Reviewerly/exHarmony huggingface dataset:
Three-Factor Authentication Scheme Based on Searchable Encryption and Biometric Fingerprint

The current paper is proposing a three-factor authentication (3FA) scheme based on three components. In the first component a token and a password will be generated (this module represents the kernel of the three-factor authentication scheme - 3FA). In the second component a pass-code will be generated, using to the token resulted in the first phase. We will use RSA for encryption and decryption of the generated values (token and pass-code). For the token ID and passcode the user will use his smartphone. The third component uses a searchable encryption scheme, whose purpose is to retrieve the documents of the user from the cloud server, based on a keyword and his/her fingerprint. The documents are stored encrypted on a mistrust server (cloud environment) and searchable encryption will help us to search specific information and to access those documents in an encrypted content. We will introduce also a software simulation developed in C# 8.0 for our scheme and a source code analysis for the main algorithms.
Investigations of the Methods of Time Delay Measurement of Stochastic Signals Using Cross-correlation with the Hilbert Transform

The article presents the results of simulation studies of four methods of time delay estimation for random signals using cross-correlation with the Hilbert Transform. Selected models of mutually delayed stochastic signals were used in the simulations, corresponding to the signals obtained from scintillation detectors in radioisotope measurements of liquid-gas two-phase flow. Standard deviations of the values of the individual functions were designated and compared, along with standard deviations of time delay estimates determined on their basis. The obtained results were compared with the results for classic cross-correlation function (CCF). It was found that for the analysed range of the signal-to-noise ratio (SNR): 0.2 ≤ SNR ≤ 5, the lowest values of standard deviation of time delay estimates were obtained for the CCFHT function (cross-correlation with the Hilbert Transform of the delayed signal).
Towards Post-Quantum Cryptography Using Thermal Noise Theory and True Random Numbers Generation

The advent of quantum computers and algorithms challenges the semantic security of symmetric and asymmetric cryptosystems.Thus, the implementation of new cryptographic primitives is essential.They must follow the breakthroughs and properties of quantum calculators which make vulnerable existing cryptosystems.In this paper, we propose a random number generation model based on evaluation of the thermal noise power of the volume elements of an electronic system with a volume of 58.83 cm 3 .We prove through the sampling of the temperature of each volume element that it is difficult for an attacker to carry out an exploit.In 12 seconds, we generate for 7 volume elements, a stream of randomly generated keys of 187 digits that will be transmitted from source to destination through the properties of quantum cryptography.
Foreground detection by ensembles of random polygonal tilings

In this work a novel region-based approach for the detection of foreground in video sequences is presented. The model consists of an ensemble of layers or tilings, where each tiling represents, by means of randomly chosen parallelogram regions, the background of the scene. Currently, the image size of video surveillance cameras far exceeds one megapixel (more than 1024 × 768), and pixel-based proposals are poorly suited for near real-time ratios. Therefore, the analysis by pixel is replaced by an analysis by region, improving the final resolution by overlapping regions or parallelograms with different shapes and sizes. Thus, for each frame, each region estimates the probability of belonging to the foreground or background, to finally compute the consensus foreground mask among all the tilings. With this proposal, it is possible to detect the foreground in high resolution sequences, a process that is not feasible using pixel-level techniques. Several experiments have been carried out by employing a wide range of videos. A qualitative and quantitative comparison with the state-of-the-art algorithms is performed by using a well-known video dataset benchmark. The results show the feasibility of our proposal compared with higher resolution methods.
Surviving in the Digital Environment: Does Survival Processing Provide an Additional Memory Benefit to Password Generation Strategies?

People encouraged to think about their survival when encoding information experience a memory advantage even when compared to other tried-and-true methods. Despite the evidence in support of the survival-processing advantage, how this advantage might apply to current-day issues has received little attention. This study aimed to determine if password generation strategies modified to encourage fitness-related encoding would yield an additional benefit in password recall. Participants recruited from online and college samples generated passwords using a sentence-based mnemonic (pilot experiment and Experiment 1) or person-action-object strategy (Experiments 2 and 3), in survival or control scenarios. Results support the plausibility of implementing different strategies for password generation, particularly the person-action-object strategy, and the importance of providing users instructions with which to create their passwords. Although the results illustrated the value of using scenarios for password creation, they did not show evidence of an additional fitness-related benefit, for possible reasons that we discuss.
The Data Provision Game: Researching Revenue Sharing in Collaborative Data Networks

