Reviewerly/exHarmony huggingface dataset:
Quality of Experience using Deep Convolutional Neural Networks and future trends

The development of immersive display technology enables to represent the details of contents more naturally by providing a more realistic viewing environment while increasing immersion. In parallel, quality of experience (QoE) has been dealt with and discussed from both academy and industry to grade consumer products from the quality perspective. However, for quantification of QoE, it is very challengeable to analyze the human perception more accurately, even if it has been studied in many decades. Currently, there is no solid methodology to verify human perception as a closed-form objectively due to the limitation of human perception analysis. Recently, the deep convolutional neural network (CNN) has emerged as a core technology while breaking most performance records in the area of artificial intelligence via intensive training in accordance with the massive dataset. The main motivation of this paper lies in finding new insight into human perception analysis for QoE evaluation through visualization of intermediate node values. This new QoE assessment approach enables us to figure out the human visual sensitivity without using any prior knowledge. Toward the end, we provide a novel clue of how to obtain visual sensitivity, which is expected to be essentially applied for future QoE applications. In addition, we discuss future applications in QoE assessment with respect to the display types.
Reinforcement Learning Method with Internal World Model Training

Reinforcement learning can be applied to various tasks such as developing motion controllers for humanoid or insect-like robots, playing board games and video games. However, reinforcement learning requires a large number of trials for learning appropriate behaviors in an environment that required physics simulations due to the computational costs of simulations. We propose a model inspired by human brain simulation. The agent builds the internal model based on the environment and trains in a simulated environment by the internal model in addition to training in the actual environment. As a result, the agent acquires the walking movement with fewer trials.
Secret Sharing Based SM2 Digital Signature Generation using Homomorphic Encryption

SM2 is an elliptic curve public key cryptography algorithm released by the State Cryptography Administration of China. It includes digital signature, data encryption and key exchange schemes. To meet specific application requirements, such as to protect the user's private key in software only implementation, and to facilitate secure cloud cryptography computing, secret sharing based SM2 signature generation schemes have been proposed in the literature. In this paper a new such kind of scheme based upon additively homomorphic encryption is proposed. The proposed scheme overcomes the drawback that the existing schemes have and is more secure. It is useful in various application scenarios.
Recommender Systems for Software Requirements Engineering: Current Research &amp; Challenges

Recommender systems have been used to improve software requirements engineering activities. This paper reviews literature on recommender systems for requirements engineering research since 2012. It discusses the common techniques used in recommender systems, the issues addressed by the recommender systems found in the literature, and challenges that still need to be addressed. Approaches to address some of the challenges are proposed.
Intensity-aware GAN for Single Image Reflection Removal

Single image reflection removal is a challenging task in computer vision. Most existing approaches rely on carefully handcrafted priors to solve the problem. Contrast to the optimization-based methods, an intensity-aware GAN with dual generators is proposed to directly estimate the function which transforms the mixture image into the reflection image itself. From the observation that the reflection layer has more discriminating power in the region with low intensity than that in the region with high intensity, the proposed architecture better describes the characteristic of the model. Moreover, a reflection image synthesis method based on the screen blending model is also presented. Experimental results demonstrate that the results of reflection removal are satisfactory in real cases while comparing with state-of-the-art methods.
Weight Pair Group Average Mean Clustering for Interval-valued Data

Weight Pair Group Average Mean clustering is one of several methods of agglomerative hierarchical clustering. It is a very popular method for computing hierarchical clusterings. Currently, clustering methods rely dissimilarity measures for interval-valued data uses representative point distance. Our work apply Weight Pair Group Average Mean clustering to interval-valued data based on the Range Euclidean Metric is an reliable alternative to be used to uncertainty quantification from symbolic interval-valued data.
Ways of Compromising Blockmesh

Nowadays blockchain is used in many spheres of our life. But even though this idea promises a high level of security and each new protocol seems to improve the technology, it is still not perfect. In this article, we concentrate on the new blockchain protocol, called "Blockmesh". The article starts with a brief introduction into the blockchain technology in general and then moves on to describing Blockmesh, its features and ways of compromising it. To test our assumptions we'll create the environment, simulating the system work. Our goal is to review the possibilities and weaknesses of the new protocol and to explore the cases of Blockmesh providing a higher level of security.
Problems of determining the admissibility and appropriateness of digital (electronic) evidence in criminal proceedings

