Reviewerly/exHarmony huggingface dataset:
Local Semantic Structure Captured and Instance Discriminated by Unsupervised Hashing

Recently, unsupervised Hashing has attracted much attention in the machine learning and information retrieval communities, due to its low storage and high search efficiency.Most of existing unsupervised Hashing methods rely on the local semantic structure of the data as the guiding information, requiring to preserve such semantic structure in the Hamming space.Thus, how to precisely represent the local structure of the data and Hashing codes becomes the key point to success.This study proposes a novel Hashing method based on self-supervised learning.Specifically, it is proposed to utilize the contrastive learning to acquire a compact and accurate feature representation for each sample, and then a semantic structure matrix can be constructed for representing the similarity between samples.Meanwhile, a new loss function is proposed to preserve the semantic information and improve the discriminative ability in the Hamming space, by the spirit of the instance discrimination method proposed recently.The proposed framework is end-to-end trainable.Extensive experiments on two large-scale image retrieval datasets show that the proposed method can significantly outperform current state-of-the-art methods.
Optimization Model and Algorithm for Multi-Label Learning

This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem.Finally, this paper uses some classical optimization algorithms to solve these optimization problems, the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.
Design and Development of Metering Management System of Water-Supply Network Based on LBS

With the development of social economy and the acceleration of urbanization, the demand of water-supply in China is increasing. The normal operation of urban water supply network is of great significance to the full utilization and sustainable development of water resources. However, the leakage problem of water supply pipe network is still serious in our country, which not only causes the waste of water resources, affects the economic benefits of water-supply enterprises, but also endangers the safety of urban construction, in this paper, the LBS service is fully integrated into the metering management system of the water-supply network, so that the management of the water-supply network is not limited by the region, and the self-positioning technology of the mobile client is used in combination with the visual geographic information system, a more convenient metering management system is developed to realize the real-time monitoring of water-supply network.
[DC] Situated augmented reality: beyond the egocentric viewpoint

Augmented reality (AR) is well suited for situated visualization (SV), a method to represent data in context, with potential in many situations. Furthermore, AR-based SV poses challenges and research prospects. A vital one is the egocentric viewpoint limitation of the users, which reduces their ability to explore all the available information. To create new approaches that will overcome this limitation, this work began by understanding the relevance of viewpoints in typical AR-based SV scenarios, characterizing the approaches already proposed in order to tackle it. The new proposed approaches should be evaluated in different settings to improve and validate them, as well as propose guidelines for some relevant scenarios.
Anytime Algorithm for Cell-based DBSCAN by Connecting Randomly Selected Cells

With the growing interest in big data, speed-up techniques for clustering are required. The density-based spatial clustering of applications with noise (DBSCAN) has been well known in database domains. Since the DBSCAN algorithm was first proposed, several speed-up methods have been introduced. In the previous work, cell-based DBSCAN as a fast DBSCAN algorithm that divides the whole dataset into smaller cells and connects them to form clusters was proposed. In this study we propose a novel clustering algorithm called anytime algorithm for cell-based DBSCAN. The proposed algorithm connects some randomly selected cells and calculates the clustering result at high speed. Next, it repeats this process, which improves the accuracy of clustering, thereby yielding the precise results. Experimental results demonstrated that the proposed algorithm can calculate the clustering results with high accuracy at high speed.
Robust Corner Detection Using Linear Fitting Error Estimation

Image corners have been widely used in image processing and computer vision applications. For a variety of contour-based corner detection methods the key step is curvature estimation, which reflects the bending strength of a curve. Corner detection aims to achieve the lowest possible computational cost and maximal detection accuracy. Instead of estimating the discrete curve directly like most contour-based corner detectors, we propose to utilize the linear fitting error to measure the corner response strength of the contour points. The proposed method consists of three aspects: First, a small curve segment is parameterized to two curves; then the minimum linear fitting errors with respect to the above two curves are estimated by employing the least-squares fitting technique; Finally, the estimated minimum fitting errors are considered as the local bending strength of the small curve segment for corner finding. The experimental results show that the proposed corner detection algorithm outperforms the five state-of-the-art corner detection methods in terms of localization error and average repeatability.
Educational Management System

