Reviewerly/exHarmony huggingface dataset:
Attack and Improvement of a Hidden Vector Encryption Scheme

Hidden Vector Encryption (HVE) is a new kind of attribute-based encryption in which a vector is hidden in the ciphertext or linked with the secret key. In ESORICS 2014, Phuong et al. proposed an HVE scheme with constant-size ciphertext which is constructed in the prime order setting. In this paper, we show that Phuong et al.’s scheme is not vector-hiding due to public parameters in their scheme leak some information about vectors. Furthermore, an improved HVE scheme is proposed in the prime order setting and its security is proven in the security model. Comparison shows our scheme has more efficient in decryption than current other HVE schemes.
A Systemic Functional Linguistic Approach to the Analysis of CFL Textbooks

In the view of much functionally oriented language education research, the goal of teaching and learning is for learners to become competent in using the appropriate forms of language for making meaning in particular contexts.Due to its systematic focus on the meaning-making mechanisms of language, systemic functional linguistics (SFL) has been recognized as having useful applications in educational settings.However, SFL-trained teachers need to be able to access teaching materials appropriate for the application of a functional approach in their teaching contexts.This study examines these issues in the context of the teaching of Chinese as a Foreign Language (CFL).It analyzes four CFL textbooks for beginners to explore the extent to which the presentation of grammatical knowledge and the types of activities used in the textbooks can raise learners' awareness of the meaning-making function of language.For this purpose, the linguistic framework of SFL is used to analyze how grammatical instruction and apparatus are presented in these textbooks.The findings from this analysis show that the types of activities in the four textbooks examined are not always sufficiently contextualized to enable students to understand the key relationship between forms and meanings in each case: three out of the four textbooks do not provide sufficient opportunities for students to become aware of the systemic nature of the language and to discover how language users make systematic choices in specific contexts.This study shows the efficacy of the SFL model in textbook analysis: an SFL-based framework can be helpful for teachers in selecting textbooks as well as in providing suggestions for authors to redesign textbooks, by encouraging textbook writers to present language as a meaning-making resource.
Interactive digital platform and cyber-physical systems in medical education

Relevance . The success and progress of medical education are inherently linked to the achievements of fundamental and applied sciences and depend on the degree of curriculum fulfilment with advanced digital technology effectiveness. The article considers new forms of learning organization based on digital platforms. Information and communication technologies (platforms) allow effective distant coordination of the academic paths for large numbers of students and strict unbiased control over the implementation of assigned tasks. The article considers the specific features of medical digital platforms, algorithmic management forms, necessity and importance of cyber-physical systems, and gives examples of single robotic element implementation used in learning platf orms. Materials and Methods . The publication selection criteria were: papers published after 2000; relevance to the keywords "Education", "Medical Education", and "Patient Simulation"; publications included in the databases "ScienceDirect" (Scopus), "IEEE", or "NCBI". Results . Twenty-seven scientific publications were selected by the inclusion and exclusion criteria. Conclusion . The online learning platform formed by a set of transformed traditional curricula allows for a full access of students to learning resources and can stimulate the teaching staff competencies, which is, in general, a relevant and promising direction for improving the effectiveness of the learning process.
A professional strategy for Bitcoin and Ethereum using Machine Learning for Investors

Forecasting economic goods market recoveries is challenging due to the volatility and uncertainty of the market's structure. Since the introduction of machine learning and increased computer power, programmable forecasting approaches have been found to be particularly effective in predicting stock values. Random Decision Forest, K - Nearest Neighbors, and Linear Regression were among the natural processing techniques employed in this study to forecast the costs of popular crypto currencies like Bit coin and Ethereum over the next few years. The model is given financial data on stock prices from the start of the day to the end of the day. Bitcoin's value may or may not improve in the future because it has been on the market for a decade. Ethereum was created in 2015 and is now the second most popular crypto currency on the market. The value of Ethereum is nearly comparable to that of Bit coin. After a few years, it is doubtful whether bit coin will be available on the market. As a result, buyers seek forecasts in order to invest in the right crypto currency and profit from it. The user can use this forecast to anticipate the future of both crypto currency prices. To assess the value of crypto currencies, we used three forecasted analytic techniques, and we can compare the accuracy of the three algorithms to see which one is the best.
An Integrated Approach to Non-Invasive Diagnosis of Dementia Using Natural Language Processing and Machine Learning

