Reviewerly/exHarmony huggingface dataset:
An Efficient and Universal Real-Time Data Integrity Verification Scheme Based on Symmetric Key in Stream Computing System

The integrity of real-time data streams has not been solved for a long time and has gradually become a difficult problem in the field of data security. Most of the current data integrity verification schemes are constructed using cryptographic algorithms with complex computation, which cannot be directly applied to real-time stream computing systems. Aiming at the above issue, this paper adopts the Carter–Wegman MAC method, pseudo-random function and symmetric cryptography mechanism to construct the Real-Time Data Integrity Verification scheme based on symmetric key in stream computing systems (RT-DIV), which converts a one-time MAC to a multiple-time MAC and retains the advantage of security performance. Then, a security analysis is given under the standard model. Finally, experiments and data analysis are conducted in a simulated environment, and the experimental results show that the RT-DIV scheme can effectively guarantee the integrity of real-time data streams. Furthermore, the RT-DIV scheme lays the foundation for the secure application of the stream computing system.
Efficient Information-Theoretic Large-Scale Semi-Supervised Metric Learning via Proxies

Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most existing semi-supervised metric learning methods rely on the manifold assumptions to mine the rich discriminant information of the unlabeled data, which breaks the intrinsic connection between the manifold regularizer-building process and the subsequent metric learning. Moreover, these methods usually encounter high computational or memory overhead. To solve these issues, we develop a novel method entitled Information-Theoretic Large-Scale Semi-Supervised Metric Learning via Proxies (ISMLP). ISMLP aims to simultaneously learn multiple proxy vectors as well as a Mahalanobis matrix and forms the semi-supervised metric learning as the probability distribution optimization parameterized by the Mahalanobis distance between the instance and each proxy vector. ISMLP maximizes the entropy of the labeled data and minimizes that of the unlabeled data to follow the entropy regularization, in this way, the labeled part and unlabeled part can be integrated in a meaningful way. Furthermore, the time complexity of the proposed method has a linear dependency concerning the number of instances, thereby, can be extended to the large-scale dataset without incurring too much time. Experiments on multiple datasets demonstrate the superiority of the proposed method over the compared methods used in the experiments.
An Industrial Load Classification Method Based on a Two-Stage Feature Selection Strategy and an Improved MPA-KELM Classifier: A Chinese Cement Plant Case

Accurately identifying industrial loads helps to accelerate the construction of new power systems and is crucial to today’s smart grid development. Therefore, this paper proposes an industrial load classification method based on two-stage feature selection combined with an improved marine predator algorithm (IMPA)-optimized kernel extreme learning machine (KELM). First, the time- and frequency-domain features of electrical equipment (active and reactive power) are extracted from the power data after data cleaning, and the initial feature pool is established. Next, a two-stage feature selection algorithm is proposed to generate the smallest features, leading to superior classification accuracy. In the initial selection phase, each feature weight is calculated using ReliefF technology, and the features with smaller weights are removed to obtain the candidate feature set. In the reselection stage, the k-nearest neighbor classifier (KNN) based on the MPA is designed to obtain the superior combination of features from the candidate feature set concerning the classification accuracy and the number of feature inputs. Third, the IMPA-KELM classifier is developed as a load identification model. The MPA improvement strategy includes self-mapping to generate chaotic sequence initialization and boundary mutation operations. Compared with the MPA, IMPA has a faster convergence speed and more robust global search capability. In this paper, actual data from the cement industry within China are used as a research case. The experimental results show that after two-stage feature selection, the initial feature set reduces the feature dimensionality from 58 dimensions to 3 dimensions, which is 5.17% of the original. In addition, the proposed IMPA-KELM has the highest overall recognition accuracy of 93.39% compared to the other models. The effectiveness and feasibility of the proposed method are demonstrated.
Mosaicing Technology for Airborne Wide Field-of-View Infrared Image