With the development of university education, the demand for educational affairs is increasing, and university management has received more and more attention. It is urgent to improve the efficiency of managing universities. Educational Management Information System is a system designed for managing teaching affairs. This system turns the manual work management mode into a system management mode. This method not only saves time, but also improves the quality of teaching. The programming language used in the system is C#, the development environment is Visual Studio 2017, and the using database is SQL Server2012 database.
A Novel Monocular Camera Obstacle Perception Algorithm for Real-Time Assist in Autonomous Vehicles

This paper aims at reducing the processing required for manoeuvring of autonomous vehicle by a novel image processing algorithm for obstacle detection and localization. A monocular camera has less data overhead and bypasses the issues of synchronization and stereo calibration. However, estimating 3D information using a single image from a monocular camera is non-trivial primarily due to ambiguity in the scale of the scene arising from limited information of the surroundings. This paper proposes a novel “Calibration Lane a low-latency real-time algorithm for lane assist, obstacle detection and avoidance using a single measurement in world coordinates. It allows the perception pipeline to be extremely light-weight and requiring a single monocular camera. The algorithm is validated and verified experimentally.
Adaptive ACOR to solve the Loney’s solenoid electromagnetic problem

This paper studies and proposes a new set of ACOR (Ant Colony optimization for Real domains) algorithms dedicated to electromagnetic problems. The adaptive ACO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> adjust their parameters during the iterative process in order to control how the exploration and exploitation are involved and performed in the global optimum search. The strategy is to smoothly alternate between exploration and exploitation, with the first one predominant in the initial stages of the algorithms and the second towards the end. Though the proposed algorithms can be applied to any kind of optimization problem they are tuned and their efficiency is proven on an electromagnetic benchmark problem, namely Loney's solenoid.
Suppressing Features That Contain Disparity Edge For Stereo Matching

Existing networks for stereo matching usually use 2-D CNN as the feature extractor. However, objects are usually continuous in spatial, if an extracted feature contains disparity edge (the representation of this feature on original image contains disparity edge), then this feature usually not occur inside the region of an object. We propose a novel attention mechanism to suppress features containing disparity edge, named SDE-Attention (SDEA). We notice that features containing disparity edge are usually continuous in one image and discontinuous in another, which means that they usually have a greater difference in two feature maps of the same layer than features that don't contain disparity edge. SDEA calculate the weight matrix of the intermediate feature map according to this trait, then the weight matrix is multiplied to the intermediate feature map. We test SDEA on PSMNet, experimental results show that our method has a significant improvement in accuracy and our network achieves state-of-the-art performance among the published networks.
Nonnegative Unimodal Matrix Factorization

We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analytical chemistry. We propose a simple but naive brute-force heuristics strategy based on accelerated projected gradient. It is then improved by using multi-grid for which we prove that the restriction operator preserves the unimodality. We also present two preliminary results regarding the uniqueness of the solution, that is, the identifiability, of NuMF. Empirical results on synthetic and real datasets confirm the effectiveness of the algorithm and illustrate the theoretical results on NuMF.
An Approach for Pose Invariant Face Recognition System Using Log-Gabor Feature

Abstract Biometrics is the process based on humans’ characters like behavioural and physical. Among the biometrics traits, face recognition has attained much importance for the authentication of individuals. Face recognition with pose variation is one of the major challenges among the illumination and expression. The system is widely used as surveillance which can withstand in places like ATMs, Airports, and Streets to track the criminals. The face recognizing mainly depends on the feature extraction from the face. Project aim is to extract those features which are invariant even though the pose changes. In the proposed work face recognition by using Log Gabor features is addressed. Firstly, colour images with variation in poses are captured followed by the extraction of facial features components like eyes, nose, mouth, and chin through Log Gabor features. The identification of the person is done using KNN classifier where the matching is carried out by calculating the Euclidean distance between the training and test pairs. Finally, experimental results show the performance of the system for different number of the users.
Progress in Face Recognition Over Evolutionary Deep-Indicators Algorithm

