Reviewerly/exHarmony huggingface dataset:
Dual-Channel Convolutional Recurrent Networks for Session-Based Recommendation

Jingjing Wang Lap-Kei Lee Nga-In WuRecommender systems assist a Web application user in satisfying their needs or interests based on the user profile and past activities. Yet due to privacy and other concerns, some applications and services only keep anonymous information. A session-based recommender system (SRS) predicts the next item by exploring only anonymous user-item behavior orders during ongoing sessions. Recurrent neural networks (RNNs) and their two variants have dominated the research on SRS. However, there are two shortcomings in these RNN-based methods: (1) RNNs easily generate false dependencies because RNNs assume all adjacent items are highly dependent on each other; (2) the sequentially connected architecture of RNNs can only capture the point-level dependencies but ignoring neglecting the union-level dependencies. This paper proposes a Dual-channel Convolutional Recurrent Neural Network (D-CRNN) model to address these problems. This hybrid model leverages RNN to explore complex long-term dependencies and combines CNN to extract the union-level context features, which help to reduce the noise. The hybrid model was evaluated on three commonly used real-world datasets. The experimental results on Diginetica dataset D-CRNN showed an improvement of 5.8% and 4.8% respectively in terms of Recall@10 and MRR@10, demonstrating the effectiveness of D-CRNN on the session-based recommendation.
Petrogenesis of Gabbroic Intrusions in the Central Eastern Desert of Egypt: Implications for Neoproterozoic Post-Collisional Magmatism Associated with Najd Fault System

Gabbroic intrusions of El Faliq area belong to Precambrian basement rocks of central Eastern Desert of Egypt as a part of the Arabian-Nubian Shield (ANS). They form fresh, undeformed elliptical body elongated in a NW-SE trend following the main sinistral strike-slip faults of the Najd Fault System. Mineralogical and geochemical inferences suggest that they were derived from hydrous tholeiitic mafic magmas having arc-like geochemical fingerprints resembling post-collisional gabbroic intrusions of Saudi Arabia. Despite the arc-like signatures, the fresh and undeformed nature together with field geology relations indicate that the studied gabbroic intrusions post-date the main collisional phase supporting their emplacement after subduction cease and during post-collisional stage. So, the arc-like fingerprints were most likely inherited from the previous subduction event in the ANS. Indeed, the high (La/Sm)N, negative Nb and positive Pb anomalies, and LILE-enrichment suggest contribution from subduction components. Moreover, super-chondritic Zr/Hf ratios and negative Ti anomalies suggest contribution from carbonatitic liquids. So, it seems that poly-phase metasomatism was involved in the genesis of the gabbroic rocks as do mafic magmatism in central southern Italy.Najd faults and shear zones which formed during post-orogenic crustal extension associated with Pan-African orogenic collapse facilitate lithospheric delamination. The delamination process enables rapidly rising of the asthenosphere mantle and melting due to adiabatic release of pressure. Melt-rock reaction process likely played important role in gabbroic rocks genesis through interaction of asthenosphere melts with lithosphere mantle rocks during ascent. HREE fractionation suggests mixing between melts from both spinel- and garnet-bearing peridotites. We suggest that the melting was triggered by passive upwelling of asthenosphere mantle due to stretching and thinning of the lithosphere during extensional tectonism following the Pan-African Orogeny.
Application of Big Data and Artificial Intelligence in Computer Networks

The information technology represented by big data processing technology and artificial intelligence, it is widely used in computer network, which effectively improves the stability of computer network and the control level of computer network. The application scenarios of major AI industries have transitioned from fragmented to deep integration. The digital transformation of enterprises presents diversified needs. As a “leading enterprise” in the field of intelligent transportation, Hisense takes AI core technology as the engine and creates an algorithm warehouse of AI core technology, including image analysis, knowledge atlas and data integration. In the application of video analysis technology, automotive intelligent devices and intelligent applications are inseparable from AI. The use of big data and artificial intelligence technology can obtain a large amount of information and digest valuable information in the shortest possible time.
SentNet : a system to recognise human sentiments in real time

Detecting sentiments through videos would prove to be an important part as nowadays, the conferences, gatherings are being held online due to global pandemic. Teaching has now turned into e-teaching modes. Even doctors are consulting the patients through online video conferencing. So, automated detection of the facial sentiment of the other person becomes the foremost concern. This paper proposes a real-time sentiment analysis end-to-end system using Raspberry Pi to capture videos that are passed into the proposed model after extracting image frames. The model used in the simulator has been trained using the MobileNet model. A distinguished dataset is used which contains features to analyze sentiments of people belonging to multiple ethnicities. The dataset consists of videos, so for training the deep learning model, frames are extracted from the videos. After that, faces are detected from these frames using two methods namely, Haar cascade(OpenCV) and MTCNN. Three different datasets are discussed: PU dataset, FER2013 cleaned dataset and Mixed (PU + FER13 cleaned) dataset. Model is trained using transfer learning on MobileNet Model and finally, hyperparameter tuning for attaining best accuracy. The final best test accuracy obtained is 74.42%, 97.50% and 88.73% in the first, second and third methods respectively.
PZLAST: an ultra-fast sequence similarity search tool implemented on a MIMD processor

