Reviewerly/exHarmony huggingface dataset:
Post-Quantum Encryption Scheme with Supersingular Isogenies

This article discusses the questions related to the modification of SIDH (Supersingular Isogeny Diffie-Hellman Key Exchange) cryptoalgorithm. SIDH is a post-quantum algorithm which can be used to establish a shared secret key between two parties. Since several quantum attacks have been discovered recently, classical Diffie-Hellman algorithm is considered insecure and SIDH has become a much better alternative. Article provides a mathematical model of the algorithm with necessary definitions and formal description of the key exchange process. Possible attacks against SIDH are also discussed with detailed review of existing methods that can be used to prevent them. The most effective attack against SIDH is GPST attack that was discovered in 2017. Its formal model is examined with exploring necessary adjustments to make this attack ineffective. Based on this result, a modification which ensures security against the GPST attack is implemented. The article contains a description of the implementation with samples of its interface. Performance of the implemented software is estimated by the Mann-Whitney U-test.
A Consistency Enhanced Deep Lmser Network for Face Sketch Synthesis

Existing face sketch synthesis methods extend conditional generative adversarial network framework with promising performance. However, they usually pre-train on additional large-scale datasets, and the performance is still not satisfied. To tackle the issues, we develop a deep bidirectional network based on the least mean square error reconstruction (Lmser) self-organizing network, which is a further development of autoencoder by folding along the central hidden layer. Such folding makes the neurons on the paired layers between encoder and decoder merge into one. We model the photo-to-sketch mapping by an Lmser network and build a sketch-to-photo mapping by a complement Lmser sharing the same structure. The bidirectional mappings form an alternation system. We devise a supervised alternating consistency for the system, by minimizing the deviation between the alternatively mapped pattern and the ground-truth. Enhanced by the consistency constraints along the bidirectional paths, the model achieve a significant improvement in terms of Fréchet Inception Distance (FID). Experiments demonstrate the effectiveness of our method in comparison with state-of-the-art methods, and reduce the FID from 34.1 to 28.7 on the CUFS dataset and from 18.2 to 11.5 on the CUFSF dataset.
Sentiment Analysis of Twitter Posts in English, Kannada and Hindi languages

The World Wide Web has always been a huge repository for information. With this, an era of social networking came into existence that allowed people across the globe to connect and communicate with each other. The data present here is of various types like text, images, videos, audios, etc. The major part of the internet includes textual data which is written in different human-understandable languages. Recently, there has been tremendous increase in this data written in different languages over social media. This paper analyzes the semantic nature of a text, classifying them as either positive or negative for English, Hindi and Kannada languages. The following classification task is resolved with a CNN-LSTM-based neural network.
Outlier Detection for Pandemic-Related Data Using Compositional Functional Data Analysis

Abstract With accurate data, governments can make the most informed decisions to keep people safer through pandemics such as the COVID-19 coronavirus. In such events, data reliability is crucial and therefore outlier detection is an important and even unavoidable issue. Outliers are often considered as the most interesting observations, because the fact that they differ from the data majority may lead to relevant findings in the subject area. Outlier detection has also been addressed in the context of multivariate functional data, thus smooth functions of several characteristics, often derived from measurements at different time points (Hubert et al. in Stat Methods Appl 24(2):177–202, 2015b). Here the underlying data are regarded as compositions, with the compositional parts forming the multivariate information, and thus only relative information in terms of log-ratios between these parts is considered as relevant for the analysis. The multivariate functional data thus have to be derived as smooth functions by utilising this relative information. Subsequently, already established multivariate functional outlier detection procedures can be used, but for interpretation purposes, the functional data need to be presented in an appropriate space. The methodology is illustrated with publicly available data around the COVID-19 pandemic to find countries displaying outlying trends.
Reviewing rounds prediction for code patches

Abstract Code review is one of the common activities to guarantee the reliability of software, while code review is time-consuming as it requires reviewers to inspect the source code of each patch. A patch may be reviewed more than once before it is eventually merged or abandoned, and then such a patch may tighten the development schedule of the developers and further affect the development progress of a project. Thus, a tool that predicts early on how long a patch will be reviewed can help developers take self-inspection beforehand for the patches that require long-time review. In this paper, we propose a novel method, PMCost , to predict the reviewing rounds of a patch. PMCost uses a number of features, including patch meta-features, code diff features, personal experience features and patch textual features, to better reflect code changes and review process. To examine the benefits of PMCost , we perform experiments on three large open source projects, namely Eclipse, OpenDaylight and OpenStack. The encouraging experimental results demonstrate the feasibility and effectiveness of our approach. Besides, we further study the why the proposed features contribute to the reviewing rounds prediction.
FallStop — A deep learning approach to real-time fall detection and monitoring of vital parameters

