Reviewerly/exHarmony huggingface dataset:
Adversarial attack to fool object detector

Abstract State-of-the-art deep neural network-based models have a loophole that they are prone to adversarial attacks. However, only a few attacks are demonstrated on object detection and these adversarial attacks have a limitation that they require tuning of hyperparameters which is very time-consuming. To address the problem, we propose a simple and computationally efficient technique in terms of the average number of iterations that is, Plug-n-Play Adversarial Attack (PPAA) in which constrained uniform random noises are used to generate perturbations. The proposed method is tested on the Microsoft Common Objects in Context (MSCOCO) dataset, using a state-of-the-art object detection algorithm named RetinaNet. The results show that the proposed PPAA reduced the average number of iterations to 8.64 which is 1/5th of DAG and achieves a comparable convergence rate that is 96.48% while keeping the perturbations quasi-imperceptible to human eyes, that quantifies to 1.2 * 10–2.
Typology of innovation strategies for petrochemical enterprises

Abstract The article defines the role of the petrochemical industry in the development of the economy as a whole. The key problems of innovative development of the Russian petrochemical complex that are typical for the current stage are identified. The purpose of the study is to analyze the organizational and economic processes of formation and implementation of innovative strategies for the development of petrochemical enterprises and practical recommendations for improving their performance. To achieve this goal, the role of the components of the innovative potential of enterprises in the development of the economy is determined; the analysis of directions of development of innovative activity of the main foreign players in the market of petrochemical is carried out; strategies of innovative development of petrochemical enterprises are formed. It is concluded that in the era of digitalization of industry, the priority strategy for the development of the petrochemical complex is resource conservation.
RandAdminSuite: A New Privacy-Enhancing Solution for Private Blockchains

Blockchain is an innovative technology which is used by cryptocurrencies as a public, immutable ledger for recording transactions, while more recent versions of Blockchain can also record smart contracts and other assets. Blockchain can be viewed as a distributed platform that holds transactional records without the involvement of a central authority, and where ensuring decentralization, transparency and security is of paramount importance. But the transparency requirement, absolutely necessary for improving trust among the blockchain's users, came with a price: lack of privacy. In most of the blockchains which are based on Bitcoin's Blockchain, anyone can query the blockchain and see all the transactions. This introduces a privacy issue which needs to be addressed. Although there are a few solutions for mitigating the privacy concern, we consider that true anonymity must be built-in, not added on trough extensions to the base protocol. We propose a novel solution, called RandAdminSuite, that addresses the blockchain privacy problem through a comprehensive approach that covers the blockchain architecture itself, transaction mechanism and cryptocurrency as well. RandAdminSuite offers some improvements over the concept of currency rewards for transaction processing nodes.
Multi-Scale Convolutional Features Network for Semantic Segmentation in Indoor Scenes

Semantic segmentation is one of the most fundamental techniques for visual intelligence, which plays a vital role for indoor service robotic tasks such as scene understanding, autonomous navigation and dexterous manipulation. However, semantic segmentation of indoor environments poses great challenges for existing segmentation techniques due to the complex overlaps, heavy occlusions and cluttered scenes with objects of different shapes and scales, which may lead to the loss of edge information and insufficient segmentation accuracy. And most of the semantic segmentation networks are very complex and cannot be applied to mobile robot platforms. Thus, it is of significant importance for ensuring as few network parameters as possible while improving the detection of meaningful edges in indoor scenes. In this paper, we present a novel indoor scene semantic segmentation method that can refine the segmentation edges and achieve a balance between accuracy and model complexity for indoor service robots. Our approach systematically incorporates dilated convolution and rich convolutional features from the intermediate layers of Convolutional Neural Networks (CNN), which is based on two motivations: (1) The middle hidden layer of CNN contains a lot of potentially useful information for better edge detection which is, however, no longer present in latter layers in traditional structures. (2) The dilated convolution can change the size of receptive field and obtain multi-scale feature information without losing the resolution and introducing any additional parameters. Thus we propose a new end-to-end Multi-Scale Convolutional Features (MSCF) network to integrate the dilated convolution and rich convolutional features extracted from the intermediate layers of traditional CNN. Finally, the resulting approach is extensively evaluated on the prestigious indoor image datasets of SUN RGB-D and NYUDv2, and shows promising improvements over state-of-the-art baselines, both qualitatively and quantitatively.
Video security against deepfakes and other forgeries

