Reviewerly/exHarmony huggingface dataset:
Gaze collaboration patterns of successful and unsuccessful programming pairs using cross-recurrence quantification analysis

Abstract A dual eye tracking experiment was performed on pairs of novice programmers as they traced and debugged fragments of code. These programming pairs were categorized into successful and unsuccessful pairs based on their debugging scores. Cross-recurrence quantification analysis (CRQA), an analysis using cross-recurrence plots (CRP), was used to determine whether there are significant differences in the gaze collaboration patterns between these pair categories. Results showed that successful and unsuccessful pairs can be characterized distinctively based on their CRPs and CRQA metrics. This study also attempted to interpret the CRQA metrics in relation to how the pairs collaborated in order to provide a somewhat clear picture of their relevance and meaning. The analysis results could serve as a precursor in helping us understand what makes a programming pair more successful over other pairs and what behaviors exhibited by unsuccessful pairs that should be avoided.
Framework for faster key search using related‐key higher‐order differentialproperties: applications to Agrasta

The relevance of the related-key model is usually controversial. However, in some cases, related-key properties have already been used to reduce the effective key length of the cipher in the single-key model. Hence, research into this direction can be helpful to bridge the gap between theory and practice aspects of the related-key model. Motivated by this challenge, the authors develop a new framework to provide further evidence that deterministic related-key characteristics can be utilised in the single-key model. The authors describe a sound framework for utilising related-key higher-order differential distinguishers that can beat the boundaries given by exhaustive key search. The data required is only one known as plaintext–ciphertext pair if the number of ciphertext bits matches the key length. From a theoretical point of view, the connection between related-key higher-order differential properties and the security of cryptographic primitives in the single-key model are precised. From a practical point of view, the proposed framework is used to evaluate the security of Agrasta cipher which is a variant of Rasta cipher presented at CRYPTO 2018. The proposed method is the first analysis of Agrasta reduced to three rounds that performs better than exhaustive key search and is independent of the used linear layers.
Edge-Preserving Stereo Matching Using Minimum Spanning Tree

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches.In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary.We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency.Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution.Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result.Finally, we run our method on several Middlebury and KITTI datasets.The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.
Finding and Counting Permutations via CSPs

Permutation patterns and pattern avoidance have been intensively studied in combinatorics and computer science, going back at least to the seminal work of Knuth on stack-sorting (1968). Perhaps the most natural algorithmic question in this area is deciding whether a given permutation of length n contains a given pattern of length k. In this work we give two new algorithms for this well-studied problem, one whose running time is $$n^{k/4 + o(k)}$$ , and a polynomial-space algorithm whose running time is the better of $$O(1.6181^n)$$ and $$O(n^{k/2 + 1})$$ . These results improve the earlier best bounds of $$n^{0.47k + o(k)}$$ and $$O(1.79^n)$$ due to Ahal and Rabinovich (2000) resp. Bruner and Lackner (2012) and are the fastest algorithms for the problem when $$k \in \varOmega (\log {n})$$ . We show that both our new algorithms and the previous exponential-time algorithms in the literature can be viewed through the unifying lens of constraint-satisfaction. Our algorithms can also count, within the same running time, the number of occurrences of a pattern. We show that this result is close to optimal: solving the counting problem in time $$f(k) \cdot n^{o(k/\log {k})}$$ would contradict the exponential-time hypothesis (ETH). For some special classes of patterns we obtain improved running times. We further prove that 3-increasing (4321-avoiding) and 3-decreasing (1234-avoiding) permutations can, in some sense, embed arbitrary permutations of almost linear length, which indicates that a sub-exponential running time is unlikely with the current techniques, even for patterns from these restricted classes.
On some methodological problems of digital transformation of education

The purpose of the article is to consider the main directions of digital transformation of education and methodological problems arising in this process. The analysis of the literature shows that pedagogical science does not keep pace with the rapid process of digital transformation of education, a unified methodological base has not been developed, there is no common understanding of this process in the scientific community and of new scientific terms that are being formed in this process. There is also no scientific justification for the ongoing changes in education. Continuous process of reforming, modernization, improvement of educational systems does not yield results, but leads to accumulation of fatigue of teachers from reforms, emergence of psychophysiological and methodical problems in training. When reforming the content of education, mistakes are made associated with insufficient reliance on fundamental pedagogical knowledge, lack of balance between traditions and innovations in education. The emerging problems can be solved only as a result of the development of a common methodological framework and common scientific approaches in the digital transformation of education.
Color Blindness Correction using Augmented Reality

