Reviewerly/exHarmony huggingface dataset:
Text Classification of News Using Transformer-based Models for Portuguese

This work proposes the use of a fine-tuned Transformers-based Natural Language Processing (NLP) model called BERTimbau to generate the word embeddings from texts published in a Brazilian newspaper, to create a robust NLP model to classify news in Portuguese, a task that is costly for humans to perform for big amounts of data. To assess this approach, besides the generation of embeddings by the fine-tuned BERTimbau, a comparative analysis was conducted using the Word2Vec technique. The first step of the work was to rearrange news from nineteen to ten categories to reduce the existence of class imbalance in the corpus, using the K-means and TF-IDF techniques. In the Word2Vec step, the CBOW and Skip-gram architectures were applied. In BERTimbau and Word2Vec steps, the Doc2Vec method was used to represent each news as a unique embedding, generating a document embedding for each news. Metrics accuracy, weighted accuracy, precision, recall, F1-Score, AUC ROC and AUC PRC were applied to evaluate the results. It was noticed that the fine-tuned BERTimbau captured distinctions in the texts of the different categories, showing that the classification model based on this model has a superior performance than the other explored techniques.
A Study of Users' Privacy Preferences for Data Sharing on Symptoms-Tracking/Health App

Symptoms-tracking applications allow crowdsensing of health and location related data from individuals to track the spread and outbreaks of infectious diseases. During the COVID-19 pandemic, for the first time in history, these apps were widely adopted across the world to combat the pandemic. However, due to the sensitive nature of the data collected by these apps, serious privacy concerns were raised and apps were critiqued for their insufficient privacy safeguards. The Covid Nearby project was launched to develop a privacy-focused symptoms-tracking app and to understand the privacy preferences of users in health emergencies.
Time‐varying learning rate for recurrent neural networks to solve linear equations

Consider there exist many time‐varying factors during the recurrent neural networks training in piratical application engineering. In this paper, different from the time‐invariant case, a time‐varying learning rate is presented to accelerate the convergence speed for the recurrent neural networks (RNNs) when used to solve the linear simultaneous equations. Theoretical analysis shows that the neural networks with the time‐varying learning rate can globally converge to the theoretical solution; moreover, if the linear activation function is used, the model will be exponential convergent to the theoretical solution, and if the sign‐bi‐power activation function is used, the state solution by the neural networks will be convergent in finite time. Computer simulation results further substantiate that the time‐varying learning rate can be used for accelerating the convergence rate of the RNN models.
Human-Machine Convergence and Disruption of Socio-Cognitive Capabilities

People who have access to smartphone or computers are already using AI. A complex set of algorithm works in search engines, social networking sites, voice recognition applications, navigation applications, OTT platforms and video games. Human-machine convergence has been made possible because of the science and technology. Artificial Intelligence and Machine Learning are the contemporary technologies which is being used in several sectors for better precisions like medical diagnostics, remote sensing etc. There is a tremendous amount of potential for AI and ML to help individuals develop their mental and interpersonal skills. Since quite some time, researchers have been looking at what effect, if any, artificial intelligence may have on the decision-making process of humans. In the same way that it is used in the decision-making processes of the scientific and corporate communities, artificial intelligence is also being employed in the legal and gambling industries. Software and algorithms based on artificial intelligence (AI) have the potential to simulate human behaviour and predict its effects. Many people are of the opinion that artificial intelligence will improve the quality of human judgement and decision making, particularly in high-stakes circumstances like war. Investigating the structural, relational, and cognitive bases of artificial intelligence's function as a facilitator of decision-making is the goal of this line of study.&#x0D;
Intelligent Course Recommender Chatbot Using Natural Language Processing

Selecting elective courses in university is challenging for students as they do not know if the courses fit their interests and provide relevant knowledge or skills for their future professions. In some cases, students may register for courses without truly understanding the courses, eventually leading to course selection mistakes. A system that can recommend courses based on student's preferences is deemed necessary to address this problem. This paper proposed an intelligent course recommender system that helps students find suitable courses based on their strengths and interests. It consists of two phases. First, an intelligent course matching engine is designed and developed. The student's input is processed using natural language processing. A convolutional neural network is used to perform Part-of-Speech tagging. Keywords are identified from the processed input, and keyword matching is performed between the student's input and the courses' keywords. The most relevant courses are identified. Second, a chatbot is developed to implement the developed intelligent course matching engine. The chatbot captured student's preferences using a human-like conversation and recommended the identified most relevant courses to the students. The system is evaluated by a group of students in Universiti Malaysia Sabah. The evaluation of the usability and functionality results shows the acceptability of the proposed system, although some future work is needed based on the feedback received.
Consistency-preserving propagation for SMT solving of concurrent program verification

