Reviewerly/exHarmony huggingface dataset:
Performance-Enhanced Integrity Verification for Large Memories

Modern memory systems on cloud servers are vulnerable to many security threats including memory integrity attacks. To protect user data, secure infrastructures like Intel SGX have deployed cryptographic memory protection mechanisms such as MAC+integrity tree. However, using an integrity tree can significantly increase the latency of memory data accesses and thus decrease performance. Although there are many tree optimizations that have obtained performance improvements, the overhead of using an integrity tree remains high. This overhead has become even worse with the recent increase of cloud memory size, making integrity protection impractical on cloud servers.We argue that most prior optimization works do not consider the architecture features of cloud server processors and thus miss the opportunity for further performance improvement. Based on this, we propose Parallelized-Compressed-Prefetched-Tree (PCPT), a tree optimization design tailored for cloud servers. PCPT consists of three methods including 1) parallelizing the memory accesses along a tree path to shorten the critical path, 2) compressing data cache lines and storing counters together with the data to reduce meta data accesses, and 3) prefetching in a tree-aware manner. We evaluate PCPT using 25 benchmarks drawn from 3 suites, and we show that PCPT improves the performance of the state-of-the-art by over 35%.
An Accountability-Oriented Generation approach to Time-Varying Structure of Cloud Service

In the current cloud service development, during the widely used of cloud service, it can self organize and respond on demand when the cloud service in phenomenon of failure or violation, but it may still cause violation. The first step in forecasting or accountability for this situation, is to generate a dynamic structure of cloud services in a timely manner. In this research, it has presented a method to generate the time-varying structure of cloud service. Firstly, dependencies between tasks and even instances within a job of cloud service are visualized to explore the time-varying characteristics contained in the cloud service structure. And then, those dependencies are discovered quantitatively using CNN (Convolutional Neural Networks). Finally, it structured into an event network of cloud service for tracing violation and other usages. A validation to this approach has been examined by an experiment based on Alibaba’s dataset. A function integrity of this approach may up to 0.80, which is higher than Bai Y and others which is no more than 0.60.
Preliminary Security Analysis, Formalisation, and Verification of OpenTitan Secure Boot Code

We perform a preliminary security analysis of the initial boot stage for the OpenTitan silicon root of trust, including formalisation and verification of relevant security goals using both bounded model checking and (unbounded) model checking. We further report on a potential vulnerability in the platform and show how it can be reproduced using formal modelling and argue that co-verification would be able to detect such vulnerabilities for high assurance projects.
On Using Cryptographic Technologies in Privacy Protection of Online Conferencing Systems

Due to the widespread of COVID-19, online conferencing turns into the most popular application that connects people all over the world. The race between platforms in terms of quality and utilities becomes more intense than ever. However, such virtual/online conferencing applications are vulnerable to multiple attacks. Among them, privacy-related issues have not been taken seriously, leading to the problem of infringing upon users’ confidentiality and privacy. This paper is designed to provide a pragmatic model that applies cryptography technology to online conferencing to protect users’ information and guarantee the service quality of the online conferencing after integration.
Content of YouTube videos on cassava production and processing in Nigeria

&#x0D; &#x0D; &#x0D; This study examined content of YouTube videos on cassava production and processing posted in Nigeria between 2009 and 2019. Purposive sampling method was used in selecting 155 YouTube videos with cassava production and processing contents. Primary data on video source, content, duration, quality, number of views, subscribers, likes, comments, presentation format and year of upload were obtained by watching these videos. Data were analysed using frequency counts, percentages, means, standard deviation. Many (53.5%) of the videos were on processing of cassava, while most (80.0%) of the videos were relatively recent (2014 - 2019). Private individuals (38.7%) and media houses (34.2%) were the main sources of videos on cassava production and processing on YouTube. The major content of the video was on agronomic practices in cassava production (12.9%) and value addition (11.0%). Also, 66.0% of the videos had video description (descriptive texts), 36.8% had mobilizing information while 52.3% had a video quality of 720p. Furthermore, 72.9% of the videos had between 1-to-7-minute runtime and these videos had more likes than dislikes with mean values of 92.8 and 5.6 respectively. The mean number of views, subscribers and comments were 11,138.1, 179,537.6 and 13.6, respectively. The presentation patterns in the videos were in form of news (24.5%) and documentary (20.0%). YouTube videos on cassava production and processing were well viewed with a substantial number of subscribers. More YouTube videos on cassava production and processing should be produced with varied contents by different stakeholders in the agricultural sector.&#x0D; &#x0D; &#x0D;
Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR

In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.
CR-UNet: Context-rich UNet for liver segmentation from CT volumes

Automatic segmentation of liver from CT volumes is essential for diagnosis and treatment of liver cancer. In order to achieve accurate segmentation of liver, we proposed a novel 3D residual UNet with context collection module (CCM). The proposed CCM can capture the long-range correlations between all pixels, thereby producing rich and dense contextual information with full-image dependency. We applied CCM at the end of contraction path of UNet to ensure the features fed into expanding path rich in contextual information. Therefore, our model is more accurate and reliable for liver edge segmentation. After extensive experiments, our model finally achieved a state-of-the-art result as Dice per case score of 0.961 and Dice global score of 0.963.
A Fraud Detection System Using Machine Learning

Financial services are used everywhere and function with high complexity. With the increase in online transacting, frauds too are increasing alarmingly. An automated Fraud Detection System is thus required. With millions of transactions taking place, it is practically impossible to detect frauds manually with good speed and accuracy. We propose a system that provides a robust, cost effective, efficient yet accurate solution to detect frauds in both online payment transactions and credit card payments. The proposed solution is a Machine Learning model that will serve the purpose of detecting “fraudulent” and “genuine” transactions in real time. This is beneficial for all sectors that are even mildly aligned to finance. The solution will help them analyse based on various factors if the ongoing transaction can be harmful and will prevent many unfortunate incidents.
Chess without draws

The prevalence of draws in chess is an increasing concern: in the ICCF’s 2017 ‘MT van Oosteram’ Correspondence event, all 56 games were drawn. This article reports a ‘Mobility Chess’ proposal by ‘Aloril’ which addresses this problem. He prefers to retain his anonymity but should be regarded as the primary author of this paper. He has defined a logical sequence of increasingly subtle and decreasingly rewarded ‘winning’ goals G k . Each position in itself associates with both an immediate achievement A j of goal G j – and an ultimate, best-achievable goal G k , k ⩽ j. Therefore, Aloril has eliminated the draw in Mobility Chess and shown how Chess can reduce the incidence of draws as much as required.
Spatial Convergence of Federated Learning in Large-Scale Cellular Networks

The deployment of federated learning in a wireless network, called federated edge learning (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. The derived spatial convergence rate is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. Combing the derived results, intuitive guidelines are given for large-scale FEEL network provisioning and planning to reduce the model training latency without violating the learning accuracy.
A One-Pass Distributed and Private Sketch for Kernel Sums with Applications to Machine Learning at Scale

Differential privacy is a compelling privacy definition that explains the privacy-utility tradeoff via formal, provable guarantees. In machine learning, we often wish to release a function over a dataset while preserving differential privacy. Although there are general algorithms to solve this problem for any function, such methods can require hours to days to run on moderately sized datasets. As a result, most private algorithms address task-dependent functions for specific applications. In this work, we propose a general purpose private sketch, or small summary of the dataset, that supports machine learning tasks such as regression, classification, density estimation, and more. Our sketch is ideal for large-scale distributed settings because it is simple to implement, mergeable, and can be created with a one-pass streaming algorithm. At the heart of our proposal is the reduction of many machine learning objectives to kernel sums. Our sketch estimates these sums using randomized contingency tables that are indexed with locality-sensitive hashing. Existing alternatives for kernel sum estimation scale poorly, often exponentially slower with an increase in dimensions. In contrast, our sketch can quickly run on large high-dimensional datasets, such as the 65 million node Friendster graph, in a single pass that takes less than 20 minutes, which is otherwise infeasible with any known alternative. Exhaustive experiments show that the privacy-utility tradeoff of our method is competitive with existing algorithms, but at an order-of-magnitude smaller computational cost. We expect that our sketch will be practically useful for differential privacy in distributed, large-scale machine learning settings.
Multi-Scale Dynamic Convolution for Classification

Convolutional neural network has achieved a lot of success in the field of computer vision in the recent years. With the rapid development of convolutional neural network, most image classification task has achieved significant performance improvement. Although some progress has been made in the research of image classification methods, there are still some deficiencies. For example, many existing methods are difficult to adaptively mine the feature importance within the sample and the feature correlation between sample scales. In order to solve the above shortcomings, this paper mainly studies image classification based on dynamic adaptive learning. In this paper, Multi-Scale Dynamic Convolution (MSDC) is proposed and verified on the standard image classification data set. Our method can be adjusted adaptively according to different scales of input data. The experimental results show that the proposed method exceeds the relevant comparison methods.
On transform-domain decoding of generalized Gabidulin codes : Dedicated to the memory of Ernst M. Gabidulin (1937–2021)