The development of information technology, along with its indisputable advantages, has brought to our lives a number of negative phenomena related to the illegal use of computers and telecommunications. However, the issue of using digital information as evidence in the criminal procedural legislation of Ukraine remains almost unsettled, in particular, the place of digital evidence in the system of procedural sources of evidence (digital evidence is difficult to unambiguously attribute to material evidence or documents) remains unclear. Criminal proceedings raise problems regarding the correct assessment of digital (electronic) evidence for their identity and admissibility, which certainly does not contribute to the effective use of digital technologies and sources of information in national proceedings. The purpose of the article is to research the problematic issues of determining the appropriateness and admissibility of digital (electronic) evidence during criminal proceedings, as well as to identify and disclose individual principles for their proper procedural evaluation. The research deals with the current state of theoretical studies of the issue of the appropriateness and admissibility of digital evidence both in Ukrainian criminal procedure science and abroad. The peculiarities of the requirements for the assessment of traditional evidence and digital evidence in criminal proceedings are analyzed. The author identifies the features of obtaining (collecting) digital evidence, given their intangible nature, with a view to their further positive evaluation by admissibility and appropriateness criteria. Taking into account the international experience, the author concludes that there is a need to distinguish separate principles of admissibility and availability for digital evidence, revealing their content. The urgent need to settle this issue, both at the legislative level and through appropriate judicial clarification, is substantiated. It is emphasized that in view of the specific nature of digital (electronic) evidence to ensure their authenticity and reliability in criminal proceedings is associated with the promptness of investigative actions, mandatory involvement of an expert, professional training of all subjects of evidence and steady adherence to recommendations for working with digital evidence.
Innovation in Educational Process of Computer Networks

This paper presents the activities carried out to enhance the teaching process of computer networks courses at secondary schools and universities. Work is done as a result of the IT Akademia national project. The paper provides wide overview of these activities, all following the exploratory learning techniques. Main goals include creation of methodological guides for secondary schools. Then preparation of handbook and innovation of existing computer networking courses at the Technical University of Kosice (TUKE). Educational materials were created and reviewed by the experts in the field of computer networks, including both secondary school and university instructors. Developed educational tools complement existing curriculum and increase competences of both, students and instructors (teachers). All outputs of the project further enable individuals with poor English language skills easier understanding of the topics, since all of the materials are in localized the native language.
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition

Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models, and generally yields improved performance and faster training times. The technique of pre-training on one task and then retraining on a new one is called transfer learning. In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks. We perform three sets of experiments with varying levels of similarity between source and target tasks to investigate the behaviour of different types of knowledge transfer. We transfer both parameters and features and analyse their behaviour. Our results demonstrate that no significant advantage is gained by using a transfer learning approach over a traditional machine learning approach for our character recognition tasks. This suggests that using transfer learning does not necessarily presuppose a better performing model in all cases.
Surface Gesture as Extension Usage Scenario of Smart Watch

Smart watch has become one of the essential electronic equipment now. However, usage scenarios of smart watch are still relatively limited. In this paper, we propose a new smart watch scenario called surface gesture by adding an infrared camera in strap position. Smart watch with surface gesture function can use gesture to control itself, and also control other devices. In our hardware simulator platform, surface gesture basically meets requirement of extension usage scenarios.
Markov and Conditionally Markov Processes: from Gaussian to Elliptical

Conditionally Markov (CM) processes, including the reciprocal processes as an important subclass, are gaining momentum as a generalization of the Markov process based on conditioning. This paper aims to extend the concept of the Gaussian CM process and some of its key results to the elliptical case. An elliptical process describes a stochastic process having jointly elliptically contoured distributions, which is the largest class of processes ensuring linearity of conditional expectation. However, it is shown in this paper that a nonsingular elliptical process is CM if and only if it is a Gaussian CM process. Towards the goal of extension, we first define a new property of processes, called weaker Markov, by relaxing the strict independence for the Markov property to a weaker condition of semi-independence. We then combine it with conditioning and the elliptical randomness to define the conditionally weaker Markov (CWM) elliptical process. The newly defined process is much larger than the Gaussian CM process, but can be characterized by a linear model of the same form as in the Gaussian case. Moreover, it is proven that the two processes also share almost the same results in optimal filtering and smoothing.
Multilingual Semantic Textual Similarity using Multilingual Word Representations