Data sharing between companies enables formerly unexploited potential for data-driven business applications. While it is clear how the owner of the business application will profit from the shared data, it is still in question how other stakeholders are incentivized to share data across company borders. We developed the data provision game based on a synthesized view of data value chains as a mathematical framework to analyze the economic interactions and incentives in a collaborative data network. We thereby emphasize revenue sharing as an important managing activity as everyone who contributes to a business application should profit from it in order to set right incentives. Further, we differentiate between entity-borne and network-borne activities. Finally, we formulated Shapley Pricing and Leave-One-Out Pricing as two options for revenue sharing mechanisms, which set incentives for welfare-optimal participation and data quality.
Framing in Context

A higher proportion of the population, than ever before, is in the older age range. Diseases and conditions such as dementia, where old age is a considerable risk factor, are likely to increase. The increased incidences of dementia have extensive social and economic implications and will increase stress on existing healthcare systems and social structures. With no cure for dementia on the horizon attention is turning to how to improve the quality of life of people living with dementia. In designing to support quality of life, it is important to understand not only the individual needs and wants, but also the broader context of care. This includes the perspectives of local, national and international stakeholders in dementia care. Human–computer interaction researchers and designers are increasingly taking on the challenges and opportunities of working in the complex field of dementia. They have made valuable contributions to the well-being and quality of life of people both living with dementia and stakeholders in their care. In this chapter, we show the importance of understanding context and provide an overview of current developments in relation to challenges in dementia. The knowledge and skills discussed and approaches, methods, methodologies and tools used are transferable and can be adapted and used for other marginalized and disadvantaged groups in society.
Efficient Design Approach of Specific Sequential Circuits using Novel Reversible Gates

Reversible structures are very prominent designs in recent trends. Because it gives less power consumption and area. As R launderer said if it use reversible circuits, then KTln2 power does not dissipate from the circuit in the form of heat. If there is no heat dissipation, energy will not be lost from the circuit. And another advantage of reversibility is, any input or output signal can be obtained at any stage. In this paper, some sequential circuits are designed using reversible logic. In reversible logic, the main problem is garbage outputs. By these garbage outputs, the delay constrains are increased. So in this paper, circuits are designed with fewer garbage outputs i.e., having less delay. Here in this paper, flip-flops, counters, and LFSR are designed using reversible logic with fewer garbage outputs. The designs are constructed using Verilog HDL in RTL style. The results are simulated using Xilinx ISE 14.7.
Cost function based on the self-organizing map for parameter estimation of chaotic discrete-time systems

Parameter estimation problem of dynamical systems is an important task in controller design and system identification. In this paper, a novel approach is proposed towards parameter estimation of discrete dynamical systems with chaotic behaviors. Here, we utilize models of attractors to find unknown parameters of a real systems. This method relies on introducing a new cost function based on self-organizing maps (SOM) of measured data obtained from the system. In addition, theoretical justifications and computational complexity analyses are presented regarding the efficiency of SOM-based cost function. Experimental results on several benchmarks showed suitable performances of the proposed cost function compared to previously published cost functions such as Mean-Squared Error (MSE), Return Map Fingerprint (RMF), and Gaussian Mixture Model (GMM).
Image Inpainting for Irregular Holes Using Extreme Learning Machine

Image inpainting is usually framed as a constrained image generation problem. It is a method that helps to reconstruct the lost or deteriorated parts of images as well as video. The main focus in image inpainting technique is how precisely to generate the corrupted pixels in an image. Traditional inpainting algorithms are unfortunately not well adapted to handle such corruptions as they rely on image processing techniques that cannot properly infer missing information when the corrupted holes are too large. In this paper, a single pass method of inpaintng method is used which does not require any back propagation for training purpose, hence saving time for training the model. The objective of the proposed model is to reconstruct large continuous regions of missing or deteriorated parts of an image. In this paper, role of Extreme Machine Learning is a single pass neural network model, where we train our model based on region surrounding the corrupted region. Each image is divided into two sections: the missing part that we try to reconstruct, and the context. The network does not require any region to be in some defined shape as it can also work on arbitrary regions. Final evaluation is based on the average element-wise L2 distance between the corrupted image and the original image for the regions which is to be regenerate. We have also used PSNR value for comparison between original image and the image which we reconstruct.
Virtual reality gesture recognition based on depth information