We have developed an ultra-fast sequence similarity search tool named PZLAST on a MIMD processor PEZY-SC2. In this paper, we show the merit of MIMD features in reducing the load imbalance among the threads. They are also effective in the implementation of the alignment for long sequences. Additionally, we point out two problems related to the implementation on an ultra-parallel computation accelerator as follows: (1) Deciding the optimal amount of inputs prior to the run is extremely difficult and usually even impossible, and (2) Keeping up the parallelism efficiently throughout the whole computation is not always possible. A feedback strategy and an accumulation strategy are proposed to overcome these problems and their results are shown to be valuable in reducing the accidental memory overflow in runtime and speeding up the processing time.
Image Relevance on Websites and Readability

The World Wide Web has become a major source of information for acquiring knowledge and information. People hardly have time to sit down and read anything lengthy anymore so graphical contents are sometimes more effective for the readers than just the written text. However, irrelevant graphical contents on the web equally contribute to poor readability, distracting the reader from the focus of the reading. The main objective of this paper is to help web designers and developers to construct better web pages from a readability point of view. We propose a new methodology to measure the relevancy of text images on a webpage based on their similarity with the webpage text. The methodology combines different techniques to extract text from images and read text from web pages in order to find relevancy between them. This approach was used to analyze 50 different educational websites in Pakistan to automatically find the relevancy of their image. Our results indicate that the images which are irrelevant to the context of the page and poor-quality images cause lower relevancy scores. Thanks to this study, web designers can improve the readability of their web pages by modifying the graphical content according to the recommendations done.
MRI Breast Tumor Extraction Using Possibilistic C Means and Classification Using Convolutional Neural Network

Breast cancer is the most leading cancer disease which demolishes many women lives for the past few decades. It can be prevented and reduce the death rate by proper treatment and by diagnosis at early stage. Mammogram and MRI scanning are preferable for analyzing the internal functionality of breast in detail, and in our study, we used MRI images with various sequences like T1-W, T2-W and T1-contrast enhanced for validation. Our approach uses gaussian filters for preprocessing, probabilistic C means for tumor segmentation and convolutional neural network for tumor classification. Our approach yields 98% segmentation accuracy and 95% classification accuracy, and its performance is compared with the existing methods like FCM, FCM-VES, hybrid K-means for segmentation and SVM, KNN and HOS–SVM for classification. We apply various qualitative measures such as entropy, eccentricity, dice coefficient, MSE, PSNR, F-score, sensitivity, specificity and accuracy for validation. For our work, we utilized online dataset like BI RADS and clinical dataset and proved that our approach needs average of 6 s for processing the breast cancer images.
Skin Cancer Classification Systems Using Convolutional Neural Network with Alexnet Architecture

Skin cancer is one of three malignant cancers that are often found in Indonesia and can cause death, apart from cervical cancer and breast cancer. Skin cancer diagnosis done manually by a dermatologist through a biopsy and microscopic process. However, this process takes a long time and has a risk of accidents in the biopsy process. Whereas early diagnosis shows that more than 90% can be cured, while delay in diagnosis shows less than 50% can be cured. For this reason, this research was conducted to design and implement a system that can classify skin cancer in order to facilitate the skin detection process to be faster. This study applied the Convolutional Neural Network (CNN) method using the Alexnet architecture to classify skin cancers into four categories, namely dermatofibromas, melanoma, nevus pigmentosus, and squamous cell carcinoma. Experiments were carried out using a dataset obtained from the International Skin Imaging Collaboration (ISIC) dataset of 4000 images of skin cancer conditions of dermatofibroma, melanoma, nevus pigmentosus, and squamous cell carcinoma, consisting of 1000 images in each class. The amount of training data used is 2700 skin cancer images. While the number of validation data used is 900 images. Then the remaining 400 images were used as test data. The experimental results using training data and validation data show that the best optimizer is Adam and the use of a learning rate of 0.001 produces an accuracy value of 99% and a loss value of 0.0596 on the applied Alexnet architecture. From the Alexnet model, the test data were successfully detected according to their respective classes with the test data accuracy value of 97.5%, loss value of 0.1272 and the precision, recall and f1-score value around 0.96–0.99. Based on the results of the system performance, it shows that the applied model promises to be an early detection tool for skin cancer by dermatologists so that it can reduce the dangerous effect due to the delay in diagnosing skin cancer.
Self-distilled Self-supervised Depth Estimation in Monocular Videos

