Reviewerly/exHarmony huggingface dataset:
Improving recent side-channel attacks against the DES key schedule

Abstract Recent publications consider side-channel attacks against the key schedule of the Data Encryption Standard (DES). These publications identify a leakage model depending on the XOR of register values in the DES key schedule. Building on this leakage model, we first revisit a discrete model which assumes that the Hamming distances between subsequent round keys leak without error. We analyze this model formally and provide theoretical explanations for observations made in previous works. Next we examine a continuous model which considers more points of interest and also takes noise into account. The model gives rise to an evaluation function for key candidates and an associated notion of key ranking. We develop an algorithm for enumerating key candidates up to a desired rank which is based on the Fincke–Pohst lattice point enumeration algorithm. We derive information-theoretic bounds and estimates for the remaining entropy and compare them with our experimental results. We apply our attack to side-channel measurements of a security controller. Using our enumeration algorithm we are able to significantly improve the results reported previously for the same measurement data.
Image Deblurring using Multi-Scale Dilated Convolutions in a LSTM-based Neural Network

This work describes the development of a compact neural network for the blind deconvolution and restoration of a blurred image. A scale-based, convolutional, long-short term memory (LSTM) network is developed for image deblurring. In this network, multi-scale information is obtained through the use of dilated convolutions and this is shared between scales using recurrent connections. The proposed network is designed to be of low-parameter count and to deblur an image without the use of prior information. We show the effectiveness of this approach through evaluation with industry standard datasets (GOPRO [2] and Kohler [10]), and we compare our results with those of two other state-of-the-art blind deblurring networks. Our results show that a comparable sharp image can be recovered more efficiently with a significant reduction in the total number of network parameters.
Unsupervised Noise Reduction for Nanochannel Measurement Using Noise2Noise Deep Learning

Noise reduction is an important issue in measurement. A difficulty to train a noise reduction model using machine learning is that clean signal on measurement object needed for supervised training is hardly available in most advanced measurement problems. Recently, an unsupervised technique for training a noise reduction model called Noise2Noise has been proposed, and a deep learning model named U-net trained by this technique has demonstrated promising performance in some measurement problems. In this study, we applied this technique to highly noisy signals of electric current waveforms obtained by measuring nanoparticle passages in a multistage narrowing nanochannel. We found that a convolutional AutoEncoder (CAE) was more suitable than the U-net for the noise reduction using the Noise2Noise technique in the nanochannel measurement problem.
Sentiment analysis of popular-music references to automobiles, 1950s to 2010s

In recent years, there has been a scholarly debate regarding the decrease in automobile-related mobility indicators (car ownership, driving license holding, VMT, etc.). Broadly speaking, two theories have been put forward to explain this trend: (1) economic factors whose impacts are well-understood in principle, but whose occurrence among young adults as a demographic sub-group had been overlooked, and (2) less well-understood shifts in cultural mores, values and sentiment towards the automobile. This second theory is devilishly difficult to study, due primarily to limitations in standard data resources such as the National Household Travel Survey and international peer datasets. In this study we first compiled a database of lyrics to popular music songs from 1956 to 2015 (defined by inclusion in the annual "top 40"), and subsequently identified references to automobiles within this corpus. We then evaluated whether there is support for theory #2 above within popular music, by looking at changes from the 1950s to the 2010s. We demonstrate that the frequency of references to automobility tended for many years to increase over time, however there has more recently been a decline after the late 2000s (decade). In terms of the sentiment of popular music lyrics that reference automobiles, our results are mixed as to whether the references are becoming increasingly positive or negative (machine analysis suggests increasing negativity, while human analysis did not find a significant association), however a consistent observation is that sentiment of automobile references have over time become more positive relative to sentiment of song lyrics overall. We also show that sentiment towards automobile references differs systematically by genre, e.g. automobile references within 'Rock' lyrics are in general more negative than similar references to cars in other music genres). The data generated on this project have been archived and made available open access for use by future researchers; details are in the full paper.
FaceShapeGene: A disentangled shape representation for flexible face image editing

