Reviewerly/exHarmony huggingface dataset:
Crowd Sourcing Application for Chennai Flood 2015

Presently social media plays a vital role in people's life. During the Chennai floods of November–December 2015, victims and also the relief centers have used the social media for sharing the information regarding the disaster. The crowd sourced data which speeds up disaster management actions such as rescue and relief services to the victims. Though the social media can provide effective disaster relief services, it does not provide an essential coordination for sharing information, resources, and plans among distinct relief organizations. However, proposed open source crowdsourcing platform overcomes this issue by offering a powerful capability for collecting information from disaster scenes. It also visualizes the interactive map which was used to crowdsource the information and generates reports as well. The reports and the database generated are helpful to the government and NGO's for the preparedness of the disaster in future i.e., for relief decision making. The article describes the usage of open source crowd sourcing platform for Chennai flood disaster, 2015.
Thoughts on the Application of Artificial Intelligence in Exceptional Child Education

The rapid development of artificial intelligence technology has set off a wave of artificial intelligence in the field of education. In the field of special children's education, the application of artificial intelligence has made little progress. Based on the comprehensive analysis of the current artificial intelligence in the practice of ordinary children's education, the article combined the urgent needs of the current special children's education, and designed the application of artificial intelligence technology in special children's education, hoped to provide new wisdom and methods for the development of special children's education.
Semantic Segmentation of Grey-Scale Traffic Scenes

In this paper we propose a novel architecture called KDNet that takes into account both spatial and temporal features for traffic scene labelling. One advantage of convolutional networks is the ability to yield hierarchical features that has been proved to bring high-quality result for scene labelling problem. We demonstrate that including temporal features has even better impact on segmenting the scene. We make use of convolutional long short-term memory cells in order to allow our model to take input at many different time steps. The backbone of our model is the well known fully convolutional network called FCN-8s. The model is built in an end-to-end manner, thus eliminating post-processing steps on its output. Our model outperforms FCN-8s at a significant margin on grey-scale video data.
Research on Data Mining Technology for Fault Correlation Analysis of Power Communication Network

A large number of alarm data sets are generated during the operation of power communication network, which implies necessary relationships between faults and operation status. In this paper, Apriori algorithm is adopted to mine association rules for the alarm data sets in power communication network According to defects of Apriori algorithm and characteristics of alarm database, the data in the database is divided into several sub-datasets and then processed separately. In this paper, the improved Apriori algorithm is named as D_Apriori (Disintegrated Apriori algorithm). According to the characteristics of each sub-datasets, an improved optimized particle swarm optimization algorithm is adopted to match the corresponding appropriate support and confidence, so that Apriori algorithm can mine the hidden correlation information more efficiently and comprehensively. Experimental results show this method can improve the operation efficiency effectively and mine more effective fault association rules hidden in a large amount of data.
The oretical basis of inclusive education in higher education institutions

The article deals with the problems of inclusive education in higher education institutions. Inclusive education is the basis and the most effective form of qualitative education for people with special needs. An important component of the inclusive approach is psychological and pedagogical provision of inclusive education in the HEI as an organized system aimed at the development of all components of the personal potential of the subjects of the educational process, regardless of their capabilities: reflexive, cognitive, and activity. The state policy and policy of the Higher School for the Development of Inclusive Education should be aimed not only at motivating people with disabilities to higher education, but also to consider the factors of the social environment and their impact on the development of higher education in general. So, developing an inclusive educational environment, is acquired by students who have a serious motivation for vocational training, their potential capabilities and abilities are often higher than the average student. As a result, after a long time the state receives not just a qualified specialist, but, above all, a motivated professional. In the conditions of the demographic decline of certain periods of development of society, when the HEI attracts a large number of students, including the so-called unpopular specialties. Inclusive education has a broad social dimension, since not only the higher school should be inclusive, but our society, with the support of the state, business and public organizations, should be inclusive. Inclusive education, being one of the main forms of realization of the right to education for persons with disabilities, should become a legally-established institution that has all the necessary components, from the preparation of the complete package of regulatory documents, the definition of norms and principles of appropriate funding, mechanisms for the creation of special conditions and principles of adaptation of the educational environment for students with special educational needs
Towards a Legal Risk Assessment

