Reviewerly/exHarmony huggingface dataset:
An explicit expression for Euclidean self-dual cyclic codes of length 2 over Galois ring GR(4,m)

For any positive integers m and k, existing literature only determines the number of all Euclidean self-dual cyclic codes of length 2k over the Galois ring GR(4,m), such as in Kiah et al. (2012) [17]. Using properties for Kronecker products of matrices of a specific type and column vectors of these matrices, we give a simple and efficient method to construct all these self-dual cyclic codes precisely. On this basis, we provide an explicit expression to represent accurately all distinct Euclidean self-dual cyclic codes of length 2k over GR(4,m), using binomial coefficients. As an application, we list all distinct Euclidean self-dual cyclic codes over GR(4,m) of length 2k explicitly, for k=4,5,6.
Virtual Reality: History, Applications, and Challenges for Human Factors Research

Research into the viability of virtual reality (VR) systems goes back nearly five decades. Even so, Human Factors researchers utilizing this type of technology are by most estimates still relatively rare in the field. The following paper focuses on how virtual reality technology is evolving into a viable tool during the research process. A brief background followed by an overview of the major terminology relating to VR is followed by human factors related applications for use. The paper concludes by discussing how VR can be used in education and specific research being conducted to examine metacognitive skills for engineering students.
On the Depth of Gestalt Hierarchies in Common Imagery

Apart from machine learning and knowledge engineering, there is a third way of challenging machine vision – the Gestalt law school. In an interdisciplinary effort between psychology and cybernetics, compositionality in perception has been studied for at least a century along these lines. Hierarchical compositions of parts and aggregates are possible in this approach. This is particularly required for high-quality high-resolution imagery becoming more and more common, because tiny details may be important as well as large-scale interdependency over several thousand pixels distance. The contribution at hand studies the depth of Gestalt-hierarchies in a typical image genre – the group picture – exemplarily, and outlines technical means for their automatic extraction. The practical part applies bottom-up hierarchical Gestalt grouping as well as top-down search focusing, listing as well success as failure. In doing so, the paper discusses exemplarily the depth and nature of such compositions in imagery relevant to human beings.
Privacy Preservation of Aggregated Data Using Virtual Battery in the Smart Grid

Smart Meters (SM) are IoT end devices used to collect user utility consumption with limited processing power on the edge of the smart grid (SG). While SMs have great applications in providing data analysis to the utility provider and consumers, private user information can be inferred from SMs readings. For preserving user privacy, a number of methods were developed that use perturbation by adding noise to alter user load and hide consumer data. Most methods limit the amount of perturbation noise using differential privacy to preserve the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. Additionally, users may desire to select complete privacy without giving consent to having their data analyzed. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. The level of noise can be set to make user data differentially private preserving statistics or break differential privacy discarding the benefits of data analysis for more privacy. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman key exchange for symmetrical encryption of transferred data and a two-way challenge-response method for authentication.
Faithful Fit, Markerless, 3D Eyeglasses Virtual Try-On

Virtual try-on allows people to check the appearance of accessories, makeup, hairstyle, hair color, clothes, and potentially more on themselves. In this paper, we propose an eyewear virtual try-on experience that is performed on a 3D face reconstructed from an input image allowing the user to see the virtual face and eyeglasses from different viewpoints. The try-on process takes into account real face and glasses sizes to provide a realistic fit estimation; it is fully automated and only requires a face picture and selection of eyeglasses frames to test.
Quality Guarantees for Autoencoders via Unsupervised Adversarial Attacks

Autoencoders are an essential concept in unsupervised learning. Currently, the quality of autoencoders is assessed either internally (e.g.. based on mean square error) or externally (e.g.. by classification performance). Yet, there is no possibility to prove that autoencoders generalize beyond the finite training data, and hence, they are not reliable for safety-critical applications that require formal guarantees also for unseen data.To address this issue, we propose the first framework to bound the worst-case error of an autoencoder within a safety-critical region of an infinite value domain, as well as the definition of unsupervised adversarial examples that cause such worst-case errors. Technically, our framework reduces the infinite search space for a uniform error bound to checking satisfiability of logical formulas in Linear Real Arithmetic. This allows us to leverage highly-optimized SMT solvers, a strategy that is very successful in the context of deductive software verification. We demonstrate our ability to find unsupervised adversarial examples as well as formal quality guarantees both on synthetic and real-world data.
A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems

Evolutionary and swarm intelligence algorithms are used as optimization algorithms for solving the clustering problem. One of the most popular optimization algorithms is the Grey Wolf Optimizer (GWO). In this chapter, we use GWO on seven medical data sets to optimize the initial clustering centroids represented by the individuals of each population at each iteration. The aim is to minimize the distances between instances of the same cluster to predict certain diseases and medical problems. The results show that solving the clustering task using GWO outperforms the other well-regarded evolutionary and swarm intelligence clustering algorithms, by converging toward enhanced solutions having low dispersion from the average values, for all the selected data sets.
The comparison of k-modes clustering and ROCK clustering to the poverty indicator in Samadua Subdistrict, South Aceh

Abstract The cluster methods including k-modes clustering and ROCK clustering are rarely used for analyzing categorical data. Much of the research data, however, are categorical, such as the poverty indicator dataset. Many studies have examined each of these two methods, but there is still limited study for comparing them. This study compared between k-modes clustering and ROCK clustering on a dataset of poverty indicators at a category scale. There were 1449 households with the lowest 40% income in Samadua Sub-district, South Aceh Regency were used as data in this study. The result of this research was that the optimal number of clusters for k-modes clustering was 2. Similarly, the optimal number of clusters for ROCK clustering was 2 with a threshold value (θ) of 0.29. The SW/SB and R-Squared values for the k-modes clustering were 0.0149 and 0.7558, respectively, compared to 0.0682 and 0.5732 for ROCK clustering. From these two values, it can be concluded that the k-modes clustering is better than ROCK clustering. The significant difference between the two clusters based on deciles of households with the lowest 40% income of the k-modes clustering is ownership of defecation facilities, ownership of television and motorbikes.
Polsar Image Classification based on Optimal Feature and Convolution Neural Network

This paper proposes a new polarimetric synthetic aperture radar (PolSAR) image classification method which uses optimal feature selection and convolutional neural network (CNN). Firstly, original image features are extracted by performing target decomposition on PolSAR data. Then, in order to reduce feature dimension and improve image classification accuracy, a group of optimal and high correlation property features are selected with the method of filtering and wrappering from the original one. Finally, the optimized CNN model is designed to implement PolSAR image classification. Experiments with measured data show that the algorithm can choose a more effective polarization feature set and achieve better classification results.
Differential Privacy Preserving Data Publishing Based on Bayesian Network

Privacy-preserving data publishing is a hot issue in the field of privacy protection. Differential privacy is a burgeoning technology of privacy protection which provides a powerful privacy mechanism and does not make restrictive assumptions on the attacker's background knowledge. At present, there does not have an effective way to generate Synthetic high-dimensional data with differential privacy technology. Aiming at the issue of high-dimensional privacy data publishing, this paper proposed a method called APrivBayes, which altered the structure of Bayesian network to make it adapting differential privacy mechanism. Then proposed a first node selection mechanism based on attribute correlation degree for the new structure of Bayesian network. Through theoretical analysis and experimental evaluation, this method improves the effect of network and reduces Laplace noise effectively, while protecting personal privacy and improving the usability of published data.
Real-Time Thermal Face Identification System for Low Memory Vision Applications Using CNN

Image based face identification systems have attained optimal performance. However, the design of such systems often involves some issues related to extreme light conditions and privacy protection, among others. Since several years, Face Identification (FI) based on thermal images using deep neural networks (DNN) has received significant attention. Yet, the majority of the FI systems developed through DNN’s need huge computational power; those systems are not suitable for the devices with memory limitations. In this paper, we proposed a new CNN framework based on depthwise separable convolutions for real-time face identification for low memory vision applications. The lack of publicly available thermal datasets makes very hard the research and developing of new techniques. In this work, we further present a new large-scale thermal face database called “ST_UNICT_Thermal_Face”. As per our analysis, the evaluation of the learnt model using the data obtained in the single-day (without temporal variations), it might not stable over time. One of the main reasons behind the development of this database for the real-time evaluation of the proposed model depends on the fact that most thermal face identification systems are not stable over time and climate due to insufficient time data. The evaluation results exhibit that the proposed framework is suitable for the devices having limited memory and which is stable over time and different indoor environmental conditions.
Network Video Traffic Classification Using Feature Fusion

Network traffic classification is essential for network management and quality of service (QoS) support. In order to improve the classification performance of network traffic, this paper proposes two novel feature fusion methods, which merge the original features with generated features or packet size histogram features extracted from the traffic flows. Since some generated features are more effective than or complementary to the original statistical features, the compound feature sets are employed, including original and generated features. Feature selection is done to select the optimal feature subset from the compound features by removing redundant ones. Experiments are conducted on a real network video traffic dataset, which verify the effectiveness of the proposed approaches. Compared with two existing methods, the proposed methods have better classification performance.
H-PS: A Heterogeneous-Aware Parameter Server With Distributed Neural Network Training

