Reviewerly/exHarmony huggingface dataset:
QuadSeal: Quadruple Balancing to Mitigate Power Analysis Attacks with Variability Effects and Electromagnetic Fault Injection Attacks

Side channel analysis attacks employ the emanated side channel information to deduce the secret keys from cryptographic implementations by analyzing the power traces during execution or scrutinizing faulty outputs. To be effective, a countermeasure must remove or conceal as many as possible side channels. However, many of the countermeasures against side channel attacks are applied independently. In this article, the authors present a novel countermeasure (referred to as QuadSeal ) against Power Analysis Attacks and Electromagentic Fault Injection Attacks (FIAs), which is an extension of the work proposed in Reference [27]. The proposed solution relies on algorithmically balancing both Hamming distances and Hamming weights (where the bit transitions on the registers and gates are balanced, and the total number of 1s and 0s are balanced) by the use of four identical circuits with differing inputs and modified SubByte tables. By randomly rotating the four encryptions, the system is protected against variations, path imbalances, and aging effects. After generating the ciphertext, the output of each circuit is compared against each other to detect any fault injections or to correct the faulty ciphertext to gain reliability. The proposed countermeasure allows components to be switched off to save power or to run four executions in parallel for high performance when resistance against power analysis attacks is not of high priority, which is not available with the existing countermeasures (except software based where source code can be changed). The proposed countermeasure is implemented for Advanced Encryption Standard (AES) and tested against Correlation Power Analysis and Mutual Information Attacks attacks (for up to a million traces), and none of the secret keys was found even after one million power traces (the unprotected AES circuit is vulnerable for power analysis attacks within 5,000 power traces). A detection circuit (referred to as C-FIA circuit) is operated using the algorithmic redundancy presented in four circuits of QuadSeal to mitigate Electromagnetic Fault Injection Attacks. Using Synopsys PrimeTime, we measured the power dissipation of QuadSeal registers and XOR gates to test the effectiveness of Quadruple balancing methodology. We tested the QuadSeal countermeasure with C-FIA circuit against Differential Fault Analysis Attacks up to one million traces; no bytes of the secret key were found. This is the smallest known circuit that is capable of withstanding power-based side channel attacks when electromagnetic injection attack resistance, process variations, path imbalances, and aging effects are considered.
Beyond Statistical Analysis in Chaos-Based CSPRNG Design

The design of cryptographically secure pseudorandom number generator (CSPRNG) producing unpredictable pseudorandom sequences robustly and credibly has been a nontrivial task. Almost all the chaos-based CSPRNG design approaches invariably depend only on statistical analysis. Such schemes designed to be secure are being proven to be predictable and insecure day by day. This paper proposes a design and instantiation approach to chaos-based CSPRNG using proven generic constructions of modern cryptography. The proposed design approach with proper instantiation of such generic constructions eventually results in providing best of both worlds that is the provable security guarantees of modern cryptography and passing of necessary statistical tests as that of chaos-based schemes. Also, we introduce a new coupled map lattice based on logistic-sine map for the construction of CSPRNG. The proposed pseudorandom number generator is proven using rigorous security analysis as that of modern cryptography and tested using the standard statistical testing suites. It is observed that the generated sequences pass all stringent statistical tests such as NIST, Dieharder, ENT, and TestU01 randomness test suites.
A Field-Sensitive Security Monitor for Object-Oriented Programs

In this paper, we propose a sound method to synthesize a permissive monitor using boolean supervisory controller synthesis that observes a Java program at certain checkpoints, predicts information flow violations and applies suitable countermeasures to prevent violations.We introduce an approach for modeling heap and information flow via heap.To improve permissiveness, we train the monitor and remove false positives by executing the program along with its executable model.If a security violation is detected, the user can define sound countermeasures, including declassification to apply in checkpoints.We prove that the monitored program ensures localized delimited release in case of declassifying information and termination-insensitive noninterference in case of no declassification.We implement a tool to automate the whole process and generate a monitor.Our method is evaluated by applying it on the Droidbench benchmark and one real-life Android application.
Delivering Newspapers Using Fixed Wing Unmanned Aerial Vehicles