The happens-before orders have been widely adopted to model thread interleaving behaviors of concurrent programs. A dedicated ordering theory solver, usually composed of theory propagation, consistency checking, and conflict clause generation, plays a central role in concurrent program verification. We propose a novel preventive reasoning approach that automatically preserves the ordering consistency and makes consistency checking and conflict clause generation omissible. We implement our approach in a prototype tool and conduct experiments on credible benchmarks; results reveal a significant improvement over existing state-of-the-art concurrent program verifiers.
Identification of Political Hate Speech Using Machine Learning-Based Text Toxicity Analysis

The maintenance of civility during political campaigns has taken a huge blow during recent times with ever increasing toxicity in speeches targeting polarization of people for electoral gains. This is however creating a big divide in the society by the proliferation of cyber-hate speech, which is threatening the integrity and harmony of societies. The term “hate speech” refers to the use of words or phrases that are threatening, derogatory, or insulting to a specific individual or group. Users of social media in India are increasing rapidly, and this is coupled with an increase in the frequency with which cyber-hate speech targets specific segments of society or individuals based on their caste, color, or creed. An online environment free of hostility and bigotry has remained a key focus area of academics’ attention. In the present study, some contemporary political data from the Twitter platform is being used to identify hate speech with the help of machine training and learning methods including the text-based natural language processing (NLP). To achieve the identified goal, several political tweets across different ideologies have been taken into consideration. There are many different ways to collect and organize emotions and personality traits. Analysis of the processed dataset has been done using an ensemble of popular machine learning algorithms, and the results indicate the comparative performance of the methods.
Machine Recognition of Map Point Symbols Based on YOLOv3 and Automatic Configuration Associated with POI

This study is oriented towards machine autonomous mapping and the need to improve the efficiency of map point symbol recognition and configuration. Therefore, an intelligent recognition method for point symbols was developed using the You Only Look Once Version 3 (YOLOv3) algorithm along with the Convolutional Block Attention Module (CBAM). Then, the recognition results of point symbols were associated with the point of interest (POI) to achieve automatic configuration. To quantitatively analyze the recognition effectiveness of this study algorithm and the comparison algorithm for map point symbols, the recall, precision and mean average precision (mAP) were employed as evaluation metrics. The experimental results indicate that the recognition efficiency of point symbols is enhanced compared to the original YOLOv3 algorithm, and that the mAP is increased by 0.55%. Compared to the Single Shot MultiBox Detector (SSD) algorithm and Faster Region-based Convolutional Neural Network (Faster RCNN) algorithm, the precision, recall rate, and mAP all performed well, achieving 97.06%, 99.72% and 99.50%, respectively. On this basis, the recognized point symbols are associated with POI, and the coordinate of point symbols are assigned through keyword matching and enrich their attribute information. This enables automatic configuration of point symbols and achieves a relatively good effect of map configuration.
Protego: Efficient, Revocable and Auditable Anonymous Credentials with Applications to Hyperledger Fabric

Recent works to improve privacy in permissioned blockchains like Hyperledger Fabric rely on Idemix, the only anonymous credential system that has been integrated to date. The current Idemix implementation in Hyperledger Fabric (v2.4) only supports a fixed set of attributes; it does not support revocation features, nor does it support anonymous endorsement of transactions (in Fabric, transactions need to be approved by a subset of peers before consensus). A prototype Idemix extension by Bogatov et al. (CANS, 2021) was proposed to include revocation, auditability, and to gain privacy for users. In this work, we explore how to gain efficiency, functionality, and further privacy, departing from recent works on anonymous credentials based on Structure-Preserving Signatures on Equivalence Classes. As a result, we extend previous works to build a new anonymous credential scheme called Protego. We also present a variant of it (Protego Duo) based on a different approach to hiding the identity of an issuer during showings. We also discuss how both can be integrated into Hyperledger Fabric and provide a prototype implementation. Finally, our results show that Protego and Protego Duo are at least twice as fast as state-of-the-art approaches based on Idemix.
Adversarial supervised contrastive learning

