Reviewerly/exHarmony huggingface dataset:
Data-free Knowledge Distillation via Adversarial

Network Compression is a challenging task, but it is crucial for using the deeper network in the low-performance device. If the original training datasets could be obtained, the traditional network compression approaches are useful for training a compact deep model. This paper proposes a novel framework for knowledge distillation without original training datasets via Generative Adversarial Network(GAN). We arrange the fixed pre-trained deeper network and the compact network as the discriminators to generate the training dataset. We also use the deeper network and the compact network as the generators, then introduce one simple full connection network as the discriminator to compress the complex network. We propose (i) a series of new images generation loss functions. (ii) a knowledge distillation method via generating adversarial networks. Finally, we show the superiority of our approach by contrasting with SOTA by benchmark datasets.
Assistive Signals for Deep Neural Network Classifiers

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model’s confidence score regardless if it’s under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. ‘Assistive Signals’ also illustrate bias of ML models towards certain patterns in real-life objects.
Assessing the Cold Start Problem in Adaptive Systems

This study presents a comparison of the following methods for estimating task difficulty in terms of the so-called "cold start" problem - during the initial phase of the introduction of the adaptive educational system to the public. The data originates from the item-based online programming course made available on the RunCode online learning platform. The dataset contains 50055 submissions on 76 tasks uploaded by 299 learners. The reference task difficulty values have been estimated with the Item Response Theory Graded Response Model on the full dataset. Results of this study show, that for the smallest size of the sample n = 5 learners, the Elo rating algorithm achieves a reasonably high correlation of 0.702. For small sizes of the sample n = 5, 10, and 20 learners, the Elo algorithm delivers slightly better estimates than the Glicko rating algorithm and outperforms the proportion correct (PC) method. For the size of the sample n = 50, the Glicko algorithm delivers better results than the Elo, and the difference between algorithms and the proportion correct method decreases.
Ensemble Model for Multi-Source Cross-Domain Sentiment Classification with Little Labeled Data

We investigate the problem task of multi-source cross-domain sentiment classification with little labeled data, which is a common problem faced by some online store owners. We propose a knowledge-diverse ensemble model which is capable of automatically capturing important topical knowledge used for cross-domain sentiment classification. Through multiple stages of constructing new pseudo-data for training, it can maintain the diversity of the captured knowledge by different base learners. Experiments on a real-world product review dataset show that our proposed model has a good performance even under the little labeled data constraint.
Social reproduction of generations in the face of digital challenges

The article describes the global processes that are transforming the world (migration and integration processes, inclusive education, digitalization of education, socio-psychological gap between generations, etc.). Global tendencies have changed the social space of people's life - “cultural gaps”, “social bottom”, “spiritual crisis of parent-child relations and intergenerational ties”, etc. have appeared, as modern challenges of society, affecting the social reproduction of generations. The problem of social reproduction, the significance of which is associated with the need for the development of sociality, the construction of the social world in the event chronotope, has been actualized; preservation of the social and historical memory of the people, the self-identity of the national community, the “core” of the spiritual image of the nation, etc. A powerful challenge in modern society is digitalization, which has changed the forms of communication and social roles, created a new virtual space for self-presentation, self-expression, while the risk is the loss of cultural identity, blurring the lines between generations, etc. The article presents the mechanisms of integration of traditional and digital technologies of social reproduction of generations, the difference of which lies in the actualization of children's interest in the historical and cultural values of the people, in the organization of joint activities to create virtual historical museums, etc. Social reproduction of generations is a complex and long-term process, the success of which depends on the unity of activities in the professional, parenting and children's community.
A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions

