Reviewerly/exHarmony huggingface dataset:
Multi-label Text Classification with Label Correction under Noise

Multi-label text classification (MLTC) is a fundamental but difficult problem in text mining, the goal of MLTC is to assign a set of most relevant labels for the given document. While existing supervised training of deep learning models for MLTC usually requires a large number of noise-free labeled samples, which is quite expensive and time-consuming or even impractical in the real-world as label annotations are inevitable error-prone. To handle such a situation, we introduce learning multi-label text classification with noisy labels, and propose an end-to-end method called Multi-label Text Classification with Label Correction under Noise (LCN). LCN contains two modules: a label correction module and a classification module. In the label correction module, a group of prototypes for each class is learnt with the help of label semantic and feature information. These prototypes are then used to calculate the similarity between the extracted deep features to correct the labels of each training sample. In the classification module, the classifier combines the original noisy labels and corrected labels of each sample as supervised information to guide the training procedure. The two modules are combined in a unified framework and trained in an alternative manner. Extensive experimental results on two multi-label text benchmark datasets validate the effectiveness of LCN and show its advantages to the state-of-art methods.
Image Forgery Detection using Machine Learning with Fusion of Global and Local Thepade's SBTC Features

Image forgery is manipulating digital images to hide or change some useful information contained in the images. Images are considered the most effective way to convey information, and manipulating this information sometimes creates havoc. The action of tampering with images that are done either for fun or to give false evidence has resulted in a disaster in some cases. It is done in such a way that it cannot be determined by the naked human eye, so many people have implemented various types of machine learning algorithms, which they have implemented with handcrafted features to determine different types of forgery and whether an image is forged or not. These algorithms are used to extract the digital signature differentiating whether an image has been tampered with or not. Various techniques have been implemented for either fine or coarse image splicing, whereas a technique dealing with both needs to be devised. For this, our proposed work focuses on different types of machine learning classifiers and 10-fold classification. The attempted values of n for the machine learning classifier include 2,3,4. The different types of classifiers include Random Forest, Random tree, support vector machine, Logistic Regression, Naive Bayes. These classifier models are trained on comofod, casia v2.0 datasets. Accuracy increase is observed when a fusion of Thepade's Sorted Block Truncation Coding (i.e., Thepade's SBTC) local and Thepade's SBTC global feature tables.
Extrinsic Self-Calibration of the Surround-View System: A Weakly Supervised Approach

An SVS usually consists of four wide-angle fisheye cameras mounted around the vehicle to sense the surrounding environment. From the images synchronously captured by all cameras, a top-down surround-view can be synthesized, on the premise that both intrinsics and extrinsics of the cameras have been calibrated. At present, the intrinsic calibration approach is relatively well-developed and can be pipelined, while the extrinsic calibration is still immature. On one hand, the existing manual calibration schemes are usually reliable, but need to be conducted by professionals in specific sites, which is undoubtedly cumbersome. On the other hand, the majority of the existing self- calibration schemes are based on low-level features and their stability and robustness are usually unsatisfactory. As far as we know, an effective extrinsic self-calibration scheme designed specially for the SVS is still lacking. To fill such a research gap to some extent, we propose a novel self-calibration scheme which follows a weakly supervised framework, namely WESNet (Weakly-supervised Extrinsic Self-calibration Network). The training of WESNet consists of two stages. First, we utilize the corners in a few calibration site images as the weak supervision to roughly optimize the network by minimizing the geometric loss. Then, after the convergency in the first stage, we additionally introduce a self-supervised photometric loss term that can be constructed by the photometric information from natural images for further fine-tuning. Besides, to support training, we totally collected 19,078 groups of synchronously captured fisheye images under various environmental conditions. To our knowledge, thus far this is the largest surround-view dataset containing original fisheye images. By means of learning prior knowledge from the training data, WESNet takes the original fisheye images synchronously collected as the input, and directly yields extrinsics end-to-end with little labor cost. Its efficiency and efficacy have been corroborated by extensive experiments conducted on our collected dataset. To make our results reproducible, source code and the collected dataset have been released at https://cslinzhang.github.io/WESNet/WESNet.html.
Is Your Time Well Spent Online?: Focusing on Quality Experiences Through a User-Centered Recommendation Algorithm and Simulation Model

Spending an uncontrolled quantity and quality of time on digital news and social media platforms can negatively influence mental health and decrease cognitive abilities. In this paper, we propose a sequential news recommendation system employing deep reinforcement learning to capture the user’s short and long-term interests while blending social news with micro-learning informative news items that can help users derive useful outcomes out of their online presence. In the absence of a publicly available dataset, we developed a simulation model to synthesize data and evaluate the proposed news recommendation system. We train and evaluate our model on synthesized data and show an improvement in user satisfaction.
“Through a Glass, Darkly”: Lessons Learned Starting over as an Electronic Resources Librarian