The advent of unmanned aerial vehicles (UAVs) or drones, as they are popularly known as, has opened a plethora of areas where they can be used and efficiently deployed to perform tasks that are otherwise redundant to humans. Companies across the world are contemplating the applications of UAVs in the delivery of items. We, in this paper, plan to incorporate a class of UAVs called fixed wing UAVs for the delivery of newspapers. Our aim is to determine the exact location where a newspaper parcel needs to be dropped from a UAV so that it reaches its intended destination. Forces of gravity and air drag have been considered in our calculations. Based on the permissible flying ranges, the weight of the newspaper parcel and the drag experienced, our equations calculate the point of parcel drop.
Unsupervised Domain Adaptation VIA Cluster Alignment with Maximum Classifier Discrepancy

One way of addressing the problem of unsupervised domain adaptation (UDA) is to perform adversarial training between two classifiers and their shared feature extractor. The two classifiers are enforced to detect the misaligned regions between the source and target domains, while the feature extractor aligns the features by confusing the classifiers. Although this method yields improvement, it ignores the relationship among target neighbors, which may consequently limit the model performance. In this work, we propose a new alignment strategy based on the "cluster assumption" to ensure the aligned target features preserve their clusters by avoiding overlap with decision boundaries. Furthermore, to make the aligned features more compact, we constrain them to be ro-bust against adversarial perturbation using the different views of the classifiers. Extensive experiments demonstrate the effectiveness of our solution on various datasets.
Auto-weighted concept factorization for joint feature map and data representation learning

Concept factorization (CF) is an effective matrix factorization model which has been widely used in many applications. In CF, the linear combination of data points serves as the dictionary based on which CF can be performed in both the original feature space as well as the reproducible kernel Hilbert space (RKHS). The conventional CF treats each dimension of the feature vector equally during the data reconstruction process, which might violate the common sense that different features have different discriminative abilities and therefore contribute differently in pattern recognition. In this paper, we introduce an auto-weighting variable into the conventional CF objective function to adaptively learn the corresponding contributions of different features and propose a new model termed Auto-Weighted Concept Factorization (AWCF). In AWCF, on one hand, the feature importance can be quantitatively measured by the auto-weighting variable in which the features with better discriminative abilities are assigned larger weights; on the other hand, we can obtain more efficient data representation to depict its semantic information. The detailed optimization procedure to AWCF objective function is derived whose complexity and convergence are also analyzed. Experiments are conducted on both synthetic and representative benchmark data sets and the clustering results demonstrate the effectiveness of AWCF in comparison with some related models.
Automated Text line Segmentation and Table detection for Pre-Printed Document Image Analysis Systems

Document Image Processing is considered one of the widely used technologies today. It has proved to be extremely useful in digitizing physical documents to preserve them for longer times. The primary challenge in this work is to extract the overlapping lines from handwritten document images and table detection from pre-printed documents. In this research paper, we present a line segmentation and table detection algorithm suitable for pre-printed and handwritten documents. Initially, the input document image is preprocessed and a baseline is created for offline every peak to carry out line segmentation. Further, table detection is accomplished with the use of edge detection techniques and morphological operations.
Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer’s disease neuroimaging initiative

Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.
Educational Reform in the Era of Artificial Intelligence

The rapid development of artificial intelligence has brought opportunities and challenges to human society. It promotes the progress of society and the development of science and technology, and also increases the unemployment rate and the ethical dilemma of technology. The weakness of artificial intelligence lies in the lack of creativity. If human beings want to have core competitiveness in the era of artificial intelligence, they must change education and enhance creativity. Education reform should be carried out from three aspects: one is to cultivate scientific and technological literacy; two is to cultivate data literacy; three is to cultivate humanistic quality.
The new physicality of data

The mass digitisation of data arguably began in the late 1980s with the mainstream adoption of new low-cost desktop computers in the workplace and visions of a paperless office. The term reflected a focus on the digitisation of existing processes. Such comparisons continued with the arrival of the Internet and worldwide web in the mid-1990s, with visions of e-commerce replacing traditional bricks-and-mortar shops. In the first two decades of the 21st century, we have entered an era where much of the data created is now digital by design and default yet simultaneously integrated with physical objects and real-world interactions. This article explores some of the innovations made possible by this new physicality of data, from the birth of a digital twin to the death of privacy, and the growing urgency for new approaches to data governance and information lifecycle management.
Secure E-Health using Images Steganography

