Reviewerly/exHarmony huggingface dataset:
LS4BUCC: A Low Overhead Storage Architecture for Blockchain Based Unmanned Collaborative Cognition System

In unmanned collaborative cognition scenarios, traditional IoT systems are facing with data security and node privacy problems. However, blockchain together with data encryption technique enables the trustless network due to the tamper-proof, anonymity and unbreakable properties. Nevertheless, there are still some shortages when combining blockchain with IoT systems, for example the storage consumption. Generally speaking, validation nodes in blockchain network need to store a complete copy of the ledger so as to verify transactions and reach the whole network consensus. However, in unmanned collaborative cognition scenarios, many devices' storage capacity are limited. Therefore, in order to solve the storage problem, we propose LS4BUCC, a low overhead storage architecture regarding to the new features of unmanned collaborative cognition systems. It selectively stores data according to the value density. Based on three storage mechanisms and two supportive methods, LS4BUCC can reduce the data storage and maintain the system security and efficiency at the same time.
Multiple deep CNN for image annotation

Achieving better performance has always been an important research target in the field of automatic image annotation. This paper draws on the current popular deep learning model for the field of automatic image annotation. We propose a multiple convolutional neural networks (CNN) combination model for image annotation, which achieves satisfactory performance. First of all, we use three classical convolutional neural networks, and subsequently we examine the annotation accuracy for each CNN model. Then we take full advantage of the powerful feature representation capabilities of deep CNN, thus the last two layers of the deep CNN are extracted for each model and merged to form a new combined feature. Finally, we form our combination models by concatenating these features from each CNN model, and utilize these concatenated features to linear SVM classifier for image annotation. Experimental results on ImageCLEF2012 image annotation dataset illustrate that our combination method outperforms the traditional classifiers and the individual CNN models.
A surrounding point set approach for path planning in unknown environments

Abstract Due to uncertainties caused by not having full information about the map, path planning in unknown environments is considered to be a more complex problem than in known environments. Consequently, appropriate decisions have to be made at each movement, as the trace depends on which parts of the map have been recognized, and how paths have been locally generated. Therefore, the present paper develops a mechanism for path planning in unknown environments by finding points that surround the importantly considered obstacles. The proposed mechanism can efficiently generate paths within the recognized region, since only subsets of obstacles and points are considered for planning a path, rather than taking all discovered obstacles and all grid points into account; also, in the proposed mechanism, only the newly discovered points by a movement are subject to being processed for updating the path. In experiments, the path planning from the start to the goal was simulated by recognizing the map based on a sensor with a radius. Then, a comparative experiment based on a well-known existing method was used to evaluate the performance of the proposed method. The results demonstrate that the proposed approach can achieve significant efficiency, as well as successfully reach the goal based on the partial information without wandering the map.
Neural adaptive admission control framework: SLA-driven action termination for real-time application service management

KEYWORDS: Neural autonomous controlapplication service managementcloud computingservice modellingservice level agreement
ECG Classification Based on Long Short-Term Memory Networks

The automatic analysis of electrocardiogram (ECG) data using deep learning has become an important method for the diagnosis of cardiovascular disease. In this paper, we proposed a LSTM-CNN hybrid model based on long short-term memory network (LSTM) and convolutional neural network (CNN) to complete short-term ECG positive anomaly classification tasks. The model can independently learn the structural features of ECG signals and have a certain memory and inference function, and deep mining of temporal correlation between the ECG signal points. Evaluated on the MIT-BIH Arrhythmia Database (MIT-BIH-AR), the experimental results show that the proposed algorithm achieves an accuracy of 99.7%, sensitivity of 99.69%, and specificity of 99.7%, respectively. Over 150,000 short-term ECG clinical records in the Chinese Cardiovascular Disease Database (CCDD) were evaluated for model performance with an accuracy of 93.39%, a sensitivity of 91.18%, and a specificity of 95.21%. The experimental results show that the LSTM-CNN model has an efficient and accurate classification performance on large-scale clinical ECG data.
Research on Knowledge Graph Application Technology

