Reviewerly/exHarmony huggingface dataset:
A Data Quality Management of Chain Stores based on Outlier Detection

For successfully analyzing data in the business of chain stores, the quality of data recorded in their shops or factories is a key factor. Data quality management is an important practical issue because data qualities widely vary depending on the managers or workers of many stores in the chain. In this paper, we present a data quality evaluation method for shops in chain businesses based on outlier detection and then, we apply this method to a dataset observed in real chain stores, which provide tire maintenance for vehicles. To evaluate the data quality of each shop, we use data about trucks tire information such as tread depth, tread pattern, and distance which was recorded by the shops at maintenance time to calculate low-quality data by using outlier detection methods with reliable experimental data and practical knowledge. Some outlier detection methods such as Isolation Forest and one-class Support Vector Machine are applied to detect anomalous tire information, which is used to calculate datas abnormal rate in each shop. Our result showed that with this kind of data, Isolation Forest is outstanding than other methods because Isolation Forest is designed to detect few and different outliers. The proposed method can support better maintenance services for customers as well as be able to get more correct data from these shops, which will be useful for the next research.
Efficient, Geometrically Adaptive Techniques for Multiscale Gaussian-Kernel SVM Classification

Single-scale Gaussian-kernel support vector machines (SVM) have achieved competitive accuracy in many practical tasks; however, a fundamental limitation of the underlying model is its use of a single bandwidth parameter which essentially assumes that the training and test data has a uniform scale everywhere. In cases of data with multiple scales, only one parameter may be unable to fully capture the heterogeneous scales present in the data. In this paper, we present two efficient approaches to constructing multiscale Gaussian kernels for SVM classification by following the multiple-kernel learning research by Gonen and Alpaydin (J Mach Learn Res 12: 2211–2268, 2011) and a self-tuning spectral clustering procedure introduced by Zelnik-Manor and Perona (Advances in Neural Information Processing Systems 17:1601–1608, 2004) in the unsupervised setting, respectively. The resulting kernels adapt to the different scales of the data and are directly computable from the training data, thus avoiding expensive hyperparameter tuning tasks. Numerical experiments demonstrate that our multiscale kernels lead to superior accuracy and fast speed.
Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors

Abstract How to construct an effective sample affinity matrix is an important problem for subspace clustering, and most existing subspace clustering algorithms pursue the affinity matrix in a single space. In this paper, we propose a novel computational framework for subspace clustering, called Complementary Subspace Clustering (CSC) at first, where the affinity matrix is constructed in a pair of complementary spaces which provide different and complementary constraints on the affinity matrix. Many existing structural priors on self representation and dimensionality reduction can be seamlessly integrated into the CSC framework. Then under this framework, we explore a simple and effective subspace clustering algorithm by respectively introducing two basic priors - sparse representation and spatial closeness - into the referred pair of spaces. Moreover, a kernel variant of the proposed clustering algorithm is present. Extensive experimental results demonstrate that although only basic priors are involved, the explored algorithms from the CSC framework can improve the clustering performances significantly when the number of the sample classes is relatively big.
Topology Learning in Radial Dynamical Systems With Unreliable Data

Many complex engineering systems admit bidirectional and linear couplings between their agents. Blind and passive methods to identify such influence pathways/couplings from data are central to many applications. However, dynamically related data streams originating at different sources are prone to corruption caused by asynchronous time stamps of different streams, packet drops, and noise. Such imperfect information may be present in the entire observation period and, hence, is not detected by change detection algorithms that require an initial clean observation period. In this article, we provide a novel approach to detect the location of corrupt agents as well as present an algorithm to learn the structure of radial dynamical systems despite corrupted data streams. In particular, we show that our approach provably learns the true radial structure if the unknown corrupted nodes are at least three <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hops</i> away from each other. Our theoretical results are further validated in a test dynamical network.
Cardinality Analysis to Classify Malicious Domain Names

Abuse of the Internet (e.g., spam mailing, phishing, and other antisocial behaviors) is increasing. Blocking such abuses with blacklists is used as a typical countermeasure. A blacklist contains malicious hosts on the Internet reputed to send spam mail, employ phishing, and engage in other antisocial behaviors. By retrieving the blacklists each time when users receive emails, users can reject spam mails. However, it is difficult to keep up with the new malicious hosts created every day, so research is required to maintain the blacklist. In this study, we propose a classification method whose recall rate (97.3%) is higher than that of the best single public blacklist (48.9%). Cardinality analysis, which identifies the access pattern of multiple clients to various domain names, is the main technique of the proposed method. We explain the method together with the results of experiments use 26M domain name system query-response data.
5G Wireless Intelligent Propagation Channel Modelling Based on Deep Residual Network