Falls can cause bone breakage, immobility and can have a negative impact on the victim's life. The elderly are prone to falls as their eyesight, hearing and reflexes tend to weaken with age. Falls among this population is a growing problem that can be prevented. Many times these falls result in death, in order to avoid this, immediate medical aid needs to be provided to the fall victim. This paper proposes a novel fall detection algorithm and a system for tracking daily movement as well as the vitals of an individual. In addition to this an alert message is sent to the victims emergency contact if a fall or any abnormality in the vitals of the individual is detected. This is performed using You Only Look Once (YOLO) object detection algorithm, Openpose, machine vision and internet of things(IOT).
Temp-Frustum Net: 3D Object Detection with Temporal Fusion

3D object detection is a core component of automated driving systems. State-of-the-art methods fuse RGB imagery and LiDAR point cloud data frame-by-frame for 3D bounding box regression. However, frame-by-frame 3D object detection suffers from noise, field-of-view obstruction, and sparsity. We propose a novel Temporal Fusion Module (TFM) to use information from previous time-steps to mitigate these problems. First, a state-of-the-art frustum network extracts point cloud features from raw RGB and LiDAR point cloud data frame-by-frame. Then, our TFM module fuses these features with a recurrent neural network. As a result, 3D object detection becomes robust against single frame failures and transient occlusions. Experiments on the KITTI object tracking dataset show the efficiency of the proposed TFM, where we obtain 6%, 4%, and 6% improvements on Car, Pedestrian, and Cyclist classes, respectively, compared to frame-by-frame baselines. Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline. Our code is open-source and available at https://github.com/emecercelik/Temp-Frustum-Net.git.
Divide and Scramble - A Recursive Image Scrambling algorithm utilizing Rubik's Cube

Divide and Scramble - An image scrambling algorithm based on the Rubik's cube is presented in this paper. The strength and robustness of the algorithm are based on the number of permutations of the Rubik's cube and its simple yet chaos-inducing rotation methods. The algorithm uses a recursive approach that runs in linearithmic time. This makes the algorithm practical for real-time applications, which involve the concepts of cryptography. The algorithm begins by dividing the image into two halves and then, pixels from each half are combined to form a cube which is scrambled using the moves generated by a pseudo-random number generator function. The generated halves are further scrambled using the algorithm in a recursive manner. The scrambling algorithm is flexible enough to be paired up with any given diffusion, confusion, or substitution methods to obtain complex and secure encryption. Moreover, the pseudo-random number generator function used can also be replaced by other random number sequence generators like chaotic maps. The strength and robustness of the algorithm are backed by the obtained results from the experiments performed. Experimental results also show that the image quality is not compromised by the algorithm while resisting common crypto-attacks like differential and statistical attacks.
Relational cost analysis in a functional-imperative setting

Abstract Relational cost analysis aims at formally establishing bounds on the difference in the evaluation costs of two programs. As a particular case, one can also use relational cost analysis to establish bounds on the difference in the evaluation cost of the same program on two different inputs. One way to perform relational cost analysis is to use a relational type-and-effect system that supports reasoning about relations between two executions of two programs. Building on this basic idea, we present a type-and-effect system, called ARel, for reasoning about the relative cost (the difference in the evaluation cost) of array-manipulating, higher order functional-imperative programs. The key ingredient of our approach is a new lightweight type refinement discipline that we use to track relations (differences) between two mutable arrays. This discipline combined with Hoare-style triples built into the types allows us to express and establish precise relative costs of several interesting programs that imperatively update their data. We have implemented ARel using ideas from bidirectional type checking.
Balancing of Samples in Class Hierarchy

In real-world classification problems, it is often the case that some classes (head classes) have large numbers of samples and the other classes (tail classes) have small numbers of samples. Such imbalance problems have been widely studied for a long time, and various methods have been proposed, such as oversampling from tail classes or heavy weighting to tail classes. However, these approaches lose the effectiveness when the number of classes is very large and imbalance is remarkable. Such a problem is called a long-tailed problem where there are a few head classes and many tail classes. In this paper, we construct a class hierarchy (a binary tree) where the numbers of samples are almost balanced in left and right children of each node. Some experiments demonstrated the effectiveness of the proposed approach.
Learning-based Tensor Decomposition with Adaptive Rank Penalty for CNNs Compression