The development of the Internet has mainly gone through three stages. The first stage can be defined as the era of document interconnection. At this time, the main task of the Internet is to provide corresponding layout content for readers to read.
Design and Application Research of VR/AR Teaching Experience System

This paper is based on the current situation of teaching, such as the lack of contextual interaction in the learning experience, the single situation, and the poor teaching experience and utilizes virtual reality (VR) and augmented reality (AR) technology to solve these problems. With the immersive, interactive and imaginative features of virtual reality technology, the virtual somatosensory virtual learning space with human-computer interaction is designed. The superposition of augmented reality technology is used to create an AR superposition learning space that combines real world and virtual space. According to the extension of VR/AR teaching thinking dimension, the teaching experience system design framework is constructed, and the physical model of experience system is proposed. Through data mining and teaching feedback, the learning experience is adjusted. Then design a personalized teaching experience system which can enhance learning experience and improve learning interest.
Automated generators of examples and problems for studying computer algorithms

Purpose The purpose of this study is to investigate students’ decisions in example-based instruction within a novel self-regulated learning context. The novelty was the use of automated generators of worked examples and problem-solving exercises instead of a few handcrafted ones. According to the cognitive load theory, when students are in control of their learning, they demonstrate different preferences in selecting worked examples or problem solving exercises for maximizing their learning. An unlimited supply of examples and exercises, however, offers unprecedented degree of flexibility that should alter the decisions of students in scheduling the instruction. Design/methodology/approach ASolver, an online learning environment augmented with such generators for studying computer algorithms in an operating systems course, was developed as the experimental platform. Students’ decisions related to choosing worked examples or problem-solving exercises were logged and analyzed. Findings Results show that students had a tendency to attempt many exercises and examples, especially when performance measurement events were impending. Strong students had greater appetite for both exercises and examples than weak students, and they were found to be more adventurous and less bothered by scaffolding. On the other hand, weak students were found to be more timid or unmotivated. They need support in the form of procedural scaffolding to guide the learning. Originality/value This study was one of the first to introduce automated example generators for studying an operating systems course and investigate students’ behaviors in such learning environments.
The Development of the Digital Wellness Toolkit

The advent of Information and Communication Technologies have brought with it numerous advantages and disadvantages. One area of concern relates to wellbeing in a digital environment: issues regarding cyber safety and cyber safety, although prevalent, are not frequently addressed in school curricula in South Africa. Faced by a lack of training material for schools and communities on cyber safety and security, and drawing from its experience in the tertiary environment, the African Centre of Excellence for Information Ethics developed the Digital Wellness Toolkit. This toolkit seeks to address this gap on four levels: pre-primary age; primary school; secondary school and local community training. In so doing, the role of the South African government is underscored, since by creating and disseminating training programmes on the ethics of emerging technologies, it realises its responsibility towards eGovernance. This paper provides an overview of the history of the development of the Digital Wellness Toolkit together with a discussion of the theoretical and methodological approaches to the design of the various Digital Wellness curricula which form part of the toolkit.
The dual of a constacyclic code, self dual, reversible constacyclic codes and constacyclic codes with complementary duals over finite local Frobenius non-chain rings with nilpotency index 3

Over finite local Frobenius non-chain rings with nilpotency index 3 and when the length of the codes is relatively prime to the characteristic of the residue field of the ring, the structure of the dual of γ -constacyclic codes is established and the algebraic characterization of self-dual γ -constacyclic codes, reversible γ -constacyclic codes and γ -constacyclic codes with complementary dual are given. Generators for the dual code are obtained from those of the original constacyclic code.
Semantic locality‐based approximate knowledge graph query

Summary RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the “schema‐free” feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top‐k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top‐k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD‐4 benchmark confirm our solution's superiority on both effectiveness and efficiency.
MASKER