Based on the engineering big data measured by Huawei Mobility Base Station and the application of electronic map, this paper establishes the radio wave feature engineering by combining the Cognitive Radio (CR) and ResNet. Based on the statistical results of channel large-scale fading characteristics, including path loss (PL), shadow fading (SF) and small-scale fading characteristics, the actual Reference Signal Receiving Power (RSRP) of the channel is obtained to modify the traditional empirical model formula of wireless channel, and the model is used to accurately predict the wireless signal coverage in the new environment, so as to greatly reduce the cost of base station construction and improve the network construction efficiency.
Incomplete Information Competition Strategy Based on Improved Asynchronous Advantage Actor Critical Model

In recent years, game theory has been widely used in the field of deep learning, mainly including intelligent competition strategies of complete information games and incomplete information games. This paper focuses on incomplete information games, and proposes a low-dimensional semantic feature based on category coding and an incomplete information competition strategy based on the improved Asynchronous Advantage Actor-Critic (A3C) network model. First, the A3C network model in deep reinforcement learning is adopted in the competition strategy, and its network structure is improved according to the semantic features based on category coding. The improved A3C model is implemented in parallel by a series of "workers". The "workers" is a new deep learning model structure proposed in this paper. Secondly, this article combines supervised learning and Deep Reinforcement Learning (DRL) to propose a new competitive strategy. Through conducting a large number of real-time experiments with human players on online competitive websites, the comparison with the existing methods in terms of the ratio of winning and losing and the ranking rate, the experimental results indicate the superiority of the new competitive strategy.
Shifting the Balance-of-Power in STV Elections

In the context of increasing automation of Australian electoral processes, and accusations of deliberate interference in elections in Europe and the USA, it is worthwhile understanding how little a change in the recorded ballots could change an election result. In this paper we construct manipulations of the ballots in order to change the overall balance of power in an Australian Federal Senate election – the upper house of Parliament. This gives, hopefully tight, over-estimations of the Margin of Victory (MOV) for the party or coalition winning the Senate. This is critical information for determining how well we can trust the reported results, and how much auditing should be applied to the election process to be certain that it reports the true result. The challenge arising in Australian Federal Senate elections is that they use a complicated Single Transferable Vote (STV) method for which it is intractable to compute the true MOV, hence we must rely on greedy methods to find small manipulations.
Research on non-blocking communication of airport data based on dynamic thread pool

In order to solve the shortcomings including the sending and receiving high delay, the high memory utilization and the high CPU(Central Processing Unit) utilization of the traditional airport scene monitoring system data, several kinds of data communication in the airport scene monitoring system are illustrated. Besides, the mechanism of non-blocking communication of airport data based on dynamic thread pool is researched, and the parameters of the data sending and receiving delay time, the memory utilization of and the CPU utilization are analyzed. By comparing some communication methods including single-thread communication, multi-thread communication and dynamic thread pool non-blocking communication, the related theory is verified by programming. By the experiment results, the non-blocking data communication mechanism based on dynamic thread pool can be applied to the data interaction of the related airport systems with less data delay time, lower CPU utilization and lower memory utilization.
Hybrid Filtering Recommendation System for Libraries

The use of information filtering technology can effectively avoid information overload. The reader can use the library recommendation system to search the books of interest, and the retrieval process automatically filters out the information that the reader is not interested in, and plays a role in optimizing the traditional book retrieval function. This paper takes the book borrowing history of the university information management system as the data source, intelligently predicts the reader’s reading preference, and combines the algorithms of hybrid filtering technology to design the library recommendation system. It uses jieba participle, and D3.js js visualization method is implemented systematically, which effectively enhances the reader’s book retrieval experience and achieves faster access to the purpose information.
Convolutional Neural Network Decoder for Enhanced Hadamard Error Correcting Code and Its Application in Video Watermarking

Error Correcting Codes play an essential role in the digital communication. Especially a new digital technology like video watermarking demands sufficient error correcting capabilities, because of very high compression ratio (about 1:200). Normally the watermarks can barely survive such massive attacks, despite very sophisticated embedding strategies. In this paper, the authors introduce a new approach for Error Correcting Code based on 2D Hadamard Code and convolutional Neuronal Network (CNN). The main idea is that the 2D-Hadamard code words can be represented as 2D basis images. The errors cause a noise in these basis images. The decoding procedure of this 2D-Codewords is realized by a CNN, which was before trained with these basis images. With this approach, it is possible to overcome the theoretical limit of error correcting capability of (d − 1)/2 bits, where d is a minimum Hamming distance. To prove the efficiency and practicability of this new 2D Hadamard Code, the method was applied to a video Watermarking Coding Scheme. The Video Watermarking Embedding procedure decomposes the initial video through Multi-Level Interframe Wavelet Transform. The low pass filtered part of the video stream is used for embedding the watermarks, which are protected respectively by CNN based 2D Hadamard Code.
Dynamical User Intention Prediction via Multi-modal Learning

