Reviewerly/exHarmony huggingface dataset:
Part-Based Pedestrian Attribute Analysis

Visual pedestrian attributes are very important for person re-identification. Due to the difficulties in obtaining identifiable face and body shots in surveillance scenarios, clothing appearance attributes become the main cue for identification. In this paper, we propose a part-based pedestrian attribute analysis method upon clothing appearance. First, to alleviate pose misalignment, 25 anatomical key-points are located by OpenPose algorithm and then 9 body parts are extracted. Besides, 3 poses are recognized via constraints on key-points. Second, for each part image, the main color feature is extracted by ColorName algorithm, and the texture classification feature is extracted by CNN network combined with SVM model. Finally, for pedestrian pair with certain poses, a weighted similarity fusion algorithm based on the color and texture feature is applied to compute the total similarity of two sets of body parts. Experimental results on pedestrians in surveillance videos demonstrate the effectiveness of our method.
Method of Applying Df-pn Algorithm to On-the-fly Controller Synthesis

Discrete controller synthesis is a method that involves using game theory to automatically generate a controller for achieving a system goal. This method is used in artificial intelligence for planning self-adaptive systems, in which it is necessary to shorten the time taken to generate a plan. Discrete controller synthesis generates a controller from an environment model and requirement model. The environment model represents the behavior of the system’s external environment as a finite state machine and is often constructed by parallel composition, which causes a state explosion. As a result, a controller cannot be synthesized within a realistic amount of memory or time. An on-the-fly method called directed controller synthesis (DCS) was developed by Daniel Ciolek. DCS partially expands and checks the environment model during exploration to avoid the state explosion caused by parallel composition. DCS uses a best-first search algorithm and has open lists, which drastically increases the size of the open list when searching for a large-scale problem and lowers search efficiency. Therefore, we propose a method of applying the df-pn algorithm, which is used when playing shogi (Japanese chess) on a computer, particularly tsume-shogi (a type of shogi problem). This algorithm is an iterative deepening depth-first search algorithm that does not have an open list but uses a hash table to store search history. Through experiments comparing our method with DCS, we were able to attain faster controller synthesis with our method than with DCS for large-scale problems.
Challenges and Research in Virtual Worlds and Augmented Reality in the Educational Field

New technologies and opportunities to modernize and make teaching more dynamic emerge, as well as to switch from the ordinary traditional teaching method to a different format, which makes the student the protagonist of the construction of his knowledge. It is precisely in this context that new forms of use of technology in the educational field emerge, among which are the virtual worlds (MV) and augmented reality (AR), which are the objects of analysis in this chapter. Taking the basic premises on these topics, this chapter aims to help the reader understand the process of development and application of virtual worlds and augmented reality in education in order to discuss the inherent difficulties, practicalities, advantages, challenges, and trends. Thus, this chapter aimed to present the reader with the importance of reflecting on this context, seeking to show how each of these technologies has been applied in the educational field, being based on reports of empirical and academic experiences of the authors and other researchers.
Improvements for NLP Models by Considering Language Differences

Abstract Aspect extraction has been playing a critical role in opinion mining of product reviews. Most of the existing works tackling various Natural Language Processing tasks focus on English corpus and barely talked about the effect of language differences when apply the algorithm to other languages. Based on a previous aspect extraction neural model and its application to text data in Chinese, this paper analyzed possible reasons and proposed plausible approaches to address the importance of handling language differences in Natural Language Processing tasks. By bringing the importance of adaptiveness of language models to attention, future NLP research work might produce more robust and comprehensive work regardless of the impact of language differences.
Stochastic Neural Control using Raw Pointcloud Data and Building Information Models

Recently, there has been a lot of excitement surrounding the use of reinforcement learning for robot control and navigation. However, many of these algorithms encounter difficulty navigating long or complex trajectories. This paper presents a new mobile robot control system called Stochastic Neural Control (SNC), that uses a stochastic policy gradient algorithm for local control and a modified probabilistic roadmap planner for global motion planning. In SNC, each mobile robot control decision is conditioned on observations from the robot sensors as well as pointcloud data, allowing the robot to safely operate within geometrically complex environments. SNC is tested on a number of challenging navigation tasks and learns advanced policies for navigation, collision-avoidance and fall-prevention. Three variants of the SNC system are evaluated against a conventional motion planning baseline. SNC outperforms the baseline and four other similar RL navigation systems in many of the trials. Finally, we present a strategy for transferring SNC from a simulated environment to a real robot. We empirically show that the SNC system exhibits good policies for mobile robot navigation when controlling a real mobile robot.
The multi-scale channel attention full connection network for edge detection