In Natural Language Processing, doing the semantic textual similarity (STS) task between monolingual sentences is itself taxing. In this paper, we extend this problem to the multilingual STS scenario, making it even more challenging. We approach this problem using a multilingual representation of words where words having similar meaning across different languages are aligned. We prepare a very large multilingual STS dataset by scraping several multilingual government, insurance, and bank websites. Because these websites have only positive examples for doing multilingual STS we develop an algorithm for generating negative examples using latent Dirichlet allocation and OpenAI-GPT. We are able to train well performing models for datasets combining English, French, and Spanish. We get very good transfer performance across languages as well as domains. We also show our model's state-of-the-art paraphrase detection performance on the Microsoft Research Paraphrase Corpus.
An Improved Differential Privacy Algorithm Using Frequent Pattern Mining

Facing with a large number of long transactions, computation time-consuming of differentially private top-k pattern mining (DP-topkP) is particularly prominent especially when the minimum threshold min_sup gradually decreases or the transactions gradually increase. In this paper, we propose an improved DP-topkP algorithm. Inspired from truncated transactions, a mechanism of length selection is firstly exploited in order to predispose the transaction database. Then, the closed frequent pattern is adopted to reduce the size of the candidate frequent item set obtained by the FP-Growth algorithm. Extensive experiments on both pumsb-star and kosarak datasets demonstrate the effectiveness and feasibility of the improved DP-topkP compared with DP-topkP.
The Impact of Merged Data in Big Data Platform: A Case Study

Handling big data is a challenging task, which, in some cases, may raise performance issues. How data are collected and stored has a high impact on performance, when, for example, some data analysis is needed, or when an organization needs to run some queries on the data. In this paper, we study the impact of the block size in a big data environment and especially in HDFS, by presenting a special use-case that illustrates and compares four possible scenarios, distinguished by the organization of the data (multiple files vs. a single merged file) on a big data environment. We perform our comparison by running a query on each scenario, and observing the running times. As expected, the results show that working with a single file in a multiple-node environment yields the best results.
Enhancement Algorithms for Images in Coal mines base on Dark Light Denoising Networks

Facing the low contrast and heavy noises in images taken from coal mine tunnels, this paper designs a new depth neural network for low-contrast denoising, denoted as the LCDNN, based on the powerful denoising ability of the SSDA and the feature leaning ability of deep neural network. The proposed network consists of a contrast enhancement module and a denoising module, each of which is a separate SSDA. The enhanced image is obtained by minimizing the noise and improving the contrast. The proposed network was applied to denoise and enhance low-contrast images taken in a coal mine tunnel. The enhancement effect was compared with that of several popular image enhancement methods. The results show that the LCDNN outperformed the contrastive methods in all areas, leading to high contrast, low noise, rich details and good visual effect. The proposed network offers an effective tool to automatically learn basic signal features and noise structure from low-contrast images.
Session-based Recommendation with Context-Aware Attention Network

Session-based recommendation aims to generate recommendation results based on user's anonymous session. Previous studies model the session as a sequence and use Recursive Neural Network (RNN) to represent user behavior for recommendations. Although achieved promising result, previous studies ignore the relationship between session's items and external context of session, which fails in revealing the intrinsic relation between them. To tackle the problem mentioned above, we propose a novel method, i.e., Session-based Recommendation with Context-Aware Attention Network, SR-CAAN, which enhances the ability of modeling the user preference by combining sequence prediction with session external context aware method. In the proposed method, we incorporate external knowledge with Knowledge Graph (KG) to obtain the external context of session by using attention mechanism. Each session is presented as a composition of the external context of session and user's long-short term interest is obtained by Recurrent Neural Networks (RNNs). Experiments conducted on real word datasets demonstrate that SR-CAAN outperform the state-of-the-art significantly.
Web SDR-based QoS Monitoring System for Wireless Networks Analysis

This article discusses the use of the WebSDR platform, which allows using the software-defined radio (SDR) technology to independently configure the receiver for several users simultaneously. It allows not only listening the sound, but also monitor wireless networks of various ranges (including mobile communication network, broadcasting). This paper describes a monitoring system prototype based on the RTL - SDR broadband radio scanner dongle in relation to the analogue radio broadcasting in the 87.5 - 108.0 MHz band and. An option of using the developed system in practice is presented.
Requirements Engineering in Cloud and Service-Oriented Systems: A Systematic Mapping Study