With the rapid development of computer technology, the information revolution is constantly affecting and changing our lives. Today’s virtual reality technology is no longer just a virtual model for viewing. Experiencers are more willing to personally interact with the virtual environment in a virtual scene to increase their sense of gaming experience. Today’s virtual reality interaction methods mainly interact through the handles that come with VR helmets. Previously, some scholars have researched gesture interaction in virtual environments. They use data gloves to obtain information about hand movements. This method makes users have to wear heavy data gloves, which limits the natural movement of their hands. To overcome this shortcoming, the need for low‐cost vision‐based virtual reality gesture interaction is becoming more and more urgent. Traditional gesture recognition technologies based on artificial modeling include template‐based matching, data‐based gloves, and machine learning. As machine learning gesture classifiers, Hidden Markov Model (HMM) and Support Vector Machine (SVM) [1] have been widely used. However, in the hand motion detection of ordinary cameras, the image is prone to speckles, lighting changes, and other factors, which seriously affects the performance of the classifier, making it difficult to accurately and efficiently detect the first image when the background is complex and the lighting is affected. In recent years, deep learning‐based object detection methods have achieved great breakthroughs [2]. In the past decade, many deep learning‐based dynamic gesture recognition algorithms have been introduced. In the case of changing lighting conditions, in order to improve the classification accuracy of gesture recognition [3]. Therefore, this article chooses a depth camera combined with HTC VIVE device for gesture recognition in virtual reality. First obtain the depth image of the hand through the depth camera, then label the depth image, and then learn through the deep learning network hand movement training [4], preprocess the detected human hand image, and recognize it by hand detection The position of the hand in the image is obtained, and the movement of the hand is recognized as a result of recognition. The algorithm in this paper uses the advanced deep learning network YOLOv3, and changes the network structure and multi‐scale structure based on the original network. This algorithm is compared with other deep learning algorithms for gesture recognition, and the recognition effect is better than other algorithms in the same category.
Robust matching cost function based on evolutionary approach

This paper proposes a novel stereo matching method with a matching cost function learned from training data. Because the cost function includes a considerably large number of parameters required to select their values, it is nearly impossible to manually select the values. We employ an evolutionary algorithm to automatically optimize the parameter values for each dataset. Using Middlebury, KITTI 2012, and KITTI 2015 dataset, we compare the proposed stereo matching method with state-of-the-art stereo matching methods that can achieve real-time computation. Experimental results show that the proposed method outperforms the real-time state-of-the-art stereo matching methods.
Personalizing Technology-Enhanced Learning for Cultural Visits

The personalized fruition of cultural heritage, especially with the health crisis we are currently experiencing, poses challenges to the learning and educational processes that could benefit from the use of intelligent tools. By emphasizing the interactivity of the learning process, in particular, the recent Technology Enhanced Learning (TEL) methodologies can represent an enabling factor for the construction of the common knowledge. This paper pursues the idea of integrating different heterogeneous artificial intelligence techniques for the personalized administration of stimuli in a dynamic learning environment. With this in mind, this paper describes a system, called WikiTEL and its first instantiation to Cultural Heritage visits in the Matera city.
Do the Urgent Things first! - Detecting Urgency in Spoken Utterances based on Acoustic Features

In the future, spoken dialogue systems will have to deal with more complex user utterances and should react in an intuitive, comprehensible way by adapting to the user, the situation and the context. In rapidly changing situations, like talking to a highly automated car, it is highly relevant to react adequately to quick urgent interjections whether within one utterance or as interruptions of ongoing actions/dialogues. A first step is the detection of urgency in user utterances. Therefore, we developed a user study based on gamification simulating such short-term urgent situations. With this study, we collected data for a first analysis of features from the audio signal, which are promising for detecting urgent utterances. In the game "What is it?" participants had to find a symbol consisting of three characteristics from a set via speech. Their search was regularly interrupted by a time limited urgent task. The data obtained show that features only from the audio signal can be used to distinguish between urgent and non-urgent utterances. Further analysis reveals that certain features of the audio signal represent different phases of the data set better or worse. We distinguish, among other things, between the phases Transition and Decline, which represent the shift from non-urgent to urgent speech and vice versa. These shifts are recognizable and can occur in rapid change. We identified several classification methods to detect successfully urgent speech in each phase.
Improving the effectiveness of subject facets in library catalogs and beyond: a MARC-based semiautomated approach