To alter the hue of an image in real-time to correct color blindness using a mobile application.
Image Segmentation Algorithm Based on Particle Swarm Optimization with K-means Optimization

In order to achieve accurate and efficient image segmentation, this paper proposes a Particle swarm optimization (PSO) algorithm and k-means aggregation hybrid image segmentation algorithm, which aims to solve the problem of selecting the initial center of k-means clustering and improve the disadvantages of easily falling into local optimal. Firstly, the image is de-noised and the processed color image is converted into HSV space to improve the color quality. Then, global search ability is improved by adjusting the inertia weight and learning factor in the particle swarm optimization algorithm. Finally, the local search is performed by switching to the k-means algorithm according to the particle fitness, so that the clustering center is constantly updated to achieve rapid convergence. Segmentation results show that the proposed algorithm can segment images accurately, and have a higher segmentation efficiency than k-means algorithm, traditional particle swarm optimization and k-means algorithm (PSOK).
Morse Code Representation using EMG Signals

Morse code is the most basic emergency communication method. It expresses alphabet and numbers by using the combinations of dots and dashes. Because of its simple structure which only contains dots and dashes, Morse code is expressed in a variety of ways. Among the methods of expressing Morse code, methods that use human body movements such as finger gesture, tongue movements and eye blinking have been studied. Our research uses surface electromyography (EMG) signals generated by hand motion. We expressed Morse code according to the duration between folding and stretching the fist. Experimentally, we have shown that the performance is 100% accuracy in recognizing 26 alphabets.
Machine Learning based Sentiment Analysis using Graph Based Approach

In the recent times, deriving high quality information from the ever increasing amount of text data is highly desirable in many application domains. Analysis and summarization of online review data is one such domain which demands an effective sentiment analysis technique for optimal decision making. This paper proposes an efficient sentiment analysis model by using a popular graph based method and some standard machine learning techniques in order to produce an overall sentiment using a limited and important set of keywords. A graph based technique using degree centrality measure is used specifically for extracting only the important and relevant keywords. The proposed model produces significant results which are validated with four different datasets and three different classifiers.
Open Data Resources for Clean Energy and Water Sectors in India

With the wave of digitalisation, institutions across countries are pushing for the creation of open data and their governance. FAIR Data Principles have initiated the publishing of open research data to the key stakeholders and practitioners in the low- and middle-income countries to meet their developmental goals through practical usage in problem-solving. Open Data, which is part of the Open Science movement, has transformed the regime structure at a transnational level for the governance of critical issues surrounding water and energy. This paper provides a baseline survey to look into the various open data initiatives in the areas of water and clean energy across countries in general and India in particular. Given the multifaceted challenges around the water-energy nexus existing in India, it is critical to identifying the open data initiatives and studying their governance at the country level. Since governance requires the participation of various institutions and multiple stakeholders, the research aims at highlighting the various initiatives such as participation of institutions and the application of Creative Commons (CC) licensing terms in the open data governance for clean energy and water sectors in India.
Implementation of Voice Recognition in Disaster Victim Detection Using Hidden Markov Model (HMM) Method

In this study, the authors used the voice recognition system as a supporter in the detection of disaster victims to streamline the rescue team in finding victims of the disaster. With the help of unmanned aircraft (drones) a speech recognition system will be apply in the micro computer installed on the drone. Sounds that are detected are general sounds that are usually spoken by someone when they are in need of help or help and certainly the sound can be understood by humans, the voice list is contained in the next discussion, and the sounds will be used as training data which later becomes the research dataset. In the implementation the author uses the Hidden Markov Model (HMM) method as a method used for searching words or the best words detected as help or assistance requests, and also the author uses Mel-Frequency Cepstral Coeffecient (MFCC) as an additional method for extracting the characteristics of each sound at dataset. Then when the test results find the input sound that matches the dataset, the system will send the location of the system which is obtained from the Global Positioning Systems (GPS) module to the rescue server.
Modified Gingerbreadman Chaotic Substitution and Transformation Based Image Encryption

In this chapter, a combined Pseudo Hadamard transformation and modified Gingerbreadman chaotic substitution based image encryption algorithm is proposed. Intrinsic properties of images such as high inter-pixel redundancy and bulk data capacity, encryption is done in two stages: transformation and substitution. Pseudo Hadamard transformation reduces correlation between the adjacent elements in the host image and entropy is increased by subjecting it to modified Gingerbreadman chaotic substitution. The initial conditions for modified Gingerbreadman chaotic generator is considered from 128 bits secrete key. The random sequence generated by modified Gingerbreadman chaotic generator is introduced in the substitution stage of encryption process to diffuse the pixel values of cipher image after transformation. The cipher images are subjected for various security analysis and the results obtained are better compared to many existing techniques.
MedGaze: Gaze Estimation on WCE Images Based on a CNN Autoencoder