The class of maximum rank distance generalized Gabidulin codes, suggested by Kshevetskiy and Gabidulin in 2005, extends the class of Gabidulin codes. For generalized Gabidulin codes we propose an efficient decoding algorithm based on the skew transform and the skew shift register synthesis.
Automated Pipe Inspection Based on Image Processing

The gas that keeps us warm or water that we need for our survival are all transported to our crowded cities from rural areas via many underground pipelines. Generally, the inspection of these pipelines is performed visually using videos recorded by a camera mounted on a mobile system which moves inside the pipelines and controlled by an operator. The video recorded by this mobile system is later analysed by operators to find anomalies that may present at the interior of these pipelines. However, this method is human dependent and rather slow. This paper introduces a highly automated system that is capable of performing fast and high precision inspection of dents via creating 3D profiles using a special laser profiler mechanism and image processing algorithms. In the proposed system, a laser source is used to project a laser beam onto a canonical mirror to form a ring shape laser light on the pipe’s internal surface. Images of the laser ring profile are acquired with an omnidirectional camera; the circular shape of the laser ring profile disturbed when the laser passes through dents. A fast boundary following is developed to detect these changes in the profile by computing Euclidean distance between each measured point on the laser ring and the ideal centre of the profile. The locations and characteristics of dents are determined by means of odometry sensors and the proposed image processing techniques, respectively.
A method for authenticating digital records for healthcare systems

The healthcare digital records include personal and sensitive information about the patient. These records are easily being distributed via different platform for clinical diagnosis purpose. This may lead to compromise with the security of the transmitted records. A fusion-based watermarking algorithm to secure medical records using non-subsampled contourlet transforms (NSCT), fast Walsh-Hadamard transform (FWHT) and Schur-decomposition is proposed in this paper. In the first part of the paper, mark images are fused using the NSCT algorithm, which generate final mark image. In the second part, using NSCT, FWHT and Schur-decomposition hide the fused-mark into the host medical image. The final part is marked data encryption based on hyperchaotic and DNA to provide the integrity and authenticity of the record. Objective measures confirm that the strength of the suggested algorithm since it is also suitable for cloud environment. Further, it is demonstrated that the proposed algorithm has a strong ability to extract hidden mark than conventional ones.
Post-quantum Resettably-Sound Zero Knowledge

We study post-quantum zero-knowledge (classical) protocols that are sound against quantum resetting attacks. Our model is inspired by the classical model of resetting provers (Barak-Goldreich-Goldwasser-Lindell, FOCS '01), providing a malicious efficient prover with oracle access to the verifier's next-message-function, fixed to some initial random tape; thereby allowing it to effectively reset (or equivalently, rewind) the verifier. In our model, the prover has quantum access to the verifier's function, and in particular can query it in superposition.
3D User Interface in Virtual Reality

Three-dimensional user interfaces (3D UI) allow users to interact with virtual objects, environments, or information on the physical or virtual space. With the development of virtual reality technology, Tons of VR products tend to occupy the market quickly. However, the formulation of interface design specifications and the enhancement of user experience always be ignored. More than one-third of the users interrupted their experience during the market research because they did not understand the program operation. 3D UI is fundamental to Interactive in virtual space. Thus, this article introduces the basic overview of virtual reality, focusing on analyzing the main design differences between traditional and three-dimensional interfaces. This study's specific objective was to propose three-dimensional interface construction strategies mainly include: 1) Building a natural harmonious interaction relationship between man and machine; 2) Driving interface experience optimization through mental models. In particular, this research tries to establish the Paradigm of Theoretical Research of 3D user interface, which provides theoretical support for design in the future.
Ensuring Smart Agriculture System Communication Confidentiality Using a New Network Steganography Method

