Reviewerly/exHarmony huggingface dataset:
Computing Maximal Unique Matches with the r-index

In recent years, pangenomes received increasing attention from the scientific community for their ability to incorporate population variation information and alleviate reference genome bias. Maximal Exact Matches (MEMs) and Maximal Unique Matches (MUMs) have proven themselves to be useful in multiple bioinformatic contexts, for example short-read alignment and multiple-genome alignment. However, standard techniques using suffix trees and FM-indexes do not scale to a pangenomic level. Recently, Gagie et al. [JACM 20] introduced the $r$-index that is a Burrows-Wheeler Transform (BWT)-based index able to handle hundreds of human genomes. Later, Rossi et al. [JCB 22] enabled the computation of MEMs using the $r$-index, and Boucher et al. [DCC 21] showed how to compute them in a streaming fashion. In this paper, we show how to augment Boucher et al.'s approach to enable the computation of MUMs on the $r$-index, while preserving the space and time bounds. We add additional $O(r)$ samples of the longest common prefix (LCP) array, where $r$ is the number of equal-letter runs of the BWT, that permits the computation of the second longest match of the pattern suffix with respect to the input text, which in turn allows the computation of candidate MUMs. We implemented a proof-of-concept of our approach, that we call mum-phinder, and tested on real-world datasets. We compared our approach with competing methods that are able to compute MUMs. We observe that our method is up to 8 times smaller, while up to 19 times slower when the dataset is not highly repetitive, while on highly repetitive data, our method is up to 6.5 times slower and uses up to 25 times less memory.
Monitoring AI systems: A Problem Analysis, Framework and Outlook

Knowledge-based systems have been used to monitor machines and processes in the real world. In this paper we propose the use of knowledge-based systems to monitor other AI systems in operation. We motivate and provide a problem analysis of this novel setting and subsequently propose a framework that allows for structuring future research related to this setting. Several directions for further research are also discussed.
On Dual-Use Information Technology [President’s Message]

While I am writing this column, the Russia–Ukraine war is raging. As bombings, destruction, and human suffering flood the daily news, I deeply feel the pain of our Ukrainian colleagues, those who have friends and family in the affected areas, those who had to put their studies and careers on hold to fight for their survival. I also acknowledge the agony of those around the world who are watching the developments in horror, trying to comprehend why such insanity was necessary.
Biologically inspired deep residual networks for computer vision applications

Deep neural network has been ensured as a key technology in the field of many challenging and vigorously researched computer vision tasks. Furthermore, classical ResNet is thought to be a state-of-the-art convolutional neural network (CNN) and was observed to capture features which can have good generalization ability. In this work, we propose a biologically inspired deep residual neural network where the hexagonal convolutions are introduced along the skip connections. The performance of different ResNet variants using square and hexagonal convolution are evaluated with the competitive training strategy mentioned by [1]. We show that the proposed approach advances the baseline image classification accuracy of vanilla ResNet architectures on CIFAR-10 and the same was observed over multiple subsets of the ImageNet 2012 dataset. We observed an average improvement by 1.35% and 0.48% on baseline top-1 accuracies for ImageNet 2012 and CIFAR-10, respectively. The proposed biologically inspired deep residual networks were observed to have improved generalized performance and this could be a potential research direction to improve the discriminative ability of state-of-the-art image classification networks.
SECURE DATA SHARING IN CLOUD

Abstract—now a days the use of Cloud computing is increasing due to its numerous features such as providing storage ,sharing video,audio content cloud computing can be defined as diiferent service which is uses the internet ,services like storage over a cloud cloud computing provide different features but now a days security is everyone concern but the majority of cloud platforms fail to provide the solution for this as there primary concern is storage so here we have discuused about the methodolgy about securing data over a cloud Index Terms—cloud computing, data, security, encryption, decryption, algorithm, sha 5, aes, des, rsa, Cp-Abe
Quantum aliasing: a negative influence of data scarcity on quantum machine learning