Abstract Edge detection algorithms based on deep learning have made great progress due to its super feature extraction and presentation ability in recent years. However, existing edge detection algorithms encounter some problems that image features extracted by networks are not sufficient, and the generated edges are fuzzy with large gap compared to the ground truth (GT). To solve the abovementioned problems, we have proposed a multi-scale channel attention full connection network model for edge detection (MAED) in this work. The proposed network consists of three parts: multi-level feature extraction part, multi-scale deep supervise part and fusion part. In addition, by introducing the "attention" mechanism into the networks, underlying features of the input image can be fully extracted and the training convergence speed can be accelerated simultaneously. To evaluate the performance of the proposed algorithm, we conducted a comprehensive study on some commonly-used datasets. The final experimental results demonstrated that our network achieves excellent performance based on both qualitative and quantitative analysis.
Z-Embedding: A Spectral Representation of Event Intervals for Efficient Clustering and Classification

Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this paper, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. More concretely, each event interval sequence is represented by a bipartite graph by following three main steps: (1) creating a hash table that can quickly convert a collection of event interval sequences into a bipartite graph representation, (2) creating and regularizing a bi-adjacency matrix corresponding to the bipartite graph, (3) defining a spectral embedding mapping on the bi-adjacency matrix. In addition, we show that substantial improvements can be achieved with regard to classification performance through pruning parameters that capture the nature of the relations formed by the event intervals. We demonstrate through extensive experimental evaluation on five real-world datasets that our approach can obtain runtime speedups of up to two orders of magnitude compared to other state-of-the-art methods and similar or better clustering and classification performance.
WITHDRAWN: Smart English Vocabulary Translation Simulation Based on FPGA Software and Hardware Co-Simulation Test

Translation is the activity of inter language conversion and the creation of language and its users. Among them, another method is to deal with and adjust the external interference information by using the related convolution network nervous system and special programming program, so as to solve the problem of ambiguity of English characters to a certain extent. In this process, the most important point is to scan the data information for pictures, and then store the data in the next step, which is to a great extent conducive to the smooth development of the follow-up work. By scanning the pictures recorded in English at the computer terminal, we detect and analyze the data contained in them to confirm whether the files are reserved for the relevant data in the stored procedure. We find the corresponding hidden communication path in different sliding windows, and finally find that there are many images with confirmed specific attributes in the hidden channel of the window, which also ensures the optimum of the research results. In the process of finding the hidden track corresponding to the sliding window, we mark the single hidden channel by image scanning and recording, and separate the data by using the names of different attributes, thus facilitating the later information collection. Through further analysis of the research data, we can find that the modified hidden information channel through the limit interval is superior to other kinds of information channels. By comparing the data, we find that the two kinds of scene images are different to some extent. In order to get the data results further, we need to make different attempts. By analyzing the test results, we find that the method can help us to obtain the latest data progress, and also show the advantages of the technology.
Study on Integrative Control between the Stereoscopic Image and the Tactile Feedback in Augmented Reality

The precise integrative control between the stereoscopic image and the tactile feedback is very essential in augmented reality[1]-[4]. In order to study this question, this paper will introduce a stereoscopic-imaging and tactile integrative augmented-reality system, and a stereoscopic-imaging and tactile integrative algorithm. The system includes a stereoscopic-imaging part and a string-based tactile part. The integrative algorithm is used to precisely control the interaction between the two parts. The results for testing the system and the algorithm demonstrate the system to be perfect through 5 testers' operation and will be presented in the last part of the paper.
LOAD: LSH-Based $$\ell _0$$-Sampling over Stream Data with Near-Duplicates