We propose new approach for facial recognition based on an Evolutionary Deep-indicators Algorithm. The main purpose of this strategy is to reach a model which can compete with human vision sensibility. This optimized model revolves around a facial chromosome that benefits from all paradigms in the same area ; such as Gaussian model, Deep learning, Eigen vector, Evolutionary mechanism. The intelligent model will be then implemented on Jetson TK1 to check its speed in response time compared to a regular CPU. This powerful tool is a NVIDIA's embedded Linux development platform featuring a Tegra K1 system on chip SOC which contains a central processor unit CPU, a graphic processor unit GPU and an image signal processor ISP in single chip. Our work is experimented using AT&T Face database seen its popularity across the international community and it provides very significant results, reaching 97.5%, compared to existing models with real time systems RTS tendencies.
Looking Through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic

Nowadays living environments are characterized by networks of inter-connected sensing devices that accomplish different tasks, e.g., video surveillance of an environment by a network of CCTV cameras. A malicious user could gather sensitive details on people’s activities by eavesdropping the exchanged data packets. To overcome this problem, video streams are protected by encryption systems, but even secured channels may still leak some information. In this paper, we show that it is possible to infer visual data by intercepting the encrypted video stream of a surveillance system, and how this may be leveraged to track the movements of a person inside the secured area. We trained an automatic classifier on a computer graphic simulator and tested it on real videos, with standard encryption protocols. Experiments proved the transferability of the classifier trained on synthetic sequences, succeeding in the detection of up to four different walking directions on real videos, with a limited amount of intercepted traffic.
On Composers and Computers

Abstract The chapter describes the manifold interactions between composers and computers in the creative process. Over time, this dialogue dating back to the 1950s has transcended the paradigm of the traditional compositional process and lead to a number of interesting implications and side effects in the context of current trends in algorithmic composition, generative music, and computational creativity. It is not just the computer but also the choice of software and a specific view on the musical material that are the decisive factors in generative and analytical approaches (which are often interlinked), where strategies of musical representation and the choice of a particular mapping are of crucial importance. Finally, the chapter examines how creativity can be defined and located in this interaction between human and machine.
Cyberbullying detection based on word curve representations using B-spline interpolation

With the advancement of technology and the rise of social media, people start to get worried about the increase of cyberbullying. Therefore, researches on cyberbullying have soared remarkably in recent years. Machine learning and natural language processing techniques are helpful to generate a model that automatically detects cyberbullying actions. However, most of the existing approaches do not preserve the information of word order. This problem causes a large loss in the text's meaning. In this study, we propose a new text representation based on the idea of making fix-length text data using B-Spline interpolation. Then, 1D-Convolutional Neural Network is used to classify cyberbullying and non-cyberbullying documents. We experiment the proposed method on a text dataset crawled from the site Formspring.me. The experimental result shows that the proposed methods outperform the conventional methods in cyberbullying detection
KRISHI SETU - BRIDGING THE VALUE GAP

Krishi Setu is an attempt to eliminate middlemen so that farmers get maximum profit from the trade chain. Our portal is a simple website where a farmer can upload the quantity of harvested crops and wholesalers can purchase as per requirements and availability. Some peculiar features of the website include news related to farming, current prices of crops, and multilingual support that will be visible on the dashboard. Website also consists of features which will determine crop disease by uploading a picture of the crop. The consumer will get recommendations on their purchases as per season and locality. The buyer can visit the place or have courier services integrated. We would choose the most optimized path if the goods are supposed to be delivered to another location and there are drop points that come in between. This also reduces delivery cost-efficiently.
Bi-Level Style and Prosody Decoupling Modeling for Personalized End-to-End Speech Synthesis

End-to-end framework can generate high-quality and high-similarity speech in the personalized speech synthesis task. However, the generalization of out-of-domain texts is still a challenging task. Limited target data leads to unacceptable errors and poor prosody and similarity performance of the synthetic speech. In this paper, we present a bi-level function decoupling framework to realise separate modeling and controlling for solving above problems. Firstly, on the style representation modeling level, compared with the conventional methods that use single embedding to model all the text dependent discrepancies, it is proposed that the speaker embedding and prosody embedding are modeled separately based on the reference audio and phonetic posteriorgram (PPG) by a multi-head attention mechanism. Secondly, on the model structure level, the decoder model structure is factored into average-net and adaptation-net, where the duration prosody controlling and speaker timbre imitation are mainly designed in relatively separate areas. Experimental results on Mandarin dataset show that the proposed methods lead to an improvement on both robustness, naturalness and similarity.
A container cloud scheduling strategy based on QoS

