Reviewerly/exHarmony huggingface dataset:
Post Quantum Digital Signature Change in IOTA to Reduce Latency in Internet of Vehicles (IoV) Environments

In the last decade, the Internet of Things has witnessed exponential growth in demand and development. The growth of IoT and the increasing need for automation have led to an exciting application in the transportation industry, namely the Internet of Vehicles (IoV). Simply put, IoV systems are vehicular ad-hoc networks (VANETs) enhanced by integrating them with automation over the internet. As briefly discussed in this paper, the IoV systems guarantee various advantages like a much safer and more efficient future. However, the primary challenge at hand is implementing IoV at the end-user level. Of the varied building blocks present on the road to the development of IoV, secure data transfer is an essential requirement, especially with recent advances in quantum computing. IOTA is a Distributed Ledger Technology (DLT) specifically designed to secure IoT data transmissions. This paper discusses the advantages of implementing IOTA for secure and private IoV systems. The paper suggests replacing IOTA’s current post-quantum signature scheme Winternitz One Time Signature (W-OTS) with CRYSTALS Dilithium. These two post-quantum signature schemes are evaluated against each other to establish that employing Dilithium reduces both latency and signature space required, bringing a significant improvement to IOTA. This improvisation is beneficial, especially for the fast-paced functioning of IoV environments.
Moroccan sign language recognition based on machine learning

More than 5% of the world's population (466 million people) suffer from a disabling hearing loss: 4 million are children. People with hearing loss usually communicate through spoken language and can benefit from assistive devices such as cochlear implants. However, deaf people have profound hearing loss and use sign language to communicate with others, which involves little or no hearing. To facilitate communication between deaf people and normal people who do not know sign language, we have proposed in this paper a system that allows textual transcription of sign language. The developed system will be able, in a first step, to recognize the sign language alphabet using machine learning and image processing. Simulation results have shown the efficiency of the developed model.
D-NEXUS: Defending text networks using summarization

Sentiment analysis is an important tool for understanding consumer sentiment in e-commerce platforms. Usually, it is performed using Deep Neural Networks (DNNs), owing to their strong predictive and generalization capabilities. Unfortunately, DNNs are prone to adversarial attacks, which involve introducing imperceptible changes in the data with the deliberate motive of “fooling” the target model. It can lead to far-reaching consequences and pose an alarming issue to the credibility of e-commerce platforms. The existing text-based defenses, such as spelling correction and adversarial training, are largely ineffective against state-of-the-art adversarial attacks, most of which deal in word replacement, insertion, and deletion. We introduce an effective transformation-based defense strategy, D-NEXUS (DefeNding tEXt networks Using Summarization). It overcomes the drawbacks of existing defenses by summarising the input text before feeding it into the target model. Our extensive experiments on publicly available datasets show that D-NEXUS successfully defends against state-of-the-art attacks, in a time-efficient manner.
Authorship Attribution with Temporal Data in Reddit

Context: The practicality brought by the use of smartphones has resulted, in recent years, in greater interaction through online social networks. Problem: Social networks can influence users both positively and negatively, one of the negative impacts is the spread of fake news. In this context, identifying the correct source of information or whether the information is true becomes an extremely relevant activity. Solution: This paper presents an approach for authorship attributions that combines text mining and temporal analysis techniques. IS Theory: This work is under the Social Network Theory, in particular, the user interaction through a forum network model, in which each post creates a comment thread and the user can reply or not inside the thread. Method: This work is a controlled experiment and it aims to extend a previous case study that used a classification between two and ten authors. The results were validated through a quantitative approach. Summary of Results: Among 10 authors, classification results had more than 97% of accuracy with chars feature having more than 99% of accuracy, among 100 authors all features presented more than 70% of accuracy. Contributions and Impact in the IS area: The main contribution of this works is to validate the authorship attribution in a big data context, using significant features and a robust classifier model.
Between resistance and collaboration: A teacher's professional activity in the Third Space