Deep neural networks have become one of the popular techniques used in many research and application areas including computer vision, natural language processing, etc.As the complexity of neural networks continuously increasing, the training process takes a much longer time and requires more computation resources.To speed up the training process, a centralized distributed training structure named Parameter Server (PS) is widely used to assign training tasks to different workers/nodes.Most existing studies considered all workers having the same computation resources.However, in a heterogeneous environment, fast workers (i.e., workers having more computation resources) can complete tasks earlier than slow workers and thus the system does not fully utilize the resources of fast workers.In this paper, we propose a PS model with heterogeneous types of workers/nodes, called H-PS, which can fully utilize the resources of each worker by dynamically scheduling tasks based on the current status of the workers (e.g., available memory).By doing so, the workers will complete their tasks at the same time and the stragglers (i.e., workers fall behind others) can be avoided.In addition, a pipeline scheme is proposed to further improve the effectiveness of workers by fully utilizing the resources of workers during the time of parameters transmitting between PS and workers.Moreover, a flexible quantization scheme is proposed to reduce the communication overhead between the PS and workers.Finally, the H-PS is implemented using Containers which is an emerging lightweight technology.The experimental results indicate that the proposed H-PS can reduce the overall training time by 1.4x -3.5x when compared with existing methods.
Insulator Intelligent Recognition Based on Machine Vision and Edge Calculation

Insulator is one of the most widely used components in the power grid. Identifing and locating insulator accurately and efficiently is the basis of intelligent evaluation of insulator defects based on UV imaging. The traditional insulator identification and location is mostly done by the back-end server, which has poor real-time performance. Based on this, this paper proposes an insulator intelligent recognition method based on machine vision and edge calculation, builds an edge calculation platform and loads the labeled insulator data set into SSD model for training. The accuracy of recognition is 91.46% and the model file is transplanted to the edge device. At the same time, the edge equipment is equipped with a visual sensor, which can identify the real-time video stream of the insulator. It has a good real-time performance and provides a new scheme for the fault detection of the external insulation equipment.
Single Image with Large Sky Area Dehazing Based on Structure-texture Decomposition

Traditional dehazing methods based on restoration are prone to color distortion and noise amplification when dealing with hazy image with large sky region. To improve dehazing effect, we proposed a dehazing algorithm based on image structure-texture decomposition and reconstruction. Hazy image is decomposed into high-frequency texture layer and low-frequency structure layer by total variation. Discrete cosine transform is used to generate an image mask to separate sky area and non-sky area. The texture layer is denoised by the mask, and the structure layer is dehazed by dark channel prior. The media transmission is corrected by color attenuation prior. Finally, the denoised texture layer and the dehazing structure layer are reconstructed to obtain dehazed image. Experiment results show that, compared with state-of-art algorithms, our method has great dehazing effect on non-sky region, and the sky region after dehazing is smooth without color distortion and noise.
Research on Relation Extraction Method Based on Similar Relations and Bayesian Neural Network

Abstract The solidification of network parameter values for most relation extraction models after training makes the model overconfident in the process of prediction and classification tasks. Moreover, the interference of similar relation in the text data will affect the effect of relation extraction. We propose a relation extraction model based on relation similarity and Bayesian neural network. This model uses the logistic regression loss function to make the training parameters closer to the target relation and away from the similar relation, thereby eliminating the interference of the similar relation in the relation extraction task. In addition, it is also possible to learn a probability distribution from the weights of the Bayesian-LSTM (Bayesian-Long Short-Term Memory) neural network, and retain the corresponding uncertainty on the basis of obtaining long-distance dependence information using LSTM, so that the model learns more data features while performing regularization at the weight level. The model also uses an attention mechanism to pay more attention to useful information. The experimental results on the Wikipedia data set TACRED (TAC Relation Extraction Dataset) data set show that the proposed method effectively improves the effect of the entity relation extraction model.
inDepth: Force-based Interaction with Objects beyond A Physical Barrier