Contrastive learning is prevalently used in pre-training deep models, followed with fine-tuning in downstream tasks for better performance or faster training. However, pre-trained models from contrastive learning are barely robust against adversarial examples in downstream tasks since the representations learned by self-supervision may lack the robustness and also the class-wise discrimination. To tackle the above problems, we adapt the contrastive learning scheme to adversarial examples for robustness enhancement, and also extend the self-supervised contrastive approach to the supervised setting for the ability to discriminate on classes. Equipped with our new designs, we proposed adversarial supervised contrastive learning (ASCL), a novel framework for robust pre-training. Despite its simplicity, extensive experiments show that ASCL achieves significant margins in adversarial robustness over the prior arts, proceeding towards either the lightweight standard fine-tuning or adversarial fine-tuning. Moreover, ASCL also shows benefits for robustness to diverse natural corruptions, suggesting the wide applicability to all sorts of practical scenarios. Notably, ASCL demonstrate impressive results in robust transfer learning.
Security Revisited: Honeytokens meet Google Authenticator

In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security.
Applying Web Crawler Technologies for Compiling Parallel Corpora as one Stage of Natural Language Processing

over the past decade, the amount of information on the internet has increased. A large amount of unstructured data, referred to as big data on the web, has been created. Finding and extracting data on the internet is called information retrieval. In the search for information, there are web crawler tools, which are a program that scans information on the internet and downloads web documents automatically. Search robot applications can be used in various fields, such as news, finance, medicine, etc. In this article, we will discuss the basic principle and characteristics of search engines as an example to build parallel corpora, as well as the classification of modern popular crawlers, strategies and current applications of crawlers. Finally, we will end this article with a discussion of future directions for research on crawlers.
A V2X Reputation System with Privacy Considerations

Inter-vehicle communications can enable a wide array of novel applications that improve transportation safety and efficiency. However, due to the adversarial environment in which vehicles may operate, it is important to ensure information integrity. To achieve this, we present a blockchain-based reputation mechanism that allows participants to assess the trustworthiness of the received data based on the existing reputation score of the sender. After each data exchange, the receiver rates the sender by uploading an evaluation to the blockchain. To ensure that no tracking of the participants is possible, despite the open nature of the blockchain, we propose an address rotation scheme based on Zero Knowledge Proofs. Vehicles have the capability to change their blockchain address at regular intervals, while at the same time keeping their reputation score. Finally, we conduct experiments and simulations to evaluate our proposed system. The obtained results show that information integrity is ensured even in the presence of large number of adversaries and that privacy protection can be achieved at a reasonable cost.
A Model-Checking Framework for the Verification of Move Smart Contracts

As the popularity of distributed ledger technology and smart contracts continues to grow, so does the number of decentralized applications and their potential exposure to expensive exploits. The need for strong vulnerability detection tools is critical. Move is a recently developed smart contract language with safety and security at the core of its design containing formal verification tools embedded into the language. Currently, these tools can only verify local properties within a single Move function. They cannot verify global properties that result from multiple function executions. In this paper, we introduce VeriMove, an extension of the VeriSolid correct-by-design model checking framework that supports the Move language. We show that model checking is a feasible method to formally verify global properties in Move smart contracts.
Real-time sub-pixel binocular ranging based on FPGA

Binocular stereo vision is an essential subject of computer vision research with numerous implications in 3D reconstruction. Binocular stereo vision algorithms, however, are computationally demanding and require a high processing units capability, Some conventional systems cannot meet the requirements of real-time and precision. To solve these issues, we employ a software-hardware co-design approach based on FPGA (Field Programmable Gate Arrays). According to the peculiarities of binocular stereo vision, We separate the system tasks into software and hardware portions that can fully utilize FPGA hardware acceleration and ARM (Advanced RISC Machine) data management. The parallel processing capabilities of FPGAs enable a comprehensive pipeline design synchronized with the same system clock to synchronously run multiple image processing components, considerably enhancing processing performance. This paper propose an improved image processing system that realizes binocular ranging through center point matching, We evaluated the design on the AXU3EG development board. The experimental results show that the system has high real-time performance. The system features a faster image processing rate and less expensive hardware than the conventional stereo matching ranging approach, and the ranging precision may be down to the sub-pixel level.
Patient Full Control over Secured Medical Records Transfer Framework Based on Blockchain