This chapter presents an SLA brokering framework that includes innovative risk-aware assessment techniques which facilitate the clarification of database and “ownership” rights of data and evaluate the probability of SLA failure. It uses the web service agreement specification (WS-Agreement) as a template and extends prior work on risk metrics from the OPTIMIS project to facilitate SLA creation between service consumers and providers within typical cloud brokerage scenarios. However, since the WS-Agreement allows for an automated mechanism between only two parties and does not cover the use of an intermediary within the agreement process, I use the specific work carried out in the AssessGrid project that includes a brokerage mechanism and pays considerable attention to addressing a risk assessment.
Generation of Random Sequences Based on Exponential Numbers

We propose a novel random number or random sequence generator using a set of large exponential numbers. The decimal pattern of an exponential number is first defined which demonstrates high level of uniformity. We can apply a set of decimal patterns to form a codeword Matrix for generating random numbers and sequences. Simulation results show that pattern with length larger than 100 can reach to \(99.9\%\) uniformity level if decimal digits are transformed into binary data. This provides potential capability for the codeword Matrix to serve as the entropy source for cryptography applications.
Semi-Supervised Nonlinear Feature Selection on Attributed Networks

The accelerating research on attributed networks with high-dimensional node attributes in various data mining tasks highlights the significance of feature selection on networked data. In view of the availability of class labels, many feature selection methods are proposed in a semi-supervised manner as data with partial labels are more accessible to us in various scenarios. More often than not, features and labels are correlated in a nonlinear way that is more intricate than linearity. In these circumstances, vast majority of existing linear algorithms could not work well since they select features according to how well the feature can linearly explain the variance of labels. Moreover, although some methods focus on nonlinear feature selection, with the neglect of the link relations between data, they are difficult to be applied to attributed networks. In this paper, we investigate how to achieve nonlinear feature selection on attributed networks with the help of both labeled and unlabeled data. Methodologically, we propose a novel semi-supervised nonlinear framework FS-GCN based on graph convolutional networks (GCNs) to select high-quality features, which can elaborately catch the nonlinear dependency between nodal attributes and class labels. Experimental results on several real-world datasets validate the superiority of FS-GCN in terms of the quality of selected features, and its robustness in the condition of the low label rate.
Secure Outlook View of Trustworthiness and Attacks in Cloud Computing

Cloud computing has turned into an inexorably famous venture model where the assets are accessible on-request to the clients. It encourages in getting to the substance over the web autonomously with no reference to the fundamental facilitating framework. As of now, various kinds of trust model are led against the different cloud administrations and resources, which focus on their accessibility, administration level understandings, and execution. This paper displays a top to bottom investigation of the different sorts of the trust prediction model and attacks proposed for the cloud computing condition and groups them dependent on the Evidence based, Hardware based, Service Level Agreements (SLA) based and Secure Cloud based Trust. Furthermore, it gives a survey analysis of the vulnerabilities used in resource based attacks about the cutting edge arrangements introduced in the writing to avert, distinguish, or manage every sort of trustworthiness in the cloud computing environment.
Pumping, with or Without Choice

We present the first machine-checked formalization of Jaffe and Ehrenfeucht, Parikh and Rozenberg’s (EPR) pumping lemmas in the Coq proof assistant. We formulate regularity in terms of finite derivatives, and prove that both Jaffe’s pumping property and EPR’s block pumping property precisely characterize regularity. We illuminate EPR’s classical proof that the block cancellation property implies regularity, and discover that—as best we can tell—their proof relies on the Axiom of Choice. We provide a new proof which eliminates the use of Choice. We explicitly construct a function which computes block cancelable languages from well-formed short languages.
An Agent Approach to Manage Heterogeneous and Distributed Knowledge

Emphasis on knowledge and information is one of the challenges of the 21st century to differentiate the intelligent business enterprises. Enterprises have to develop their organization in order to capture, manage, and use information in a context of continually changing technology. Indeed, knowledge and information are completely distributed in the information network of the company. In addition, knowledge is, by nature, heterogeneous, since it is provided from different information sources like the software, the technical report, the meeting statements, etc. The authors present in this article the architecture of a multi-agent system, which allows the capitalization of the distributed and heterogeneous knowledge. They then present how the agents help business experts to design ontologies in detailing this problem and how the agents extract knowledge from different user databases by using a semantic approach.
End-to-End Language Identification Using a Residual Convolutional Neural Network with Attentive Temporal Pooling