Benefited from recent artificial intelligence evolution, Automatic Speech Recognition (ASR) technology has achieved enormous performance improvement and wider application. Unfortunately, ASR is also heavily leveraged by speech eavesdropping, where ASR is used to translate large volume of intercepted vocal speech into text content, causing considerable information leakage. In this work, we propose MASKER -- a mobile security enhancement solution to protect the mobile speech data from ASR in eavesdropping. By identifying ASR models' ubiquitous vulnerability, MASKER is designed to generate human imperceptible adversarial noises into the real-time speech on the mobile device (e.g. phone call and voice message). Even the speech data is exposed to eavesdropping during data transmission, the adversarial noises can effectively perturb the ASR process with significant Word Error Rate (WER). Meanwhile, MASKER is further optimized for mobile user perception quality and enhanced for environmental noises adaptation. Moreover, MASKER has outstanding computation efficiency for mobile system integration. Experiments show that, MASKER can achieve security enhancement with an average WER of 84.55% for ASR perturbation, 32% noise reduction for user perception quality and 16× faster processing speed compared to the state-of-the-art method.
Assertion Detection in Clinical Natural Language Processing: A Knowledge-Poor Machine Learning Approach

Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients' condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.
A Systematic Literature Review of Improved Knowledge Management in Agile Software Development

Agile Software Development (ASD) is an adaptive software development approach that easily adapts to changing software requirements. It offers an advantage in time management but has disadvantages such as lack of software documentation and knowledge management. This research aims to understand more about research development in the knowledge management improvisation in Agile Software Development by collecting various themes of improved area and method used. To achieve this goal, 226 articles written in 2009-2018 are screened by using Kitchenham method to produce 15 best articles. This systematic literature review (SLR) results in a summary of improvements in knowledge management. The summary includes various approaches of several themes such as documentation, tools or technology, and others. The areas that need improvement are tools for supporting communication and documentation. The suggested improvement that has been proposed by researcher focuses mostly on artifact documentation, decision making, effort estimation and tools. In these studies, research question can be identified, analyzed, and answered.
Product Application to Recognize the Marker Through Augmented Reality

This paper presents a methodology to recognize the matrix marker at minimum latency time. Latency is the biggest challenge in the field of Augmented Reality (AR). Marker detection approach in AR has been conducted for several years, but there is no system or method to reduce the latency. Although, currently some methods use marker-less augmented reality system, but complexity in detection process, the marker based recognise approach is proposed. Here, we propose matrix marker recognise algorithm and mathematical formulation to detect the marker at different distance and luminous conditions. The position and quadrilateral areas which may seem like marker have been processed by posit class & IP prototype tool from Aforge.net framework. Experimental results compare the latency using rectifier, without rectifier and ideal value of system under different distance and luminous conditions.
Bio-inspired Optimization of Type-2 Fuzzy Controllers in Autonomous Mobile Robot Navigation

In this paper we perform a comparison of using type-2 fuzzy logic in two different bio-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA). Each of these methods is enhanced with a methodology for dynamic parameter adaptation using interval type-2 fuzzy logic, where based on some metrics about the algorithm, like the percentage of iterations elapsed or the diversity of the population, we aim at controlling its behavior and therefore control its abilities to perform a global or a local search. To test these methods two benchmark control problems were used in which a fuzzy controller is optimized to minimize the error in the simulation with nonlinear complex plants.
Multi-object tracking using deformable convolution networks with tracklets updating

Many multi-object tracking methods have been proposed to solve the computer vision problem which has been attracting significant attentions because of the significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, hybrid deformable convolution neural networks with frame-pair input and deformable layers for multi-object tracking are presented. The object tracking method trained using two successive frames can predict the centers of searching windows as the locations of tracked targets to improve the accuracy and robustness of object tracking. Histogram of Oriented Gradient and CNN features are extracted as appearance features to measure similarities between objects. Kalman filter and Hungarian algorithm are used to create tracklets association which indicates the location and the trajectories of tracked targets. To solve the problem of object transformation, we construct a novel sampling strategy for off-line training with the idea of augmenting the special sampling locations in the convolution layers and pooling layers with additional offsets. Experiments on the popular challenging datasets show that the proposed tracking system performs on par with recently developed generic multi-object tracking methods, but effective for dense geometric transformation objects and with much less memory. In addition, the proposed tracking system can run in a speed of over 75 (24) fps with a GPU (CPU), much faster than most deep networks-based trackers.
Efficient Astronomical Data Condensation Using Fast Nearest Neighbors Search

