Reviewerly/exHarmony huggingface dataset:
Ballot Mechanism Design Based on Blockchain Methodologies

Ballot mechanism design is proposed to improve existing ballot procedures. Presently, direct election of government officials at various levels is still under the mechanism of hand counting. Occasionally, the outcome from a hand count could still save the day because of the mistakes or inconsistent standards. Nowadays, network architecture and IoT devices are well developed. Based on the infrastructure, the ballot mechanism is designed by adapting blockchain methodologies to solve the problems of existing election procedures. In ballot mechanism, blockchain methodologies are adopted including cryptography, peer-to-peer transaction, data storage. In which, the complete cryptography is necessary for protecting personal information and defending outside destruction. Secure hash algorithm is a methodology to convert meaningful text into a meaningless string. Digital signature is adopted to ensure the vote was made by the person who has the related private key. Peer-to-peer transmission between voters and election center server to reduce the exchange errors. Each vote will be stored in the blockchain, where is under the secure mechanism, data is hardly altered and records are stored permanently. The goal of ballot mechanism design is aim to raise the election efficiency and accuracy, improve the manual counting procedures, and reduce cost of human resource and misjudgment through the blockchain methodologies.
Competition of Strategies in jSO Algorithm

A newly proposed variant of the efficient jSO algorithm employing competition of eight strategies (cSO) is proposed. The main idea is to select the most proper strategy and adapt the setting to each solved problem. One more mutation variant and one more type of crossover are added to jSO, and moreover, the popular mechanism of Eigen coordinate system is applied. All eight strategies compete to be used in the next generations based on the successes in previous generations. The proposed cSO method has more wins over jSO significantly in more real-world problems than fails. The original jSO strategy is never the most frequently used strategy, compared with other newly employed strategies.
Markov Neural Network For Guidance, Navigation and Control

The paper proposes a Markov neural network for guidance, navigation, and control (GNC) designs. The proposed neural network is a temporal neural network evolving through the state that is the outcome of the Markov property of a stochastic dynamic system. At a specific time, a neural network represents either the complicated nonlinear behaviors of a dynamic system or a complicated GNC design problem. This neural network influences its future neural network only through the state, which is the special property to distinguish itself from other artificial neural networks. The proposed Markov neural network is directly solved by deep learning techniques. This paper demonstrates that the proposed Markov neutral network results in more accurate system identification than conventional approaches. It also discusses benchmark GNC design examples to be efficiently solved by the proposed Markov neural network, indicating that GNC designs and state of the art artificial neural networks of deep learning are systematically fused.
Medical image encryption via lifting method

A new method to enhance image encryption is proposed. It is based on lifting transform and edge detection technique, the edge detection is selected as a way that it increases the capacity of hidden data, which smooth (non-edge) area hide small amount of information as compared to edge area. Initial ly, the lifting transform will apply on entering image. For approximate part, the simplest edge detection technique is applied, then, the image into N×N blocks is divided, the selection of key for edge will be different about non-edge block. To make the proposed method better enough, different techniques are used, one for the edge block and another for non-edge block. The proposed method is proved resists in most of security attacks. Further different testes analysis as entropy, correlation and histogram analysis is used to verify the performance of proposed method.
Secure and Dynamic Outsourcing Computation of Machine Learning in Cloud Computing

This paper presents a novel, secure and dynamic mechanism to train machine learning models that achieve membership privacy. Our protocol falls in the two-server-aided model and allows one server to perform most of computations and allows another server to provide auxiliary computation. In addition, users distribute their private data among two non-colluding but untrusted servers who train neural network on the adaptively select data using secure multi-party computation (MPC). In our protocol, only the selected members can reconstruct a predefined secret and jointly decrypt the computation result. This protocol is proven to be secure in the semi-honest model and passive adversary settings, and show that security is maintained even if the unselected users' drop out at any time.
Binarization Trees and Random Number Generation