Predicting the intention of users for different commodities has been receiving more and more attention in many applications, such as the decision of awarding bonus and the recommendation of commodity in E-commerce. Existing methods treat customer-to-commodity data as a flat data sequence while ignoring intrinsic multi-modality nature. Observing that different modalities (e.g., click ratios, collection, and purchase amount), as well as the elements within each modality, contribute differently to the prediction of purchasing intention. Besides, existing methods cannot handle the sparsity problem well. As a result, they cannot predict the user intention with sparse data. To address these issues, in this paper, we present a novel Dynamical User Intention Prediction via Multi-modal Learning (DUIPML) method to integrate different types of data to dynamically predict the user intention while reducing the impact of sparse data, which can be well applied on the practical bonus awarding scenario. Specifically, we firstly design a multi-modal fusion strategy to integrate different types of behavior information to obtain the initial user intention of each customer for each category. Next, we treat different clients with different preferences as two modalities and proposed a multi-modal alignment strategy to explore the latent correlations between different clients. After that, we communicate knowledge between the two clients based on the correlations to complete and enrich the user intention for each other, and thus to alleviate the issue of data sparsity. We apply the enriched user intention for the practical bonus awarding scenario on the Taobao platform in Alibaba group. Experiments on benchmark multi-modal datasets and the realistic E-commodity scenarios show that our method significantly outperforms related representative approaches both on effectiveness and adaptability.
Generation Method of Binary Patterns Based on the Space-Time Combination with Projector Defocusing

This paper proposes a novel generation method of binary patterns based on the space-time combination with projector defocusing technique. The linear superposition theory of the fringe projection profilometry system is described. In terms of the principle and the digital binary defocusing technique, through the accurate sequential control of hardware, a sinusoidal function with wavelength is sampled in "time" and "space" directions to calculate the widths of the projected binary patterns. Then, a series of corresponding square fringe patterns are generated and projected to form a high-quality sinusoidal fringe pattern in the defocused projection volume (i.e., the measured volume). Finally, the performance of our proposed approach is demonstrated by the simulations and experiments, e.g., the phase root-mean-square error of a simulation with three-step phase-shifting method can reach about 0.0286 Rad. The precision level is similar to that of the compared optimization method. Fortunately, our proposed method is more convenient and less time-consuming because the optimization process is avoided.
Using iPads in Technical Editing: An Untapped Resource

In this brief paper, I discuss an extended pilot study focused on using iPads in a technical editing course. From fall 2017 to fall 2019 (and ongoing), I introduced students to using iPads for digitally editing PDFs. End-of-semester student reflections of this experience reveal various attitudes about both the technology and the process. As I reviewed these student reflections certain themes emerged: a reluctance to use unfamiliar products, atypical ways of accessing and editing documents, issues of sustainability, and future of the editing processes.
Synthesizing Patterns for Videos Based on the Adversarial ConvNet

Abstract In this paper, we proposed the application of the adversarial ConvNet mode with back-propagation algorithm and weighted method for synthesizing dynamic patterns. A multi-layer spatiotemporal filter is designed in the adversarial ConvNet to capture the spatiotemporal information that may be lost in the convolution phase. The model defines probability distributions. The problem of pattern design is to obtain the best synthesis by training these probability models and maximizing the objective likelihood function. In the training phase, multiple layers parameters of filters are updated through the matching between the synthesized videos generated based on the adversarial trained ConvNets and the observed training videos. The maximum likelihood learning algorithm is used to realize the synthesis of real natural image patterns. The effectiveness of this method is evaluated on public DynTex datasets and compared with other technology through the experimental results.
Fast and Efficient Decision-Based Attack for Deep Neural Network on Edge

Deep Neural Networks (DNN) are very effective in high performance applications such as computer vision, natural language processing and speech recognition. However, these networks are vulnerable to adversarial attacks that infuses perturbations in the input data which are imperceptible to human eyes. In this paper, we propose a novel decision-based targeted adversarial attack algorithm which exposes the vulnerability of the underlying DNN when implemented on a resource constrained computing edge. Experimental results show that the proposed model performs 4 seconds(s) faster on an average, in a single perturbed image generation than the state of the art RED-attack, while consuming 15% less time for the entire dataset.
Hardware Acceleration of Decision Tree Learning Algorithm

