Reviewerly/exHarmony huggingface dataset:
Network Embedding Method Based On Extractive Summary

Redundant or low quality sampling sequences are used in existing network embedding methods based on random walk. A network embedding method based on extractive summary is proposed to generate high-quality node embedding. A selective gate network is used by the role of the node in the overall sequence. A decoder based on extractive abstract is designed by prediction and sampled condition of the node. Firstly, by using the control characteristics of the selective gate network, the hidden state vectors containing the attribute information are filtered. The environment vectors that can effectively represent the key information of nodes are acquired. It achieves the extraction of important information of the node. Furthermore, the environment vector is decoded by the extractive-abstract-based decoder. The redundant nodes in the original sampling sequence are removed, which further improves the classification accuracy. With the datasets of Cora, Citeseer and Wiki, the proposed method is applied to network node classification, and outperforms several mainstream baseline methods.
Motion Capture System by Spatio-Temporal Integration of Multiple Kinects

This paper proposes a motion capture system consisting of multiple Kinects. For obtaining correct body poses, we propose a method that generates pseudo-synchronous pose data from asynchronous Kinects and integrates the poses eliminating wrongly estimated data. Experimental results confirm that the method works effectively for capturing a moving person, which has been difficult to capture using asynchronous Kinects.
Ensuring High QoE for DASH-Based Clients Using Deterministic Network Calculus in SDN Networks

HTTP Adaptive Streaming (HAS) is becoming the de-facto video delivery technology over best- effort networks nowadays, thanks to the myriad advantages it brings. However, many studies have shown that HAS suffers from many Quality of Experience (QoE)-related issues in the presence of competing players. This is mainly caused by the selfishness of the players resulting from the decentralized intelligence given to the player. Another limitation is the bottleneck link that could happen at any time during the streaming session and anywhere in the network. These issues may result in wobbling bandwidth perception by the players and could lead to missing the deadline for chunk downloads, which result in the most annoying issue consisting of rebuffering events. In this paper, we leverage the Software-Defined Networking paradigm to take advantage of the global view of the network and its powerful intelligence that allows reacting to the network changing conditions. Ultimately, we aim at preventing the re-buffering events, resulting from deadline misses, and ensuring high QoE for the accepted clients in the system. To this end, we use Deterministic Network Calculus (DNC) to guarantee a maximum delay for the download of the video chunks while maximizing the perceived video quality. Simulation results show that the proposed solution ensures high efficiency for the accepted clients without any rebuffering events which result in high user QoE. Consequently, it might be highly useful for scenarios where video chunks should be strictly downloaded on- time or ensuring low delay with high user QoE such as serving video premium subscribers or remote control/driving of an autonomous vehicle in future 5G mobile networks.
Comparison of Cloud Computing Security Threats and Their Counter Measures

One of the major driving forces in the current technological world, is the concept of cloud computing. Many companies and organizations have their precious data stored on various cloud computing platforms. Organizations depend on cloud computing will effectively manage the excessive data quantity and handle them. In such a scenario, a breach of that data or any major threat can cause huge damage to the image and financial position of the company. Due to a security breach, if all of those data is leaked to an unauthorized intruder, it can lead to severe manipulations of that data and cause huge losses to the assets of the company. Hence, various counter measures to tackle those security issues are discussed in this paper. All the possible data issues are listed out and the various methods to counter them are discussed.
Distance Calculation of an Object in a Stereo Vision System

The ability of a machine to capture 3D information from the real world duplicating the ability of human vision is one of the great interest in science and industry. Works on stereo vision duplicating the real world environment for machines to adapt the ability of human vision by many researchers. Stereo camera based distance calculation of an object from an observer taken in an image is performed and height of the object is also calculated. Stereo parameters of both the cameras are estimated using Camera calibration. Rectification of the images from both the camera is performed so that they are in the same plane. Disparity mapping between the left and the right images is estimated. Using the disparity map and stereo parameters of both the cameras then the 3-Dimensional (3D) view of the scene is constructed. The distance of the object is calculated using Euclidean distance by determining the centroid of the bounding box and the camera and finally height of the object is calculated. Results shows the output of the object being placed at the distance between 10 to 3 m. The average error in the distance calculation is 1.74%.
Autonomous Vehicles Diagnosis Platform(AVDP) based on deep learning and loopback