An m-extracting procedure produces unbiased random bits from a loaded dice with m faces, and its output rate, the average number of output per input is bounded by the Shannon entropy of the source. This information-theoretic upper bound can be achieved only asymptotically as the input size increases, by certain extracting procedures that we call asymptotically optimal. Although a computationally efficient asymptotically optimal 2-extracting procedure has been known for a while, its counterparts for m-ary input, m > 2, was found only recently, and they are still relatively complicated to describe. A binarization takes inputs from an m-faced dice and produce bit sequences to be fed into a binary extracting procedure to obtain random bits. Thus, binary extracting procedures give rise to an m-extracting procedure via a binarization. A binarization is to be called complete, if it preserves the asymptotic optimality, and such a procedure has been proposed by Zhou and Bruck. We show that a complete binarization naturally arises from a binary tree with m leaves. Therefore, there exist complete binarizations in abundance and Zhou-Bruck scheme is an instance of them. We now have a relatively simple way to obtain an asymptotically optimal and computationally efficient m-extracting procedure, from a binary one, because these binarizations are both conceptually and computationally simple. The well-known leaf entropy theorem and a closely related structure lemma play important roles in the arguments.
Simple and Accurate Classification Method Based on Class Association Rules Performs Well on Well-Known Datasets

Existing classification rule learning algorithms use mainly greedy heuristic search to find regularities in datasets for classification. In recent years, extensive research on association rule mining was performed in the machine learning community on learning rules by using exhaustive search. The main objective is to find all rules in data that satisfy the user-specified minimum support and minimum confidence constraints. Although the whole set of rules may not be used directly for accurate classification, effective and efficient classifiers have been built using these, so called, classification association rules. In this paper, we compare "classical" classification rule learning algorithms that use greedy heuristic search to produce the final classifier with a class association rule learner that uses constrained exhaustive search to find classification rules on "well known" datasets. We propose a simple method to extract class association rules by simple pruning to form an accurate classifier. This is a preliminary study that aims to show that an adequate choice of the "right" class association rules by considering the dependent (class) attribute distribution of values can produce a compact, understandable and relatively accurate classifier. We have performed experiments on 12 datasets from UCI Machine Learning Database Repository and compared the results with well-known rule-based and tree-based classification algorithms. Experimental results show that our method was consistent and comparative with other well-known classification algorithms. Although not achieving the best results in terms of classification accuracy, our method is relatively simple and produces compact and understandable classifiers by exhaustively searching the entire example space.
A Proposal for Account Recovery in Decentralized Applications

Decentralized applications achieve stronger security and fault tolerance by placing trust in the majority of users instead of a single third party. However, it also means that there will be no administrator to handle unexpected situations. Because there is no trusted central management service, a decentralized application can do little when a user loses their private key that unlocks the tokens and other assets the user possesses, and users are responsible for keeping exclusive control of their private keys. Therefore, as decentralized applications are becoming mainstream, and more and more non-tech savvy users began to use these decentralized apps, account recovery will be a much-needed feature. In this paper, we propose a method to recover an account by an arbitration process that utilizes the information asymmetry between the regular users and trade partners an individual may have had over the life of their blockchain account for which they lost access. Our method involves a spam filter to separate legitimate requests from malicious or spam requests, a robust voting mechanism backed by game-theoretic insights, and an extensive set of controls to avert any attacks.
Priest on Negation

What conception of negation a dialetheist might have, in holding that a statement and its negation can both be true, has been the subject to considerable debate. Several of the issues in play in this area—such as the unique characterization of negation, and the interplay between contrariety and subcontrariety—are broached here by considering some positions taken on them by Graham Priest and assorted critics (in particular, Hartley Slater and Jean-Yves Béziau). Some of the more intricate points, as well as detailed discussions of commentators (including those just mentioned as well as Heinrich Wansing) on Priest are handled in explicitly labelled Digressions and in two Appendices.
Discovering High Influence Co-location Patterns from Spatial Data Sets

The co-location pattern is a subset of spatial features that are frequently located together in spatial proximity. However, the traditional approaches only focus on the prevalence of patterns, and it cannot reflect the influence of patterns. In this paper, we are committed to address the problem of mining high influence co-location patterns. At first, we define the concepts of influence features and reference features. Based on these concepts, a series of definitions are introduced further to describe the influence co-location pattern. Secondly, a metric is designed to measure the influence degree of the influence co-location pattern, and a basic algorithm for mining high influence co-location patterns is presented. Then, according to the properties of the influence co-location pattern, the corresponding pruning strategy is proposed to improve the efficiency of the algorithm. At last, we conduct extensive experiments on synthetic and real data sets to test our approaches. Experimental results show that our approaches are effective and efficient to discover high influence co-location patterns.
Illumination Invariant Thermal Face Recognition using Convolutional Neural Network