This article examines the experience of a starting electronic resources librarian, who transitioned to the role after working for a vendor and as a faculty member. The author's experience demonstrates the complex, often ambiguous nature of the role, as supported by the professional literature. The article concludes with a call for greater product support and transparency on the part of vendors. Although anecdotal, the author's experience should interest new ERLs as well as library staff and vendors with whom they communicate. It should also interest those who are considering so-called “alternative academic” (non-professorial) careers with their doctoral degrees.
Detection of Cache Side Channel Attacks Using Thread Level Monitoring of Hardware Performance Counters

Modern multiprocessor systems adopt optimization techniques to boost the speed of execution. These optimizations create vulnerabilities that can be exploited by attackers, thus causing security breaches. The hierarchical structure of cache memory where the Last Level Cache is a super set of previous levels and is shared between multiple cores of the processors creates an attack vector for cache side-channel attacks (SCA). In such attacks, the attacker is able to trace the pattern of victim process execution and correspondingly retrieve secret information by monitoring the shared cache. Mitigation techniques against such attacks trade off security against overall system performance. Hence, mitigation only when an attack is detected is needed. We propose an architecture-agnostic approach that uses hardware performance counters at run time and at thread level instead of current state of the art which use counters at system level to detect cache SCA. The proposed approach reduces the false positives by 48% when compared with system level approaches. Thus, the trade off with performance is also reduced and hence, the proposed approach is especially significant for embedded systems where processor cycle time is a limited resource.
Solar Energy Resource Assessment Using GHI and DNI Satellite Data for Moroccan Climate

Solar energy is in rapid development in Morocco. Thanks to its geographical and climatic location, the Kingdom of Morocco is privileged to develop large-scale solar energy exploitation. The knowledge of the solar potential in a given site is crucial for most solar energy applications. The present study aims to evaluate the energy potential in several cities in Morocco to provide guidance to solar energy users about the available amount of energy. The assessment was done using 10 years of data from the Helioclim3 satellite database. In addition to the solar potential analysis, we performed a classification of the days to determine the dominant climatology based on the clear sky index. The results showed the dominance of clear days toward all study sites, and selected sites may be a promising location for solar projects with annual global horizontal irradiation reaching 2075 kWh/m2 and direct normal irradiation reaching 2463 kWh/m2.
COVID-19’s Impact on the Quality of Educational Process and the Academic Performance as Viewed by IT Students: A Case Study in Text Mining

In this paper, we describe the results of the text mining and sentiment analysis case study with the aim to analyze the COVID-19’s impact on the quality of the educational process and the academic performance of IT students of Virumaa College of Tallinn University of Technology (TalTech) through an essay assignment. In this study, we analyze and compare students’ feedback about their studies in the lockdown during the first and the second wave of the COVID-19 pandemic. To identify students’ sentiments and determine the potential influencing factors descriptive textual analytics, sentiment analysis techniques and textual data visualizations are used. As a result of this study the recommendations were developed to improve online learning and students’ motivation and emotional state. The methodology of the analysis provided in this paper can be helpful for further using as well as implemented by any higher education institution.
On a generalization of the concept of normal numbers

The paper considers the generalization of the concept of normal numbers in the context of the classical s-th representation of real numbers, in relation to the Q_s-representation, first considered by M. Pratsiovytyi. The result of I. Nivena and H. Zukerman is deepened in relation to the metric theory of normal E. Borel numbers. It is shown that the set of all Q_s-normal numbers has a Lebesgue measure 1. The connection between the property of normality and the uniform distribution of the sequence of numbers generated by the shift operator in relation to the corresponding number is established. It was found that the set of all numbers of the segment [0; 1] for which the corresponding sequence generated by the operator of left-hand shift Q_s-digits is uniformly distributed has a full Lebesgue measure. The corresponding theorems deepen the results of the metric theory Q_s-decompositions of real numbers of the segment [0; 1] obtained by M. Pratsiovytyi and G. Torbin.
The Choice of Kernel Function for One-Class Support Vector Machine

Support vector machines (SVM) are mainly used in classification problem, like character recognition, face recognition, pedestrian detection, text classification and other fields. Isolation forest can be used to anomaly detection (outlier detection), which requires relatively small samples from large data sets. SVM has several kernel functions including RBF, linear, polynomial and radial. In this paper, the experiments on different real-world data sets are performed to demonstrate the importance of kernel choice and the corresponding sensitivity of the algorithm. This paper mainly explores the performance of various main kernel functions in different data sets in OCSVM, and compares the final effect of OCSVM and isolation forest in processing the same data set. Furthermore, this paper also compares the OCSVM method using RBF kernel with adaptive boosting.
The DSW Model: An Efficient Approach for Single Web Services Modeling