This paper describes a system of steps which aims at proving the ownership of video content, detecting tampered original videos and distinguishing between authentic and counterfeit videos. This system achieves its purpose by using the interdisciplinary technologies of image processing namely face detection, recognition and semi fragile watermarking along with data security algorithm of AES encryption. Our paper aims at optimizing each step in this system to reduce the overall computational count.
Summary of the 2019 Activity Detection in Extended Videos Prize Challenge

Despite previous data collection efforts and benchmark studies, progress in activity detection technologies has been slow, especially with applications that meet practical needs for the video analytics domain. In this paper, we discuss the results from the Activity detection in Extended Video Prize Challenge (ActEV-PC) that was sponsored by IARPA. The goal of the ActEV-PC was to promote robust automatic activity detection system development and to reduce the detection error rate. To examine the ability of activity detection systems in different aspects, we opened a competition to the public and ran evaluations (as a task under the ActivityNet workshop at CVPR 2019) with two different phases: an open leaderboard evaluation and a sequestered data evaluation. The Video and Image Retrieval and Analysis Tool (VIRAT) dataset was used for the open leaderboard evaluation while the Multiview Extended Video with Activities (MEVA) dataset was used for the sequestered data evaluation. Eighteen target activities were defined for detection. In this paper, we present results and findings from the two-phase ActEV-PC competition. Eighteen teams from academia and industry participated in the competitions and three top performers received a cash award (funded by IARPA). The winners were presented at the ActivityNet Workshop at CVPR 2019.
Self-Admitted Technical Debt classification using LSTM neural network

Context: In software development, new functionalities and bug fixes are required to ensure a better user experience. Sometimes developers need to implement quick changes to meet deadlines rather than a better solution that would take longer. These easy choices, known as Technical Debts, can cause long-term negative impacts because they can bring extra effort to the team in the future. One way to detect technical debts is through source code comments. Developers often insert comments in which they admit that there is a need to improve that part of the code later. This is known as Self-Admitted Technical Debt (SATD). Objective: Evaluate a Long short-term memory (LSTM) neural network model to identify design and requirement SATDs from comments in source code. Method: We performed a controlled experiment to evaluate the quality of the model compared with two language models from literature in a labeled dataset. Results: Our model results outperformed the other models in precision, improving average precision in approximately 8% compared to auto-sklearn and 19% compared to maximum entropy approach, however, the LSTM model achieved worse results in recall and f-measure. Conclusion: We found that the LSTM model can classify with better precision but needs a larger training, so it can improve on the detection of negative cases.
Comparative Evaluation of the Effects of Motion Control on Cybersickness in Immersive Virtual Environments

The commercialization and lowering costs of consumer grade Virtual Reality (VR) devices has made the technology increasingly accessible to users around the world. The usage of VR technology is often accompanied by an undesirable side effect called cybersickness. Cyber-sickness is the feeling of discomfort that occurs during VR experiences, producing symptoms similar to those of motion sickness. It continues to remain one of the biggest hurdles to the widespread adoption of VR, making it increasingly important to explore and understand the factors that influence its onset. In this work, we investigated the influence of the presence/absence of motion control on the onset and severity of cybersickness in an HMD based VR driving simulation employing steering as a travel metaphor. Towards this end, we conducted a between subjects study manipulating the presence of control between three experimental conditions, two of which (Driving condition and Yoked Pair condition) formed a yoked control design where every pair of drivers and their yoked pairs were exposed to identical vehicular motion stimuli created by participants in the driving condition. In the other condition (Autonomous Car condition), participants experienced a program driven autonomous vehicle simulation. Results indicated that participants in the Driving condition experienced higher levels of cybersickness than participants in the Yoked Pair condition. While these results don’t conform to findings from previous research which suggests that having control over motion reduces cybersickness, it seems to point towards the importance of the fidelity of the control metaphor’s feedback response in alleviating cybersickness. Simply allowing one control their motion may not readily alleviate cybersickness but could instead increase it in such HMD based VR driving simulations. It may hence be important to consider how well the control metaphor and its feedback matches users’ expectations if we want to successfully mitigate cybersickness.
Predictive Visual Analysis of Speech Data using Machine Learning Algorithms