How do I look if I have the same nose shape as my favorite star? Existing methods for face image manipulation generally focus on modifying predefined facial attributes, editing expressions and changing image styles, where users cannot control the shapes of specific semantic facial parts freely in the generated face image. The facial part shapes are described by their geometries and need to be controlled by continuously generating geometric parameters. Therefore, the existing methods that work with discretely labelled attributes are not applicable on this task. In this paper, we propose a novel approach to learn the disentangled shape representation for a face image, namely the FaceShapeGene, which encodes the shape information of the semantic facial parts into separate chunks in the latent space. It allows users to freely recombine the part-wise latent chunks of a face image from other individuals to transfer a specified facial part shape, just like gene editing. Experimental results on several tasks demonstrate that the proposed FaceShapeGene representation correctly disentangles the shape features of different semantic parts. Comparisons to existing methods show the superiority of the proposed method on facial parts editing tasks.
Adversarial Reconstruction for Outdoors Insulator Anomaly Detection and Recognition in High-Speed Railway Traction Substation

As an indispensable part of the high-speed railway traction substation,the outdoors insulator ensures the normal operation of transmission network and defends the stability of transmission line. In order to avoid the serious transmission fault and decrease the economic loss, finding a effective way to find the anomaly regions of insulator is a meaningful issue. Therefore, a method is presented for outdoors insulator anomaly detection and recognition in high-speed railway traction substation based on adversarial reconstruction in this paper. First, the R3Det is employed to fix the position of insulator from the input image of traction substation. At the second stage, the insulator image which located from the first stage is entered into our designed generative adversarial networks to reconstruct input image. In the end, the structural similarity algorithm is adopted to compare the difference between the input image and the generated image to find the anomalous region, then template matching is used for recognition of the anomalous area. Test results on Heishan traction substation show the effective performance of our designed method.
Variational Deep Embedding Clustering by Augmented Mutual Information Maximization

Clustering is a crucial but challenging task in pattern analysis and machine learning. Recent many deep clustering methods combining representation learning with cluster techniques emerged. These deep clustering methods mainly focus on the correlation among samples and ignore the relationship between samples and their representations. In this paper, we propose a novel end-to-end clustering framework, namely variational deep embedding clustering by augmented mutual information maximization (VCAMI). From the perspective of VAE, we prove that minimizing reconstruction loss is equivalent to maximizing the mutual information of the input and its latent representation. This provides a theoretical guarantee for us to directly maximize the mutual information instead of minimizing reconstruction loss. Therefore we proposed the augmented mutual information which highlights the uniqueness of the representations while discovering invariant information among similar samples. Extensive experiments on several challenging image datasets show that the VCAMI achieves good performance. we achieve state-of-the-art ACC results for clustering on MNIST (99.5%) and CIFAR-10 (65.4%) to the best of our knowledge.
Generating antibunching light by beam-splitting photon number resolved detection from the superbunching squeezed vacuum

Beam-splitting photon number resolved (PNR) detection is one of the typical non-Gaussian operations in quantum optics. In this paper, we show that, by such an operation, the antibunching light can be generated by performing the beam-splitting PNR measurements of the superbunching squeezed vacuum light to subtract the odd photons. This is because that the squeezed vacuum is superposed by the even-photon Fock states, while its odd-photons subtracted state is just superposed by the odd-photon Fock states and the superposed probability of the single-photon state is significantly high. Hopefully, beam-splitting PNR detection could also be used to implement other desired non-Gaussian operations for engineering various nonclassical light, as the PNR detectors have been well developed in recent years.
Application Research of Computer Simulation in Basketball Teaching