In this work, we tackle the problem of end-to-end language identification from speech. To this end, we propose the use of a residual convolutional neural network aiming at exploiting the ability of such architectures to take into account large contextual segments of input data. Moreover, in order for variable input lengths to be supported by the proposed setting, a self-attention mechanism is employed on top of the final convolutional layer. This results in a learnable temporal feature pooling scheme that allows for embedding varying duration utterances into a fixed dimension space. Evaluation is performed on data containing ten oriental languages under different test conditions, namely: short-duration recordings, confusing languages trials, as well as a set of trials in which non-target unseen languages are included. End-to-end evaluation of the proposed framework is thus shown to significantly outperform well-known benchmark methods under considered evaluation conditions.
Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems: Extended Abstract

In this work, we address traffic management of multiple payload transport systems (PTS) comprising of non-holonomic robots. Each PTS is a loosely coupled rigid robot formation carrying a payload. We ensure each PTS traverses its desired trajectory while avoiding collisions with other PTS and static obstacles in various kinds of complex environments. Each PTS has one leader and multiple followers and the followers maintain a desired distance and angle w.r.t the leader using a decentralized leader-follower control architecture. We showcase through simulations that our strategies help manage traffic for a large number of PTS moving around.
Servo Control of Brush DC Motor with Variable Load

Abstract In this article, in view of the characteristics of the cash circulation module of financial machines, such as fast note entering and going out of the note box and large change of load, a control system of fuzzy speed regulation of the note box based on the existing brush driven DC motor through simple feedback signals is designed, so as to realize the stable note output of the note box under varying load. Through experimental verification, after the speed adjustment of the note box, the change in the delivery speed of note has been greatly reduced, and the time change rate of each note transmission has been controlled below 4%, which meets the design expectations and meets the requirements of the system, and can be used as a reference for design in related fields.
Efficient Feature Extraction for Person Re-Identification via Distillation

Person re-identification has received increasing attention due to the high performance achieved by new methods based on deep learning. With larger networks of cameras being deployed, more surveillance videos need to be parsed, and extracting features for each frame remains a bottleneck. In addition, the feature extraction needs to be robust to images captured in a variety of scenarios. We propose using deep neural network distillation for training a feature extractor with a lower computational cost, while keeping track of its cross-domain ability. In the end, the proposed model is three times faster, without a decrease in accuracy. Results are validated on two popular person re-identification benchmark datasets and compared to a solution using ResNet.
DTR: A Novel Topic Generate Algorithm Based on Dbscan and TextRank

With the advent of the era of big data, information overload has become a universal problem. How to extract valuable information from the complicated data, and let users quickly understand the main content of these data has become the focus of research. Current topic generation is a way to solve this problem. Some unsupervised topic models generate results for the probability distribution of topics and words, which are less explanatory to the topic, unable to generate subject matter that can be read smoothly. Supervised topic classification methods rely too much on tagged training data and are not universally applicable. This paper proposes a novel topic generation algorithm DTR, which is based on Dbscan and TextRank. By combining text clustering with abstract algorithm, it can automatically generate refined and understandable document topics. At the same time, the similarity analysis method is used to quickly process the document, which narrows the data range of the generated topic and improves the accuracy of the generated topic. The effectiveness of this algorithm is verified by experiments based on a large amount of real data.
Protection of Information From the Influence of Noise on the Transmission of Video Data in a Geoinformation System

The chapter describes parallel-hierarchical technologies that are characterized by a high degree of parallelism, high performance, noise immunity, parallel-hierarchical mode of transmission and processing of information. The peculiarities of the design of automated geoinformation and energy systems on the basis of parallel-hierarchical technologies and modified confidential method of Q-transformation of information are presented. Experimental analysis showed the advantages of the proposed methods of image processing and extraction of characteristic features.
Data Driven QoE-QoS Association Modeling of Conversational Video

In recent years, the variety and volume of multimedia services have increased exponentially. Like most of other multimedia services, the conversational video service has stringent quality of service and experience requirements. In order to better support users QoE (Quality of Experience) and allocate network resources more effectively, this paper focuses on the QoS (Quality of Service)-QoE association modeling of conversational video flows. Through statistical analysis of the network video flow data, statistical distribution association between QoE and some network layer QoS parameters of the conversational video service is studied by use of the ITU-T P.1201.1 model, and relevant distribution functions are fitted. From the perspective of user's experience, the QoS parameter recommendation indices are given for each MOS (Mean Opinion Score) value interval, and a QoE connected QoS model is established. Through the verification and comparison with an existing work, the rationality of the recommended QoS indices is demonstrated, which can help the network operators strike a balance between saving network resources and satisfying user's quality of experience.
Research and Top-level Framework Design on Unified Resource Management of Big Data in Strategic Consulting