The interpretation of medical images depends on physicians' experience. Over time, physicians develop their ability to examine the images, and this is usually reflected on gaze patterns they follow to observe visual cues, which lead them to diagnostic decisions. In the context of gaze prediction, graph and machine learning methods have been proposed for the visual saliency estimation on generic images. In this work we preset a novel and robust gaze estimation methodology based on physicians' eye fixations, using convolutional neural networks combined with regularization methods, on medical images taken during Wireless Capsule Endoscopy (WCE). Furthermore, we present a novel dataset of physicians' eye fixation patterns which was used for the training of the neural network model. The model was able to achieve 68.5% Judd's Area Under the receiver operating Characteristic (AUC-J).
Complex Fuzzy Sets with Application in BCK/BCI-Algebras

As a generation of fuzzy set, the notion of complex fuzzy set which is an innovative concept is introduced by Ramot, Milo, Friedman and Kandel. The purpose of this article is to apply complex fuzzy set to BCK/BCI-algebras. The notions of a complex subalgebra and a complex left (right) reduced ideal in a BCK/BCI- algebra are introduced, and related properties are investigated. Characterizations of a complex subalgebra are provided, and the homomorphic image (preimage) of a complex subalgebra and a complex left (right) reduced ideal.
Fitch's Paradox and Level-Bridging Principles

Fitch’s Paradox shows that if every truth is knowable, then every truth is known. Standard diagnoses identify the factivity/negative infallibility of the knowledge operator and Moorean contradictions as the root source of the result. This paper generalises Fitch’s result to show that such diagnoses are mistaken. In place of factivity/negative infallibility, the weaker assumption of any ‘level-bridging principle’ suffices. A consequence is that the result holds for some logics in which the “Moorean contradiction” commonly thought to underlie the result is in fact consistent. This generalised result improves on the current understanding of Fitch’s result and widens the range of modalities of philosophical interest to which the result might be fruitfully applied. Along the way, we also consider a semantic explanation for Fitch’s result which answers a challenge raised by Kvanvig.
Detection of HEVC Double Compression with Different Quantization Parameters Based on Property of DCT Coefficients and TUs

High Efficiency Video Coding (HEVC) double compression detection is of great significance in video forensics. In this paper, a novel algorithm of detection of HEVC video double compression with different Quantization Parameters (QPs) based on Property of DCT Coefficients and TUs (PDCT) feature is proposed. Firstly, the impact of Transforming Unit (TU) type and QP on Discrete Cosine Transform (DCT) coefficients is analyzed. Next, the PDCT feature is extracted based on the statistical characteristics of DCT coefficients and TUs. Then, the framework of our proposed algorithm is given, consisting of four steps described in detail. Finally, experiments are conducted on high definition videos, which are more common in HEVC, while excluding the influence of video content on the detection accuracy. Experimental results and the corresponding analysis are also provided, demonstrating the superiority of the proposed algorithm to the current state-of-the-art algorithm of detecting HEVC double compression with different QPs.
APPLICATION OF KOHONEN SELF-ORGANIZING MAP TO SEARCH FOR REGION OF INTEREST IN THE DETECTION OF OBJECTS

Today, there is a serious need to improve the performance of algorithms for detecting objects in images. This process can be accelerated with the help of preliminary processing, having found areas of interest on the images where the probability of object detection is high. To this end, it is proposed to use the algorithm for distinguishing the boundaries of objects using the Sobel operator and Kohonen self-organizing maps, described in this paper and shown by the example of determining zones of interest when searching and recognizing objects in satellite images. The presented algorithm allows 15–100 times reduction in the amount of data arriving at the convolutional neural network, which provides the final recognition. Also, the algorithm can significantly reduce the number of training images, since the size of the parts of the input image supplied to the convolution network is tied to the image scale and equal to the size of the largest recognizable object, and the object is centered in the frame. This allows to accelerate network learning by more than 5 times and increase recognition accuracy by at least 10 %, as well as halve the required minimum number of layers and neurons of the convolutional network, thereby increasing its speed.
Web-Based Environmental Learning Information System in SMA Angkasa Lanud Husein Sastranegara Bandung