This paper proposes `Autonomous Vehicles Diagnosis Platform(AVDP) based on deep learning and loopback' to improve the safety of Autonomous driving vehicles. DLPP consists of an On-board Gateway Module (OGM) and a Self-diagnosis Module of Part (SMP). While traditional vehicle gateways were used to convert messages from the vehicle, OGM not only has the translation capabilities of existing gateways, but also uses loopback to determine whether a fault has occurred in the sensor or part. The payload, which is used by OGM to judge the sensor normally, is sent to SMP for self-diagnosis. The Self-diagnosis Module of Part (SMP) diagnoses parts itself by using the payloads transferred from the OGM. Because the SMP is designed based on an LSTM algorithm, it diagnoses a vehicle's fault by considering the correlation between previous diagnosis result and current measured parts` data. In the experiment, in the result of experiment, As the set of test data increases, the SMP increases accuracy and there is little difference in uptime, so the high accuracy SMP is more suitable for vehicle diagnostics than these two neural network models.
Voice-Input Multimedia Information Retrieval System Based on Text-to-image GAN

In this paper, we develop an integrated multimedia information retrieval system. By utilizing text-to-image Generative Adversarial Network and image-to-text model, the developed system enables users to retrieve an objective content utilizing voice as an input with high accuracy. Experimental results show the effectiveness of the developed system.
Driven state transfer and state storage in a tight-binding chain using two local barriers

A theoretical scheme to realize quantum state transfer and state storage in a uniformly coupled tight-binding chain is introduced in this paper. Two controllable gate voltages acting as local barriers are applied onto specific sites of the system, which separate the chain into three regions. By setting two gate voltages being equal, we show that an initially localized quantum wave packet undergoes perfect periodic revivals, allowing for perfect quantum state transfer between two nonadjacent spatial regions of the system. We also show that the wave packet can be trapped in its initial region by setting two gate voltages being unequal, which relates to the problem of storing quantum information. Moreover an efficient time-dependent quantum state transfer protocol is presented by smoothly varying the two gate voltages. Significantly, in our setup, the transferred state can be trapped, with a high fidelity of storage, at the end of the transfer protocol.
Theorising Information Security Policy Violations

Information system security threats perpetuates organisations in spite of enormous investments in security measures. The academic literature and the media reflect the huge financial loss and reputational harm to organisations due to computer related security breaches. Although technical safeguards are indispensable, the academic literature highlights the ‘insider threat’. Organisational employees pose a significant threat, considering, they already have access to the organizations’ information systems. It’s a matter of how they use/abuse it. This article explores the theoretical foundation in the domain of information systems security policy violations. The academic databases are queried for key theories in computer compliance/non-compliance. These theories are examined for theoretical development. A problem area is identified and subsequently, a theoretical model is proposed in an attempt to explain: Why employees violate information systems security policies?
An Effective Source Selection Algorithm for Filling Missing Tuples

Completeness is one of the central criteria for data quality, and the completeness of data becomes particularly important. Specifically, incomplete data refers to a data set that does not contain enough information to answer the query, which can be divided into missing the values and tuples. This paper presents a technique of leveraging other data sources to fill missing tuples in target data. However, accessing too many data sources introduces a huge cost, so we investigate how to select a proper subset of sources to fill the missing tuples. Firstly, we define the gain model of sources and introduce the optimization problem of source selection from the perspective of missing tuples, in which the gain is maximized with the cost under a threshold. For filling the missing tuples, we propose a data source selection strategy based on a genetic algorithm. Experimental results show high performance on both the effectiveness of our algorithm.
Soft Computing-Based Control System of Intelligent Robot Navigation

This paper focuses on the study of intelligent navigation techniques which are capable of navigating a mobile robot autonomously in unknown environments in real-time. We primarily focused on a soft computing-based control system of autonomous robot behaviour. The soft computing methods included artificial neural networks and fuzzy logic. Using them, it was possible to control autonomous robot behaviour. Based on defined behaviour, this device was able to deduce a corresponding reaction to an unknown situation. Real robotic equipment was represented by a Lego Mindstorms EV3 robot. The outcomes of all experiments were analysed in the conclusion.
Intelligent Manufacturing Information Security Sharing Model Based on Blockchain

The development of the manufacturing industry reflects the productivity in a country or region to a certain extent. The informatization plays an increasingly important role in manufacturing. At present, there is still a phenomenon of insufficient transparency and low security performance in the manufacturing industry, which hinders the further development of the manufacturing industry. This paper proposes an intelligent manufacturing information security sharing model based on blockchain technology. The blockchain system can store product information and transaction information in the blocks which can achieve traceability of product information. The information is strengthened in real time, and the consensus mechanism and encryption algorithm are used to ensure that the product cannot be tampered. This information-sharing mechanism has promoted the development of smart manufacturing to a certain extent.
Bestow: A Location-based Mobile Application for Religious Donors in Context of Bangladesh