In this work, we investigate approaches to leverage self-distillation via predictions consistency on self-supervised monocular depth estimation models. Since per-pixel depth predictions are not equally accurate, we propose a mechanism to filter out unreliable predictions. Moreover, we study representative strategies to enforce consistency between predictions. Our results show that choosing proper filtering and consistency enforcement approaches are key to obtain larger improvements on monocular depth estimation. Our method achieves competitive performance on the KITTI benchmark.
Formalization and Processing of Data Requirements for the Development of Next Generation Railway Traffic Management Systems

Railway Traffic Management Systems (TMSs) handle data from multiple railway subsystems, including Rail Business Services (such as interlocking, RBC, maintenance service, etc.) and external services (such as passenger information systems, weather forecast, etc.). In turn, the data from these subsystems are described in several models or ontologies contributed by various organizations or projects which are in a process of converging or federation. The challenge of the Shift2Rail OPTIMA project, which is implementing a communication platform for virtual testing of new applications for railway TMS, is to allow the exchange of data between different services or users and to support new traffic management applications, enabling access to a large number of disparate data sources. In this paper, the core activities of the OPTIMA project related to the formulation and standardization of a common data model are described. A new Common Data Model is developed based on standardized data structures to enable the seamless exchange of large amounts of data between different and heterogeneous sources and consumers of data, that contributes to the building of next generation of a more effective and efficient railway TMS suitable to offer precise and real-time traffic information to railway operators and other end users.
One Time Password-Based Two Channel Authentication Mechanism Using Blockchain

Using Fog Nodes, also known as IOT devices are increasing everyday with more and more home automation, industry automation, automobile automation, etc. Security threats for these devices are also increasing. One of the threats is impersonating one fog node, stealing data and taking control of the network which is also known as the Sybil attack. To provide security, most fog devices use one step or two step authentication and sometimes use encryption. With static passwords, there is a chance of compromise by password sharing and leaking. Some weak encryption algorithms used are also compromised. Data about fog nodes in the network is stored in a weak database and is tampered. OTP-based Two Channel Authentication Mechanism (OTPTAM) to authenticate the fog nodes with metadata stored in Blockchain Database and communicate using channels encrypted with Elliptical Ciphers can solve the majority of these problems. Metadata of the nodes like Bluetooth MAC address, network mac address, telephone number are all stored in the blockchain and the OTP is exchanged via these channels to ensure the authenticity of the fog nodes.
A Human in Every APE: Delineating and Evaluating the Human Analysis Systems of Anti-Phishing Entities

We conducted a large-scale evaluation of some popular Anti-Phishing Entities (APEs). As part of this, we submitted arrays of CAPTCHA challenge-laden honey sites to 7 APEs. An analysis of the “click-through rates” during the visits from the APEs showed strong evidence for the presence of formidable human analysis systems in conjunction with automated crawler systems. In summary, we estimate that as many as 10% to 24% of URLs submitted to each of 4 APEs (Google Safe Browsing, Microsoft SmartScreen, Bitdefender and Netcraft) were likely visited by human analysts. In contrast to prior works, these measurements present a very optimistic picture for web security as, for the first time, they show presence of expansive human analysis systems to tackle suspicious URLs that might otherwise be challenging for automated crawlers to analyze. This finding allowed us an opportunity to conduct the first systematic study of the robustness of the human analysis systems of APEs which revealed some glaring weaknesses in them. We saw that all the APEs we studied fall prey to issues such as lack of geolocation and client device diversity exposing their human systems to targeted evasive attacks. Apart from this, we also found a specific weakness across the entire APE ecosystem that enables creation of long-lasting phishing pages targeted exclusively against Android/Chrome devices by capitalizing on discrepancies in web sensor API outputs. We demonstrate this with the help of 10 artificial phishing sites that survived indefinitely despite repeated reporting to all APEs. We suggest mitigations for all these issues. We also conduct an elaborate disclosure process with all affected APEs in an attempt to persuade them to pursue these mitigations.
Performance of Explainable AI Methods in Asset Failure Prediction