Current diagnosis of dementia revolves around the usage of lengthy clinical tests such as brain scans and blood tests that are also often invasive for the elderly populations that cognitive impairment is common to. A rising trend in care for dementia is the usage of qualitative tests like neuropathological and cognitive assessments to serve as preliminary diagnosis, especially since these do not require invasive procedures and instead are accessible to delicate individuals. Machine learning techniques have slowly gained traction within healthcare, especially as algorithms have the unique ability to process a large amount of quantitative and qualitative data to simple and often binary outputs. Machine learning algorithms are thus particularly suited to the task of diagnosis of dementia. This paper aims to provide a qualitative and non-invasive method for diagnosis of dementia in patients and utilizes a combination of machine learning and natural language processing (NLP). All data for testing and training of the model was sourced from the Behavioral Risk Factor Surveillance System (BRFSS), an annual survey assessment conducted by the CDC. In our proposed model, a conversational chatbot collects data from individuals surrounding multiple qualitative predictors, and these values are then fed into a machine learning algorithm to provide an output for presence of dementia. The strongest performing algorithm was a random forest algorithm, which outperforms the current state-of-the-art, with an AUC-PR score of 0.832 and an MCC score of 0.53. Models like those proposed by our paper have the potential to revolutionize effective care for patients facing dementia, making diagnosis much quicker and easier to access.
RS-Stack: Defense against Stack Buffer Overflow Attack with Random Shadow Stack

Shadow stack is a method to prevent stack buffer overflow attack. However, in the existing shadow stack solutions, it is often supposed that the shadow stack is safe, but the reality is not always the case. To overcome it, this paper presents a stack buffer overflow attack defense method based on dynamic shadow stack, which is called RS-Stack. The outstanding advantage of RS-Stack is that the shadow stack address is random, and it is difficult for attackers to locate the shadow stack. In order to further improve the security, RS-Stack separates the shadow stack address for protection, which makes it difficult for attackers to destroy the contents of the shadow stack. RS-Stack provides double protection for shadow stack. We have partly implemented RS-Stack, and the experiment shows that the shadow stack address splitting protection introduce heavily performance overhead. In order not to affect the performance too much, the address split protection is optional, and this mechanism is only used in the environment with high security requirements.
Ga-DQN: A Gravity-aware DQN Based UAV Path Planning Algorithm

Unmanned aerial vehicles (UAVs) path planning refers to exploring the optimal flight trajectory from the starting point to the destination that satisfies the UAV under specific constraints such as maneuverability and environmental information constraints, which is a crucial technology for the UAV mission planning. In order to enhance the efficiency and safety of the UAV path planning task, a new autonomous UAV path planning system based on deep reinforcement learning is proposed in this article. At the beginning, a new action guidance strategy based on the Deep Q-Network (DQN) algorithm is introduced via deploying the Gravity-aware Deep Q-Network (Ga-DQN) method. This strategy can effectively assist the UAVs to avoid the obstacles in the specific state. For balancing the efficiency and safety of the task, a reward scheme that introduces a safety counting mechanism is proposed to provide global guidance for the agent in Deep Reinforcement Learning (DRL). The simulation results under different obstacle densities show that the proposed novel strategy can obviously behave robust and greater efficiency compared to the traditional methods.
A Hybrid Approach for Moving Object Detection Using TV-L1 Features and RPCA–MOG

Robust Principal Component Analysis (RPCA) has recently been active everywhere for dimension reduction in image processing, display and pattern recognition.Methods based on low-rank sparse representations, which make some specific significant assumptions, have recently received a lot of attention in background modelling.Meanwhile, a powerful analysis framework is needed to handle background areas or foreground motion at various scales.this paper presents a hybrid approach along with total variation L1 (TV-L1) features and reproductive RPCA model in low rank background subtraction modelling and sparse matrix with mixture of Gaussians (MoG) as foreground modelling.The hybrid structure with TV-L1 features imposes a hierarchical RPCA on the singular values of the low-rank component and MOG sparsity indicators.The proposed work was evaluated on the CDnet2014 (ChangeDetection.net)dataset, obtained result as accuracy was 92.9%, 86.7% 95.7% for Highway, Escalator, and Indoor respectively.The proposed method is compared with traditional methods and obtained relative reconstruction error is 0.01529 as a lower side.
Low latency BCH decoder using the affine polynomial over the finite field

The paper proposes a low latency BCH decoder with low complexity using parallel computation and simplifying locating errors by finding the roots of the affine polynomial over finite fields. The proposed design can be implemented on low-cost hardware platforms while applicable in very low latency information systems.
A Discrete Moth Flame Algorithm for Feature Selection

