Reviewerly/exHarmony huggingface dataset:
Social Media Sentiment Classification for Tunisian Dialect: A Deep Learning Approach

Social media becomes nowadays a valuable resource for posting and expressing opinions about services or products. Sentiments about a product or a service offered to companies are too valuable in this era than ever before. Indeed, knowing whether their customers are expressing positive, neutral or negative sentiments toward their products can be of a high importance for these companies. Hence, extracting the sentiments from comments (or reviews) in Tunisian dialect and written with Tunisian Arabizi script is a challenge compared to other languages. Although, we are getting better with each passing year, but it is still a long way to go to handle Tunisian dialect. In this paper, we focus on sentiment analysis of Tunisian dialect comments posted on social media. More precisely, we propose an interesting deep learning approach for sentiment classification. Thus, we used three corpora, experimented and evaluated four deep learning models: CNN, LSTM, Bi-LSTM and GRU. Our deep neural networks have achieved good results for Tunisian dialect sentiment classification. Best results are obtained based on GRU model.
Revocable Hierarchical Attribute-Based Signatures from Lattices

Attribute-based Signatures (ABS) allow users to obtain attributes from issuing authorities, and sign messages whilst simultaneously proving compliance of their attributes with a verification policy. ABS demands that both the signer and the set of attributes used to satisfy a policy remain hidden to the verifier. Hierarchical ABS (HABS) supporting roots of trust and delegation were recently proposed to alleviate scalability issues in centralised ABS schemes. An important yet challenging property for privacy-preserving ABS is revocation, which may be applied to signers or some of the attributes they possess. Existing ABS schemes lack efficient revocation of either signers or their attributes, relying on generic costly proofs. Moreover, in HABS there is a further need to support revocation of authorities on the delegation paths, which is not provided by existing HABS constructions. This paper proposes a direct HABS scheme with a Verifier-Local Revocation (VLR) property. We extend the original HABS security model to address revocation and develop a new attribute delegation technique with appropriate VLR mechanism for HABS, which also implies the first ABS scheme to support VLR. Moreover, our scheme supports inner-product signing policies, offering a wider class of attribute relations than previous HABS schemes, and is the first to be based on lattices, which are thought to offer post-quantum security.
Semantically Driven Machine Learning-Infused Approach for Tracing Evolution on Software Requirements

Software engineering forms a well-structured application of engineering methodologies for software development. It forms a branch under engineering discipline which is associated with all the facets of software production. The entire information regarding a particular software is documented and stored in configuration management repositories of a particular organization in the form of software requirement specifications (SRSs). Information about several such software requirements, software paradigms, etc., are then tweeted on developer’s Twitter handles, especially during the release of particular software. Through this paper, a novel approach has been put forth for the recommendation of tweets adhering to the developer’s community and SRS documents to the users in correspondence to their queries. Various machine learning and Web mining techniques have been explored and implemented for tweets classification, grouping, ranking, and prioritization. The results obtained advocate for the proposed methodology to be classified as a highly effective approach.
Experimental Evaluation of e.MMC Data Recovery

In this paper, we explore the data recovery procedures from e∙MMCs. The e∙MMC is one of the “managed” flash memory devices that are popularly used in modern digital devices as their storage media. The e∙MMC, which consists of flash memory and the flash memory controller, optimizes the data input/output between the host device and the non-volatile memory through its standardized protocol. Its standardized structure and protocol makes forensic physical data acquisition simpler than handling the raw flash memory. However, its secure data purging features, such as Secure Erase and Sanitize, make data recovery from e∙MMC a challenging task. In this research, we investigate inside the e∙MMCs, and evaluate advanced data recovery procedures. By reverse engineering the structures of e∙MMCs and accessing the internal flash memory, we discover that securely erased data is still recoverable from the internal flash memory. In some models, more than 99% of the securely erased data can still be recoverable by accessing the flash memory inside the e∙MMCs. The data extraction method, along with experimental data recovery evaluation, will be explored in this paper.
Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation

Abstract Although deep neural networks (DNNs) are high-performance methods for various complex tasks, e.g., environment perception in automated vehicles (AVs), they are vulnerable to adversarial perturbations. Recent works have proven the existence of universal adversarial perturbations (UAPs), which, when added to most images, destroy the output of the respective perception function. Existing attack methods often show a low success rate when attacking target models which are different from the one that the attack was optimized on. To address such weak transferability, we propose a novel learning criterion by combining a low-level feature loss, addressing the similarity of feature representations in the first layer of various model architectures, with a cross-entropy loss. Experimental results on ImageNet and Cityscapes datasets show that our method effectively generates universal adversarial perturbations achieving state-of-the-art fooling rates across different models, tasks, and datasets. Due to their effectiveness, we propose the use of such novel generated UAPs in robustness evaluation of DNN-based environment perception functions for AVs.
The Potential Role of Fintech and Digital Currency for Islamic Green Financing: Toward an Integrated Model