Extensive research on machine learning models, which in the majority are black-boxes, created a great need for the development of Explainable Artificial Intelligence (XAI) methods. Complex machine learning (ML) models usually require an external explanation method to understand their decisions. The interpretation of the model predictions are crucial in many fields, i.e., predictive maintenance, where it is not only required to evaluate the state of an asset, but also to determine the root causes of the potential failure. In this work, we present a comparison of state-of-the-art ML models and XAI methods, which we used for the prediction of the RUL of aircraft turbofan engines. We trained five different models on the C-MAPSS dataset and used SHAP and LIME to assign numerical importance to the features. We have compared the results of explanations using stability and consistency metrics and evaluated the explanations qualitatively by visual inspection. The obtained results indicate that SHAP method outperforms other methods in the fidelity of explanations. We observe that there exist substantial differences in the explanations depending on the selection of a model and XAI method, thus we find a need for further research in XAI field.
Use of Expert Information Systems in Regional Monitoring Research (Experience of Don State Technical University)

Purpose: the purpose of this research is the analysis of the creation and experience of the application of the first information system in the Russian Federation, combining sociological, health, and socio-psychological indicators of the regional general education system in a single information environment. Methodology: The methodological apparatus of the information system ensures the receipt of a monitoring result—solving the current tasks of expert control of the health protection activities of educational organizations in the region while relying on the socio-psychological indicators of students. Results: Algorithms of using expert information systems in solving managerial problems of professional field by means of digital technologies have been obtained. Originality: Opportunities to expand the scope of monitored indicators in accordance with the requests and current tasks of the professional sphere are presented. Prospects for the use of expert information systems for a wider range of management tasks are formulated.
Sentence-level Sentiment Analysis Using GCN on Contextualized Word Representations

Sentiments expressed in opinions on social networks have played an increasingly significant impact in solving various social problems. Improving the effectiveness of sentiment analysis methods on social networks is still of interest to several scientists. A notable and robust development direction is sentiment analysis methods based on graph convolutional networks (GCNs). This paper introduces a model called contextual-based GCN for sentence-level sentiment analysis by considering the following steps: (i) Sentences are converted into contextualized word representation vectors based on the combination of the bidirectional encoder representations from the transformer model and bidirectional long short-term memory. (ii) The contextualized word representations are used to construct a sentence graph as a feature of nodes. (iii) A GCN model with two convolutional layers was used to learn the structure-aware node representations on the sentence graph. (iv) The softmax classifier was used for the sentence-level sentiment analysis. Experimental results on benchmark datasets showed that, unlike other methods, the proposed method can extract more context information from the opinions to obtain a better representation of the graph structure and learn better structure-aware nodes represented on the graph. The proposed method has improved the performance in terms of accuracy of the conventional methods from 2.2 to 3.2% points.
South Africa’s Taxi Industry as a Cybersecurity-Awareness Game Changer: Why and How?

The South African economy is not only the second largest in Africa, but it is also the most diversified, industrialized and technologically advanced on the continent. This technological advance is evidenced by its citizenry’s comparatively high-level of digital connectivity and interconnectedness. On the reverse side, South Africa (SA) has the third highest number of cybercrime victims internationally. Maintaining and expanding its competitive technological advantage requires of South Africa to have a robust national cybersecurity endeavour. This needs to include an innovative, high-impact cybersecurity awareness campaign that effectively reaches a diverse population. This paper’s primary aim is to propose such a high-impact drive, namely a broad-based national cybersecurity awareness campaign that levers the South African minibus taxi industry. The paper’s three objectives pertain to the ‘why’ and ‘how’ of such a campaign. The paper’s first objective is to substantiate the need for a broad-based cybersecurity awareness campaign (in short: why is it needed?). The second objective is to substantiate why the taxi industry constitutes an optimal platform for a game changing campaign. Thirdly, the paper advances a proposition on a Taxi Industry Cybersecurity Awareness Campaign (TICAC) (i.e. how can the taxi industry be a game changer?). We qualify the TICAC as a tentative, high-level conceptual proposition subject to much further research on the theoretical/academic and practical levels. It is hoped that the paper would be of value to also other countries – developed and developing – in utilising private and/or public transport industries as platforms for cybersecurity awareness initiatives.
The Threat of Quantum Computing to SMEs

Small and medium-sized enterprises (SMEs) are an important economic factor. Nevertheless, they are less well-prepared for information security threats than larger companies due to various factors. The severity of the threat may also depend on the industry. Currently, quantum computers are under development and are not yet ready for widespread practical use, but they can already show significant performance improvements over classical computers in specialized fields. Their particular capabilities allow them to accomplish specialized tasks (such as decrypting certain previously secure encryption methods). Therefore, information should already be cryptographically prepared for the use of quantum computing. If we transfer the findings of SMEs being ill-prepared for IT threats to the potential threat to information security posed by the code breaking capabilities of quantum computers, the need for action in this area becomes clear. In particular, the post-quantum cryptography movement can be a solution for SMEs in this regard. Since many SMEs are not yet aware of this threat, it is important to raise their awareness. Based on these findings, further research questions arise that could be answered in future studies.