Audio sense analysis using automated speech recognition is an emerging research field where opinions or feelings are natural audio displayed by a speaker is detected. Still, text- based sentiment detection is relatively unexplored and very less used in physical word despite its huge possible implementation area because Talking and listening improves information if it is nice then it encourage to listen to you which helps better understanding the things and to form a relationship. Talking have many emotions in it; this is not just nice thing or good news, but also anger, fear, criticism, or blame hence can be categories in positive, neutral or negative communication. In this new architecture, we perform a predictive visual analysis of big data collecting from various speaking source and here we perform graphical and non-graph analysis and visualize it using machine learning algorithms (Naive Bayes). And we perform weighted word cloud visualizations, which give improved semantic insights. The results obtained by this work can help in finding good teacher, orator, speaker, and examine self-talking skill.
Survey on the Patent Law Awareness and the Entrepreneurial Trend of Greece’s Graduates of Technology Institutes

In information technology industry, the development of new technology achievements influences and inspires the business world, serves and contributes to the progress of society.This rapidly evolving technological advancement needs a legal framework in order to obtain social and commercial status.Patent Law is the legal science and patent is the legal tool that adds value to intangible assets and therefore technology scientists are motivated to ensure their invention with a patent and also dominant technology enterprises are competing for the development of a strong patent portfolio.For the first time in Greece, combining the fields of intellectual property law and technological innovation, we created a survey using a questionnaire that was distributed in 2019, to graduated students of technology institutes in Greece.The purpose of the present survey is to clarify the knowhow of students as for the legal point of view of technology.We intend to find out, whether Greece's potential inventors have beyond their technological background, the skills and knowledge to support a potential innovative idea.The results of the survey were that students have confidence in the institution of patents; however they lack the knowledge of patenting technological inventions.At the same time they have a positive attitude and motivation for entrepreneurship in the technological industry.Consequently, it is vital to apply measures in education, for the next generation technical inventors to have the knowhow of utilizing their intellectual asset in their own free will.Then, we can refer to integrated scientist technocrats who can protect their intellectual property and manage a competitive business.
Convolutional Neural Network For Predicting The Spread of Cancer

Detecting the spread of cancer using digital images requires expertise from a pathologist, takes time and vulnerable to human error. In our work, we offer an approach to using a convolutional neural network (CNN) to predict the presence of cancer by using pathological images. We have developed a CNN framework, applied data augmentation then trained our model using a single GPU (Graphic Processor Unit) provided by Google Collaborator. We have used data published in the form of a Kaggle dataset. Datasets are small pathology images which are derived from Camelyon16, namely PatchCam. This database consists of approximately 200,000 images categorized into two classes: cancer and no cancer. We have split the data with a proportion of 70% for training and 30% for validation. We can report satisfactory results regarding the 200,000 images in the training and validation processes with accuracy reaches 0.92. The fl scores have found to be 0.94 for class no cancer and 0.91 for class cancer, while the AUC grade has 0.98. Besides the presentation of competitive results, we discuss how such automatic systems may improve the cognitive capabilities of clinical experts.
Object Depth Measurement from Monocular Images Based on Feature Segments

Depth measurement technology plays an important role in the field of machine vision, and the depth measurement methods based on monocular vision have received more and more attention. However, previous depth measurement schemes based on single feature points and least squares calculation are susceptible to feature matching errors. To this end, this paper proposes a new object depth measurement method from monocular images based on feature segments. We use two images taken by the same camera and the pose information provided by GPS/IMU device to perform object depth measurement. The experiment based on the visual simulation software shows that the proposed method can improve the accuracy of the measurement results with a good robustness.
University of Education: Structure and Main Components of Management

&lt;p style="text-align:justify"&gt;The article analyzes the evolution of pedagogical systems and the practice of universities in the world. The work reveals the essence and main structural components of the university education, as a special institution of higher education, designed to become a systemic solution to the problem of management and development of teacher education. The model presented by us includes the following components: education, as a platform for transferring innovative solutions into practice; Mega-Science -a system of research collaborations that provides advanced solutions to global problems; technologies -research constructs according to the Lego principle, generating local problem-solving based on a cluster; implementation -a system of investment decisions on the implementation of developments in the practice of activity; innovation is a system for the formation of products open to use that stimulate the development of education. The paper presents the main results of these developments, conclusions are made about the need to form a university of education as a special system operating within the framework of the concept of open pedagogical education and aimed at creating a federal scientific and educational hub of global importance for reproduction and the formation of human capital in scientific, engineering activities, the generation of new solutions in the field of training and development of teaching staff in.&lt;/p&gt;
Content-Based Search in Lecture Video: A Systematic Literature Review