Docker-based container cloud technology is the current research hotspot. The resource scheduling policy in the container cloud plays a decisive role in the resource utilization and cluster performance of the data center. At the same time, resource scheduling also affects the quality of service all the time. This paper studies and analyzes the scheduling strategy, and proposes an evaluation model based on multi-dimensional resource weight. Considering the factors such as CPU, memory, disk, bandwidth and the number of containers, the QoS model is established and the scheduling algorithm is improved to obtain the optimal allocation of resource scheduling. Experimental results show that the new scheduling method ensures a lower violation rate of SLA and makes the multi-dimensional resource utilization of the cluster higher.
Interrelated information pair extraction algorithm of visual attention for form documents

Information extraction technology of form documents plays an important role in facilitating the retention and retrieval of forms. The existing algorithms for form information extraction usually lacks certain accuracy and universality. In this paper, an algorithm of structured key information extraction for forms is proposed, which takes into account image features and text layout of forms, and is universal to different kinds of forms. First, a keywords and key information detection network is designed to detect information blocks. Then considering the characteristics of text layout based on human visual attention mechanism, an algorithm of matching keywords and corresponding information based on position relationships is proposed. Finally, the classification of information is realized by fuzzy matching of keywords, so as to extract the structured information quickly and accurately. Experiments show that compared with existing form information extraction systems, this algorithm realizes the extraction of structured information from form documents based on image features and text layout, greatly improving accuracy and universality.
A Testing Approach While Re-engineering Legacy Systems: An Industrial Case Study

Many organizations use legacy systems as these systems contain their valuable business rules. However, these legacy systems answer the past requirements but are difficult to maintain and evolve due to old technology use. In this situation, stockholders decide to renovate the system with a minimum amount of cost and risk. Although the renovation process is a more affordable choice over redevelopment, it comes with its risks such as performance loss and failure to obtain quality goals. A proper test process can minimize risks incorporated with the renovation process. This work introduces a testing model tailored for the migration and re-engineering process and employs test automation, which results in early bug detection. Moreover, the automated tests ensure functional sameness between the old and the new system. This process enhances reliability, accuracy, and speed of testing.
Designing and Evaluating Intelligent Agents’ Interaction Mechanisms for Assisting Human in High-Level Thinking Tasks

In the twenty-first century, people need to learn and apply high-level thinking skills, such as decision-making, problem-solving, and critical thinking, in their daily lives. Traditionally when in need, people can get help from human assistants to complete these thinking tasks, yet, such human experts are not always accessible. Intelligent agents, such as physical robots and virtual chatbots, are promising alternatives to the human assistants in these tasks. My doctoral research focuses on designing intelligent agents to assist human users and evaluating the impact of their interaction mechanisms (i.e., way of behaving) on user experience. Specifically, my works surround this theme by proposing: 1) an anticipation-autonomy framework that models the service robot's proactivity in decision-making support contexts (decision-making); 2) a bot assistant that helps users to solve the problem of writing poor-quality comments in online mental health communities either by assessing the writing performance or recommending writing examples (problem-solving); and 3) a chatbot in comparison to a non-conversational tool for facilitating people to read academic papers critically (critical thinking). My dissertation contributes to the design of intelligent agents for helping people in high-level thinking tasks and insights for developing appropriate interaction mechanisms for these agents in the future.
Research on Multi Pose Facial Feature Recognition Based on Deep Learning

A multi pose facial feature recognition method based on deep learning is proposed. Firstly, the face direction is determined by eyes and mouth combined with face features, and then the face is segmented according to the face direction to get the approximate forward candidate region of the face, which is then verified by the layered structure depth learning algorithm. Multi pose face detection algorithm based on face feature and deep learning algorithm. Most of the background regions are eliminated quickly by using skin color features, and the candidate regions are segmented according to the face direction determined by face geometry features, and the candidate regions are classified by using depth learning algorithm. The candidate regions are classified by deep learning algorithm, and the multi pose facial features are accurately recognized. This method can avoid the problem of increasing the complexity of the algorithm, such as multi pose face extended feature or pose detector, so as to improve the search speed and accuracy. The experimental results show that the multi pose facial feature recognition method based on deep learning can detect the multi pose face quickly and has good robustness to expression and occlusion.