Service-oriented computing attracted the attention of researchers nowadays. Hence, the quality of service-oriented systems became crucial. A web service is the main building component of service-oriented systems, in which some aspects must be taken into consideration while building such systems. These aspects include quality, performance, efficiency, reliability and availability of web services. However, minor studies take into consideration single web service modeling, or their proposed models were too complex and time consuming. Thus, in this paper, the Direct Single Web service (DSW) model is proposed as a model-based testing approach for modeling single web services in a direct and time efficient model. The DSW model uses Web Services Description Language (WSDL) files to define a single web service. Hence, it generates a directed graph from web service WSDL files. It can be used for test case generation, services search, finding faults and optimization. The DSW model proved to be efficient in terms of: (1) coverage, covering all parts of WSDL files, (2) time, where the graph construction consumes 15-25% of the parsing time for WSDL files, and (3) accuracy in comparison with other approaches.
Indonesian Abusive Tweet Classification based on Convolutional Neural Network and Long Short Term Memory Method

Twitter is one of the famous social media applications in Indonesia. The study will classify data to detect hate speech on Twitter into two classes: abusive and non-abusive. The abusive class will contain tweets in the form of words that contain negative elements or hate speech, while the non-abusive class contains tweets in the form of words that do not contain negative elements. The desired purpose of this research is to help those who need this research to be able to use it to supervise others from hate speech and find the perpetrators who do it. The data used is secondary data taken from the Kaggle website. The data contains tweets that have been classified based on connotations of abusive or non-abusive. Several processes are carried out to achieve the desired goal, including data acquisition, preprocessing, classification, evaluation, and content detection. The algorithms used in the study were LSTM and CNN. Both algorithms produce the same performance relatively. It’s just that CNN is slightly superior. The accuracy value of CNN is 0.9131 or 91.31%, the precision is 0.9131 or 91.31%, the recall is 0.9131 or 91.31%, and the F1-Score of 0.9131 or 91.31%. Meanwhile, for LSTM, the accuracy value is 0.9104 or 91.04%, the precision is 0.9104 or 91.04%, the recall is 0.9104 or 91.04%, and the F1-Score is 0.9104 or 91.04%
Design of a Small Sweeping Robot Based on STM32

Abstract With the continuous improvement of people’s living standards and the emergence of various intelligent appliances, a household floor sweeping robot is designed in this subject, which can automatically clean the ground in the room. The robot takes STM32F103ZET6 single chip microcomputer as the controller, uses two DC motors and L298N driving chips to drive the robot to walk, and uses ultrasonic sensors to detect indoor obstacles, The infrared sensor is used to detect the indoor edge position, the straight travel control of the robot is realized through the coding disk combined with PID algorithm, and the turning control is realized through differential control and PWM speed regulation. The robot can realize the garbage cleaning in the regular room, and has a certain application prospect.
PatchGAN-Based Depth Completion in Autonomous Vehicle

Depth completion is a challenging task and it serves as the fundamental function for many vision handling operation in autonomous vehicle. It aims to generate a dense depth map from upsampling the sparse input data collected from LIDAR sensor. This paper proposed a GAN architecture with a two-branch autoencoder in the generator that exploits two different modalities and performs a fusion strategy between two modalities. Furthermore, the architecture is evaluated on the KITTI datasets and KITTI benchmark and proved to have a competitive performance..
HybridPKE: A forward-secure non-interactive quantum-safe hybrid key exchange scheme

The schemes submitted to NIST call for standardization of post-quantum cryptography have some common drawbacks in terms of decryption failure probability, scalability, and switching issues from pre-quantum to post- quantum cryptography. In this paper, we address all these issues and propose a novel quantum-safe scheme HybridPKE for key exchange and encryption. HybridPKE is based on Ring Learning with Error lattice problem and uses Advance Encryption Standard symmetric-key algorithm. The scheme is non-interactive as both the parties establish the same key without sharing any reconciliation information. HybridPKE provides forward secrecy and authenticated quantum-safe communication with zero decryption failure. The uniqueness of this paper lies in the first to integrate both pre-quantum and post-quantum scheme to provide a robust hybrid key exchange. The paper presents the efficient implementation and supported parameter set of purposed HybridPKE scheme by fine-tuning the parameters through rigorous experimentation and security analysis under different attacks.
Data security

This chapter, written by a global expert on data protection, combines the journey of food with the journey of data. As much as we wish to provide for the quality, safety, defense, security, and authenticity of food, we must also provide for the same dimensions as they apply to data.
Role-Playing Games as a Strategy to Incorporate Social Skills in Electrical Engineering Programs [Education Corner]