Purpose This paper puts forward a MARC-based semiautomated approach to extracting semantically rich subject facets from general and/or specialized controlled vocabularies for display in topic-oriented faceted catalog interfaces in a way that would better support users' exploratory search tasks. Design/methodology/approach Hierarchical faceted subject metadata is extracted from general and/or specialized controlled vocabularies by using standard client/server communication protocols. Rigorous facet analysis, classification and linguistic principles are applied on top of that to ensure faceting accuracy and consistency. Findings A shallow application of facet analysis and classification, together with poorly organized displays, is one of the major barriers to effective faceted navigation in library, archive and museum catalogs. Research limitations/implications This paper does not deal with Web-scale discovery services. Practical implications This paper offers suggestions that can be used by the technical services departments of libraries, archives and museums in designing and developing more powerful exploratory search interfaces. Originality/value This paper addresses the problem of deriving clearly delineated topical facets from existing metadata for display in a user-friendly, high-level topical overview that is meant to encourage a multidimensional exploration of local collections as well as “learning by browsing.”
An Autoencoder Based Approach to Defend Against Adversarial Attacks for Autonomous Vehicles

Boosted by the evolution of machine learning technology, large amount of data and advanced computing system, neural networks have achieved state-of-the-art performance that even exceeds human capability in many applications [1] [2] . However, adversarial attacks targeting neural networks have demonstrated detrimental impact in autonomous driving [3] . The adversarial attacks are capable of arbitrarily manipulating the neural network classification results with different input data which is non-perceivable to human.
Performance Evaluation of Graphical User Interfaces in Java and C#

With the growing usage and migration of interactive applications and systems into today's environment, the significance of the graphical user interfaces (GUIs) increases as they act as the gates into using systems efficiently. Therefore, extensive efforts are spent to enhance the usability of the GUIs. However, most of the works focus on testing the functional properties of applications' GUIs rather than nonfunctional features such as performance. For this reason, it is worthwhile to assess the performance of various GUI components at runtime. In this paper, the most popular programming languages used to create GUI based applications namely, Java and C# were compared experimentally to evaluate their performance in terms of the creation and manipulation of GUI components/controls. The experimental results of this study, which is based on 32 testing scenarios, showed that Java outperformed C# in all test scenarios. This might be because of the “HotSpot” performance engine that Java uses. This study is useful for developers to get insights into the performance of different GUI components provided by different programming languages. Also, it helps them to choose the right programming language for their GUI based applications hence enhance the overall applications' performance and user satisfaction.
Organized Cyber-Racketeering: Exploring the Role of Internet Technology in Organized Cybercrime Syndicates Using a Grounded Theory Approach

In recent years, cyber-racketeering has become a prevalent means of cyberattack, severely jeopardizing nations, organizations, and individual users. However, because previous research has been focused on individual crimes, insider security threats, and compliance, the serious consequences of organized cybercrimes have received little attention. To investigate organized cyber-racketeering, we explore how organized cybercriminals apply organizational techniques to systematically commit cyberattack on individuals and businesses. Based on 80 court cases of successfully prosecuted cybercrimes, this article is one of the first to scientifically examine organized crime syndicates that are engaged in cyber-racketeering. We use the grounded theory approach to investigate organized cyber-racketeering. To ensure the scientific rigor and realism of this study, we examine these court cases to develop a process theory based on the results of interpretive and iterative analyses conducted from multiple perspectives on organized cyber-racketeering. We establish a framework for organized cyber-racketeering to shed light on its characteristics and the crucial role of Internet technology (IT) in the implementation and operation of cyber-racketeering activities. The proposed framework consists of motive, organization, learning and development, and execution. This article contributes both to enhancing the understanding of how organized cybercriminals operate and profit from cyber-racketeering using information and communications technology (ICT) and to improving countermeasures that may be taken by practitioners.
Research on multi-mode online measurement based on universal ranging machine learning model

The focus of this article is that it is difficult to effectively measure the universal distance through the use of conventional parameters. This paper uniquely proposes the use of depth-limiting machine learning methods to model the universal distance measurement model, which can effectively perform multi-modal online measurement of the data measurement machine learning model. The feature extraction is unique to this article. The model uses the Softmax software to fine-tune the logarithm to verify the correctness of the propagation algorithm. It can effectively verify the measured distance and obtain multimodal data online. Recognition accuracy and strong robustness are also one of the characteristics of this article. This article has high academic value and innovative features, and is of great significance for the study of machine learning models.
Research on Side-Channel Attack Based on the Synergy between SNR and Convolutional Neural Networks

Abstract The data encryption process in the encryption chip will be leaked by means of timing operation time, probing data collecting operation power and electromagnetic signal interception, which makes side channel attack possible. Nowadays, the research on template creation of template attacks has shifted from Gaussian distribution to the use of machine learning algorithms to create templates. With many parameters, it is difficult to find a suitable network structure. Based on many experimental studies, the experience of a convolutional neural network structure suitable for side channel analysis is summarized and proposed.