Vision-based automatic detection and tracking of on-road vehicles is a demanding research area in intelligent transportation systems (ITS). Several investigation reports are available in the literature in this research field using various machine learning (ML) techniques. This article proposes a method for on-road vehicle detection and tracking in varying weather conditions by modifying the structure of you only look once (YOLO), a recently developed deep learning model in computer vision for detecting objects. ML-based systems repurpose classifiers to detect the vehicles, whereas in YOLO model, a single convolutional neural network (CNN) predicts the detecting bounding boxes and the class probabilities for those boxes together in one evaluation from the full images. As the entire detection is performed using a single network, the detection process can be viewed as an end-to-end system. In this work, 16 convolutional layers of YOLO have been used along with two fully connected layers at the end. The vehicles have been detected in varying weather conditions. Two different public data sets, namely CDNet 2014 and LISA 2010 data sets have been used to evaluate the system performance, and it outperforms state-of-the-art results.
A Multiple SVMs Classifier in Three-Way Decisions Framework for Text Classification

Text classification has been recognized as an essential technique for handling and organizing text data. However, the classic text classifiers cannot clearly describe the difference between relevant and irrelevant information because of uncertainty. The underlying reason is that it is very hard for most text classifiers to explicitly describe the large uncertain boundary between two classes. A three-way decisions based framework is an interesting methodology for dealing with uncertainties in binary classification. However, it is not easy to effectively integrate the framework with a popular classifier (e.g. SVM). By integrating the distinct aspects of three-way decisions theory and the capacities of a support vector machine (SVM), a Multiple-SVMs classifier is proposed in this paper to address this issue. The proposed approach starts from the strategy of partitioning the training set into three regions, namely, positive, negative and boundary regions, to ensure the certainty of extracted knowledge for describing relevant information. Based on these three regions, an innovative and effective probabilistic feature-weighting approach has been proposed to accurately weight the representative terms. The model then organizes training samples to design a Multiple-SVMs classifier which is capable of predicting the classes of each document. Experimental results on the standard datasets RCVI and R21578 show that the proposed method significantly outperforms other state-of-the-art methods in different popular measures.
New Results on Asymmetric Single Correcting Codes of Magnitude Four

An error model with asymmetric single error with magnitude four is considered. In this paper, the constructions of codes correcting single error of magnitude four over \mathbb Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2<sup>a</sup>3<sup>b</sup>r</sub> are studied which is equivalent to construct B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> r) sets. Firstly, we reduce the construction of a maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> r) set for a ≥ 4 and gcd(r,6)=1 to the construction of a maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-3</sup> 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> r) set. Furthermore, we will show that maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](8·3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> r) sets can be reduced to maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> r) sets and also give lower bounds of maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](12r) and B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2·3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> r) sets. Finally, we give a necessary and sufficient condition on the existence of perfect B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](p) set for prime p.
CNN-based Person Recognition System for Masked Faces in a post-pandemic world

With everyone now covering their faces with a face mask to avoid contagion of the COVID-19 virus, it becomes an increasingly difficult challenge for the facial recognition systems to identify people wearing masks. Algorithms devised before the pandemic for facial recognition often fail in this context, and consequently a need to understand the working of facial recognition algorithms when presented with occluded faces arises. This project aims to develop a new lightweight Convolutional Neural Network-based algorithm to resolve this issue. The proposed model gives a comparable accuracy with similar models developed in the past. Further, the proposed algorithm is used to create a robust system to ensure adherence of COVID-19 protocols in a real-world environment.
Disparity Estimation from Holoscopic Elemental Images

Holoscopic 3D imaging system, also known as integral imaging, is an innovative 3D imaging principle that overcomes key limitation of traditional 2D imaging issues such as depth, scalability and multi-perspective with its simplistic form of 3D data acquisition and visualisation which provides robust and spatial information of the real world 3D scene. In this paper, an innovative 3D map generation technique is proposed which produces accurate 3D map of a scene from a single holoscopic 3D image based on angular information preserved in its elemental images. 3D depth map is generated from the elemental images based on the semi-global block-based matching algorithm. A weighted least squares filter is utilised to minimise the noise in the resulting disparity image. The evaluation result outperformed state of the art 3D depth generation techniques and the proposed technique enlarges the industrial application of 3D imaging applications such as AR/VR, inspection, robotics, security and entertainment.
Exploiting Doppler in Bernoulli Track-Before-Detect

Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Recently, an approach using a Bernoulli track-before-detect (TBD) filter has been proposed for an airborne scanning radar in the maritime domain [1]. This work focused on a non-coherent fast scanning mode where the dwell-time (time-on-target) was very short. The current paper explores the benefits of using Doppler information in TBD when the radar is operating in a slower scanning mode. A new TBD filter, which exploits the Doppler information in the form of a point measurement is developed and analysed, with significant improvement demonstrated over the traditional fast scanning operation.
The Unprecedented Rise in Cybercrime and the Role of the Human Vulnerability Factor

This chapter is primarily intended to firstly define and review the literature in cybersecurity and vividly shed light on the mechanisms involved in the social engineering phenomenon. It will discuss the various attempts at network intrusion and the steps typically taken in the implementation of cyber-thefts. The chapter will provide the rationale behind the justification of why humans are considered to be the weakest link in these attacks. The study will also explain the reasons for the rise in cybercrimes and their impact on organizations. In closing, the chapter will put forward some recommendations to serve as preventative measures and solutions to the threats and vulnerabilities posed by cyber-attacks. Finally, measures, such as conducting regular, thorough, and relevant awareness training, frequent drills, and realistic tests, will be addressed with a view to maintaining a steady focus on the overall discipline of the organization, thereby hardening the component of the network that is the softest by nature—the human vulnerability factor.
Analysis of Monetary Policy Decisions of the National Bank of Romania with Text Mining Techniques

This paper uses text mining techniques in order to carry out an analysis of the monetary policy decisions of the National Bank of Romania (NBR). The analysis highlights the main topics of debate of the NBR Board that revolve around inflation, reference rates for monetary policy, rising consumption and the current account deficit and the need to keep current monetary policy rates constant in the regional economic context that characterized the year 2019 and the massive shift in the beginning of 2020 with the start of the coronavirus outbreak.
An Automatic Emotion Analysis of Real Time Corona Tweets

Emotion Analysis from text is a recent research field originated from Sentiment Analysis. The Sentiment Analysis identify the sentiments and classify them to positive, neutral, or negative sentiments based on the text. Emotion Analysis ocus to detect and recognize emotions through the text expression which are anger, disgust, fear, happiness, sadness and surprise. The real time applications of emotion analysis can be widely applied in software engineering, website customization, education, and gaming domains. Emotion Analysis can be done by gathering social media data such as twitter, reviews, blogs. Emotion Analysis can be done on the public opinion on a particular product or on a particular topic. In this paper we are focusing to do emotion analysis of the public on real time recent corona tweets. The tweets are collected through twitter application interface. The collected real tweets are applied to pre-processing strategies to remove inconsistent and redundant factors and the pre-processed tweets are visualized through word cloud. The emotions scores are obtained using nrc_sentiment dictionary which contains basic emotions and both sentiments of positive or negative. The emotions score levels of the public are identified from the Corona Tweets and depicted through graphical analysis.
A new Cybersecurity Strategy for IoE by Exploiting an Optimization Approach

Today's companies are increasingly relying on Internet of Everything (IoE) to modernize their operations. The very complexes characteristics of such system expose their applications and their exchanged data to multiples risks and security breaches that make them targets for cyber attacks. The aim of our work in this paper is to provide an cybersecurity strategy whose objective is to prevent and anticipate threats related to the IoE. An economic approach is used in order to help to take decisions according to the reduction of the risks generated by the non definition of the appropriate levels of security. The considered problem have been resolved by exploiting a combinatorial optimization approach with a practical case of knapsack. We opted for a bi-objective modeling under uncertainty with a constraint of cardinality and a given budget to be respected. To guarantee a robustness of our strategy, we have also considered the criterion of uncertainty by taking into account all the possible threats that can be generated by a cyber attacks over IoE. Our strategy have been implemented and simulated under MATLAB environement and its performance results have been compared to those obtained by NSGA-II metaheuristic. Our proposed cyber security strategy recorded a clear improvment of efficiency according to the optimization of the security level and cost parametrs.
Research on Teaching Exploration Practice under the Background of Multimedia Information Fusion