Multi-detector parallel scanning is derived from the traditional airborne panorama camera, and it has a great lateral field of view. A wide field-of-view camera can be used to obtain an area of remote sensing image by whisk broom mood during the flight. The adjacent image during acquisition should cover the overlap region according to the flight path, and then the regional image can be generated by image processing. Complexity and difficulty are increased during the regional image processing due to some interference factors of aircraft in flight. The overlap of the acquired regional image is constantly variable. Depending on the analysis of the imaging geometric principle of a wide field-of-view scanning camera, this paper proposes the rigorous geometric model of geoposition. The infrared image mosaic technology is proposed according to the features of regional images through the SIFT (Scale Invariant Feature Transform) operator to extract the two best-matching point pairs in the adjacent overlap region. We realize the coarse registration of adjacent images according to image translation, rotation, and a scale model of image geometric transformation, and then the local fine stitching is realized using the normalized cross-correlation matching strategy. The regional mosaic experiment of aerial multi-detector parallel scanning infrared image is processed to verify the feasibility and efficiency of the proposed algorithm.
Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method.
Information security of hospital computer network based on SAE deep neural network

Abstract As the pace of hospital informatization construction continues to accelerate, information network technologies are being used more and more extensively in the medical industry. These advanced technologies make medical businesses more and more dependent on industry information and data, which brings about network system security issues that cannot be ignored. To strengthen the daily operation and management of hospitals, ensure the stable operation of computer systems, and do a good job in protecting the security of hospital computer system network information, this paper designs a risk assessment method for hospital computer network information security based on SAE deep neural network and analyzes the main factors affecting the security of hospital computer system network information. The experimental results prove that the proposed method can effectively improve the reliability of the evaluation results and ensure the accuracy of the evaluation results. According to the obtained information security model, it can effectively guide the construction and application of hospital computer network information systems, optimize the system network, and promote the development of hospital informatization.
Spectral Salt-and-Pepper Patch Masking for Self-Supervised Speech Representation Learning

Recent advanced systems in the speech recognition domain use large Transformer neural networks that have been pretrained on massive speech data. General methods in the deep learning area have been frequently shared across various domains, and the Transformer model can also be used effectively across speech and image. In this paper, we introduce a novel masking method for self-supervised speech representation learning with salt-and-pepper (S&amp;P) mask which is commonly used in computer vision. The proposed scheme includes consecutive quadrilateral-shaped S&amp;P patches randomly contaminating the input speech spectrum. Furthermore, we modify the standard S&amp;P mask to make it appropriate for the speech domain. In order to validate the effect of the proposed spectral S&amp;P patch masking for the self-supervised representation learning approach, we conduct the pretraining and downstream experiments with two languages, English and Korean. To this end, we pretrain the speech representation model using each dataset and evaluate the pretrained models for feature extraction and fine-tuning performance on varying downstream tasks, respectively. The experimental outcomes clearly illustrate that the proposed spectral S&amp;P patch masking is effective for various downstream tasks when combined with the conventional masking methods.
The Role of Transformational and Transactional Leadership on Turnover Intentions in Tanzanian Commercial Banks: The Moderating Effect of Perceived Organizational Support

Purpose: This study is aimed at examining the role of transformational and transactional leadership on turnover intentions in Tanzanian commercial banks. Further, the study scrutinizes the moderating effect of perceived organizational support.&#x0D; Design/Method/Approach: The study used a quantitative approach. Data were collected from 231 employees working in head offices of three commercial banks in Tanzania. Data were analyzed by using descriptive and inferential statistics.&#x0D; Findings: The study found that both transformational and transactional styles of leadership are used in commercial banks. Further, each leadership style plays a significant role in reducing turnover intentions. Perceived organizational support was also found to be a significant moderator of the relationship between each leadership style and turnover intentions.&#x0D; Theoretical Implications: This study extends the literature on the role of a relationship between transformational and transactional leadership styles on turnover intentions in Tanzanian commercial banks.&#x0D; Practical Implications: Commercial banks have to make use of both transformational and transactional leadership styles in order to obtain positive outcomes in retaining their employees. Moreover, better results could be achieved once employees perceive the presence of organizational support.&#x0D; Originality/Value: As per the existing empirical literature, it is the first study to be conducted in the Tanzanian context.&#x0D; Research Limitations/Future Research: This study used a quantitative method only. Future studies may consider using a mixed approach to get more insights from respondents. Data collection was done in three commercial banks in Tanzania. A more comprehensive study to be done in bank branches is therefore recommended.&#x0D; Paper Type: Empirical&#x0D; JEL Classification: M12, M19, M53
The Detection of Students' Abnormal Behavior in Online Exams Using Facial Landmarks in Conjunction with the YOLOv5 Models