A crucial and important task in machine learning is feature selection (FS).The primary goal of the feature selection task is to minimize the dimension of the feature set while preserving performance accuracy.In order to address the FS task, a discrete moth flame algorithm that is combined with levy flights (DL-MFA) is presented in this research.The proposed DL-MFA imitates the natural navigational patterns of moths.The moths move along a straight line at a constant angle in the direction of the true light source (the moon) known as transverse orientation.Additionally, moths are drawn to artificial lights like fires and because of the close proximity; they constantly adjust their flying angles, creating a spiral path.In order to maintain healthy population diversity and increase the global search capabilities of the algorithm, the levy flight search technique is also used as a regulator of the moth position updating mechanism.The five swarm intelligence algorithms (SIAs) are contrasted with the proposed algorithm using measures such as entropy, purity, completeness score (CS), and homogeneity score (HS).For evaluating fitness, the SSE fitness function is utilised.The outcomes have shown that the proposed algorithm achieved purity values in the range 90% to 100%, and entropy 10% to 50%.Proposed DL-MFA has also achieved homogeneity score and completeness score up to 50%.These results prove that the proposed algorithm is better than its state-ofthe-art competitors.
Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases

E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3 involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification was greater than that of the two test scenarios that were suggested, which was 49.43%.&#x0D;
Hybrid Convolutional Neural Network with Residual Neural Network for Breast Cancer Prediction Using Mammography Images

BC (Breast Cancer) is predominantly prevalent in women and is the leading cause of mortality due to cancers which can be reduced using mammography screenings.The use of CNNs (Convolution Neural Networks), a type of deep learning method has proven to be highly successful in image identifications.However, the quality of acquired mammographic images is found to be low while being used by detection models and hence this work proposed hybrid MLTs (Machine learning Techniques) for overcoming low-quality issues in the prediction of BCs.Initially, Statistical correlation analysis-based pre-processing is introduced for improving classifier performances followed by a hybrid model which predicts BCs effectively.This work also introduces a novel building block called Fuzzy Scoring based Resnets (Residual Networks) and CNNs called FS-Resnet CNNs for optimizing networks.The proposed FS-Resnet CNN model is computationally efficient, less sensitive to noises, and efficient in memory usage.Experimental results show that the proposed model achieves 95% accuracy ,95.45% precision, 93% recall rate, 94.21% f-measure and 18.113(S)-time complexity.
Hand Gesture Recognition in Complex Background

Hand gestures are a form of nonverbal communication, and gesture recognition systems used for interpreting them are applied in various fields like robot control, sign language, graphic editor control, virtual environment, appliance control, 3D modelling, and so on. For many years, several vision-based gesture recognition systems have been investigated, but owing to differences in illumination conditions and background complications, it remains a challenging task. In this paper, we propose a real-time hand gesture recognition system that can recognize and categorize hand motions against a complicated background. We apply a hand segmentation method which is made out of adaptive background removal and a skin color-based threshold to overcome issues mentioned before. The Histogram of Oriented Gradients (HOG) features were used to train a Support Vector Machine (SVM) classifier, and gesture classification was evaluated.
Transformer with Spatio-Temporal Representation for Video Anomaly Detection

With the popularity of smart surveillance devices and the increase of people’s security awareness, video anomaly detection has become an important task. However, learning rich multi-scale spatio-temporal information from high-dimensional videos to predict anomalous behaviors is a challenging task due to the large local redundancy and complex global dependencies among video frames. Although Convolutional Neural Network (CNN) has extraordinary bias induction capabilities, their inherent localization limitations lead to their lack of ability to capture long-term spatio-temporal features. Therefore, we propose a Transformer with spatio-temporal representation for video anomaly detection. The network combines the convolution operation with the Transformer operation, and uses the convolution operation to extract shallow spatial features to facilitate the recovery of sampled images. At the same time, Transformer operation is used to encode patches and efficiently capture remote dependencies through a self-attention mechanism, and to reduce the limitations in local redundancy. Experimental results on the UCSD Ped2, CUHK Avenue and ShanghaiTech datasets demonstrate the effectiveness of the proposed network.
Deep Learning-Based Sentiment Classification of Social Network Texts in Amharic Language