Traditionally, engineering degree programs are reasonably good at transmitting technical knowledge to their students; regardless of the particular conditions of each university, lecturers usually have extensive experience in doing so. However, employers are demanding soft skills in addition to the abilities our engineers master, and strategies used to incorporate them within our compact and information-packed programs are required. This article presents an experience in promoting skills for communicating and interacting with ordinary people in technical issues, performed among electrical engineering students from the radio-frequency (RF) branch. Our proposal is to use role-playing techniques to train students to communicate with people who are afraid of electromagnetic (EM) exposure effects, which has immediate implications during 5G network deployment. Moreover, the use of forum facilities in e-learning platforms allows such techniques to be integrated within an online course.
Parallel Algorithm for Solving the Graph Isomorphism Problem

In this paper, we propose a parallel algorithm for solving the graph isomorphism problem. Our goal is to construct a suitable vertex substitution or to prove the absence of such. The problem is solved for undirected graphs without loops and multiple edges; it is assumed that the graphs can be disconnected. The question of the existence or absence of an algorithm with a polynomial complexity is currently open. Therefore, as for any time-consuming task, the question arises about speeding up its solution by parallelizing the algorithm. The RPM_ParLib library developed by the author allows developing effective applications for parallel computing on a local network under the control of the runtime environment .NET Framework. Supporting a recursive-parallel programming style, such applications have the ability to generate parallel branches of computation directly during program execution and dynamically redistribute work between computing modules. The library can be used for applications written in any programming language supported by the .NET Framework. To solve our problem and conduct a numerical experiment, several applications in the C# language were developed. The purpose of the experiment was to study the acceleration achieved due to the recursive-parallel organization of calculations. Specially generated random regular graphs with various degrees of vertices were used as the initial data. A detailed description of the algorithm and experiment, as well as the results obtained, are also given.
Semantic Segmentation of Satellite Images for Water Body Detection

Semantic Segmentation is a technique in Computer vision which is used to label different pixel classes present in imagery. When talking about Satellite images, Semantic Segmentation can be used to segment water bodies, forest area, buildings, agriculture lands, etc. In this paper, we have focused on Semantic Segmentation of Satellite images for Water Body Detection. We have used a predefined architecture U-Net as the segmentation model to detect and segment water bodies present in satellite imagery. U-Net is a better choice over other models because of its various advantages like less training data required, low inference time and fast learning. We have used sentinel-2 dataset for training and validation of the model. The images are RGB images and they are stored in jpeg format. The dataset has 2000 + images, however, we have tried to train and validate our model on minimum number of images so as to get an optimal model in less amount of data. We succeeded in achieving good results in less data. The final result is the segmented mask of a satellite image where white region is water body and black region is land area.
COMPLETE INTUITIONISTIC TEMPORAL LOGICS FOR TOPOLOGICAL DYNAMICS

Abstract The language of linear temporal logic can be interpreted on the class of dynamic topological systems, giving rise to the intuitionistic temporal logic ${\sf ITL}^{\sf c}_{\Diamond \forall }$ , recently shown to be decidable by Fernández-Duque. In this article we axiomatize this logic, some fragments, and prove completeness for several familiar spaces.
Extending Bayesian Learning to Intelligent Learning in Human Way

Bayesian learning has been successfully used in many fields to make decision or sense the outcome of causes or influences of events. However, the relations between causes and observed events are more complicated than the Bayesian inference can learn, like prediction in some psychology experiments, which should consider human experience and the interdependence of the events. This paper raises Bayesian learning to intelligent learning by putting events in a network to analysis the complicated criterions and interdependences among events with the individual probability judgments. Finally, the results of experiment used by intelligent learning illustrate more complex relations than Bayesian learning.
A Semantic Segmentation Method for Remote Sensing Images based on Deeplab v3

As a basic technology for image analysis and scene understanding, image semantic segmentation is widely used in the field of remote sensing images. It can better help humans understand the world’s scenes and analyze potential changes from the top view of the earth, which has high practical value and development prospects. In this paper, a semantic segmentation method for high-resolution remote sensing images based on DeepLab v3[9] is proposed. The network structure of this method uses the atrous spatial pyramid pooling (ASPP) to extract the multi-scale feature information of high-resolution remote sensing images. Besides, the experimental part of this paper is evaluated on the public dataset of remote sensing images named ISPRS Vaihingen. Experimental results show that the proposed method can achieve an average accuracy of 81.72% on the ISPRS Vaihingen dataset. Therefore, this method can be used as an automated tool for remote sensing semantic segmentation.