The popularity of massive open online courses (MOOCs) and other forms of distance learning has increased recently. Schools and institutions are going online to serve their students better. Exam integrity depends on the effectiveness of proctoring remote online exams. Proctoring services powered by computer vision and artificial intelligence have also gained popularity. Such systems should employ methods to guarantee an impartial examination. This research demonstrates how to create a multi-model computer vision system to identify and prevent abnormal student behaviour during exams. The system uses You only look once (YOLO) models and Dlib facial landmarks to recognize faces, objects, eye, hand, and mouth opening movement, gaze sideways, and use a mobile phone. Our approach offered a model that analyzes student behaviour using a deep neural network model learned from our newly produced dataset" StudentBehavioralDS." On the generated dataset, the "Behavioral Detection Model" had a mean Average Precision (mAP) of 0.87, while the "Mouth Opening Detection Model" and "Person and Objects Detection Model" had accuracies of 0.95 and 0.96, respectively. This work demonstrates good detection accuracy. We conclude that using computer vision and deep learning models trained on a private dataset, our idea provides a range of techniques to spot odd student behaviour during online tests.
: Software product lines extraction driven by language server protocol

Software product lines (SPL) describe highly-variable software systems as a family of similar products that differ in terms of the features they provide. The promise of SPL engineering is to enable massive software reuse by allowing software features to be reused across a variety of different products made for several customers. However, there are some disadvantages in the extraction of SPLs from standard applications. Most notably, approaches to the development of SPLs are not supported by the base language and use a syntax and composition techniques that require a deep understanding of the tools being used. Therefore, the same features cannot be used in a different application and developers must face a steep learning curve when developing SPLs for the first time or when switching from one approach to a different one. Ultimately, this problem is due to a lack of standards in the area of SPL engineering and in the way SPLs are extracted from variability-unaware applications. In this work, we present a framework based on LSP and dubbed that aims at standardizing such a process by decoupling the refactoring operations made by the user from the effect they have on the source code. This way, the server for a specific SPL development approach can be used across several development environments that provide clients with customized refactoring options. Conversely, the developers can use the same client to refactor SPLs made according to different approaches without needing to learn the syntax of each approach. To showcase the applicability of the approach, we present an evaluation performed by refactoring four SPLs according to two different approaches: the results show that a minimal implementation of the client and server applications can be used to reduce the effort of extracting an SPL up to the 93% and that it can greatly reduce or even completely hide the implementation details from the developer, depending on the chosen approach.
Counterfactual and seeing-to-it responsibilities in strategic games

The article studies two forms of responsibility in the setting of strategic games with imperfect information. They are referred to as seeing-to-it responsibility and counterfactual responsibility. It shows that counterfactual responsibility is definable through seeing-to-it, but not the other way around. The article also proposes a sound and complete bimodal logical system that describes the interplay between the seeing-to-it modality and the individual ex ante knowledge modality.
Scientific visualization for advanced deep-sea exploration equipment and underwater automatic manipulation

Scientific visualization is important in modern technological activities and engineering exploration. Due to the dark and high-pressure characteristics of deep sea, it is difficult to visualize the entire operation of deep-sea equipment. Thus, it is of great necessity to use virtual simulation technology to help people understand the operation process of some deep-sea exploration equipment on the sea floor. In this paper, science, art, and new media are combined through artistic rendering, visual processing, and the technology of virtual reality (VR) and holography, which makes the exploration of the latest deep-sea lander and intelligent submersible named “Luling” look more intuitive and smart and have more visual impact and expression. Apart from that, automatic manipulation videos of the rover robot in the deep sea captured by the Luling are effectively nested to realize the goal of virtual and real presentation. The designed scientific visualization of deep-sea equipment can not only adapt to the display output of VR, mobile phones, TV, 360° showcase, and other platforms, but also achieve immersive experience and virtual simulation learning through HTC Vive VR equipment. The technology and design way of scientific visualization in this paper is universal and suitable to the same kind of engineering simulation.
A unified network embedding algorithm for multi-type similarity measures