Analyzing astronomical observations represents one of the most challenging tasks of data exploration. It is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of Big Data problems - like data variety - are also present, datasets size represents the most significant obstacle in visualization, and subsequent analysis. The paper studies efficient data condensation algorithm aimed at providing its compact representation. It is based on fast nearest neighbor calculation using tree structures and parallel processing. The properties of the proposed approach are preliminary studied on astronomical datasets related to the GAIA mission. It is concluded that introduced technique might serve as a scalable method of alleviating the problem of data sets size.
Evolutionary Development of Growing Generic Sorting Networks by Means of Rewriting Systems

This paper presents an evolutionary developmental method for the design of arbitrarily growing sorting networks. The developmental model is based on a parallel rewriting system (a grammar) that is specified by an alphabet, an initial string (an axiom), and a set of rewriting rules. The rewriting process iteratively expands the axiom in order to develop more complex strings during a series of development steps (i.e., derivations in the grammar). A mapping function is introduced that allows for converting the strings onto comparator structures-building blocks of sorting networks. The construction of the networks is performed in such a way that a given (initial) sorting network grows progressively by adding further building blocks within each development step. For a given (fixed) alphabet, the axiom together with the rewriting rules themselves are the subjects of the evolutionary search. It will be shown that suitable grammars can be evolved for the construction of arbitrarily large sorting networks that grow with various given sizes of development steps. Moreover, the resulting networks exhibit significantly better properties (the number of comparators and delay) in comparison with those obtained by means of similar existing methods.
HydraRoute

Routing for dense circuits is a major challenge for VLSI physical design. Most routing approaches rely at least partially on a "rip-up and reroute" scheme, where solution quality and run times can be impacted profoundly by the order in which nets are routed. Other routing tools rely on backtracking methods embedded in integer linear programming solvers. In this paper, we present a novel approach which avoids backtracking, and largely eliminates the routing order considerations, by constructing a large number of routings simultaneously. By keeping "options open," our approach sidesteps conflicts. Our approach is a factor of ten faster than other recent work, reduces via counts by 30% or more, and is competitive on both wire length and completion rates. The approach is simple, scalable, and adaptable to the complex constraints of modern circuit fabrication processes.
Distributed File Sharing and Retrieval Model for Cloud Virtual Environment

Cloud-based storage services are multiplying and are being adapted mostly for data storage. At the same time, many potential problems pertaining to data storage and security are being addressed. This research provides the architecture for splitting user data and the solution in retrieving different data chunks, stored on different cloud storages. By doing this, not only the load on a single server is reduced but security and storage are efficiently used. Data would be stored and retrieved in slices, hence the chances of data forgery are diminishing. File processing will be faster as split parts would be fetched from different clouds, enabling parallel processing.
Inside out - A study of users’ perceptions of password memorability and recall

How users perceive password memorability is not well understood, despite passwords being studied thoroughly. We investigate the relationship between users’ perceptions of the memorability of a number of passwords and the users’ electroencephalogram (EEG) data collected using brain-computer interfaces (BCIs). In this study, we asked 75 participants to rank the comparative memorability of 15 blocks of 5 passwords each, while collecting their EEG data. Passwords had the same length but differed in characteristics and strength. We were able to find a consistency between how users perceive password memorability, the EEG signals elicited by the least and most memorable passwords from a list, and the actual measured strength of these passwords. Our results suggest that EEG signals could fill the gap in the absence of information about how users rank passwords, or password strength, with an 82% accuracy.
Matrix Barcode Based Secure Authentication without Trusting Third Party

The user creates a JavaScript-enabled bookmark file once and opens this file to generate a QR code every time to use secure servers. The mobile device generates a secret to encrypt his/her credentials and saves it so that the browser can be accessed, thus enabling him/her to log onto secure servers.