The Purpose of this research is to build an information system application to help teachers and students in high school in learning environmental education. Angkasa Lanud Husein Sastranegara Bandung Senior High School is an adiwiyata green school project, they also have an environmental friendly institution curriculum that is environmental education. The method used for this research is descrptive method. His method is carried out by examining an existing event and analyzed by interview and observation data collection technique. The result of this research is an application of environmental learning information systems that can help the learning process of students and teachers to achieve the appropriate target material and interactive learning so that students can easily understand the contents of the material provided.
Bypassing Non-Outsourceable Proof-of-Work Schemes Using Collateralized Smart Contracts

Centralized pools and renting of mining power are considered as sources of possible censorship threats and even 51% attacks for decentralized cryptocurrencies. Non-outsourceable Proof-of-Work (PoW) schemes have been proposed to tackle these issues. However, tenets in the folklore say that such schemes could potentially be bypassed by using escrow mechanisms. In this work, we propose a concrete example of such a mechanism which is using collateralized smart contracts. Our approach allows miners to bypass non-outsourceable PoW schemes if the underlying blockchain platform supports smart contracts in a sufficiently advanced language. In particular, the language should allow access to the PoW solution. At a high level, our approach requires the miner to lock some collateral covering the reward amount and protected by a smart contract acting as an escrow. The smart contract allows the pool to collect the collateral as soon as the miner collects any block rewards. We propose two variants of the approach depending on when the collateral is bound to the block solution. Using this, we show how to bypass previously proposed non-outsourceable Proof-of-Work schemes (with the notable exception for strong non-outsourceable schemes) and show how to build mining pools for such schemes.
An interval type-2 FCM for color image segmentation

is one of the clustering algorithms that group data objects based on similarity, in image processing, FCM divides pixels in images to different segments based on their contribution degrees to different segments.One of the popular and powerful techniques used in cluster
Virtual Environment Application that Complements the Treatment of Dyslexia (VEATD) in Children

The educational disorders that children present at an early age can cause them to not fully develop throughout their lives. In this research work a 3D virtual system that allows the child who has been diagnosed with dyslexia to complement the exercises performed in a conventional therapy is described. To achieve this an application was developed, the app consists of two games (each with three levels of difficulty), and that are part of the rehabilitation program. In each of these games virtual objects are combined with auditory messages to provide the user with an immersive experience, and to train more than one sense at a time. In the first game task, the activity asks the children to correctly locate the syllables that compose a word and for the second activity the children will listen to a word, after the games asks the children to select the correct word. This tool has been tested by a group of children (eight), with ages ranging from 8 to 12 years old, whose development can be supervised at home by their parents, since it is an intuitive and easy to use interface. The results obtained are stored in a database and in this way the medical specialist can monitor the progress of the child throughout his treatment. For the validation of this proposal the SUS usability test was used.
Cognitive Behavioral Therapy and Virtual Reality Combination in a Case of Social Phobia

In this study, examined the effectiveness of Virtual Reality (VR) based on Cognitive Behavioural Therapy (CBT) in a social phobia case. Furthermore, this study aims to increase the awareness of Virtual Reality as a psychotherapy tool for mental health professionals in Turkey and also aims to analyze the effectiveness of the treatment. It participated in the study a 32-year-old male with clinical signs of social phobia. The treatment was conducted by a clinical psychologist who has Virtual Reality training. During the treatment, the therapist used two assessment sessions followed by four sessions of standard CBT protocols after which the therapist used Virtual Reality Exposure therapy protocol for social phobia.At the end of the Virtual Reality sessions based on CBT, the patients anxiety score decreased and the coping capacity increased in social phobic situations in real-life conditions. The change in the treatment process was also evaluated with the Beck Anxiety Scale and the Liebowitz Social Anxiety Scale, and a significant decrease in anxiety scores was observed.The results show that the use of VR for exposure in the CBT can reduce social phobia and must be further evaluation of the effectiveness of VR.
Optical encryption based on double random-phase encoding: secure versus unsecure variants

A smart brute-force double random-phase encoding attack is presented that takes advantage of an unreported vulnerability: the smoothness of mean squared error (MSE) and correlation coefficient (CC) curves in a key-sensitivity analysis. The vulnerability reported here is made visible in a key-sensitivity analysis. It is shown that a modular arithmetic pre-coding provides significant robustness against this form of attack because the pre-coding creates a highly nonlinear, highly oscillatory MSE and CC curve in the key space. Knowledge of this new vulnerability and how to prevent this in the first place provides a better understanding of the robustness of various double random-phase encoding designs.