The domain of Cloud and Service-oriented requirements engineering (SORE) focuses on modelling, specifying, and analysing requirements to enable software development in a service-oriented manner. Even though Cloud and SORE has received increasing attention over the past decade, there is no clear view of the current state of knowledge. Our goal is to gain insights into the current status of Cloud and SORE research published to date. To this end, we conducted a systematic mapping study to develop a map to classify relevant literature published between 2007 and 2018. Out of the 326 studies that were examined, we selected 24 studies as primary studies. The resulting map provides an overview of the existing work on Cloud and SORE and makes it possible to identify gaps and plan and position future research. Our findings suggest that Cloud and SORE as a research context needs specific attention. More evaluation and validation research is needed to better understand the implementation and impact of the various solutions (e.g. frameworks, tools) that are proposed in the Cloud and SORE context.
Attacking VQA Systems via Adversarial Background Noise

Adversarial examples have been successfully generated for various image classification models. Recently, several methods have been proposed to generate adversarial examples for more sophisticated tasks such as image captioning and visual question answering (VQA). In this paper, we propose a targeted adversarial attack for VQA where the noise is added only to the background pixels of the image keeping the rest of the image unchanged. The experiments are done on two state-of-the-art VQA systems: End-to-End Neural Module Network (N2NMN) and Memory, Attention and Composition Network (MAC network) and three datasets: SHAPES, CLEVR, and VQA v2.0. We combine validation and test sets of SHAPES, and select 1000 image-question pairs from CLEVR validation set. For VQA v2.0, we select 500 image-question pairs from the validation set for experimentation. We study the proposed attack under two different settings: same-category and different-category; referring to whether or not the target adversarial answer lies in the same category as the original answer. For CLEVR, the proposed attack achieves 100% success rate for both the models under same-category setting and success rate of 22.3% for N2NMN and 73.9% for MAC network under different-category setting. For SHAPES, the proposed attack achieves success rate of 68.9% for N2NMN. The proposed attack also achieves high success rate for same-category setting in VQA v2.0. Furthermore, we give strong rationale behind the robustness of N2NMN to different-category attack.
On Comparison of Countermeasures against Statistical Ineffective Fault Attacks

Statistical Ineffective Fault Attacks were proposed in CHES 2018 as a new powerful, yet practical, fault model that can be used to leak information from implementations that are protected against forgery, side-channel or traditional fault attacks. The attack leaks information about messages where the attack is ineffective. In other words, when the fault targeted by the attacker matches the internal value of the state, the fault goes unnoticed by traditional countermeasures, which give the attacker enough information about the internal state of the device. In May 2019, three potential countermeasures for this type of attack were proposed. These countermeasures have different goals and strategies, making each of them more convenient in a different scenario. In this paper, we characterize the strategies used in each of these countermeasures and study the cost of implementing each of them. We also study the security advantages and disadvantages of each of them and show some of the shortcomings of these countermeasures.
CNN-based bit-depth enhancement by the suppression of false contour and color distortion

Although 10-bit monitors are getting popular, most of the available media sources are 8-bit. The inconsistence between the low-bit-depth media sources and high-bit-depth monitors should be properly solved to make full use of the high-bit-depth equipment. Simply converting low-bit-depth images/videos to high-bit-depth ones via zero-padding would result in false contour artifacts in smooth region, which greatly degrades the visual quality. In this paper, a novel auto-encoder like CNN model is proposed to convert low-bit-depth images to high-bit-depth ones. Our method can significantly suppress false contour by the use of vgg_loss (mean square error computed on pre-trained VGG-19 feature maps). However, significant color distortion would be found in some results if only vgg_loss is used. In order to suppress color distortion, range_loss is proposed which restrains the difference between the resultant pixel values and the zero-padded ones within the range of [0, S), where S is the re-quantization step. Benefit from the novel network model and the designed loss function consisting of range_loss and vgg_loss, the proposed method has comparable objective metric with state-of-the-art. In particular, our method achieves better visual quality by significantly suppressing false contour artifacts and color distortion. Those conclusions are proved by experiments, and our code can be found at https://github.com/pengcm/BE-AUTO-ext.
Estimating and Controlling Overlap in Gaussian Mixtures for Clustering Methods Evaluation

The ad hoc nature of the clustering methods makes simulated data paramount in assessing the performance of clustering methods. Real datasets could be used in the evaluation of clustering methods with the major drawback of missing the assessment of many test scenarios. In this paper, we propose a formal quantification of component overlap. This quantification is derived from a set of theorems which allow us to derive an automatic method for artificial data generation. We also derive a method to estimate parameters of existing models and to evaluate the results of other approaches. Automatic estimation of the overlap rate can also be used as an unsupervised learning approach in data mining to determine the parameters of mixture models from actual observations.