In this era of technological advancement and the goals to become digital Bangladesh by 2020, the demand for digital services is escalating rapidly. However, there is not much technological development solely focusing on religious donations in Bangladesh. As a result, people from different mosques, temples, churches or other religious foundations usually collect donations physically by visiting in person or organizing charity events which is time consuming and strenuous. It also gets gruelling to inform substantial population for help during emergencies. Additionally, uneducated people finds difficulty operating the limited number of already existing online donation platforms. Therefore, this paper proposes a donation system to overcome these obstacles by developing a mobile application. Additionally, this proposed system aims to make it easier for both educated or uneducated people to donate and overcome the hurdles of donation in current system. The proposed system was assessed by group of 65 people which included people related with various religious organizations or are teachers. The assessment lead to around 83% of the people concluding the system as desirable, efficient and user friendly.
Applying a Computer-assisted Concept-mapping Approach to the Improvement of English Reading Comprehension

The current English reading teaching in Chinese senior high schools mainly employs traditional methods, which lead to students' negative attitudes towards English reading. In order to solve these learning problems, we applied computer-assisted concept-mapping approach and conducted experimental research in 2017 in a senior high school, China. One hundred and twelve first-year students participated in the project. The research results demonstrate that the computer-assisted concept-mapping approach enhances the students' attitudes toward English reading and their reading comprehension level.
The Return Statement

Curriculum assessment is a critical but arduous task. It yields insight into the strengths and weaknesses of our programs, outlining paths for curriculum improvement. However, current methods of assessment are often viewed as burdensome, as disorganized, or even as unfruitful. A lack of consistent assessment makes determining the impact of program modifications to be extremely difficult, if not impossible. Even when issues are identified, lengthy and backloaded assessments do not leave any time to address these issues for the assessed students. To overcome the obstacles of program assessment, we have constructed the Continuous Assessment Database System (CADS). This system conducts direct assessment of student prerequisite knowledge at each semester's outset. By tracking prerequisite knowledge, we determine whether students recall the crucial elements of our curriculum. Furthermore, educators receive data on students' preparedness early in the semester, allowing for them to adapt their courses to best meet student needs. In order to generate informative reports for faculty each term, CADS maps its assessment questions to student information, to CS knowledge topics, to departmental student learning outcomes, and to taxonomic mastery levels. These reports visualize current data against historical data and present deeper examinations of significant data shifts for causal analysis. Different diagrams give insight into specific courses, while others examine student learning across the entire CS program via diverse mappings. Overall, CADS provides a model for using technology to craft an expedient form of program assessment that dynamizes a curriculum. This paper details our CADS implementation.
Impact of Agile Misconceptions Towards Organization Technology Competitiveness

Due to the digital and 4IR transformation happening across the world, it is imperative for company's survival to be technologically existent and adequately competitive. This is one of the reasons why organizations are increasingly adopting the Agile methodology. Agile has been one approach organizations adopt to effectively response to this current technological competitiveness because through Agile organizations are able to build constant, quality software that allows them to get their products and services quickly in the market while being flexible and adaptive to the continuous change. However, with the adoption of Agile comes numerous challenges, that includes various misconceptions about Agile which often hinders effective implementation of Agile projects. This paper reviews the current literature on various misconceptions around the agile methodology and how they impact the organizations technology competitiveness. A systematic literature review was conducted on two theories; first being the current misconceptions around Agile approach and secondly, the organizations technology competitiveness. The main aim of the review was to establish a connection between the existence of misconceptions in Agile environments and the impact towards the organization ability to effective technology competitiveness.
Multi-way Encoding for Robustness

Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely-used, but highly-vulnerable mapping. We demonstrate that by leveraging a different output encoding, multi-way encoding, we decorre-late source and target models, making target models more secure. Our approach makes it more difficult for adversaries to find useful gradients for generating adversarial attacks. We present robustness for black-box and white-box attacks on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN. The strength of our approach is also presented in the form of an attack for model watermarking, raising challenges in detecting stolen models.
Multi-camera Vehicle Tracking from End-to-end based on Spatial-Temporal Information and Visual Features