The role of FinTech and digital currency on green financing has gained significant attention in recent years. With the assistance of FinTech and digital currency, investors are redirecting their investment portfolio into economic activities that bring good balance between economic, environmental, and social objectives. This is to promote the well-being of humans and to mitigate climate change, biodiversity loss, and inequality. Indeed, FinTech and digital currency are touted as game-changers that help to innovate new investment instruments related to green-based projects and close the financing gap. While progress has been made in green financing, the applications of FinTech and digital currency in the context of Islamic green financing are scarce. Currently, traditional methods are used to raise funds for green-based projects or activities, thus creating a financing gap between issuers and investors. Although many issuers promote and facilitate flexibility and financial innovation, many investors lack the depth of using the power of FinTech and digital currency to harness their potential. In fact, the literature remains silent on this important gap. There is an absence of an integrated model, which could enhance the role of FinTech and digital currency in Islamic green financing. The novelty of this study lies in setting a new research direction for integrated FinTech and digital currency models that can be instrumental in further enhancing and optimizing the efficiency of Islamic green financing.
Spintronic Eyeblink Gesture Sensor With Wearable Interface System

This work presents an eyeblink system that detects magnets placed on the eyelid via integrated magnetic sensors and an analogue circuit on an eyewear frame (without a glass lens). The eyelid magnets were detected using tunnelling magnetoresistance (TMR) bridge sensors with a sensitivity of 14 mV/V/Oe and were positioned centre-right and centre-left of the eyewear frame. Each eye side has a single TMR sensor wired to a single circuit, where the signal was filtered (<0.5 Hz and >30 Hz) and amplified to detect the weak magnetic field produced by the 3-millimetre (mm) diameter and 0.5 mm thickness N42 Neodymium magnets attached to a medical tape strip, for the adult-age demographic. Each eyeblink was repeated by a trigger command (right eyeblink) followed by the appropriate command, right, left or both eyeblinks. The eyeblink gesture system has shown repeatability, resulting in blinking classification based on the analogue signal amplitude threshold. As a result, the signal can be scaled and classified as well as, integrated with a Bluetooth module in real-time. This will enable end-users to connect to various other Bluetooth enabled devices for wireless assistive technologies. The eyeblink system was tested by 14 participants via a stimuli-based game. Within an average time of 185-seconds, the system demonstrated a group mean accuracy of 72% for 40 commands. Moreover, the maximum information transfer rate (ITR) of the participants was 35.95 Bits per minute.
Small Traffic Sign Detector in Real-time Based on Improved YOLO-v4

As one of the indispensable parts of the smart transportation system, traffic sign detection is still an open challenge for the reason that traffic sign detection, one kind of small object detection, requires high precision, high reliability, and real-time speed. In this paper, a real-time detector for small traffic sign improved from YOLO-v4 is proposed. First, a transpose convolution layer is added into the backbone of YOLO-v4, named CSPDark-53, to get larger feature maps with richer semantic information of small objects. Secondly, aiming at fusing multi-scale feature maps more effectively, we present two novel attention modules, the upsampling attention module and the downsampling attention module, to calculate the channel weights and spatial masks to help the network focus on useful features and suppress invalid features. The experiments show our detector outperforms the compared models in Chinese Traffic sign dataset TT100k.
Cognitive Driving Data Visualization and Driving Style Transfer

This study proposes a visualization method for imaging cognitive driving data, which records data related to driving operations and drivers&#x2019; cognitive states. The visualization yielded an easy understanding of the characteristics of the driving operation and cognitive states. This allows intuitive comparison using images. Some differences between novice and experienced drivers can be visually embossed. Furthermore, the image style transfer algorithm using deep learning can be adopted for driving style transfer by representing the driving data as an image. Therefore, the image of an experienced driver can be used as a style image, and that of a novice driver can be modified by the style of an experienced driver. In this study, some trials of driving style transfer were attempted. Consequently, the driving style of a novice driver can be modified to clear and smooth operations like those of an experienced driver. As for the cognitive state, although a novice driver has always felt stressed up, the style of relaxed driving based on road conditions can be applied.
Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data-driven deep learning and hand-crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi-scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state-of-the-art underwater image enhancement methods.
A knowledge graph completion model integrating entity description and network structure

Purpose In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets. Design/methodology/approach The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model. Findings The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task. Originality/value The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.
Mediation Models Predicting the Level of Digital Competence of 12-14 Year Old Schoolchildren in the Area of Digital Problem Solving

Technology has become invaluable and digital competence has turned into a necessity for students. The research presented here aims to propose mediation models that explain the influence of attitudinal, technology use, and family factors on the level of digital problem-solving skills of compulsory education students (12-14 years old). A quantitative methodology with a cross-sectional design was used. We worked with a sample of 772 students from 18 Spanish educational centres according to a stratified random sampling method. For data collection, an objective assessment test was used for knowledge and skills, and a Likert scale for attitudes. Regression analyses were carried out by creating theoretical reference models based on the bootstrapping technique. The results showed that students who expressed a favourable attitude towards digital problem solving demonstrated a better level of competence, which was also positively influenced by the possession of technological devices and the frequency of their use for school and non-school activities at home.
Discovering hidden patterns among medicines prescribed to patients using Association Rule Mining Technique

