture the object automatically. We built the entire lecture recording system from scratch and performed the experiments in the real lectures. The experimental results show that our system outperforms the systems without a PDR module in terms of the accuracy and robustness.
Streaming Hate: Exploring the Harm of Anti-Banyamulenge and Anti-Tutsi Hate Speech on Congolese Social Media

Mediatized hate speech fuelled the genocide against the Tutsis in Rwanda nearly three decades ago, yet anti-Tutsi rhetoric is still circulating in the Great Lakes region, albeit under a radically changed media and political landscape. Social media and online platforms facilitate the proliferation of inflammatory and discriminatory discourses whose impact on violent conflict remains uncertain. This study examines the phenomenon by focusing on anti-Banyamulenge and anti-Tutsi hate speech in the context of resurgent violent armed conflict in the highlands of Eastern South Kivu province of the Democratic Republic of Congo (DRC). It first explores the history of the Banyamulenge in the DRC, highlighting contestations of their identity and belonging as well as their experiences and positionality in the current cycle of atrocious conflicts. It then documents and analyzes the content of hate speech and conspiracy theories circulating in audio, video, and text messages in multiple languages, and identifies the actors involved in their dissemination. The analysis unpacks how social media platforms facilitate interactions between diverse actors, including leaders of armed groups, public officials, and diaspora communities. It argues that novel transboundary networks of identity are emerging where hateful narratives and conspiracy theories are created, refined, and disseminated to larger audiences. These self-reinforcing networks diffuse incendiary nativist discourses, anti-Tutsi sentiments, dehumanizing language, and calls for genocide reaching from global to local terrains.
On-board satellite data processing to achieve smart information collection

Nowadays, it is a reality to launch, operate, and utilize small satellites at an affordable cost. However, bandwidth constraint is still an important challenge. For instance, multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, which is addressed mainly by source and channel coding aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis technique on the satellite to decide which information is effectively useful for specific target applications, before coding and transmitting. The challenge would be detecting clouds and vessels having the measurements of red-band, green-band, blue-band, and near infrared band, aiming at sufficient probability of detection, avoiding false alarms. Furthermore, the embedded platform constraints must be satisfied. Experiments for typical scenarios of summer and winter days in Stockholm, Sweden, are conducted using data from the Mimir's Well, the Saab AI-based data fusion system. Results show that non-relevant content can be identified and discarded, pointing out that for the cloudy scenarios evaluated, up to 73.1% percent of image content can be suppressed without compromising the useful information into the image. For the water regions in the scenarios containing vessels, results indicate that a stringent amount of data can be discarded (up to 98.5%) when transmitting only the regions of interest (ROI).
LAS-Transformer: An Enhanced Transformer Based on the Local Attention Mechanism for Speech Recognition

Recently, Transformer-based models have shown promising results in automatic speech recognition (ASR), outperforming models based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs). However, directly applying a Transformer to the ASR task does not exploit the correlation among speech frames effectively, leaving the model trapped in a sub-optimal solution. To this end, we propose a local attention Transformer model for speech recognition that combines the high correlation among speech frames. Specifically, we use relative positional embedding, rather than absolute positional embedding, to improve the generalization of the Transformer for speech sequences of different lengths. Secondly, we add local attention based on parametric positional relations to the self-attentive module and explicitly incorporate prior knowledge into the self-attentive module to make the training process insensitive to hyperparameters, thus improving the performance. Experiments carried out on the LibriSpeech dataset show that our proposed approach achieves a word error rate of 2.3/5.5% by language model fusion without any external data and reduces the word error rate by 17.8/9.8% compared to the baseline. The results are also close to, or better than, other state-of-the-art end-to-end models.
Secure and Private Fountain Code based Architecture for Blockchains

Recently, different architectures based on coding theory have been proposed to reduce the storage and communication costs associated with a blockchain system. However, many of these methods have high bandwidth requirements for repairing the share of a failed node or decoding a particular requested block. The bandwidth required for decoding a requested block becomes an important factor in some blockchain applications like healthcare, where historical data needs to be frequently accessed. In this work, we introduce two new architectures for blockchain-based systems, which reduce the storage and communication costs associated with blockchain&#x2019;s historical data and simultaneously provides confidentiality of the stored data. The two protocols are designed using a combination of fountain codes and a proposed communication and repair efficient secret sharing scheme. We also present a construction of the secret sharing scheme which meets our requirements.
A Bibliometric Analysis of Mobile Assisted Second Language Learning

This article reviews mobile assisted second language learning (MASLL) with a bibliometric method. The authors collected data of papers related to MASLL from Web of Science and then analyzed the sources, authors, papers, and conceptual structure. The result suggests that: the effects of technology on learning, vocabulary, personalized learning, learning environment, learner attitudes were popular topics in this field; the attention on themes related to “technology” is fading away and the focus of MASLL research is shifting to themes related to “students”. Most-cited journals, authors, and papers are also presented in this article.
URJA: A sustainable energy distribution and trade model for smart grids