To improve the teaching quality of basketball players, this paper attempts to study the virtual human motion control method. A basketball player simulation system is designed by using Visual C++ programming language, OpenGL graphic program interface language and 3DS max. By importing the motion state of virtual human in 3dsmax into the system in the form of 3DS file, the motion process of virtual human is obtained. Starting from the graphical user interface, level design, animation module and other modules, the problems of texture mapping, animation and collision detection are realized and optimized, which can assist athletes in training. The experimental results show that the system can promote the physical training of basketball players, and it can also significantly improve the running speed and jumping force of basketball players.
New cognitive sharing algorithms for cloud service management

Summary This article will present new classes of confidential data management algorithms for the protection and safeguarding of classified data. These algorithms will constitute a solution dedicated to supporting the process of safeguarding and protecting information with various meanings and in different forms. A special type of data is services, understood as information subject to the processes of management and protection. These processes will be discussed on the example of selected solutions dedicated to the concealment of information with secret character. A novelty presented in this article is the possibility to apply algorithms based on the meaning interpretation of the analyzed data in order to secure the data properly; there is also a possibility of executing the proposed solutions regardless of the structure, in which these solutions are to be implemented. An effective execution of data protection tasks will be discussed on the example of two‐stage secret protection algorithms, that is, the protection of the basic data set and the information set with meanings generated on the basis of the semantic secret analysis. Standard secret division approaches are not dependent on semantic content of shared information. In this article, we will be presented such novel approach, which allow to divide data depending on its semantic meaning or content. The problem of semantic meaning evaluation will be solved using cognitive information systems, allowing to extract meaning hidden in the splitted data.
Hidden Markov Modeling of the Cognitive Process in Strategic Thinking

Hidden Markov models (HMMs) are used to study language, sleep, and other processes that reflect probabilistic transitions between states that cannot be observed directly. We apply HMMs to data from experiments on visual location games. In these games, people choose a pixel from an image. They either have a common goal to match locations or have different goals in hider-seeker games. Eyetracking records where they look at a fine-grained time scale. Numerical salience of different locations is predicted, a priori, from a specialized vision science based neural network. The HMM shows the pattern of transitioning from hidden states corresponding to either high or low salience locations, using the eye-tracking and salience data. The transitions vary based on the player’s strategic goal; for example, hiders transition more often to low-salience states than seekers do. The estimated HMM is then used to do two useful things. First, a continuous-time HMM (cHMM) predicts the salience level of each player’s looking over the course of several seconds. The cHMM is then used to predict what would happen if the same process was truncated by time pressure choosing in two seconds instead of six, cHMM predicts seekers will match hiders 12% of the time; they actually match 15%. Second, dHMM is used to infer levels of strategic thinking from high-to-low transitions (a la Costa-Gomes et al. 2001 and others). The resulting estimates are more plausible than some maximum-likelihood procedures and models which appear to grossly underestimate strategic sophistication. Other applications of HMM in experimental economics are suggested.
Unsupervised learning based target localization method for pantograph video

Localization of structural regions in pantograph videos is the core and key to solve the problem of pantograph state detection using image processing technology. In this paper, an unsupervised learning based target localization method for pantograph video is proposed. First, an unsupervised learning approach is used to learn and estimate the pixel nodes in the same frame and neighborhood video image sequences that have undergone superpixel segmentation to achieve initialized prediction of the pantograph region and localization of the target region based on the correlation and semantic information between the pixel nodes. Secondly, we segment the target region by constructing the minimum energy function of the CRF model for the localization results. Finally, we construct pantograph video data sets in various environments. The experimental results show that the method is able to obtain accurate localization and segmentation results for different pantograph video data in different complex scenes, and has obvious superiority and robustness compared with other algorithms.
Image Electronic Evidence Screening Based on Improved SSD

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.
Big Data in Policing: Profiling, Patterns, and Out of the Box Thinking

