Reviewerly/exHarmony huggingface dataset:
Importance of Cloud Deployment Model and Security Issues of Software as a Service (SaaS) for Cloud Computing

Cloud computing, now-a-days has become the jargon in the industry of IT. It is a model that gives worldwide access to shared pools of configurable resources over the internet. That means it run several applications or programs at the same time on more than one computer. The nature and usage of the cloud computing are developing rapidly both virtually and in reality. It is making things easier for the user of internet by many of its attractive features but these features, have not just only challenged the existing security system, but have also revealed new security issues. In this paper the deployment of cloud models are discussed . The cloud models are deployed as public cloud, private cloud and hybrid cloud. Each of these models have their own advantages and disadvantages. Cloud also provides some services like software as a service (SaaS), Platform as a service (PaaS), Infrastructure as a service (IaaS) . This paper is an insightful analysis of the features as well as the securities challenges of Software as a Service (SaaS) for cloud computing.
Development and application of user review quality model for embedded system

Software industry is poised for robust and sustained growth. Many software products / applications are being developed. It is found that the software so developed must be free from defects / bugs. One major area that requires concentration is requirements analysis. The requirements from various stakeholders must be received and analyzed for their relevance in the final product. Some prioritization analysis is also required to consider some requirements over the others. Kano and AHP are known to help the developers in analyzing relative strengths of various requirements. However, after defining the requirements clearly, the developed product must be free of defects. SWFMEA is a technique to ensure the defect-free development of the software. Software User Review Defect Corrective Model (SURDCM), which has Kano and AHP models for requirements analysis cum prioritization and SWFMEA to detect possible defects and their respective effects, is used for development of a software product. It is expected that with the use of this model, the user involvement is increased to clearly specify the requirements and the possible defects and the effects are analyzed. This model encourages the user participation throughout the development of the product.
Dangerous Target Recognition of Massive Image and Video Based on Deep Learning

The vast amount of data in the video requires people to check and identify, that is, it is necessary to extract a short possible image segment from a long-time video or massive image data. Technicians are prone to fatigue and long-term high-load work, and there are inevitable omissions, and computers can continue to work reliably as long as they have electricity. The paper is aimed at the dangerous targets that may appear in massive image and video data, especially unidentified personnel, vehicles, aircraft, etc. The computer image semantic recognition system automatically and quickly detect these potential dangers (or fragments of interest), and converts these massive “suspected” image languages into natural language that users can understand, and achieves early warning. The main work carried out in this paper is: label a certain amount of data with a dangerous target; on the platform of YOLOv3, a certain amount of training is carried out; we use the trained data for the identification of dangerous item, such as guns and knives, etc. The experiments shown that the dangerous image target is effectively identified.
The Development and Evaluation of an Educational Board Game with Augmented Reality Integrating Contextual Clues as Multi-Level Scaffolding for Learning Ecosystem Concepts

The study developed an AR board game - Ecological Restoration, which employed AR as multi-level cognitive scaffoldings for learning ecosystem concepts. The advantage of this “hybrid” approach is it introduced the flexibility and adaptability to educational board games, which its learning content is generally fixed in printed cards. The game was implemented in a biology class for evaluation. Results show that students generally perceived the game as useful and ease of use for learning. Students' understanding of ecosystem concepts were also significantly improved. Implications of these findings are discussed.
On Adaptive Change Recommendation

As the complexity of a software system grows, it becomes harder for developers to be aware of all the dependencies between its artifacts (e.g., files or methods). Change impact analysis helps to overcome this challenge, by recommending relevant source-code artifacts related to a developer’s current changes. Association rule mining has shown promise in determining change impact by uncovering relevant patterns in the system’s change history. State-of-the-art change impact mining typically uses a change history of tens of thousands of transactions. For efficiency, targeted association rule mining constrains the transactions used to those potentially relevant to answering a particular query. However, it still considers all the relevant transactions in the history. This paper presents Atari, a new adaptive approach that further constrains targeted association rule mining by considering a dynamic selection of the relevant transactions. Our investigation of adaptive change impact mining empirically studies fourteen algorithm variants. We show that adaptive algorithms are viable, can be just as applicable as the start-of-the-art complete-history algorithms, and even outperform them for certain queries. However, more important than this direct comparison, our investigation motivates and lays the groundwork for the future study of adaptive techniques, and their application to challenges such as on-the-fly impact analysis at GitHub-scale.
Secure Guarded Commands