Decision Tree Classification variants are among the most popular machine learning algorithms and have been applied in various fields with success. Their versatility and popularity along with the ease to use make it imperative that solutions be found regarding its performance optimization problem, hence in this paper we tackle this issue by applying methods to optimize a Decision Tree Learning implementation (version C4.5), that will be executed in a heterogeneous computing system involving FPGA along with CPU, making use of the tools offered by the Software-Defined System On Chip (SDSoC) development platform. Initially, a profiling of the code is done, after which the computationally intensive part of the algorithm is determined, that is found to be the decision tree training part and within that part specifically, the Information Gain computations. Then the kernel has been developed as a hardware accelerated function that assumes the latter computations. The presented performance of the kernel was evaluated on a Zed platform integrated with Xilinx Zynq-7000 SoC in a FPGA-based heterogeneous system and it was shown that the accelerator can yield up to 2.48x kernel speedup in a single decision tree’s training part, compared to an embedded ARM processor based implementation.
Target Detection Experiment Method and Process Based on YOLOV3

Moving target detection is mostly based on the detection of the underlying video information. This paper discusses the five aspects of target detection concept, YOLOV3 principle introduction, experimental method and process, experimental results and discussion, and YOLOV3 application scenario. Moving target detection is the basis for achieving target recognition tracking. Since the video sequence is composed of a sequence of image frames having a certain continuity in time, the detection of the moving object in the video is performed by extracting the sequence of image frames from the video sequence according to a certain period. We use YOLOV3 for target detection experiments. The first step is to configure the model training environment. The second step is to download and open the YOLOv3 project from Github. In the third step, we use Label Img to label the data. In the fourth step, after the model training is over, we test the trained model.
Universal Adversarial Perturbation Generated by Attacking Layer-wise Relevance Propagation

The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has become an important research area of machine learning. It has been known that many state-of-the-art DNNs suffer the risk of universal adversarial perturbations, which are image-agnostic and able to lead misclassifications with high probability. In this paper, we propose a novel method to create such universal adversarial perturbations. Our approach is the first to generate universal perturbations by attacking the attention heat maps with the interpretation method, Layer-wise Relevance Propagation. It is demonstrated that our method achieves high fooling ratios on image classification DNNs pre-trained by ImageNet dataset. Moreover, our attack shows good transferability across different DNNs.
A Percussionist Understands Rhythm in Five Essays of Exactly 1,000 Words Each Not Including Titles and Subtitles

Does the first drop feel cold or heavy? Do you feel at one with nature, or are you more concerned with the future of your cashmere sweater? Do you feel inspired? Annoyed? Or, do you shake your head in exasperation and mutter, “That drop is late!”
Enhanced Opinion Classification Using Nature-Inspired Meta-Heuristics for Policy Evaluation

In today’s competitive and result-driven environment, every decision needs to be carefully weighed before implementation. The government faces a similar problem while evaluating its schemes and forming policies. The goal of this study is to recommend a suitable and optimal automated tool for a highly accurate process of sentiment analysis on government schemes. This paper studies the effects of adding a feature selection phase to the conventional opinion mining model by analyzing the impact on the accuracy of the different models. For this purpose, swarm evolutionary algorithms, namely binary cuckoo search algorithm and firefly algorithm, have been used and analyzed, coupled with TF-IDF-based feature extraction for an optimized opinion classification process. Digital India, the flagship campaign of the Indian government, has been selected as the topic of study for this research due to its significant impact on Indian society in recent years. This paper aims to examine the success of the Digital India program, while at the same time, determine the most appropriate model for future assessment of government schemes.
Mixed Catering Recommendation Model Based on Context Awareness and User Interest

In this paper, the context awareness and recommendation algorithms are studied in depth. From the recommendation of the railway 12306 high-speed rail self-service meal recommendation, the ontology model is firstly designed for the passenger situation and the catering field knowledge, and the corresponding inference rules are designed for knowledge reasoning. context recommendation; then analyze user behavior to obtain preference data, use data filling method to improve traditional user-based collaborative recommendation algorithm; finally, design a hybrid recommendation model combining user context, existing interest preference and potential interest preference. Finally, it is proved by experiments that compared with the single context awareness model and user interest model, the hybrid recommendation model proposed in this paper has a good effect in dealing with dietary recommendation strategies in different situations.