In the field of education, the emergence of multimedia information technology has led to great changes in traditional teaching methods, and the development of multimedia information technology has led the improvement of modern educational technology. Nowadays, it is playing a more and more important role in the teaching link of colleges and universities. However, while seeing the advantages of multimedia technology, we should also see that teachers still have some problems in the understanding of modern teaching methods, the appropriate use of multimedia and the understanding of multi-media operation process. From the point of view of practice and exploration, this paper expounds the problems in the application and teaching of multimedia information technology and the effect between appropriate and inappropriate, and puts forward the necessity of rational use of multimedia.
Binary Code Authorship Identification with Neural Representation Learning

Authorship identification on binary code is of great value in applications such as malware analysis, software forensics and code theft detection. Existing approaches that adopt traditional machine learning paradigm to train classifiers for authorship identification extract and select features on the basis of a deep analysis and understanding (which are generally conducted by domain experts) of the programming languages as well as programmers’ coding habits, showing limitations in the attribution accuracy and granularity. Inspired by the recent great successes of neural networks in various program analysis tasks, this paper proposes NeurAI to perform fine-grained program authorship identification by analyzing the binary codes of individual functions with a deep representation learning-based method. To evaluate the performance of NeurAI, a large dataset consisting of 147,110 functions collected from Google Code Jam is constructed. The extensive experimental results show that NeurAI can successfully capture different programmers’ coding styles preserved in the binary code, where 90% accuracy is achieved among 50 authors for the function-level binary code authorship identification task.
Enhanced Neural Architecture Search Using Super Learner and Ensemble Approaches

Neural networks, and in particular Convolutional Neural Networks (CNNs), are often optimized using default parameters. Neural Architecture Search (NAS) enables multiple architectures to be evaluated prior to selection of the optimal architecture. A system integrating open-source tools for Neural Architecture Search (OpenNAS) of image classification problems has been developed and made available to the open-source community. OpenNAS takes any dataset of grayscale, or RGB images, and generates the optimal CNN architecture. The training and optimization of neural networks, using super learner and ensemble approaches, is explored in this research. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and pretrained models serve as base learners for network ensembles. Meta learner algorithms are subsequently applied to these base learners and the ensemble performance on image classification problems is evaluated. Our results show that a stacked generalization ensemble of heterogeneous models is the most effective approach to image classification within OpenNAS.
If you want better empirical research, value your theory

Scientific progress comes from creating sound theories. However, current software engineering still mostly falls short of this goal, although its importance is widely accepted. Thus, in this paper, we discuss the importance of a successful interaction of empirical research with a strong theoretical basis and the ramifications this has. In particular, we will extensively discuss the implications on theory building and the empirical vs. theory interaction, etc. While not everything we will discuss is novel, we present a number of insights, which we at least did not see in software engineering literature. We strongly believe that a careful consideration of the insights discussed in this paper has the potential to lead to a significant improvement in software engineering research.
Curiosity Based RL on Robot Manufacturing Cell

This paper introduces a novel combination of scheduling control on a flexible robot manufacturing cell with curiosity based RL. Reinforcement learning has proved to be highly successful in solving tasks like robotics and scheduling. But this requires hand tuning of rewards in problem domains like robotics and scheduling even where the solution is not obvious. To this end, we apply a curiosity based reinforcement learning, using intrinsic motivation as a form of reward, on a flexible robot manufacturing cell to alleviate this problem. Further, the learning agents are embedded into the transportation robots to enable a generalized learning solution that can be applied to a variety of environments. In the first approach, the curiosity based reinforcement learning is applied to a simple structured robot manufacturing cell. And in the second approach, the same algorithm is applied to a graph structured robot manufacturing cell. Results from the experiments show that the agents are able to solve both the environments with the ability to transfer the curiosity module directly from one environment to another. We conclude that curiosity based learning on scheduling tasks provide a viable alternative to the reward shaped reinforcement learning traditionally used.