Traditional network embedding aims to learn representations by capturing a predefined vertex-to-vertex similarity measure. However, in practice, there are different types of similarity measures (e.g., connectivity and structural similarity), which are appropriate for different downstream applications. Meanwhile, it is hard to select the “best” similarity measure that can mostly benefit the application, considering the required domain knowledge of both application scenario and network science. It sometimes requires to cooperate these similarity measures with each other for achieving better performance.Therefore, automatically integrate multiple types of similarity measures into a uniform network embedding framework is critical to obtain effective vertex representations for a downstream application. In this paper, we address the above problem in social networks, and propose a semi-supervised representation learning algorithm. The general idea of our approach is to impose social influence, which occurs when one’s opinions, emotions, or behaviors are affected by others in a social network. Particularly, we build the connection between a user’s representation vector and the probability of her being influenced by another user to have a particular label (e.g., fraud, personal interest, etc.). We conduct efficient experiments based on six real-world datasets and find a clear improvement of our approach comparing with several state-of-the-art baselines.
External clustering validity index based on extended similarity measures

Clustering is one of the most important unsupervised approaches to organizing data in many practical applications. The application of different methods to clustering one dataset generally results in different clusterings of that dataset; for that reason, there is a need for evaluating the similarities of various clustering methods. This can be accomplished with the help of external validity indices. Although many external validity measures exist in the literature, most of them suffer from some drawbacks: they apply very strong null hypotheses, which lead to the independence of the clusterings, fixed number of clusters, and fixed cluster sizes. In addition, an acceptable measure should have some properties to be a metric. If a measure does not qualify for being a metric, the interpretation will not be easy. In this study, a novel Set Matching Index (SMI) is proposed based extended derivation of Jaccard, Dice, and Cosine measures. Thus, three new indices are introduced in this paper, i.e., Set Matching Index based on Extended Jaccard similarity measure (SMI-EJ), Set Matching Index based on Extended Dice similarity measure (SMI-ED) and Set Matching Index based on Extended Cosine similarity measure (SMI-EC) as a set matching metric. All of the metric properties are proved in the present research. For the assessment of the proposed indices performance quality, they are experimented on ten real-world and Synthetic datasets, and then the obtained results are compared with those of four popular external indices (NMI, RI, ARI and Purity) and a new index (PSI). Also to verify the performance of three proposed similarity indices, the experiments develop. We apply the proposed indices to the generated clustering solutions and compare the results. We show that the proposed indices achieve desirable results based on the external validity index properties.Moreover, SMI has simple formula with moderate and appropriate results that can be easily interpreted.
A multi-layer photonic spiking neural network with a modified backpropagation algorithm for nonlinear classification

In this work, we construct a fully connected multi-layer photonic spiking neural network (PSNN) based on excitable vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA) equipped with the modified supervised backpropagation (BP) algorithm. Different from the traditional BP algorithm, our proposed modified BP algorithm is based on optical spikes, and its activation function exploits the excitable characteristic of VCSELs-SA. It is demonstrated that this BP PSNN can be competent in linearly inseparable problems by correctly solving the exclusive or (XOR) problem. Thus, a three-layer fully connected BP PSNN is designed to perform the prediction of Human immunodeficiency virus-1 protease (HIV-1 PR) cleavage sites and achieves 91.95% classification accuracy with the modified BP algorithm, which could be a useful tool in helping to find effective inhibitors of HIV PR to fight against acquired immunodeficiency syndrome (AIDS). These results demonstrate the validity of our modified BP algorithm in dealing with nonlinear classification problems in PSNNs, which is helpful for supervised learning in the photonic neuromorphic computing architecture for future neuromorphic applications.