Energy has always been one of the fundamental elements in the growth of human society. It is crucial that we improve our energy management due to the ever-growing imbalance in demand-supply. The new wave in the energy sector is characterized by the three D's of decarbonization, decentralization, and digitalization. For a sustainable life, it is necessary that energy is tapped from alternative sources that are renewable to leave a minimal carbon footprint. To integrate different sources with the main power grid, we require two-way communication among them. This makes it indispensable that consumers are empowered to protect their privacy and make them the sole owners of their data. A good access control scheme would ensure security of data, and an efficient trading platform would ensure judicious use of the resources. Here, we propose a framework URJA with an access control scheme and an energy trading platform based on blockchain for a smart grid. The locally available renewable sources of energy are connected to the grid such that demand-supply is managed effectively without loss of efficiency and privacy. A customized consensus scheme based on trust ensures a quick operation in real-time.
A Novel Fault Detection Algorithm Based on the Single Indicator Data

With the rapid development of Server Cluster System technologies, more and more services and tasks are deployed on the Server Cluster Systems. Despite its convenience, a large number of services are mixed together and affect each other, which brings great difficulties to the detection of equipment fault and causes great losses. Nowadays, artificial intelligence for IT operations (AIOps), which utilizes data analysis and machine learning to improve the operation quality of Server Clusters, has been proposed to solve this problem. However, the existing solutions of AIOps cannot be applied to different scenarios. Therefore, in this paper, we propose a novel fault detection method for single indicators which is adaptive to various scenes. To enrich the representation of data, we first propose an interval-volatility-rate method to extract the context features of data in a fixed interval. Based on this, the convolutional neural network and long short-term memory network are employed to get the locally spatial features and temporal features of data. After that, the spatial features and temporal features are combined and input to an MLP network to perform the fault prediction. Additionally, a k − σ principle is designed to promote the sensitivity of fault detection. Experimental results show that our method outperforms other competitive models and has better scalability.
Deep Learning-Based Brain Hemorrhage Detection in CT Reports

Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance.
Automatic exposure correction algorithm for online silkworm pupae (Bombyx mori) sex classification

In the process of online sex identification and sorting of silkworm pupae by machine vision technology, the collected images of silkworm pupae gonad suffer underexposure or overexposure due to the biological characteristics of silkworm pupae and the mechanism movement. As a result, the partial texture characteristics of silkworm pupae gonad are not obvious and thus lead to a large error for the subsequent identification of male and female pupae. In view of the overexposed or underexposed problem existed in online image collecting of silkworm pupae gonad, an algorithm model is proposed in this paper. It can automatically determine whether the silkworm pupae gonad image has exposure problem according to the distribution law of gray values. Moreover, an extra correction model based on the combination of the Retinex (retina and cortex) model and the weighted least squares method is put forward to correcting the exposure of gonadal images. The experiment results demonstrate that the proposed algorithm not only can find the silkworm pupae gonad image with exposure problem completely and accurately, but also make the restored gonadal texture clearer. Furthermore, this algorithm has a good effect on the exposure correction of other objects. After the original data sets are processed by the algorithm, the correct sex identification rate in the classification network based on Resnet50 training reaches 90.52%. It is 7.08% higher than the original silkworm pupae gonad images, which can verify the effectiveness of the algorithm. And the recovery time of a single image is within 0.3 s, which can meet the needs of online automatic identification. The proposed algorithm is of great significance for the online male and female identification of silkworm pupae based on machine vision.
Deep learning-based method for detecting anomalies in electromagnetic environment situation

The anomaly detection of electromagnetic environment situation (EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment. In this paper, we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection (EMES-AD). Firstly, the convolutional kernel extracts the static features of different regions of the EMES. Secondly, the dynamic features of the region are obtained by using a recurrent neural network (LSTM). Thirdly, the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES. The structural similarity algorithm (SSIM) is used to determine whether it is anomalous. We developed the detection framework, de-signed the network parameters, simulated the data sets containing different anomalous types of EMES, and carried out the detection experiments. The experimental results show that the proposed method is effective.
Multi-Agent Graph Convolutional Reinforcement Learning for Intelligent Load Balancing

A smart Load Balancing (LB) policy based on Graph Convolutional Multi-Agent Reinforcement Learning (GC-MARL) is proposed to improve load balancing in networks beyond what can be realized by traditional methods and state of the art machine learning based approaches. GC-MARL models the network as a graph and derives through a graph convolutional method the policy that splits traffic flows across end-to-end candidate paths while meeting application QoE requirements. The proposed method uses the throughput and the delay, observed at the network level, as the key performance indicators embedded in the reward expression as opposed to observing QoE at the application level. The results confirm the effectiveness of the proposed solution in terms of KPIs (such as throughput, delay, jitter, packet loss), and KQIs (such as QoE, average video bitrate, stalling, etc…).
Multiauthority Attribute-Based Keyword Search over Cloud-Edge-End Collaboration in IoV

The development of cloud computing and edge computing makes it possible to store and share Internet of Vehicles (IoV) data on a large scale, which greatly contributes to traffic intelligence, but outsourced data confidentiality and user privacy cannot be guaranteed. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) scheme can achieve both fine-grained access control and secure data sharing. However, existing CP-ABE schemes owns high computational complexity, and the adopted single attribute authority mode is burdensome to resource-limited IoV. Thus, this paper proposes a Multiauthority Attribute-Based Keyword Search over Cloud-Edge-End Collaboration (CEABKS-MA) system, leveraging the benefits of edge and cloud resources and effectively combining with the multiauthority structure to minimize the computation and storage pressure on resource-limited parties in the system. In addition, fine-grained keyword search with support for attribute update and lightweight encryption/decryption is extended. Finally, this paper demonstrates the security and efficiency of the CEABKS-MA system through rigorous security analysis and simulation experiments.