In large-scale traffic video analysis, continuous tracking of vehicles across cameras overcomes the time and space limitations of a single camera, and is conducive to transportation design and traffic flow optimization. In this work, we propose an end-to-end framework for multi-camera vehicle detection, tracking and re-identification in complex traffic environments with urban multi-junctions, which integrates visual features and temporal-spatial information of the trajectories for optimization. Based on detection and tracking of multi-vehicles in a single camera, our method distinguishes and marks the vehicle trajectories from different intersections where they enter and exit. Then, the visual features of the same vehicle keyframes are extracted to match between the cameras of the specific matching link, while taking into account the constraint of the trajectory time. In the end, our algorithm shortens vehicle trajectories' average matching time in two cameras to 2 seconds, and the accuracy is 81.59% in the test scenarios, which greatly improves the efficiency and accuracy of vehicle re-identification.
Server-Less Cloud Computing—An Economical Solution for Business Operations

Cloud computing provided computing resources which were not imagined before by any business as solutions for their operations. Since the last five years, a new paradigm of service model has emerged which is Function as a Service (FaaS). FaaS is also referred to as server-less computing where the stateless function is responsible for computation of data. These functions are deployed on Amazon Web Services (AWS) Lambda along with other services like API Gateway and DynamoDB to efficiently run the application. The data is transferred using HTTP which is a stateless protocol and it uses its GET and POST methods to exchange data. In this paper, we analyze the deployment of an API on a conventional cloud computing model and compare it with the server-less model, taking account of all the API calls that generate costs.
Making AdaBoost Less Prone to Overfitting on Noisy Datasets

AdaBoost is perhaps one of the most well-known ensemble learning algorithms. In simple terms, the idea in AdaBoost is to train a number of weak learners in an increamental fashion where each new learner tries to focus more on those samples that were misclassfied by the preceding classifiers. Consequently, in the presence of noisy data samples, the new leraners will somehow memorize the data, which in turn will lead to an overfitted model. The main objective of this paper is to provide a generalized version of the AdaBoost algorithm that avoids overfitting, and performs better when the data samples are corrupted with noise. To this end, we make use of another ensemble learning algorithm called ValidBoost [15], and introduce a mechanism to dynamically determine the thresholds for both the error rate of each classifier and the error rate in each iteration. These threshholds enable us to control the error rate of the algorithm. Experimental simulations have been made on several benchmark datasets including Web datasets such as “Website Phishing Data Set” and “Page Blocks Classification Data Set” to evaluate the performance of our proposed algorithm.
Deep Learning Preserving Renal Dialysis Treatment Recommendation

In China, there are many patients with renal failure and the treatment period of renal failure is long, but the number of doctors is not proportional to the number of patients. Renal dialysis is a kind of blood purification technology, which has a certain significance for alleviating the symptoms of patients with renal failure and prolonging the survival period. In this case, it is meaningful to establish an efficient recommendation model to assist the doctor in determining the renal dialysis treatment plan. Based on 22039 renal dialysis treatment records of 180 patients, we proposed a recommendation model of renal dialysis treatment plan based on deep learning, which can recommend a treatment plan based on patient's physical data to provide an intelligent reference for doctors. The model can first intelligently complete the missing data and recommend treatment plans for new patients and re-visiting patients. For new patients, the model based on a deep neural network which mines large data of existing patients, recommend the optimal treatment plan. For re-visiting patients, the model based on the LSTM which personalized learns their personal diagnosis and treatment data, recommend a personalized treatment plan. The model recommends dialyzer mode, dialyzer type, anticoagulant type, and anticoagulant first dose according to the type of disease, complications, dry weight, current weight, age and gender. The model can effectively improve the doctor's diagnosis speed, improve the utilization rate of medical resources and has broad application and promotion prospects.
Performance Evaluation of Face Attribute Estimation Method Using DendroNet

There are many studies on face recognition, which identifies a person using distinctive features extracted from a face image. One of the problems in face recognition is that the accuracy of face recognition decreases due to environmental changes such as head pose, emotion, illumination, etc. Addressing this problem, soft biometrics, which uses attributes such as age and gender for person authentication, is expected to improve the accuracy of face recognition. This paper proposes a face attribute estimation method using the Convolutional Neural Network (CNN). The CNN architecture of the proposed method, called DendroNet, is automatically designed according to the relationships among attributes. Though experiments using the CelebA dataset, we demonstrate that the proposed method exhibits better performance than conventional methods.