Quantum machine Learning (QML) is an emerging technique that leverages quantum theory and the yet-to-be fully developed quantum computers. QML may help solve classification problems that cannot be resolved by deep neural network (DNN). However, QML suffers from a quantum aliasing problem that is created by the inevitable downsampling and binarization operations. QML takes classical data domain as its input, transforms it into a domain of quantum states (a subset of the Hilbert space) using quantum encoding, generates quantum feature vectors, and builds a quantum circuit on the quantum feature vectors—as a machine learning model—to perform classification tasks. Hence, quantum encoding is an important task in QML. However, in addition to the quantum aliasing problem created by the downsampling and binarization operations in the quantum encoding of the classical data to quantum bits, the data scarcity can intensify this problem into an uncontrollable state. This problem has been rarely studied in the QML research literature. Therefore, it is important to study this problem in depth and develop a robust quantum encoding scheme that improves the performance of QML under the influence of data scarcity. In this work, the performance instability of QML under the severity of quantum aliasing and the influence of data scarcity (a combined effect of data and quantum paucity) has been studied by using the TensorFlow Quantum software framework and the MNIST handwritten digits dataset. A quantum encoding—by leveraging Fast Fourier Transform (FFT) and Blackman-Harris window—that encodes the data into qubits to reduce quantum aliasing has been proposed. An empirical study shows that QML significantly improves its learning behavior and performs better than DNN under data scarcity with the use of the proposed FFT-based quantum encoding.
Analysis of Farmers’ Willingness to Use Blockchain and Influencing Factors Based on the Binary Logit Model

Blockchain is the frontier of modern science and technology, and promoting its application in agriculture is of great significance to agricultural development. Taking the farmers who are essential agricultural subjects as the research object, this paper applies the binary Logit model to investigate the farmers’ willingness to apply blockchain and its influencing factors. Based on rigorous analyses of research data and models, the main research conclusions are obtained. First, at the current stage, Chinese farmers are not very receptive to the blockchain, most of whom are unwilling to apply it to agricultural production and operation. Second, farmers’ age and participation in agricultural training exert a remarkable negative impact on their willingness to apply blockchain. Third, the education level of farmers, the highest education level of their family, the annual income of crops per mu, government subsidies, the application of agricultural information technology, and the degree of their understanding of blockchain impose a remarkable positive impact on their willingness to apply blockchain. According to the analysis results, the following suggestions are put forward: (1) strengthen education and training to improve farmers’ understanding of blockchain; (2) strengthen financial support and provide equipment subsidies and tax relief for farmers who apply blockchain; and (3) implement demonstration projects and take the lead in applying blockchain by supporting family farms, large planters, and other agricultural business entities with demonstration effects.
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness bias), where certain subgroups in a dataset (e.g. based on class, gender, etc.) are less robust than others. This bias not only persists even after adversarial training, but often results in severe performance discrepancies across these subgroups. Existing works characterize the subgroup's robustness bias by only checking individual sample's proximity to the decision boundary. In this work, we argue that this measure alone is not sufficient and validate our argument via extensive experimental analysis. It has been observed that adversarial attacks often corrupt the high-frequency components of the input image. We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i.e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary. We demonstrate that by reliably estimating adversarial vulnerability at the sample level using the proposed holistic metric, it is possible to develop a trustworthy system where humans can be alerted about the incoming samples that are highly likely to be misclassified at test time. This is achieved with better precision when our holistic metric is used over individual measures. To further corroborate the utility of the proposed holistic approach, we perform knowledge distillation in a limited-sample setting. We observe that the student network trained with the subset of samples selected using our combined metric performs better than both the competing baselines, viz., where samples are selected randomly or based on their distances to the decision boundary.
DMAFV

A device may produce invalid values due to a malfunction or infection with malicious firmware. On the other hand, many device drivers implicitly assume that devices will conform to specifications and often do not sufficiently check device input values. One approach to detect faulty device drivers is using fault injection. However, previous studies targeted the MMIO (memory mapped I/O) area and the I/O instructions, but not the DMA (direct memory access) area.
Learning Label Initialization for Time-Dependent Harmonic Extension

Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version of the Dirichlet problem on graphs and shows how to improve its solution by learning the proper initialization vector on the unlabeled nodes. Further, we show that the improved solution is at par with state-of-the-art methods used for node classification. Finally, we conclude this paper by discussing the importance of parameter t, pros, and future directions.
Coherence resource power of isocoherent states

We address the problem of comparing quantum states with the same amount of coherence in terms of their coherence resource power given by the preorder of incoherent operations. For any coherence measure, two states with null or maximum value of coherence are equivalent with respect to that preorder. This is no longer true for intermediate values of coherence when pure states of quantum systems with dimension greater than two are considered. In particular, we show that, for any value of coherence (except the extreme values, zero and the maximum), there are infinite incomparable pure states with that value of coherence. These results are not peculiarities of a given coherence measure, but common properties of every well-behaved coherence measure. Furthermore, we show that for qubit mixed states there exist coherence measures, such as the relative entropy of coherence, that admit incomparable isocoherent states.
E-learning system of teaching english language