Low-rank tensor decomposition is a widely-used strategy to compress convolutional neural networks (CNNs). Existing learning-based decomposition methods encourage low-rank filter weights via regularizer of filters’ pair-wise force or nuclear norm during training. However, these methods can not obtain the satisfactory low-rank structure. We propose a new method with an adaptive rank penalty to learn more compact CNNs. Specifically, we transform rank constraint into a differentiable one and impose its adaptive violation-aware penalty on filters. Moreover, this paper is the first work to integrate the learning-based decomposition and group decomposition to make a better trade-off, especially for the tough task of compression of 1×1 convolution.The obtained low-rank model can be easily decomposed while nearly keeping the full accuracy without additional fine-tuning process. The effectiveness is verified by compression experiments of VGG and ResNet on CIFAR-10 and ILSVRC-2012. Our method can reduce about 65% parameters of ResNet-110 with 0.04% Top-1 accuracy drop on CIFAR-10, and reduce about 60% parameters of ResNet-50 with 0.57% Top-1 accuracy drop on ILSVRC-2012.
Adversarially Generating Rank-Constrained Graphs

Graph generation is a task that has been explored with a wide variety of methods. Recently, several papers have applied Generative Adversarial Networks (GANs) to this task, but most of these methods result in graphs of full or unknown rank. Many real-world graphs have low rank, which roughly translates to the number of communities in that graph. Furthermore, it has been shown that taking the low rank approximation of a graph can defend against adversarial attacks. This suggests that testing models against graphs of different rank may be useful. However, current methods provide no way to control the rank of generated graphs. In this paper, we propose two variants of BRGAN: GAN architectures that generates synthetic graphs, which in addition to having realistic graph features, also have bounded rank. Our first variant, BRGAN-A, generates synthetic graphs competitive with state-of-the-art models, with rank equal to or lower than the desired rank. Our second variant, BRGAN-B, generates graphs of almost exactly the desired rank, but results in less realistic results. We also propose a novel rank penalty term on the generator, which allows us to control this realism-rank tradeoff.
Enhancing ICT for Quality Education in Sub-Saharan Africa

This research assesses the relevance of information and communication technology (ICT) in primary education quality in a panel of 49 Sub-Saharan African countries for the period 2000-2012. The empirical evidence is based on Two Stage Least Squares (2SLS) and Instrumental Quantile regressions (IQR). From the 2SLS: (i) mobile phone and internet penetration rates reduce poor quality education and enhancing internet penetration has a net negative effect of greater magnitude. From the IQR: (i) with the exception of the highest quantile for mobile phone penetration and top quantiles for internet penetration, ICT consistently has a negative effect on poor education quality with a non-monotonic pattern. (ii) Net negative effects are exclusively apparent in the median and top quantiles of internet-related regressions. It follows that enhancing internet penetration will benefit countries with above-median levels of poor education quality while enhancing internet penetration is not immediately relevant to reducing poor education quality in countries with below-median levels of poor education quality.
A scalable decentralized system for fair token distribution and seamless users onboarding

Tokens are digital, transferable, and programmable assets and one of the most promising tools offered by blockchains. They could enable a wide range of applications, from down to earth to futuristic. One of the main issues in achieving wide adoption of tokens is onboarding: main platforms require users to deal with specific tools such as wallets, transaction fees, key generation, and storage. The most common solution to offer a familiar experience to naive users are custodial intermediaries, which have the important drawback of centralizing the process, and keeping users away from the advantages of self-sovereign assets control. In this paper, we present a process for the distribution of digital tokens to end users, exploiting “physical” objects for initial distribution. The process is aimed at making the onboarding of users not yet accustomed to blockchain tools and concepts as easy as possible, in a secure and decentralized way.
An improved reversible information hiding scheme for Encrypted Video

In order to solve the problem of low capacity of reversible data hiding (RDH) in encrypted video, an improved histogram translation method is proposed to achieve large capacity. A two-dimensional histogram of motion vector difference was constructed and a histogram translation algorithm with high utilization rate was designed to realize reversible information hiding in encrypted video. The experimental results show that the embedding capacity of the scheme is increased by 153.3% on average, the average PSNR of encrypted video is less than 15.956 dB, and the average PSNR of decrypted stego video can reach more than 30 dB.
NIKE from Affine Determinant Programs

A multi-party non-interactive key-exchange (\(\mathsf {NIKE}\)) scheme enables N users to securely exchange a secret key \( K \) in a non-interactive manner. It is well-known that \(\mathsf {NIKE}\) schemes can be obtained assuming the existence of indistinguishability obfuscation (\(\mathsf {iO}\)).In this work, we revisit the original, \(\mathsf {iO}\)-based, provably-secure NIKE construction by Boneh and Zhandry, aiming to simplify it. The core idea behind our protocol is to replace the functionality of the obfuscator with the one of an affine determinant program (\(\mathsf {ADP}\)). Although \(\mathsf {ADP}\)s have been designed with the purpose of attaining indistinguishability obfuscation, such implication is left open for general circuits.The ingredients enabling to prove the security of our scheme stem into a more careful analysis of the branching programs needed to build \(\mathsf {ADP}\)s. In particular, we show: 1. An intuitive indistinguishability notion defined for \(\mathsf {ADP}\)s of puncturable pseudorandom functions (\(\mathsf {PRF}\)s) is sufficient to prove security for \(\mathsf {NIKE}\). 2. A set of simple conditions based on ADP’s branching program topology that are sufficient for proving indistinguishability of ADPs. We leave open the question of finding ADPs satisfying them.
A Comparison of the Fatigue Progression of Eye-Tracked and Motion-Controlled Interaction in Immersive Space