We propose inDepth, a novel system that enables force-based interaction with objects beyond a physical barrier by using scalable force sensor modules. inDepth transforms a physical barrier (eg. glass showcase or 3D display) to a tangible input interface that enables users to interact with objects out of reach, by applying finger pressure on the barrier's surface. To achieve this interaction, our system tracks the applied force as a directional vector by using three force sensors installed underneath the barrier. Meanwhile, our force-to-depth conversion algorithm translates force intensity into a spatial position along its direction beyond the barrier. Finally, the system executes various operations on objects in that position based on the type of application. In this paper, we introduce inDepth concept and its design space. We also demonstrate example applications, including selecting items in showcases and manipulating 3D rendered models.
Learning Implicit Generative Models by Teaching Density Estimators

Implicit generative models are difficult to train as no explicit density functions are defined. Generative adversarial nets (GANs) present a minimax framework to train such models, which however can suffer from mode collapse due to the nature of the JS-divergence. This paper presents a learning by teaching (LBT) approach to learning implicit models, which intrinsically avoids the mode collapse problem by optimizing a KL-divergence rather than the JS-divergence in GANs. In LBT, an auxiliary density estimator is introduced to fit the implicit model's distribution while the implicit model teaches the density estimator to match the data distribution. LBT is formulated as a bilevel optimization problem, whose optimal generator matches the true data distribution. LBT can be naturally integrated with GANs to derive a hybrid LBT-GAN that enjoys complimentary benefits. Finally, we present a stochastic gradient ascent algorithm with unrolling to solve the challenging learning problems. Experimental results demonstrate the effectiveness of our method.
Position Paper: Towards a Robust Edge-Native Storage System

Edge environments are generating an increasingly large amount of data due to the proliferation of edge devices. Accommodating this large influx of data at edge servers is a challenging issue. While some data can be processed as it is generated, others must be stored for later access. This paper proposes the features that a new edge-native storage system must possess including support for user mobility and node fluctuation. To motivate this, we first describe several emerging edge applications and their data needs. We then describe the challenges in meeting these needs. We then evaluate an out-of-the-box cloud storage system, Cassandra, to assess it's suitability as an edge storage system due to many edge-friendly features. We determined that while a cloud-based storage system can be ported to the edge meeting some of the challenges, other challenges require new solutions. Based on the challenges and the results of Cassandra case study, we propose a set of design principles for a new edge-native storage system.
A Backtracking Algorithmic Toolbox for Solving the Subgraph Isomorphism Problem

The subgraph isomorphism problem asks whether a given graph is a subgraph of another graph. It is one of the most general NP-complete problems since many other problems (e.g., Hamiltonian cycle, clique, independent set, etc.) have a natural reduction to subgraph isomorphism. Furthermore, there is a variety of practical applications where graph pattern matching is the core problem. Developing efficient algorithms and solvers for this problem thus enables good solutions to a variety of different practical problems. In this chapter, the authors present and experimentally explore various algorithmic refinements and code optimizations for improving the performance of subgraph isomorphism solvers. In particular, they focus on algorithms that are based on the backtracking approach and constraint satisfaction programming. They gather experiences from many state-of-the-art algorithms as well as from their engagement in this field. Lessons learned from engineering such a solver can be utilized in many other fields where backtracking is a prominent approach for solving a particular problem.
Additive G-codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math> and their dualities

We define additive G-codes over finite fields. We prove that if C is an additive G-code over Fq with duality M then its dual with respect to this duality CM is an additive G-code. We prove that if M and M′ are two dualities, then CM and CM′ are equivalent codes. Finally, we study the existence of self-dual codes for a variety of dualities and relate them to formally self-dual and linear self-dual codes.
The Influence of Ideological and Political Education on Employment Quality of College Students based on Association Rule Analysis

Abstract A large number of employment data of college graduates are stored in the database. It is necessary to mine a large number of unknown and useful information hidden in these employment data. This paper aims to analyze the impact of Ideological and political education on the quality of employment by using association rules. Firstly, this paper studies the calculation formula of association rules. Firstly, the concept of association rules is described. Secondly, the association rules of employment data are analyzed and designed. Secondly, the employment data of college students and the learning data of Ideological and political education of college students are mined. Finally, the employment data is analyzed and studied in detail by using software. The experimental results show that the credits of Ideological and political education are in the range of [3,4]. 38.69% of the graduates whose achievements in Ideological and political education are within the range of [3,4] belong to class II. Among the graduates whose score of Ideological and political education in 2019 is [1,2], 35.29% are not satisfied with their current situation, that is to say, their confidence in the rules is 0.3529%. It can be seen that the credit system of Ideological and political education of college students has affected the employment quality of graduates after half a year to some extent.