The article proposes to examine teachers' professional activity with the help of P. Bourdieu's theory of sociology of education and the theory of postcolonialism. According to P. Bourdieu's theory, it is revealed how the teachers' habitus, acting under the pressure of external structures, reproduces a culture that ensures the continuity of the position of dominant classes. In this respect, the teacher is similar to a colonizer sent to civilize by barbarian tribes. From a postcolonial perspective, the processes of cultural imposition are disrupted as cultural resistance in the "barbarian" tribes intensifies. Rejecting the one-way cultural movement (from colonist to colonized), postcolonialism speaks of encounters in which both sides undergo cultural change. A Third Space is formed, in which a hybrid culture is created. In this sense, the teacher can no longer be seen as the conqueror of new generations. He/ she is a social agent looking for his/ her own strategies and ways of survival. Taking over Other's culture is the teacher's resistance in professional terms and collaborating with the Other's culture. Resistance and collaboration interrupt reproduction and expand the hybrid culture. It is a state of uncertainty and tension that leads to dissatisfaction with professional activities. This interpretation seeks to explain the decline in the prestige of the teaching profession and teachers' own dissatisfaction with their work, as recorded in OECD TALIS 2018 research.&#x0D; Concept. P. Bourdieu states that the purpose of a school is to reproduce power relations. Teachers, using their authority, implement a culture that supports the position of the dominant class. However, various new studies show a decline in teachers' authority. The rupture of hierarchical connections in the process of culture imposition is being studied in postcolonialism. By applying the ideas of H. K. Bhabha, the modern teacher activity can be explained not as a cultural reproduction but as a teacher's constant encounters with the culture of the Other. A space where cultural encounters take place, K. H. Bhabha names the Third Space. Here a new hybrid culture emerges, and a school becomes open to otherness and diversity.&#x0D;
Self-Supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty

Dear Editor, This letter is concerned with self-supervised monocular depth estimation. To estimate uncertainty simultaneously, we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation with the discrete strategy that explicitly associates the prediction and the uncertainty to train the networks. Furthermore, we propose the uncertainty-guided feature fusion module to fully utilize the uncertainty information. Codes will be available at https://github.com/zhyever/Monocular-Depth-Esti-mation-Toolbox.
A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System

This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.
Security of Cloud Networks – Status, Challenges and Future Trends

Cloud based Network allows the sharing of resources which includes network, applications, software program and storage via internet. Cloud end-users can rent more than one resources consistent with their requirements, and pay simplest for the services they use. However, regardless of all cloud blessings there are numerous protection issues associated with network, hardware, information, virtualization and service providers that act as a great barrier in the adoption of cloud in the IT industry. The largest assignment in accomplishing cloud Network protection is to manage information secure. The primary troubles that get up with the upload of information to cloud are that the clients don&#x2019;t have the seeing of their information and neither do they know its position. They must to trust in the service provider to guarantee that the platform is secure, and it implements necessary protection properties to hold their information safe.
A Pedestrian Re-identification Algorithm Based on 3D Convolution and Non_Local Block

In the application of video-based pedestrian re-identification, introduced deep learning method to learn feature representation of pedestrian. In order to improve feature quality, introduced 3D convolution block as backbone network to aggregate temporal and spatial features; for issue of human body occlusion in video frames, introduced Non_Local block to capture long distance dependence between frames, and eventually eliminate the impact of occlusion. Optimal embedding scheme of 3D convolution and Non_Local block in backbone network is designed via experiments, and has proved that rich features of pedestrian can be extracted from video frames by this solution, which helps to improve the accuracy of re-identification.
A Framework Using Useful Work for Transient Committee Selections in Blockchain Consensus

We present an novel implementation for a member selection mechanism for committee-based consensus utilizing useful work execution as a precursor for selection. The framework enables distributed code execution with can ensure the code is safe to execute and provide verification of results. Our approach incorporates the Interplanetary File System (IPFS) network for storage and retrieval of code and related data. Through IPFS content-based addressing, both the source files and results are made available in a way that is tamper resistant with permanent data storage. Utilizing a code parser built using python, potential attack vectors within submitted code are identified and blocked allowing for safe remote execution. The end result is a tool that enables secure, local code execution without risk to the host machine and without the need for containerization software. Verified solutions reward participants with credits which are exchanged for participation in a committee-based consensus system in a fair and distributed manner.
SQuadMDS: A lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP

Multidimensional scaling is a process that aims to embed high dimensional data into a lower-dimensional space; this process is often used for the purpose of data visualisation. Common multidimensional scaling algorithms tend to have high computational complexities, making them inapplicable on large data sets. This work introduces a stochastic, force directed approach to multidimensional scaling with a time and space complexity of O(N), with N data points. The method can be combined with force directed layouts of the family of neighbour embedding such as t-SNE, to produce embeddings that preserve both the global and the local structures of the data. Experiments assess the quality of the embeddings produced by the standalone version and its hybrid extension both quantitatively and qualitatively, showing competitive results outperforming state-of-the-art approaches. Codes are available at https://github.com/PierreLambert3/SQuaD-MDS-and-FItSNE-hybrid.
Malay lexical simplification model for non-native speaker

Vocabulary is an important language skill that can affect a person’s understanding of a sentence. Thus, lexical simplification is the task of converting difficult words into simpler words. It is to make it easier for the reader to understand the sentences. The biggest challenge in lexical simplification is to simplify the words needed without changing the meaning of the sentence. Past studies have shown that there are weaknesses in this task, where simple words are also identified as complex words. This issue has led to the simplification of unnecessary words. The purpose of the study is to produce a complex word identification model for the Malay language into words that are more easily understood by nonnative speakers. Experiments was performed on the appropriate features to obtain the required results. Machine learning was used to ensure the results were more accurate. This study is a novelty in text simplification of the Malay language in the field of Natural Language Processing (NLP) and may be used as a preprocessing tool to improve other tasks in NLP.
Torrent: Strong, Fast Balance Discovery in the Lightning Network

The owners of channels in the Bitcoin’s Lightning Network do not disclose their balances in order to protect their privacy, and conceal payment transfers through their channels. Nevertheless, recent studies have shown that channel balances can be discovered using simple probing techniques. These techniques are, however, slow, often rely on specific control messages, and require to open a new channel for every single channel balance discovery or have limited power in discovering balances of remote channels. In this work, we present a powerful balance discovery method called Torrent that overcomes these limitations of existing methods. Unlike the existing techniques, Torrent uses multi-path payments instead of single-path payments. This—together with a novel max flow algorithm designed for the Lightning Network— allows a single probing node to push a large flow of payments through any target channel, making it more likely to discover its balance. Moreover, Torrent can speed-up balance discovery through parallel payment transfers, and pre-computation of payment paths. In addition, Torrent can operate in the absence of routing control messages that are often relied on by the existing channel discovery methods.
Image-Based Visual Servoing Techniques for Robot Control

Visual servoing involves extracting information from the image provided by cameras and integrating it into the robot control loop to achieve the desired action. Visual servoing techniques based on geometric primitives are effective for a large class of applications. In this paper, we present a comparison between points, line, and circle primitives in terms of feature error, computation time, and space velocity convergence of the vision sensor. The simulation results show the ability of visual servo techniques based on the three geometric primitives for controlling robots with superiority to line primitives in terms of computation time and speed of convergence.
Mobile Education System Based on Genetic Algorithm

In order to solve the problems existing in the current mobile education system, this paper proposes a method of mobile education system based on genetic algorithm. By analyzing the research status of mobile learning and the theory and application mode of mobile learning, this method explores the combination of mobile learning theory and application mode and China’s traditional primary and secondary education mode, then puts forward a mobile teaching model suitable for primary and secondary education, and builds a mobile teaching system on this basis. The results show that, through the analysis, it is found that most of them remain at about 10%. In practice, the occupation is slightly increased only when multiple people teach, but it also belongs to an acceptable normal level, which can meet more complex functions in the future. The function of mobile learning platform needs to be expanded. For example, mobile learning forum can provide learners with a platform for communication, discussion, and sharing learning materials.
Research on the Lightweight Deployment Method of Integration of Training and Inference in Artificial Intelligence