Eye-tracking enabled virtual reality (VR) headsets have recently become more widely available. This opens up opportunities to incorporate eye gaze interaction methods in VR applications. However, studies on the fatigue-induced performance fluctuations of these new input modalities are scarce and rarely provide a direct comparison with established interaction methods. We conduct a study to compare the selection-interaction performance between commonly used handheld motion control devices and emerging eye interaction technology in VR. We investigate each interaction’s unique fatigue progression pattern in study sessions with ten minutes of continuous engagement. The results support and extend previous findings regarding the progression of fatigue in eye-tracked interaction over prolonged periods. By directly comparing gaze-with motion-controlled interaction, we put the emerging eye-trackers into perspective with the state-of-the-art interaction method for immersive space. We then discuss potential implications for future extended reality (XR) interaction design based on our findings.
Copper mass flux and evolution of supergene orebody in the Miduk Porphyry Copper Deposit, Iran, and its application to exploration of exotic deposits

The present study used field geology and aimed to model the geological results by modern methods for examining the effects of geological features on the formation of an enriched ore in a mature supergene profile such as Miduk Porphyry Copper Deposit (MPCD). In addition, it aimed to discover which geologic controllers caused the fixation or conduction of Cu in supergene solutions. To this end, the anatomy of the supergene zones was examined by direct sampling and mapping. Analysis of fracture density helps uncover the role of fractures in conveyance of fluids. The results showed that MPCD endured a systematic supergene process, which resulted in the formation of a 200-meter enriched orebody on the top of the hypogene ore. Additionally, the topographic gradient was the most important controller in the migration of supergene solutions to northern side while the north–south trending fractures doubled the lateral flux. Furthermore, the mass balance analysis indicated that 181,000 tons of Cu laterally escaped from the supergene system. Considering the geochemical conditions in sources areas, this amount of metal was sufficient to form a proximal type of exotic mineralization. Further, drainages derived from the main deposit exhibited exotic mineralization, which supported the lateral movement of Cu from the main orebody.
Modification and Software Implementation of Mceliece Cryptosystem

This article addresses issues related to the modification of the McEliece cryptosystem. The software development presented in this article is crypto stability, the original McEliece algorithm is post-quantum, unlike its counterparts. The theoretical description of the McEliece cryptosystem was considered. More cryptographically secure combinations are considered for the selection of various parameters, and an estimate of the algorithm's strength is calculated when these parameters are taken into account or not. A mathematical model of the original algorithm was developed. It analyzes the original McEliece algorithm and modifications with leader decoding and decoding over Hamming distance. The result was confirmation that the original algorithm was the most efficient. When analyzing the modifications among themselves, it was concluded that the operating time of the modification in terms of the Hamming distance practically did not differ from the original, but significantly differed from the modification of the leader decoding. The leader decode modification was found to be the most ineffective. The program itself was written in the object-oriented Python 3 language, development environment—IDLE.
A Program Logic for Fresh Name Generation

We present a program logic for Pitts and Stark’s \(\nu \)-calculus, an extension of the call-by-value simply-typed \(\lambda \)-calculus with a mechanism for the generation of fresh names. Names can be compared for equality and inequality, producing programs with subtle observable properties. Hidden names produced by interactions between generation and abstraction are captured logically with a second-order quantifier over type contexts. We illustrate usage of the logic through reasoning about well-known difficult cases from the literature.
A Reconfigurable Heterogeneous Computing-Hardware Design With Aimed VGG16 Acceleration

At present, the convolutional layer is a key component of the neural network. With the development of convolutional recognition technology, more and more models have increased the depth of the internal convolutional layer through time iteration and are committed to more extensive and more characteristic inputs. With a large number of information input units, the computing power of the hardware needs to make up for the efficiency and cost of the algorithm. However, most of the models developed by research are based on the traditional single hardware platform, and constantly test its performance limits. Therefore, we tested and developed a hybrid hardware platform, while expanding its performance parallelism, throughput capacity, and limitations on storage bit width. We use the ZYNQ-7100 platform to test the feasibility of the proposed hardware structure for the deeper convolutional structure VGG16 and compare the power consumption of other non-configurable platforms. The tested digital architecture finally has extremely obvious advantages and provides reliable configuration capabilities to make our experiments declared successful in terms of algorithms and structures.