We develop a lightweight approach to information flow control that interacts with the use of cryptographic schemes. The language is a version of Dijkstra’s Guarded Commands language extended with parallelism, communication and symmetric cryptography. Information flow is modelled using security labels that are sets of hashed symmetric keys expressing the capabilities needed for access to data. In essence, encryption is used to encapsulate the protection offered by the information flow policy. We develop a type system aimed at tracking explicit, implicit, bypassing and correlation flows arising due to the parallel processes and the internal non-determinism inherent in Guarded Commands. The development is facilitated by the parallel processes having disjoint memories and is illustrated on a multiplexer scenario previously addressed using content-dependent information flow policies.
An Integrated Medical Image Compression Approach With Deep Learning And Binary Plane Difference

Image coding is treated as one of the important factor in multimedia communication and storage systems. In recent years with the rapid improvement in the usage of mobile internet there is a huge demand for developing a faster transmission of the multimedia data. Medical image compression has received greater importance due to its heavy demand for decreasing its size without disturbing the quality of it. Binary plane technique has attained attention in recent time due to its capability of compressing the image both in lossy and lossless modes and achieving higher quality than existing JPEG and SPIHT methods. In this paper, an effective approach of extracting the region (ROI) and non-region (NROI) of interest from MR brain images and then compress the ROI in loss less mode and NROI in lossy mode.
A Quantum Annealing-Based Approach to Extreme Clustering

Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing. We prove that this method yields optimal clustering assignments under a separability assumption, and show that the generated clustering assignments are of comparable quality to those of assignments generated by common clustering algorithms, yet can be obtained a full order of magnitude faster.
Recognition of Kannada Characters in Scene Images using Neural Networks

In recent years, there has been significant progress in recognition of text from scene images. However, very less work is done for Kannada language. The focus of this paper is to identify Kannada character set. Researchers have purported a number of deep learning procedures for natural scene text detection and recognition. In this work, basic transfer learning neural network classification models are trained with 1700 scene Kannada character images extracted from Chars74K standard dataset, and labelled individual folders as per character names. Stochastic gradient descent solver with different epoch, learning rate and momentum is applied. Also, the performance of Alex net model is improved by inducing a batch normalization layer in the hidden layers. The results show that for characteristic features of Kannada text, the combination of Vgg19 network model used with higher split ratio and stated training options and values gave 100% accuracy. Also, the modified Alex net model performed better with accuracy up to 96 percent and lower error rate of 0.0134.
Immersive Virtual Reality for the Assessment and Training of Spatial Memory: Feasibility in Individuals with Brain Injury

Immersive Virtual Reality (VR) shows promise for cognitive diagnostics and rehabilitation as it can present individuals with cognitive impairment with realistic, life-like environments, and allows to precisely record behavioral performance to infer indicators of cognitive processes. The aims of our study were to (1) determine the feasibility in using immersive VR in individuals with acquired brain injury, and detect limits in its applicability, and (2) estimate the extent to which the immersion, sense of presence, usability and general motivational aspects, and side effects affect users' experience. To that end, a novel VR task, the immersive Virtual Memory Task (imVMT), was developed and applied to measure the spatial memory. Preliminary data will be discussed with a focus on feasibility.
YOLOv3 as a Deep Face Detector

Face detection is a crucial step for several applications including surveillance, human-machine interaction, and IoT. Robustness to occlusion, pose, scale, and illumination changes are the key issues in all of these systems. After the success of CNNs in object detection, face detection has been dominated by the CNN-based methods. This paper proposes a face detector designed based on a recently introduced real time deep object detector, YOLOv3. In particular YOLOv3 network is trained as a face detector and a new model file is generated. Performance evaluation reported on WIDER data base demonstrate that the developed face detector, YOLOv3-face, improves robustness to occlusion and pose changes and it is capable of detecting faces greater than 15 pixels. Performance of YOLOv3-face compared to top 14 state-of-the-art trackers is reported in terms of precision-recall curves. It is concluded that the precision rates achieved by YOLOv3-face are very close to the top 8 trackers at tolerable false detection rates. Moreover, the computational load of YOLOv3-face detector significantly reduces with the used single pass joint optimization scheme.
The Research on Distributed Fusion Estimation Based on Machine Learning