This paper puts forward a set of top-level framework design methodology for unified data resource management aiming at the characteristics of big data, multi-source and heterogeneous, and the difficulty of unified organization and management, and completes the top-level framework of big data for strategic consulting based on this methodology for the scenario of strategic consulting. This paper investigates the unified data resource integration model at home and abroad, and proposed a set of architecture design methodology based on it. The methodology includes three aspects: metadata-driven, hierarchical organization, separation and reorganization. Then the article takes the real data as an example and gives the preliminary results of the model to prove the validity of the model.
A competition-based image saliency model

Competition for visual representation is an important mechanism for selective visual attention. The traditional global distinctiveness based saliency models usually compute the distinctiveness to measure saliency via comparing the difference of image patches in various spaces. In this paper, we propose to use an improved neural competition model to replace the comparison. The pairwise competition responses for a patch to all of the other patches are summed up to represent the distinctiveness of that patch. Particularly, the competition response is computed by a neural competition model with the dissimilarity bias and the gradient based feature inputs. Experimental results validate that the proposed model presents high effectiveness in saliency detection by outperforming nine state-of-the-art models.
Deep neural networks compiler for a trace-based accelerator

Convolutional Neural Networks (CNNs) are the algorithm of choice for image processing applications. CNNs are a highly parallel workload that leads to the emergence of custom hardware accelerators. Deep Learning (DL) models specialized in different tasks require programmable custom hardware and a compiler/mapper to efficiently translate different CNNs into an efficient dataflow in the accelerator. The goal of this paper is to present a compiler for running CNNs on programmable custom hardware accelerators with a domain-specific ISA that targets CNNs. In this work, the compiler was evaluated and tested on a hardware accelerator that was presented in [18]. The compiler uses model definition files created from popular frameworks to generate custom instructions. The model goes through static compilation and different levels of hardware aware optimizations that improve performance and data reuse of the generated program. The software also exposes an interface to run on various FPGA platforms, providing an end-to-end solution. Various CNN models were benchmarked on different systems while scaling the number of processing units.
Vehicle Positioning and Ranging with Static Traffic Camera based on 2D-3D Tracking and Re-Projection

Vehicle positioning and ranging are the current research hotspots. To attain the competition goal of the MMSP Witcomm Challenge 2019 and promote the development of the autonomous driving technology, a novel framework is proposed in this paper, which combines the 2D object tracking and 3D reprojection methodologies. Firstly, a correlation filter using the deep convolutional features is designed to detect the bounding box of the moving objects which is achieved via finding the maximum response of the initial object in the convolutional feature maps. Next the homography matrix is calculated based on the image coordinate points and the corresponding world coordinate points to project the vehicle 2D boundary into the real world. The validity of the proposed framework is verified using the dataset provided by the MMSP Witcomm Challenge 2019 competition, where our method was awarded the second-place prize.
Contrast Stretching Techniques for Enhancement of Mammograms

Contrast stretching techniques deal with manipulation of grey-levels of an image that do not properly incur utilization of the dynamic range of the display system. These are spatial processing techniques which modify or enhance the contrast of the image to yield a visually better image for specific application.
Complexity of statistical attacks on QC‐LDPC code‐based cryptosystems

Public-key cryptosystems built on quasi-cyclic (QC) low-density parity-check and moderate-density parity-check codes are promising candidates for post-quantum cryptography, since they are characterised by compact keys and high algorithmic efficiency. The main issue with this kind of system is represented by the fact that, since the decoding procedure is probabilistic, it may leak information about the secret key. In this work, the authors study cryptanalysis procedures that aim at recovering the secret key by exploiting this fact. They identify the phenomenon that is at the basis of these procedures and show that the QC structure plays an important role in the success of these attacks. They use a graph analogy to study the complexity of these attacks, and show that their feasibility strongly depends on the QC structure. They also devise an approach to perform full cryptanalysis by combining an information set decoding algorithm with some partial knowledge about the structure of the secret key.