Big Data, being a massive amount of data, which requires technologies, information architecture and systems design, and analytical methods, has a significant impact on modern society and the development of security area strategies. This paper investigates the importance of Big Data in policing and discusses the challenges arising from its appliance to maintain public order. It is presented a theoretical study, based on a literature review in the context of policing that allows the establishment of the construct in which the Portuguese National Public Security developed its own Strategic Information System. For the study, some inclusion and exclusion criteria were used to narrow the gaps for a better understanding of the subject. Even though some questions arise from using the Big Data, such as processing, profiling, parsing algorithms that can conduct excessive normalisations of the data, Big Data is leverage in policing and a tool of predictive policing.
Transmission line image mosaic based on improved SIFT-LATCH

Abstract In the transmission line inspection, in order to obtain wide-view and large-scale images, it is necessary to stitch images taken by drones. This article proposes an improved SIFT-LATCH-based UAV aerial transmission line image stitching algorithm. First, the image is processed in parallel through multi-threading. The SIFT algorithm is used to extract the feature points in the image, and the LATCH descriptor is used to describe the feature points. The sampling consensus algorithm is used to purify the feature points, and finally the translational homography matrix is used to solve the problem of black lines in the stitched image. Experiments show that the algorithm proposed in the article has the advantages of good real-time and high accuracy in splicing UAV aerial pictures of transmission lines.
Meta-path Guided Heterogeneous Graph Neural Network For Dish Recommendation System

Abstract The traditional recommendation system can hardly utilize the ingredients and flavor characteristics of the dishes, and faces the problems of sparse data and cold start, resulting in inaccurate results of the recommendation system. This paper addresses these problems by constructing a heterogeneous graph network with the interaction data between users and dishes in the food domain and taking the main and auxiliary ingredients as nodes. Also, we capture the higher-order structural information among users, dishes, and main and auxiliary ingredients by using meta-path guidance. In the meantime, we assign weights to the edges associated with user nodes and main and auxiliary ingredients nodes, which can obtain users’ preferences for main and auxiliary ingredients by using weighted GCN networks. The experiments conducted on a food domain dataset demonstrate that the meta-path-guided heterogeneous graph dish recommendation algorithm proposed in this paper is improved over the traditional recommendation algorithm.
Visual communication website design quiz personalization based on DiSC

Abstract The purpose of this research was to design a personality test website based on DiSC theory that could help high school students who are still confused about what college courses that could be chosen later based on their type of DiSC personality. The website was created in quiz form. The user would answer a few questions before knowing what their personality type. The design method used a creative strategy by targeting teenagers, especially high school students. The design was tailored to the visual and design strategies that describe the website as a friend who provided direction and enlightenment. The results are that a website can be used by users, especially high school students, to know more in its nature so that they can choose the right course majors. In the end, it is hoped that this website can be useful for users who use it.
Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.
VR Sickness Assessment with Perception Prior and Hybrid Temporal Features

Virtual reality (VR) sickness is one of the obstacles hindering the growth of the VR market. Different VR contents may cause various degree of sickness. If the degree of the sickness can be estimated objectively, it adds a great value and help in designing the VR contents. To address this problem, a novel content-based VR sickness assessment method which considers both the perception prior and hybrid temporal features is proposed. Based on the perception prior which assumes the user's field of view becomes narrower while watching videos, a Gaussian weighted optical flow is calculated with a specified aspect ratio. In order to capture the dynamic characteristics, hybrid temporal features including horizontal motion, vertical motion and the proposed motion anisotropy are adopted. In addition, a new dataset is compiled with one hundred VR sickness test samples and each of which comes along with the Discomfort Scores (DS) answered by the user and a Simulator Sickness Questionnaire (SSQ) collected at the end of test. A random forest regressor is then trained on this dataset by feeding the hybrid temporal features of both the present and the previous minute. Extensive experiments are conducted on the VRSA dataset and the results demonstrate that the proposed method is comparable to the state-of-the-art method in terms of effectiveness and efficiency.