Massive amounts of stream data nowadays almost make any real-time analysis impossible. To overcome the challenge of processing this huge amount of data, previous works typically use sampling to extract representatives and conduct analysis on this sampled dataset. In this paper, we propose LOAD, a Locality-Sensitive Hashing (LSH) based \(\ell _0\)-sampling over stream data. Instead of having the same diameter for all dimensions, LOAD utilizes the dimension-specific diameters which could fit the distribution of groups better. Therefore, LOAD always generates a better representative identification result. To facilitate the real-time analysis, we further optimize LOAD by applying LSH. Since nearest items are hashed into the same bucket with high probability, hence distinguishing the representatives becomes lightning fast. Extensive experiments show that LOAD is not only more accurate than other state-of-the-art algorithms, but also faster by an order of magnitude.
Strategy for Organization of Computational Experiments of the Functioning of Underground Water Inlets Using a Fuzzy Multiple Approach

The article provides expert assessments of fuzzy concepts, parameters, and characteristics related to the functioning of underground water intakes (ERW). The choice and justification of the ERW technological schemes is proposed based on the analysis of lithology, thickness, width and length of aquifers, initial and boundary hydrogeological, hydrological and hydrochemical conditions. The basis of the process of justifying the ERW project is a fuzzy-deterministic model of geofiltration for a single-layer aquifer, which makes it possible to directly use in the process of modelling and PR fuzzy information expressing the experience, knowledge, intuition of hydrogeologists, presented in linguistic form. And also the article proposes an algorithm for making a decision on the formation, operation and restoration of ERW for single-layer formations.
Design of flood control and operation safety management system for dams operated by China Three Gorges Corporation

China Three Gorges Corporation (CTG) currently owns 57 large dams all over the world. These dams are operated by different entities independently. To better share information and to provide efficient technical support for these dams, it's necessary to establish a management information system for safety and flood control, as a standardized operation platform, to collect real-time data, and to provide auxiliary safety analysis as well as functions such as expert consulting and emergency response for their operators. This paper describes the positioning, modules, system integration as well as innovative features of the system.
Computational Complexity Analysis of Block Ciphers of Transport Layer Security

This paper aims to analyze the computational complexity by calculating the cost in terms of the total number of primitive operations like AND, OR, XOR, SHIFT per block of data of three major block ciphers: Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), and Advanced Encryption Standard (AES) that form the backbone of Transport Layer Security (TLS). Further, the proposed analysis can be applied as a universal analysis free from any hardware or architecture.
Research on Computer Aided English Translation in the Wave of Globalization

Abstract In the context of globalization, people’s demand for translation services is growing rapidly, which leads to the increasing scale of translation. With the rapid development of science and technology, computer aided translation (hereinafter referred to as CAT) greatly improves the efficiency of translation. With the development of economic globalization, traditional machinetranslation can’t meet the needs of people on the quality and speed of English, which requires people to improve the CAT technology. Therefore, CAT as an alternative has become one of the most effective choices for translators, which will help translators to complete English translation at a higher quality and speed. At present, the core technology of CAT tools is translation memory, including translation memory system, term management system, alignment tool, translation project management tool, etc., which will better translate. Firstly, this paper analyzes the core module of CAT. Then, this paper proposes some machinetranslation processes. Finally, some suggestions are put forward.
Effects of Unbalanced Data on Radiometric Transforming Model Fitting for Relative Radiometric Normalization

Most of the existing (Relative Radiometric Normalization) RRN research focus on the automatic sample selection, while unbalanced data effect on the regression is not noted and the pre-selected sample set like the Pseudo-invariant Features (PIFs), or homogeneous pixels are directly used to solve the radiometric transforming models without considering the representativeness of the sample set. In this paper, we investigated the effects of unbalanced data, specifically on two aspects: 1) statistical properties of the estimated model parameters, 2) the normalizing accuracy of the fitted model. To make the work thoroughly, four regression methods are investigated, including Least Square Regression (LSQ), Theil-Sen estimator (TSR), Support vector machine regression (SVR), and Random forest regression (RFR). And Monte-Carlo Simulation is used to generated various sample sets with different distributions. It is demonstrated that the LSQ and the TSR are vulnerable to data unbalance, in terms of both the estimated model parameters and the normalizing accuracy of the fitted radiometric transforming model, whereas the SVR and RFR are not sensitive.
A Content-based Intelligent Label Classification System for Warehouse Management