In recent years, the continuous development of artificial intelligence has largely been driven by algorithms and computing power. This paper mainly discusses the training and inference methods of artificial intelligence from the perspective of computing power. To address the issue of computing power, it is necessary to consider performance, cost, power consumption, flexibility, and robustness comprehensively. At present, the training of artificial intelligence models mostly are based on GPU platforms. Although GPUs offer high computing performance, their power consumption and cost are relatively high. It is not suitable to use GPUs as the implementation platform in certain application scenarios with demanding power consumption and cost. The emergence of high-performance heterogeneous architecture devices provides a new path for the integration of artificial intelligence training and inference. Typically, in Xilinx and Intel’s multi-core heterogeneous architecture, multiple high-performance processors and FPGAs are integrated into a single chip. When compared with the current separate training and inference method, heterogeneous architectures leverage a single chip to realize the integration of AI training and inference, providing a good balance of training and inference of different targets, further reducing the cost of training and implementation of AI inference and power consumption, so as to achieve the lightweight goals of computation, and to improve the flexibility and robustness of the system. In this paper, based on the LeNet-5 network structure, we first introduced the process of network training using a multi-core CPU in Xilinx’s latest multi-core heterogeneous architecture device, MPSoC. Then, the method of converting the network model into hardware logic implementation was studied, and the model parameters were transferred from the processing system of the device to the hardware accelerator structure, composed of programmable logic through the bus interface AXI provided on the chip. Finally, the integrated implementation method was tested and verified in Xilinx MPSoC. According to the test results, the recognition accuracy of this lightweight deployment scheme on MNIST dataset and CIFAR-10 dataset reached 99.5 and 75.4% respectively, while the average processing time of the single frame was only 2.2 ms. In addition, the power consumption of the network within the SoC hardware accelerator is only 1.363 W at 100 MHz.
Work in Progress: Exploring Schedule-Based Side-Channels in TrustZone-Enabled Real-Time Systems

Our research demonstrates the existence of side-channel information leaks in TrustZone-enabled real-time systems. Our algorithm can infer the critical tasks’ arrival times and pinpoint when the system switches between regular and secure execution modes. By precisely obtaining such timing information, an adversary could infer the task execution patterns inside the secure system — thus putting the system’s safety, security, and integrity at risk. Considering that secure enclaves such as TrustZone are used for executing security-critical functionalities, our findings will help designers be aware of side-channel vulnerabilities and assist them in designing better, leakage-proof systems.
Twitter Bot using { rvest } , { rtweet } and a GitHub Action

The National Strategic Food Price Information Center (PIHPS) was established by the Government of Indonesia and Bank Indonesia,with the aim of providing access to the latest food price information daily for the public and at the same time supporting the formulation of inflation control policies. Officially, this information can be accessed through the website (https://hargapangan.id/) and applications on Android and iOS. There has been a voluntary effort to further disseminate this information via twitter @hargapan***, but this was being done manually and was active only temporarily. panganBot retrieves data from http://hargapangan.id using {rvest}, processes it, and publishes it to the Twitter account @panganBot using {rtweet}. This is done automatically via GitHub Action.
Improved Object Detection Method for Unmanned Surface Vehicle Using Real-Time Neural Networks

Real-time and accurate object detection is a critical (b) prerequisite for Unmanned Surface Vehicles(USVs) to perform intelligent tasks based on images or videos. While the maritime environment always encountered various extreme scenarios, such as rainy or foggy weather, strong lights and far vision, which all seriously harmed the performance of state-of-the-art methods for normal object detection when directly applied them on USV. Therefore, we proposed an improved object detection method for USV based on Yolov4, which focus on repairing the performance loss caused by the unfavorable factors under maritime environment. Firstly, we adjust the default anchor size in ordinary model which helps detect the tiny object from a far vision, as well as the anchor ratio, fitting the shape of ships more to implicitly improve the detection precision. Secondly, we take full advantage of data augmentation to increase the robustness of object detection under extreme brightness. Finally, we enriched our training data with more rainy and foggy images token from different maritime scenes which enhanced model’s ability to detect objects under extreme weather. Extensive experiments demonstrates that proposed improved method effectively achieved real-time and accurate object detection for USV under maritime environment.
Research on Post Earthquake Public Opinion Analysis Based on XLNet-BiGRU-A Algorithm

Because the traditional public opinion sentiment analysis can only be one-way analysis according to the direction of semantic information, it is impossible to fully obtain the semantics of the above and below and extract the deep semantic information in the sentence; and the public opinion comments after the earthquake are different from the general e-commerce, movie and other comments with the characteristics of short sentences and few extractable features. Aiming at the above problems, this paper proposes an emotional trend analysis model that combines XLNet, BiGRU and attention mechanism (XLNetBiGRU-A).The results show that on the self-dataset, the XLNetBiGRU-AT public opinion sentiment trend analysis model has certain advantages, and the accuracy rate is 79.96%. It was increased to 90.42%.