Multi-sensor distributed fusion estimation algorithms based on machine learning are proposed in this paper. Firstly, using local estimations as inputs and estimations of three classic distributed fusion (weighted by matrices, by diagonal matrices and by scalars) as the training sets, three distributed fusion algorithms based on BP network (BP net-based fusion weighted by matrices, by diagonal matrices and by scalar) are proposed and the selection basis of the number of nodes in hidden layer is given. Furthermore, by using local estimations as inputs and centralized fusion estimation as training set, another recurrent net-based distributed fusion algorithm is proposed, in the case that neither true states nor cross-covariance matrices is available. This method is not limited to the linear minimum variance (LMV) criterion, so its accuracy is higher than the classical three distributed fusion algorithms. A radar tracking simulation verifies the effectiveness of the proposed fusion networks.
Personalized Tutoring System for Elearning

Educational data mining is an advanced interdisciplinary research which is concerned for developing methods to analyze the performance of the students in order to improve the quality of learning. In this study, the academic, non-behavioral and behavioral features are used for analyzing the performance of the students. The students were formed as clusters based on their similar characteristics for providing remedial measures to improve their academic achievement. Finally from the output obtained, Personalized Tutoring System is used for customizing instruction based on the needs of individual learner cluster. The Knowledge based tutor will suggest appropriate e-learning resources from the resource pool.
Research on Multiple UAVs Cooperative Collision Prevention

When multiple UAVs fly missions in the same area, it is inevitable that they will collide with each other. In this paper, the collision prevention problem of multiple UAVs in the air is studied, and the model of two UAVs flying in opposite direction is established. Geometric method is used to obtain the distance between UAV flights, by adjusting the speed and Angle of flight between drones, the minimum distance between them can be changed to avoid collisions. In this paper, the shortest distance between two UAVs is changed through simulation analysis, so that multiple UAVs can avoid collisions during flight and effectively reduce the occurrence of collisions.
Risk Management of Debtor Information System At Bank XYZ Using OCTAVE Allegro Method

Risk assessment as part of the risk management of the debtor information system is conducted to assess how possible threats and vulnerabilities to the debtor's information system and assets. This study aims to analyze risk on the debtor information system at Bank XYZ, while the evaluation and analysis process is more focused on the management of critical assets owned by the bank. The analysis was carried out using the OCTAVE Allegro (Operationally Critical Threat, Assets and Vulnerability Evaluation) method. It is expected that this analysis will get the final results in the form of recommendations about the steps that must be taken to protect and manage the debtor information system critical assets at Bank XYZ.
Algorand: A Better Distributed Ledger

Blockchain is the most rapidly improving technology in distributed computing. It has come a long way from being a new paradigm which is gaining momentum in the ledger technology. Bitcoin being the first implementation of distributed ledger brought in many advantages and disadvantages. Later came the applications of the proof-of-stake algorithms into the ledger technology. All of these performed well except that each did not provide a secure way of distributed computing. Thus, researchers brought in a new concept of the cryptographic ledgers which used the whole new Byzantine Agreement protocol and the technology got to be known as the Algorand. It scaled the databases and data warehouses according to the consensus and brought around the transactions very well bringing in more advantages from its applications. The various technologies and protocols which were previously used before algorand are discussed to provide an idea as to what lead to the development of the most evolving technology in the Block- chain. One application of the algorand which is cryptographic sortition algorithm is explained for knowing of the protocols used and the efficient performance provided byalgorand.
Parallel Gym Gazebo: a Scalable Parallel Robot Deep Reinforcement Learning Platform

Deep reinforcement learning is making advances in robotics with the platforms of realistic environment simulation. However, as shown in this paper, the realistic simulation introduces vast time cost which is the bottleneck of the learning procedure. To solve this problem generally, we propose a parallel reinforcement learning platform which follows the master-slave principle and integrates learning programs with multiple distributedrobot simulators. The platform is intrinsically scalable and requires no modification to existing serially designed learning environments or algorithms. Experimental results demonstrate that our platform significantly accelerates the learning progress of robots, in direct proportion to the parallel scale. The parallelism also brings richer exploration and sampling, enhancing the performance of deep reinforcement learning algorithms compared with existing serial platforms.
Overview and analysis of methods and models for data structuring, storage and processing in the Internet

This paper aims to review and analyze known models and methods for data structuring, storing and processing in the Internet. A SWOT analysis has been made to look at the strengths and weaknesses of known models.
Application of Extreme Learning Machine in Detecting Auto Insurance Fraud