Abstract The main aim is to secure the medical records by ensuring safe communication between the sender and the receiver by adding multiple layers of security. This project is a combined approach of both encryption and steganography to give security the utmost importance. In the sender, only the authorized person can send the secret image to the receiver. In the first stage, an image steganography system is processed using confidential medical X-ray images which are hidden under the cover images. The encryption method used is based on Random Number Generator and the pixel indicator, in other words, by moving the position of the pixel in the image to encrypt the image on the transmitting side. Whereas on the receiver side, this project proposes a method to authenticate the receiver by incorporating Email Authentication and OTP Verification to decrypt the encrypted image.
Speaker verification-derived loss and data augmentation for DNN-based multispeaker speech synthesis

Building multispeaker neural network-based text-to-speech synthesis systems commonly relies on the availability of large amounts of high quality recordings from each speaker and conditioning the training process on the speaker's identity or on a learned representation of it. However, when little data is available from each speaker, or the number of speakers is limited, the multispeaker TTS can be hard to train and will result in poor speaker similarity and naturalness. In order to address this issue, we explore two directions: forcing the network to learn a better speaker identity representation by appending an additional loss term; and augmenting the input data pertaining to each speaker using waveform manipulation methods. We show that both methods are efficient when evaluated with both objective and subjective measures. The additional loss term aids the speaker similarity, while the data augmentation improves the Intelligibility of the multispeaker TTS system.
Hate Speech Detection Using Genetic Programming

There has been a steep increase in the use of social media in our everyday lives in recent years. Along with this, there has been an increase in hate speech disseminated on these platforms, due to the anonymity of the users as well as the ease of use. Social media platforms need to filter and prevent the spread of hate speech to protect their users and society. Due to the high traffic, automatic detection of hate speech is necessary. Hate speech detection is one of the most difficult classification challenges in text mining. Research in this domain focuses on the use of supervised machine learning approaches, such as support vector machine, logistic regression, convolutional neural network, and random forest. Ensemble techniques have also been employed. However, the performance of these approaches has not yet reached an acceptable level. In this paper, we propose the use of the Genetic Programming (GP) approach for binary classification of hate speech on social media platforms. Each individual in the GP framework represents a classifier that is evolved to optimize Fl-score. Experimental results show the effectiveness of our GP approach; the proposed approach outperforms the state-of-the-art using the same dataset HatEval.
A Survey on Different Scheduling Algorithms in Operating System

Allotting resources for process execution is one of the important procedures to be done in any operating system. Mostly in battery operated systems, where the resources are scarce, it becomes more challenging to have the resource management done, having promised all the processes equal opportunity of resource accesses. There are several algorithms for resource allotment developed and each one of the algorithms thrives to do resource allotment to all the processes dynamically. In this paper, a detailed review of the existing scheduling techniques for resource management has been done. Also, state-of-the-art scheduling mechanisms have been discussed and a comparison has been done among the existing state-of-the-art mechanisms. Finally, the challenges faced by scheduling techniques and how to overcome is also covered.
Different Threshold Wavelet Denoising Methods Applied In Centrifugal Fan Characteristic Signal Analysis

Wavelet denoising and the separability index of state feature class are proposed to evaluate the denoising effect. While introducing the Fisher standard discriminant rate F to measure class separability, the Fisher standard discrimination rate of each characteristic parameter after noise detection by using the rigrsure-sln soft threshold for airflow pulsation and fan noise signal are improved obviously. In this paper, the G-P algorithm is used to calculate the relationship among airflow pulsation, fan noise and vibration signal. It is found that rigrsure-sln soft-threshold method is better than other threshold method for denoising the airflow pulsation and the fan noise signal. The phase diagram of the airflow pulsation and the noise collection signal of the ventilator are disordered due to the influence of noise, and the signal after wavelet denoising shows a clear and regular chaotic attractor.
Comparative Analysis of Graph Databases for Git Data