An illumination invariant thermal face recognition using convolutional neural network is proposed. The proposed CNN model includes training phase and testing phase. The convolutional layer of CNN model is utilized to extract features. This produces a feature map in the output image and the feature maps are fed to the next layer. The output from the last pooling layer is flattened and fed into a fully connected layer (FC layer). The goal of FC layer is to employ these features for classifying the input image into various classes based on the training datasets. Compared with the traditional thermal face recognition, experimental results demonstrate the superiority of the proposed method.
Toward Data Driven Development in RoboCup

Conducting games in RoboCup incurs high cost in terms of effort, time, and money. The scientific outcome, however, is quite limited and often not very conclusive. Especially, analyzing and drawing conclusions about the performance of complex processes like decision making of an individual robot or the behavior on the team level poses a considerable challenge. Collecting more data during the competition games will help to analyze the performance of algorithms, identify errors and areas for improvement, and make more significant statements regarding the performance of the robots. In this work we investigate the possibilities for collection of the large scale RoboCup data and its analysis. We present a system for automatic recording of synchronized videos of RoboCup games and an application for exploration and annotation of large sets of RoboCup-related data. We also present data sets collected during the competitions in 2018 and an algorithm for visual detection and tracking of robots in the RoboCup videos. A first empirical evaluation shows promising results and demonstrates how such data can be integrated and used to validate robot’s behavior.
Tight Wavelet Frame Using Complex wavelet Designed in Free Shape on Frequency Domain

In this paper, we propose a construction method of a tight wavelet frame using a complex wavelet designed in a free shape on the frequency domain. This method is divided into two parts. First, based on the designed complex wavelet, we construct an approximate tight wavelet frame. Next, based on it, we construct a tight wavelet frame with minor modification. Additionally, for example, we show the construction process of the tight wavelet frame using the approximate Gabor wavelet.
Improving Result Diversity Using Query Term Proximity in Exploratory Search

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e. term frequency (tf), inverse document frequency (idf), etc. Query term proximity within matched documents is mostly under-explored. In this paper, a novel information retrieval framework is proposed, to promote the documents among all relevant retrieved ones. The relevance estimation is a weighted combination of document statistics and query term statistics, and term-term proximity is a simply aggregates of diverse user preferences aspects in query formation, thus adapted into the framework with conventional relevance measures. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The evaluation asserts the usability of QTP measures to balance several seeking tradeoffs, e.g. relevance, novelty, result diversity (Coverage and Topicality), and overall retrieval. The assessment of user search trails indicates significant growth in a learning outcome.
Thermal Face Recognition Based on Transformation by Residual U-Net and Pixel Shuffle Upsampling

We present a thermal face recognition system that first transforms the given face in the thermal spectrum into the visible spectrum, and then recognizes the transformed face by matching it with the face gallery. To achieve high-fidelity transformation, the U-Net structure with a residual network backbone is developed for generating visible face images from thermal face images. Our work mainly improves upon previous works on the Nagoya University thermal face dataset. In the evaluation, we show that the rank-1 recognition accuracy can be improved by more than $$10\%$$. The improvement on visual quality of transformed faces is also measured in terms of PSNR (with 0.36 dB improvement) and SSIM (with 0.07 improvement).
From Coarse to Fine: A Monocular Depth Estimation Model Based on Left-Right Consistency

Predicting depth from an image is an essential problem in the area of computer vision and deep learning shows a great potential in this area. However most deep Convolutional Neural Networks are need to train them using vast amount of manually labelled data, which is difficult or even scarcely possible in some special environment. In this paper, we proposed an unsupervised method based on left-right consistence with multi-loss fusion, which can perform single image depth estimation, despite the absence of ground truth data. We treat the issue as an image reconstruction problem by training our network with a combine of SSIM and Huber loss. To achieve estimation the depth from coarse to fine, we estimate a coarse map in the former layer and using bilinear sample to transmit the map to the latter layer to obtain a fine depth map. Our method achieves more accurate result on KITTI driving dataset.
Model-Driven Engineering for Delta-Oriented Software Product Lines