This paper deals with developing a novel methodology for detecting anomalous claims in auto insurance records by a neural network based Extreme Learning Machine (ELM). Initially, the raw dataset has undergone a preprocessing proce-dure and divided into the training, validation and testing sets. A pool of trained ELM classifiers is then generated by using different combinations of ELM parameters on the train set. The best ELM model is then selected by subjecting the validation set upon the trained models. Afterwards, the testing set is fed to the validated model for determining the nature of the insurance claims - legitimate/malicious. The performance of the model is demonstrated by extensive tests carried out on a widely used auto insurance dataset.
MMLT: A mutual multilevel trust framework based on trusted third parties in multicloud environments

Summary In this article, a mutual multilevel trust framework is proposed, which involves managing trust from the perspective of cloud users (CUs) and cloud service providers (CSPs) in a multicloud environment based on a set of trusted third parties (TTPs). These independent agents are trusted by CUs and CSPs and distributed on different clouds. The TTPs evaluate the CUs' trustworthiness based on the accuracy of feedback ratings and assess the CSPs' trustworthiness based on the quality of service monitoring information. They are connected themselves through the trusted release network, which enables a TTP to obtain trust information about CSPs and CUs from other clouds. With the objective of developing an effective trust management framework, a new approach has been provided to improve trust‐based interactions, that is, able to rank the trusted cloud services (CSs) based on CU's priorities via fuzzy logic. Fuzzy logic is applied to manage the different priorities of CUs, all the CUs do not have the same priorities to use trusted CSs. Customizing service ranking allows CUs to apply trusted CSs based on their priorities. Experiments on real datasets well matched the analytical results, indicating that our proposed approach is effective and outperforms the existing approaches.
Analysis of K-Means Clustering Algorithm: A Case Study Using Large Scale E-Commerce Products

E-commerce has become a crucial platform consists a large database of products with billions number of retailers and consumers. However, these products are placed into different categories according to the structure of different websites. A clustering analysis using K-Means Clustering algorithm helps in providing an insightful pattern on categories of clustered products. This analysis leads to an automatic classification model to classify the products efficiently. This paper presents a step by step cluster analysis using K-Means clustering to group e-commerce products from the online store website in Malaysia. The results show that the e-commerce products were categorized into three clusters. The most frequent words in each cluster provided a useful insight on the category of the clustered products which were hair and face, oral and pets care products. Hence, K-Means clustering analysis able to group a large data set of e-commerce products effectively.
Image Retrieval Using Fuzzy $k$-NN on Dominant Rotated Local Binary Patterns

Day to day progress in technologies, rapidly magnify the size of image archives and thus process of retrieving desired images from huge image archives is one of active research topic and it has wide range of applications. This process is more difficult when the images are affected by illumination conditions, rotations and spatial resolutions. In this paper, local level, rotation and illumination invariant texture information are computed using dominant rotated local binary pattern (DRLB) for texture and histological datasets and fuzzy k-NN with Canberra distance as kernel is applied on DRLBPs for categorizing the images then divergence among database and query images are measured using Canberra distance. The experimental outcomes provide evidence for the efficiency of proposed approach for texture and histological datasets.
Distant-Supervised Relation Extraction with Hierarchical Attention Based on Knowledge Graph

Relation Extraction is concentrated on finding the unknown relational facts automatically from the unstructured texts. Most current methods, especially the distant supervision relation extraction (DSRE), have been successfully applied to achieve this goal. DSRE combines knowledge graph and text corpus to corporately generate plenty of labeled data without human efforts. However, the existing methods of DSRE ignore the noisy words within sentences and suffer from the noisy labelling problem; the additional knowledge is represented in a common semantic space and ignores the semantic-space difference between relations and entities. To address these problems, this study proposes a novel hierarchical attention model, named the Bi-GRU-based Knowledge Graph Attention Model (BG2KGA) for DSRE using the Bidirectional Gated Recurrent Unit (Bi-GRU) network. BG2KGA contains the word-level and sentence-level attentions with the guidance of additional knowledge graph, to highlight the key words and sentences respectively which can contribute more to the final relation representations. Further-more, the additional knowledge graph are embedded in the multi-semantic vector space to capture the relations in 1-N, N-1 and N-N entity pairs. Experiments are conducted on a widely used dataset for distant supervision. The experimental results have shown that the proposed model outperforms the current methods and can improve the Precision/Recall (PR) curve area by 8% to 16% compared to the state-of-the-art models; the AUC of BG2KGA can reach 0.468 in the best case.