Abstract In past decades, the requirements that database management systems (DBMSs) must achieve have become increasingly stringent (speed, data volume). This increase in complexity led to the development of a wide range of non-relational databases strategies, each one suited for specific scenarios. In this context, Graph Database Management Systems (GDBMSs) became popular to represent social networks and other domains that can be intuitively represented as graph-like structures. In this paper, we represent Version Control System data, specifically Git, from a large software project in a graph structure and compared three popular GDBMSs: Neo4j , Janus Graph and Dgraph . We evaluated read/write operations performance for common activities, such as inserting new commits into the graph and retrieving the complete commit history of a specific project. With this contribution, researches and engineers may choose, assertively, the better solution for their needs.
The impact of internet access on cloud computing research in Africa: Analysis of bibliometric and online search data

The objective of this paper is to uncover the relationship between internet penetration and cloud computing research output and to understand the connection between the interest in cloud computing on research outputs in cloud computing from African countries. For the period of 2009 to 2017, bibliometric data on cloud computing research was retrieved from the Scopus database. Online search traffic data on the search term “cloud computing” was obtained from Google trends for the period under review. Our results show that there was a strong significant correlation between internet penetration and the total number of scholarly outputs within the given period for all countries studied, suggesting that an increase in internet penetration is directly proportional to increase in cloud computing research activities and outputs from a given region. The penetration of internet technologies contributes significantly to the advancement of research efforts and scholarly outputs in ICT-related endeavours.
The Effect of Instruction Model Using Media and Motivation on Chemical Learning Results (Study of The Effect of Instruction Models Using Media and Motivation in Learning)

Abstract This study aims to determine the effect of instruction models using animation and motivation on student chemical learning results and the interaction between instruction models and motivation. The study was conducted on all students of class XI IPA SMA Negeri 10 Medan in 3 classes and samples were taken randomly. This research is an experimental research with 2x2 factorial anava design. The factors tested were PBL model factors with Macromedia Flash, PBL models with Power Point media, while motivational factors with high and low criteria. Based on the hypothesis test at a significant level a = 0.05, it was obtained that F count (A) was 5.530, F count (B) was 29.584, and F count (AB) was 4.556, while the price of Ftable (0.05) (1.56) = 4.01 then F count &gt; F table which means there is influence and interaction of learning models using animation media and motivation on student learning results. The results of this study indicate that students who are taught with the PBL model using Macromedia Flash media on highly motivated students show the highest average of 85,667, and the average of low motivated students is 80.000. Furthermore, students who were taught with the PBL model using Power Point media on highly motivated students showed an average of 85.294, and an average of students with low motivation was 72.308. Thus it was concluded that in chemical learning the topic of buffer solutions is better to use PBL models with Macromedia Flash media.
Sample-efficient proper PAC learning with approximate differential privacy

In this paper we prove that the sample complexity of properly learning a class of Littlestone dimension d with approximate differential privacy is Õ(d6), ignoring privacy and accuracy parameters. This result answers a question of Bun et al. (FOCS 2020) by improving upon their upper bound of 2O(d) on the sample complexity. Prior to our work, finiteness of the sample complexity for privately learning a class of finite Littlestone dimension was only known for improper private learners, and the fact that our learner is proper answers another question of Bun et al., which was also asked by Bousquet et al. (NeurIPS 2020). Using machinery developed by Bousquet et al., we then show that the sample complexity of sanitizing a binary hypothesis class is at most polynomial in its Littlestone dimension and dual Littlestone dimension. This implies that a class is sanitizable if and only if it has finite Littlestone dimension. An important ingredient of our proofs is a new property of binary hypothesis classes that we call irreducibility, which may be of independent interest.
Attention-Guided Knowledge Distillation for Efficient Single-Stage Detector

Knowledge distillation has been successfully applied in image classification for model acceleration. There are also some works employing this technique to object detection, but they all treat different feature regions equally when performing feature mimic. In this paper, we propose an end-to-end attention-guided knowledge distillation method to train efficient single-stage detectors with much smaller backbones. More specifically, we introduce an attention mechanism to prioritize the transfer of important knowledge by focusing on a sparse set of hard samples, leading to a more thorough distillation process. In addition, the proposed distillation method also provides an easy way to train efficient detectors without tedious ImageNet pre-training procedure. Extensive experiments on PASCAL VOC and CityPersons datasets demonstrate the effectiveness of the proposed approach. We achieve 57.96% and 69.48% mAP on VOC07 with the backbone of 1/8 VGG16 and 1/4 VGG16, greatly outperforming their ImageNet pre-trained counterparts by 11.7% and 7.1% respectively.