The use of E-learning platforms for education in recent times has been on the rise. The purpose of this work is to provide an overview of the web application. This paper provides an overview of a web-based application as a result of the project titled “22nd Century English”. This is a web platform that arranges schedules and scheduled video conferences between students and lecturers. A lot of teachers especially English Teachers have extra time with which they can teach but without commitment, because of their commitment to their Teaching Institution. With the opportunity to handle freelance lessons through the developed application, teachers have the ability to earn extra income while helping other students outside their schools. This platform also helps students to learn English remotely without wasting time with traveling or intense commitment. Keywords: E-learning; English teaching; learn press; LMS. Keywords: LMS, learn press, e-learning, English teaching, efficiency of English learning;
Dynamic assertion for quantum circuits based on stabilizers

Quantum computing is computationally more powerful than classical one due to the features of superposition and entanglement of quantum bits (qubits). However, because of the non-cloning property, measuring qubits in superposition forces them to collapse onto classical ones, which makes traditional run-time techniques for debugging and analyzing hardware circuits infeasible. To overcome this issue, previous works proposed the concept of quantum dynamic runtime assertion. Stabilizer is an approach adopted for correcting quantum errors. The quantum state can remain unchanged after several Pauli operations. We call these Pauli operations a stabilizer. In this work, we propose to use the quantum stabilizers for dynamic runtime assertions, which requires less quantum gates and increases the detect accuracy on Noisy intermediate scale quantum(NISQ).
The Evaluation of the Academic Progress Information System SIsKA-NG Mobile Based on Heuristic and User Experience

The limitations on the Academic Progress Information System (SIsKA) application, in terms of its interface and functionality, have a direct consequence on the quality of service and monitoring student progress in the Computer Science Department of Universitas Pendidikan Ganesha.Therefore, it is necessary to overcome these limitations through research that focuses on the development and evaluation of the next generation of SIsKA (SIsKA-NG) mobile application with real-time notification advantage compared to its counterpart existing web application.The heuristic evaluation of the first stage of this mobile application development identified twenty problems where eight problems with the highest severity (Catastrophic), six problems with high priority (Major), three problems with low priority (Minor) that need to be solved, and three problems that do not need to be fixed unless there is extra time.User satisfaction was measured by using the System Usability Scale (SUS) questionnaire and gave the final score of 82.25.That score was higher than the minimal SUS standard score of 68.00 and was graded as very good, which means that users are very satisfied with using the application.To maximize more of the SIsKA-NG mobile performance, usability testing was conducted to obtain qualitative data from the same SUS respondents by using the Concurrent Think Aloud (CTA) method.CTA gave a summary result of twenty-one criticisms or suggestions that were referred to by the second stage of the SIsKA-NG mobile development.
Procedural dungeon generation for a 2D top-down game

This work examines solvability, difficulty, and engagement of procedurally generated content for a 2D, top-down game. We adapt the A* algorithm to find a solution when there are keys and locks between the starting position and the goal. It requires iteration to find a solution, since analysis of points in the dungeon alone are computationally expensive. If no solution exists, the program can detect this by showing no progress made in an iteration. We ran this on 30 dungeons, and classified their content according to the metrics of solvability, difficulty, and engagement.
Application of Statistical Software in Quantitative Data Analysis

The paper expounds on the application of Statistical Software (SS) in quantitative data analysis. Research questions were formulated and questionnaire were used for data collection. The collected data were analysed using percentage, mean and regression. From the analysis carried out, we discovered that women are fewer in the academic profession. We also find out that the most common statistical software is MsExcel, followed by Statistical Package for Social Sciences(SPSS) and the less popular statistical software is R. Also, the frequently used statistical software is SPSS, followed by MsExcel, while the less usage statistical software is R. In this paper work, it was shown that there is a positive impact of statistical software in obtaining a fast, accurate and efficient result in academic staff research work in Kogi State Polytechnic (KSP), Lokoja. The following statistical software - MsExcel, SPSS, Graphpad, Minitab and R, were highlighted and the regression analysis carried out for each statistical software were screenshot and presented. It was discovered that the five (5) different SS used gave the same result. The simple regression model obtained is Number of Death = 44+ 2.17Malaria. Recommendations were made accordingly.
Fast Left Kan Extensions Using The Chase