With the advancement of science and technology, everything is directly or indirectly related to it. In the same way, the learning method is also changing. Nowadays e-learning is one of the most popular methods of learning where lecture video plays a great role. Although lots of work have been done in the area of lecture video limited work have been done using content-based searching. This paper aims to obtain an overview of the topic of lecture video content-based search. To achieve that, a protocol for systemic literature review has been adapted and subsequently implemented from literature review protocol standards. The objective is to assess the existing inputs and outputs in the literature to create the best lecture video content-based search system. This paper reveals benefits, obstacles and challenges, and data source and contents which can be extracted and utilized for content-based search.
How users tweet about a cyber attack: An explorative study using machine learning and social network analysis

Cybercrime is a growing threat for firms and customers that emerged with the digitization of business. However, research shows that even though people claim that they are concerned about their privacy online, they do not act correspondingly. This study investigates how prevalent security issues are during a cyber attack among Twitter users. The case under examination is the security breach at the US ticket sales company, Ticketfly, that compromised the information of 26 million users. Tweets related to cybersecurity are detected through the application of automated text classification based on supervised machine learning with support vector machines. Subsequently, the users that wrote security-related tweets are grouped into communities through a social network analysis. The results of this multi-method study show that users concerned about security issues are mostly part of expert communities with already superior knowledge about cybersecurity.
Group of continuous transformations of real interval preserving tails of G2-representation of numbers

In the paper, we consider a two-symbol system of encoding for real numbers with two bases having different signs \({g_0&lt;1}\) and \(g_1=g_0-1\). Transformations (bijections of the set to itself) of interval \([0,g_0]\) preserving tails of this representation of numbers are studied. We prove constructively that the set of all continuous transformations from this class with respect to composition of functions forms an infinite non-abelian group such that increasing transformations form its proper subgroup. This group is a proper subgroup of the group of transformations preserving frequencies of digits of representations of numbers.
Trust and Reputation Smart Contracts for Explainable Recommendations

Recommendation systems are usually evaluated through accuracy and classification metrics. However, when these systems are supported by crowdsourced data, such metrics are unable to estimate data authenticity, leading to potential unreliability. Consequently, it is essential to ensure data authenticity and processing transparency in large crowdsourced recommendation systems. In this work, processing transparency is achieved by explaining recommendations and data authenticity is ensured via blockchain smart contracts. The proposed method models the pairwise trust and system-wide reputation of crowd contributors; stores the contributor models as smart contracts in a private Ethereum network; and implements a recommendation and explanation engine based on the stored contributor trust and reputation smart contracts. In terms of contributions, this paper explores trust and reputation smart contracts for explainable recommendations. The experiments, which were performed with a crowdsourced data set from Expedia, showed that the proposed method provides cost-free processing transparency and data authenticity at the cost of latency.
On Cybersecurity Education for Non-technical Learners

In this paper the topic of cybersecurity education for individuals who are not in a technical learning environment will be explored. There are currently several organizations that can certify an education program that is focused on cybersecurity or similar areas. However, for individuals who are not within the cybersecurity field, such as those who are not yet finished with their high school education, those who are within another discipline at the University or College level, or those who are outside of the education system entirely, then there is no such entities or guidelines for programs to inform them about cybersecurity and privacy related issues and topics. This does not mean though, that these individuals have any less of a need for some training in the basics of cybersecurity and privacy protection that will affect everyone's daily life. Since these individuals are not exposed to the curriculums that might enable them to recognize or gain an understanding of the reasons behind practices that are common place for everyone, such as password requirements, then there could be an even greater need for them to be exposed to it in a controlled and consistent manner. The need to understand the security and privacy concerns associated with using various services and devices will continue to increase as more devices become internet and network connected, more jobs increasingly rely on technology, and more of these connected devices are brought into homes.
Plaintext recovery attack on 3DES algorithm with different byte keys

Aiming at the problem of plaintext recovery attack on 3DES algorithm with different byte keys, a plaintext recovery method for 3DES algorithm with different byte keys is proposed. In order to strengthen the anti-attack ability of DES algorithm, 3DES algorithm is used, which includes double-key 3DES of and triple-double-key 3DES. The 3DES algorithm with different seed key length is used to recover the plaintext. The attack algorithm for the first 256-byte plaintext encrypted by 3DES algorithm are given through the single byte deviation rule and double byte deviation rule. The results show that with the increase of the corresponding quantization order, the processing effect of the method is better than that of the chaos mapping method (4.2 dB), and with the increase of the quantization order, the advantage of the method is constantly improved; the attack result of the method on 256 plaintext is shown as the 114th byte to 127th byte from 3072 bytes, so that in practical application, the plaintext encrypted by 3DES algorithm can be recovered better.