Sentiment analysis is among the main targets of natural language processing (NLP) that assigns a positive or negative value to the opinion expressed in natural language text within different contexts such as social media, forum, news, blogs, and many others. Sentiments of an under-researched language such as Amharic have received little attention in NLP applications due to the scares of enough resources to develop such methods. In this paper we combine the deep learning (CNN, LSTM, FFNN, and BiLSTM) and classical models (cosine similarity) with word embedding techniques for sentence-level sentiment classification of social media messages in Amharic language that has never been tested before. We use the Amharic Twitter dataset that consists of around 3000 text snippets. Data augmentation is applied to increase the dataset for training those models. We achieved the 82.2% accuracy using the sentence transformer and cosine similarity on the Amharic corpus.
A New Addition Law in Twisted Edwards Curves on Non Local Ring

Let $$\mathbb {F}_{q}$$ be a finite field of q elements with $$q=p^r$$ for some odd prime integer p and a positive integer r. Let $$R=\mathbb {F}_{q}[e],$$ where $$e^{2}=e.$$ The purpose of this paper is to investigate $$E_{E,a,d}(R)$$ be the twisted Edwards curves over R, with $$a,d \in R$$ . In the end of the paper, we study the complexity of this new addition law in $$E_{E,a,d}(R)$$ and highlight some links of our results with elliptic curves cryptosystem.
Where are the Children with Autism Looking in Reality?

Social difficulties are hallmarks of individuals with autism spectrum disorder (ASD), of which atypical visual attention is one of the most important characteristics. Learning and modeling the atypical visual attention of individuals with ASD have particularly important significance to related research in the fields of medical science, psychology, education etc., and many studies have been conducted in the literature. However, previous studies have two weaknesses. First, all stimuli in the conducted experiments are selected by the researchers, which are not only restricted by the objective and intention of the researchers, but also limited by the subjective cognition of the photographers. Secondly, most of these stimuli are displayed on screens with restricted and relatively small field-of-view (FOV) compared with the real world. Therefore, in this paper, we conduct the first large-scale study towards better understanding and modeling the atypical visual attention of individuals with ASD in real world. To overcome the two weaknesses mentioned above, a large-scale dataset is established which includes 300 omnidirectional images with the corresponding eye tracking data collected under virtual reality (VR) environment among 15 children with ASD and 16 typically developing (TD) controls. Moreover, a vector quantized saliency prediction model (VQSAL) is applied to better learn the visual attention patterns of both ASD and TD people under the omnidirectional condition.
Accelerated Information Dissemination for Replica Selection in Distributed Key-Value Store Systems

In distributed key-value stores, multiple replica servers are always available for each key-value access operation when the eventual consistency model is employed. Accordingly, the completion times of the key-value access operations generated by an end-user request at different servers may be of great difference, especially when the replica servers are heterogeneous and have time-varying performance. Accordingly, the replica selection algorithm is crucial to cut the response time of end-user requests. The main challenge of making replica selection for each light-weighted key-value access operation is to timely know the status of replica serves. Recently, the adaptive replica selection algorithm C3 suggests guiding the replica selection with the piggybacked information of replica server in the returned “value”. Although C3 has good performance, the poor timeliness of feedback information makes a large performance gap between C3 and the ideal replica selection algorithm. To narrow this gap, the Accelerated Information Dissemination (AID) mechanism is proposed in this paper. Specifically, AID removes the bottleneck of information dissemination at the “slow” servers by letting both “client” and “replica server” store the records about the status of replica servers and both “key” and “value” piggyback multiple records. AID is implemented in Cassandra and evaluated by experiments and large scale simulations. The results show AID can significantly improve the timeliness of feedback information, especially when the number of nodes is large. Accordingly, AID helps C3 to greatly reduce the latency.
Feature Extraction and Identification of Specific Radiation Sources Based on Axial Integral Bispectrum and Deep Residual Shrinkage Network

In the existing research on the identification of specific radiation sources, some noise inevitably occurs when classifying samples, which affects the extraction of Radio frequency fingerprint (RFF) with unique native properties, thus reducing the classification accuracy. In this paper, a feature extraction and identification method for specific radiation sources based on the integration of axial integral bispectrum features and deep residual shrinkage networks(DRSN) is proposed. First, the axial integral bispectrum is used to extract the signal features, and then the signal is denoised by soft thresholding in the deep residual shrinkage network, and the threshold is automatically set according to the situation of each sample using the attention mechanism. The experimental results show that the new method can effectively improve the classification accuracy under low signal-to-noise ratio, and in the case of low signal-to-noise ratio, the maximum distance of 62ft can achieve 98.5% classification and recognition accuracy.