We show how computation of left Kan extensions can be reduced to computation of free models of cartesian (finite-limit) theories. We discuss how the standard and parallel chase compute weakly free models of regular theories and free models of cartesian theories, and compare the concept of "free model" with a similar concept from database theory known as "universal model". We prove that, as algorithms for computing finite free models of cartesian theories, the standard and parallel chase are complete under fairness assumptions. Finally, we describe an optimized implementation of the parallel chase specialized to left Kan extensions that achieves an order of magnitude improvement in our performance benchmarks compared to the next fastest left Kan extension algorithm we are aware of.
Path planning of multi-parcel UAV based on improved simulated annealing algorithm

Aiming at the path planning problem of logistics unmanned aerial vehicles (UAVs) that can deliver multiple packages, this paper comprehensively considers the maximum travel distance of UAVs, the maximum carrying capacity, the geographical location of delivery centers and delivery points, and the priority of packages, with the shortest flight distance and the highest customer satisfaction as the objectives. A multi-objective programming model with multiple constraints is constructed and an improved simulated annealing algorithm is designed. By constructing a specific solution space and calculating the probability of accepting new solutions, the global optimal solution can be obtained by avoiding local optima as much as possible. Finally, the sensitivity of initial temperature, temperature gradient, and iteration times of simulated annealing is analyzed. The results show that the simulated annealing algorithm can efficiently plan the UAV navigation path and minimize the cost. When the initial temperature is 180℃~ 220℃, the temperature gradient is 4℃, and the number of iterations is between 200 and 300, the probability of obtaining the global optimal solution is the highest. The research results can provide guidance and reference value for the application of UAVs in logistics distribution systems.
Quantum Multipartite correlation in Optical channel

The communication in the quantum networks is based on optical channels to take the advantage of analog, optical and digital communication system. Single data which carries information over the channel can be termed as solid state atoms or photons. The reliable channel is the one which carries the same state from the sending node to destination there can be superposition of states. practically ideal channel does not exist, the optical Gaussian channel which is considered as a degradable quantum channel. The optical interfaces and standard signal format would allow interconnecting fiber optic transmission equipment through multiple networks which carries information generated by multiple users of the network. Taking the infrastructure of existing telecommunication use the quantum information so that it is secure by its nature.
Investigation of the digital technologies influence on the process transformation in cinema

Within the sectoral structure of the national economy, audiovisual industries are parts of the cultural industry. Cinema is a particularly effective means of satisfying the human need for affective self-expression and establishing emotional communication between people. To increase the audiovisual product competitiveness, an analysis of the digital technologies impact on it was carried out in order to attract an additional audience. It is noted that modern technologies have paved the way for multifunctional devices: the creative process is increasingly becoming technological in nature. The use of digital technologies has led to the creation of high-quality films. Due to technological progress, the equipment becomes available to more participants in the film business. Digital technologies have become a powerful and accessible tool that allows non-professional artists/directors to enter the cinematic space. However, for the author’s cinema, digital technologies have only become a tool to improve picture quality, as well as expand the capabilities for the artist / director to bring their ideas to life on screen.
Specific features of the training seminar in order to improve the subjective well-being of students

The purpose of this research is to evaluate the specific features of the training seminar in order to improve the subjective well-being of university students. The qualitative research method was used in this study. The study group of the research consisted of 72 university students studying at various universities in Almaty, Kazakhstan in the 2021–2022 academic year. Research data were collected with a semi-structured interview form prepared by the researchers. As a result of the research, students defined subjective well-being as looking at life positively, living life in a planned way, socialising and being a good person. University students evaluated their subjective well-being at a moderate level with suggestions of university students participating in the research to improve subjective well-being, giving importance to personal development, establishing positive relations with the environment and benefiting from activities in universities. In addition, students’ opinions on the characteristics of the training seminars for the development of subjective well-being are that they are motivating, provide multidimensional development, have rich content and are held regularly. In line with the findings obtained from the research, it has emerged that the training seminars organised for the improvement of the subjective well-being of university students should be organised frequently, provide multidimensional development and be rich in content.&#x0D; &#x0D; Keywords: Subjective well-being, educational seminars, university students