Covid-19 outbreak represents an exceptional test of the flexibility and the efficiency of patient medical records transfer among healthcare providers which ended up in boundless interruption to the healthcare industry. This public crisis has pushed for an urgent innovation of the patient medical records transference (PMRT) system to meet the needs and provide appropriate patient care. Moreover, the drawback effects of Covid-19 changed the healthcare system forever, more patients are requesting more control, secure, and smoother experience when they want access to their health records. However, the problems stem from the lack of interoperability among the healthcare system and providers and the added burden of cyber-attacks on an already stressed system call for an immediate solution. In this work, we present a secured blockchain framework that ensures patients full ownership over their medical data which can be stored in their private IPFS and later can be shared with an authorized provider. The analysis of the proposed security and privacy aspects shows promising results in providing time savings and resulted in enhanced confidentiality and less disruption in patient-provider interactions.
Eight Golden Rules Interface Analysis for Video Conference Information System

Shneiderman’s eight golden rules are leveraged in this research to analyze the user interface of the video conference information system. This system environment facilitates teachers’ and students’ online learning for Vocational High School graduates at Padang Panjang, Indonesia (SMKN 2 graduates). The backbone of this research is the eight golden rules with an experiment to determine whether a system’s user interface follows the user’s wishes (user-friendly). The population in this research was teachers and students, with a sample of 50 people. Analysis and discussion are accomplished by broadcasting questionnaires to the population. Based on the results, we derive a consistency value of a variable of 145,4. In short, the results of this study clarify that the video conference information system that we developed reaches the main objective by accomplishing a user-friendly interface.
Building 360-degree VR Video for AquaFlux and Epsilon Research Instruments

The Virtual Reality (VR) technology is being utilised immensely in recent years for the purposes of demonstration and illustration a variety of prototypes and products especially on the world wide web platform. AquaFlux and Epsilon are two advanced research instruments that were developed by a research group at London South Bank University for medical and cosmetic purposes. These two medical tools have now been marketed in more than 200 organisations globally. However, for users to master and understand comprehensively how these two devices operate they often do require an intensive on-site training which is time-consuming, costly and in some cases like the current era of COVID-19 it becomes essential to offer clients the option of remote learning whether they were nationally or internationally based. Thus, the design of a VR environment that can simulate and envision all features and functionalities of AquaFlux and Epsilon is one of the ultimate solutions. 360-degree VR videos are going to empower users with the required knowledge and provide a broad essential feeling of how AquaFlux and Epsilon instruments function.
City AR: Augmented reality navigation in the smart cities infrastructure

Smart cities are considered to satisfy the main sustainable growth purposes such as decreased poverty, increased safety, and provision of effective governmental services. At the same time, the proposed solution adds benefits by increasing the efficiency of many processes and allowing the management of processes automatically. The solutions in particular start to play an important role when it comes to creating cues about the objects, and their actions and movements inside the complex spaces. Such objects would become especially relevant by allowing a mutual interaction with the smart infrastructure and increasing the speed of the completion of the task, (i.e. quickly find an object and obtain a prompt recognition for the information about the space). In this paper, we propose an augmented reality navigation interface, whereby we find the right destination and obtain important data about objects in the environments as well as servers displaying correct actions for the implementation of tasks in the complex systems. This works only with help of the mobile phone camera and abstains from the need to use any other special equipment or GPS. That makes the solution scalable and user-friendly. The efficiency of the AR navigation interface was proven, allowing people to find their destination up to 263% faster in comparison to the conventional way, depending on the complexity of the route. The proposed solution increases sales and financial parameters in the complex environments such as the airports, forums, exhibitions, museums, malls, etc.
A Survey On Image Captioning

Image captioning is a challenging task and attracted a lot of research which is ongoing in the field of computer vision. In image captioning, we use natural language processing along with computer vision to produce the captions. Majority of the papers reviewed for this survey paper use the encoder and decoder framework, but there are lot of other techniques, like supervised and unsupervised image captioning. There is also a technique called scene graph alignment Which is used for unsupervised captioning. Some authors simply reconstructed older procedures rather than using the new ones in order to produce better results. The majority of publications utilised RNN or LSTM as the encoder and CNN as the decoder. Many centred graphs were also employed by authors to rebuild several sentences from it. Another strategy is known as the actor and critic model, in which the actor performs some task and the critic offers criticism to help the results.