With the rapid development of the e-commerce business, smart warehouse management has become a critical part of the supply chain for maintaining an optimized cost in stock logistics. The managing of inventory may require substantial labors and expenses on the order fulfillment and the label classification. This paper proposes a warehouse management system that can provide the real-time categorizing service for labels based on a fastText based word text classification algorithm. In this system, a label classifier is presented to facilitate the process of classifying massive labels based on its content. In this classifier, a Huffman tree data structure have been developed and a layered softmax structure is utilized to reduce the computational complexity. Experimental results show that the proposed method is capable of processing efficient label classification tasks where the management efficiency can be improved for the economic benefits of warehouse management.
Group Communication Needs No Consistent Transcript Distribution

Instant Messaging (IM) clients are lying to you about groups and their consistency. None of the so called secure chat applications provide the same security properties for group communication you know from face-to-face meetings. In this paper we give an overview of the challenges in group communication and introduce a solution to the transcript distribution challenge. We present a new visualization for out-of-order messages and the results of a user study to show that our approach is working for every day users.
Comparison of 2D and 3D U-Nets for Organ Segmentation

As a popular convolutional neural network, U-net developed by Ronneberger et al. at the University of Freiburg, Germany, has been widely applied in biomedical image segmentation. The network is an extension of the fully convolutional network with multi-resolution encoder and decoder structures and skip layers at each resolution. It can achieve effective segmentation results with a limited training dataset. Initially developed for multi-channel 2D images, U-net has been extended to segmenting 3D biomedical images and even multi-channel or serial 3D images by using 3D convolution kernels, down-sampling, and up-sampling blocks, and various activation functions. In this chapter, using the lung CT segmentation challenge datasets, the performance of organ segmentation is compared between 2D and 3D U-nets trained at different spatial resolutions. The network structures used herein provide intuitive, hands-on experiences about their advantages and disadvantages for multiple organ segmentation. The comparative study could be helpful to facilitate research and development of deep learning-based biomedical image segmentation algorithms.
Infrared Pedestrian Detection Based on GAN Data Augmentation

Object detection, as an important branch of computer vision, has been widely studied in recent years. However, the lack of large labeled dataset obstructs the usage of convolutional neural networks (CNN) for detecting in thermal infrared (TIR) images. Most existing dataset focus on visible images, while thermal infrared images are helpful for detection even in a dark environment. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the available labeled visible images to synthetic infrared images. Based on the original pedestrian dataset CVC-09, we use the pedestrian dataset CVC-14 to generate some labeled pedestrian infrared images. Finally, we compare original dataset with classic data augmentation and synthetic data augmentation training CNN. In addition, we explore the quality of synthetic TIR images using contrast experiments. The average precision of detection using classic data augmentation alone is 79.18%. By adding synthetic data augmentation, the average precision has improved to 82.24%. We believe that this method of synthetic data augmentation can be extended to other infrared detection applications and achieve other breakthroughs.
Web-based Design of Financial Apps: Case of Kosan 54

Abstract Financial Management is the process of financial planning in budgeting, auditing, management, control, deposit funds that are held for effective and efficient financial decisions.. As the existing financial management processes in boarding house number 54 (Kosan 54) is still manual, it is prone to mistakes during the recording and making their own bills and financial statements, along with the process of bill payment by the boarding members. Therefore, a system that can perform data processing in an integrated and well computerized system using a WEB-based system is urgently needed. The designed system is a WEB-based Financial Management Application. To build the application, a software such as MySQL database is needed, as well as using the PHP programming language. This finance management application then will be of great help in the planning of appropriate financial income and expenses incurred.
An Improved Approach to Secure Digital Audio Using Hybrid Decomposition Technique

Unlicensed access to digital audio is found to be very frequent today. Copyright and ownership issues are very common. Obsolete schemes are looking to fail to preserve the ownership of digital audio. A watermark data validates the correct belongingness of an audio file. Inculcation of any data into an audio signal is not always tolerated by audio. In this work, a robust method is proposed based on a hybrid decomposition technique in which discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) applied to successfully perform watermarking in audio signals. A watermark image of size 16 by 16 pixels is used to inculcate in the digital audio of sampling rate 44.1 kHz. The watermark image is first undergone into a cyclic encoding process in which watermark bits are encoded using the redundant bit. Then the bits are further scrambled using Arnold’s cat map. After the robust encryption, the encrypted watermark bits are embedded into the host audio using hybrid decomposition. The reverse process is applied to extricate the watermark bits. The watermarked audio is tested under various signal processing attacks and the quality of the extracted watermark image is checked using standard parameters. The quality assessment of the watermark image is found satisfactory that declares the robustness of the scheme.