Software product line engineering (SPLE) is an approach in software development that produces various products based on commonality and variability. SPLE maintains the product variations within two main phases: domain engineering and application engineering. Lack of adequate technology and tools support is one of the problems in adopting SPLE. In this research, a model-driven approach based on delta-oriented programming is proposed for SPLE. The process starts with the domain analysis phase by defining a feature diagram and Unified Modeling Language (UML) based on existing systems. While those models represent the problem domain, delta-oriented programming with abstract behavioral specification? (ABS) language is used in the solution domain. This approach is supported by automated model transformations, which transform the feature diagram and UML to ABS models. A code generator mechanism is also used to produce a running application based on ABS models. When the user selects features in this application, our tools generate the running application based on those selections. We provide a running example, a charity organization system, as a case study. Therefore, this research proposes an entire SPLE process based on a model-driven approach that covers the problem and solution domains and produces a running application.
Improving Monocular Depth Prediction in Ambiguous Scenes Using a Single Range Measurement

Depth maps are widely used in robotics, with numerous applications in agricultural tasks. Methods for estimating these from monocular images currently exist, but this is an ill posed problem which requires assumptions about object scale and camera focal length. These assumptions may not always be reasonable and how to deal with them has not been sufficiently explored in the current literature. For example, scenes in agriculture frequently violate the assumption of having a single scale per object class and represent a failure case for these methods. To avoid these assumptions when estimating depth maps, we present an approach where a single actual distance measurement is fused with a monocular image. Our results indicate that this method can outperform an image-only baseline, provided the distance measurement is sampled according to a projective model. We also found that a single measurement can significantly improve accuracy on simulated variable scale versions of two common public datasets. A hardware implementation of this approach was tested in an agricultural setting, though results were poor. Software and hardware designs are made available1.
Security status of electronic records preservation in central China

Purpose Recently, increasing importance has been given to electronic records in China, despite the lack of understanding that institutions and organizations have regarding the security status of electronic records preservation. Wuhan, the largest city in central China, serves as a case to investigate the preservation security of electronic records. Challenges to security are summarized, and solutions are proposed to support policy-developing and operational guidance. The paper aims to discuss these issues. Design/methodology/approach The Delphi method is applied to analyze the advice of nine experts, select survey items and design questionnaires. Data are collected from 34 archives in Wuhan through field surveys, oral interviews and e-mails, which are analyzed and illustrated with three representative cases. Findings Main achievements of electronic records preservation are concluded in the electronic records management system, carrier types and storage formats, and data backup. Problems are summarized as a lack of awareness and capability of defending against security risks, disaster recovery capability, and understanding of electronic record characteristics. Solutions are proposed as follows: carrying out regular security risk evaluation, adopting new technologies, implementing application-level backup, strengthening technology-related education and attracting more IT talent to join the archive discipline. In addition, it is critical to promote an understanding of the characteristics of electronic records. Originality/value This paper investigates the security status of electronic records in central China by surveys and case studies. Critical problems and corresponding solutions are raised to support policy-developing and operational guidance for the research and practice of all kinds of institutions that implement electronic records preservation.
Incremental Filter Pruning via Random Walk for Accelerating Deep Convolutional Neural Networks

Accelerating Deep Convolutional Neural Networks (CNNs) has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. Previous works utilized "smaller-norm-less-important" criterion to prune filters with smaller ࡁp-norm values by pruning and retraining alternately. This trends to narrow the model capacity for the following reasons: (1) Violent pruning. Previous works adopt a violent strategy in which all filters are simultaneously pruned, which leaving the room to retain model accuracy limited. (2) Filter degradation. Previous works simply set the pruned filter to 0 and retrained it alterately, which easily led to the loss of learning ability of filters. To solve this problem, we propose a novel filter pruning method, namely Incremental Filter Pruning via Random Walk (IFPRW). IFPRW solves the problem of violent pruning by incremental method and Filter degradation by means of random walk. When applied to two image classification benchmarks, the usefulness and strength of IFPRW is validated. Notably, on CIFAR-10, IFPRW reduces more than 46% FLOPs on ResNet-110 with even 0.28% relative accuracy improvement. Moreover, on ILSVRC-2012, IFPRW reduces more than 54% FLOPs on ResNet-101 with only 0.7% top-5 accurcacy drop. which proving that IFPRW outperforms the state-of-the-art filter pruning methods.