Hospital pharmacies are experiencing significant rise in demand for medicines for treatment and care of patients admitted to hospitals. Nevertheless, inefficient processes continue to be a problem in hospital pharmacy management, resulting in mismatch between demand and supply, time-consuming manual tasks, out-of-stock situations, medicine expiration, and excessive operating costs. Prior research has addressed various aspects of hospital pharmacy management, but limited studies have focused on discovering hidden patterns and knowledge from large volume of data stored in hospital information systems, which can help improve pharmacy operations. Primary goal of this research is to extract meaningful knowledge from hospital databases to improve pharmacy operations. Hospital pharmacy database from multispecialty hospital in India is used to examine associations among medicines prescribed to hospitalized patients using Apriori approach, which is one of the algorithms of Association Rule Mining. Data comprising 745,176 pharmaceutical item order records were gathered, processed and analyzed. Around sixty-three association rules were discovered. Different measures like support, confidence, and lift are used to evaluate the rules. Rule comprising medicines Atracurium, Febuxostat, and Multivitamins achieved the highest confidence of 87.35%. Discovered rules can help pharmacists to anticipate demand, develop better inventory system, improve operational efficiency and lead to effective healthcare system.
A System for Training Innovative Entrepreneurs with the Use of an Aggregator Platform for the Digital Economy

The paper considers some issues of training innovative entrepreneurs able to work successfully in a rapidly changing environment. The aim of the work is to create a system for training entrepreneurs of a new formation with flexible thinking, able to quickly relearn, able to generate new ideas, work in a team performing various roles—an analyst, an idea generator, a mediator, developing start-ups, new areas of technological entrepreneurship, intra-entrepreneurship using an aggregator platform for geographically distributed students. A model of the educational process has been proposed, algorithms have been developed for the work of the authors of educational and methodological materials, teachers and students within the framework of the educational process. General, structural, functional schemes and technical requirements for the functioning of the platform have been formulated. A method for implementing a scalable educational subsystem using a micro service architecture has been proposed. A tool for the formation of a unified educational and methodological knowledge base, available to all platform participants, has been proposed. A list of functional modules of the self-sufficient educational subsystem of the aggregator platform has been formed. A method has been proposed for optimizing testing of students of the subsystem using AI technologies implemented in the educational subsystem of the aggregator platform. The system allows you to train entrepreneurs-innovators of a wide class. It was tested during master classes and seminars at the international conference “Innovative Entrepreneurship 2021: Digital Expansion”, as well as during classes at the universities of Dubna, Nizhny Novgorod and Kaliningrad.
Additive Technologies for Adaptive Creativity Flexible Express Design in an Exponential Economy

The paper considers the training technology of highly professional specialists that is congruent to the condition and peculiarities of the innovative development of economic sectors. Authors offer a new method of organizing a human-centered educational process, where actual curricula are created directly during the academic semester using the method of additive technologies in education. In equivalent to 3D printing of multicomponent biological or composite products using several delivery systems for various components, we propose to design a learning process for study groups from various sources. Involving specialists in the project learning process from anywhere in the world where the Internet is available allows to ensure the process of obtaining knowledge simultaneously with the process of generating new knowledge, new technical and technological solutions, both according to any pre-selected algorithm, and with variations in the course of training. A special environment of human interaction is created, where the educational process for all participants is transformed into intellectual process of innovations generation. Authors implemented the method of SKW-matrix - a structural code of imagination, which allows to form and capitalize the results of intellectual activity obtained in the process of training. The blockchain technology ensures that the contribution of each of the participants in the formation of the generated intellectual property object is considered with the determination of the coefficient of participation in the created capital. The proposed methodology for specialists training transfers innovation into an exponential growth of the creative economy.
Filecoin

Protocol Labs, the same company that invented and maintains IPFS, created Filecoin. It expands the IPFS concept by establishing an open source decentralized storage network and incentivizes users to keep data pinned to IPFS.
Applied Holistic Mathematical Models for Dynamic Systems (AHMM4DS)

In this article, the author presents the applied holistic mathematical model for the support of dynamic system (AHMM4DS) transformation and integration processes. The AHMM4DS is based on a mixed research method that is supported by a mainly a qualitative research approach, where the main goal is to insure a strategic business competitive advantage. The AHMM4DS uses a natural programming language (NLP) and factors to support a central reasoning engine and a distributed enterprise architecture project's (EAP) concept. This article's proof of concept (PoC) presents the transformation of a dynamic systems, where the central point is the transformation of their services. A DS is managed by a transformation manager, who uses a methodology and a framework that can support and estimate the risks of implementation of a transformation process. Then he uses it to solve various types of problems. The manager is also responsible for the implementation of the DS, and during its implementation phase a transformation framework is needed.