Nowadays, the demand for natural resources is increasing, which leads to the search for new solutions to prevent its resources from becoming scarce. Indeed, this resources scarcity, such as water one, poses a threat to all of humanity. Thanks to current technologies, we are able to avoid the worst if there are going to be implemented quickly. So, several research projects are being carried out in the field of smart agriculture with the use of advanced technologies such as IoT to optimize production and reduce costs. In parallel to their works, others are focused on solving other problems according to the development of smart agriculture, in particular energy consumption and the security of exchanges between IoT objects to avoid any alteration that may impact the correct exploitation of the Smart agriculture solutions. In this paper, we propose an adaptation of a widespread techniques used in the exchange confidentiality, which is steganography, in the smart agriculture field IoT based. Our approach aims to secure exchanges with a minimum of energy consumption for common objects with a limited energy resource. The results of our experiments show low impact resources consumption when using steganography as a secure exchange solution.
Improving the Contour Saliency by Tensor Superposition

Contour integration is an important intermediate stage of the object recognition, in which line segments belonging to an object boundary are perceptually linked and segmented from complex backgrounds.This paper puts forward an improved contour integration algorithm.The method computes the vector sum of all neighboring ball voting vectors for an object image pixel to improve the saliency of edges.Four groups of experiments are performed on typical contours to compare the contour in painting effect and the precision.Experimental results show that the contour saliency can be improved by tensor superposition.
Performance Analysis of Python-based Finite Volume Solver ANUGA on Modern Architectures

The performance analysis of ANUGA, a Python-based finite volume solver on the unstructured grid for shallow water model in two dimensions, on recent Intel and AMD processor-based HPC clusters, form the focus of the present study. The analysis uses three datasets with different resolutions of the underlying triangular mesh for discretizing the region of interest. The computational workload depends on the resolution of the grid, which impacts the computation time required for the simulation of the flow. The factors influencing the parallel performance: workload distribution across different processes, and the bulk of the data exchange (the communication overhead), are studied systematically. This paper is an account of the preliminary study to understand the impact of the memory hierarchy (due to the relative sizes of cache) available on different architecture on the performance of this application.
Statistical Analysis of Text Corpus to Determine Appropriate Syllable Length for TTS

Speech synthesis is the computer generated simulation of human speech for any text input. The objective of a text to speech system is to convert an arbitrary text into its corresponding spoken waveform. Generating speech that is close to natural human speech is always a challenging issue for synthesis systems. It is a factor that depends on the kind of available database, and the algorithms that choose the appropriate speech units from the database. Since decades researchers have been exploring building the database for speech systems. The issue lies on what should be the unit of speech to be stored in the database. Since Telugu language is syllabic by nature, a statistical analysis is carried out on the text corpus of 5 million words size to identify the required vowel and biphone syllable units that can cover the major vocabulary in Telugu language. This paper presents the analysis carried out at vowel and biphone level to extract the vowel and biphone syllable speech units and estimate the coverage. The experimental analysis carried out proved that top 50% biphone units covered 25% of the total corpus.
The implementation and evaluation of the field of view in 3D PC game

This research developed a 3D third-person game, made open and closed scenes, through experiments to investigate the field of view suitable for different scenes. Playing 3D games can sometimes cause 3D discomfort or motion sickness. There are many people who have this kind of 3D discomfort. In the research on this issue, the authenticity of the game or the physiological impact of the observer was discussed in detail; however, the impact of the design of the game itself was less discussed. The field of view in the game scene is likely to cause motion sickness. This experimental method uses the adjustment method in psychophysics to test whether the player is comfortable with different scenes. According to the test of different scenarios, the average and standard deviation of the most comfortable visual field are obtained. The viewing angles of different scenes are significantly different, and the best viewing angle of a closed scene is larger than that of an open scene.
Breast Cancer Recurrence Prediction Model Using Machine Learning Technique: State of the Art, Challenges and Future Direction

Nowadays, the most common type of cancer in women worldwide is Breast Cancer (BC). BC may be detected at early stage itself using Mammograms, probably before it's spread. Recurrent BC could occur months or years after initial treatment. Cancer may occur in the same place or spread to different areas due to local or distant recurrence. Early-stage treatment is done not only to cure BC but additionally facilitate in preventing its recurrence/ repetition. In predicting the early stage of BC, a machine learning (ML) technique has been used by most of the researcher. so, the present study we focus on a review of different ML techniques which predicts the recurrence of BC and identified the issues over the past decades. Also summarized the obtained results by the researcher for evaluating their predictive model performance. The study scope, results, merits, and demerits of earlier studies have been discussed. Later, gives deep insights of learning technique and then recommended a possible solution for further improvement for BC recurrence prediction.