Reviewerly/exHarmony huggingface dataset:
MARISMA-BiDa pattern: Integrated risk analysis for big data

Data is one of the most important assets for all types of companies, which have undoubtedly grown their quantity and the ways of exploiting them. Big Data appears in this context as a set of technologies that manage data to obtain information that supports decision-making. These systems were not conceived to be secure, resulting in significant risks that must be controlled. Security risks in Big Data must be analyzed and managed in an appropriate manner to protect the system and secure the information and the data being handled. This paper proposes a risk analysis approach for Big Data environments, which is based on a security analysis methodology called MARISMA (Methodology for the Analysis of Risks on Information System), supported by a technological environment in the cloud (eMARISMA tool) already used by numerous clients. Both MARISMA and eMARISMA are specifically designed to be easily adapted to particular contexts, such as Big Data. Our proposal, called MARISMA-BiDa, is based on the main related standards, such as ISO/IEC 27,000 and 31,000, or the NIST Big Data reference architecture or ENISA and CSA recommendations for Big Data.
Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents’ health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident’s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.
Security, Privacy, Trust Management and Performance Optimization of Blockchain Technology

Blockchain is a developing technology which provides data storage, secure transactions and establishing trust in an open environment. Blockchain is widely implemented in cryptocurrency systems like Bitcoins, smart contracts, smart grids over IoT devices etc. Blockchain also has wide applications in healthcare, automobile industries, private and public sectors. This growing popularity of Blockchain is luring hackers to perform various cyber-attacks in order to detect vulnerabilities in the applied Blockchain system. A vulnerable block chain system is open for network breaches, data thefts and information manipulations. Therefore, it is important to design a Blockchain system with proper security, privacy and trust management. However, increasing security and privacy in Blockchain reduces its performance because security and privacy policies are overhead to the applied Blockchain system. Therefore, it is also important to optimize the performance of Blockchain system with proper implementation of security and privacy. This chapter discuss the current scenarios of security, privacy and trust issues in Blockchain. The chapter also discuss the proposed solutions regarding these issues. Further, the chapter discuss the performance analysis on increasing security and privacy in the Blockchain technology and proposed optimizations in the literature. This chapter is then conclude by providing few research directions for improving security, privacy and trust in Blockchain technology while keeping its performance optimized.
HGR: Hand-Gesture-Recognition Based Text Input Method for AR/VR Wearable Devices

Hand gestures, whether static or dynamic, are a field of intense study and have several potential uses for human-computer interaction in real-time systems. Static and dynamic hand gestures are rudimentary ways for human-computer interaction. This paper presents a technique for the text input method which is hand-gesture-recognition based. This compact hand-based text input system is proposed for augmented reality (AR) and virtual reality (VR) devices. To recognize and classify hand gestures, the hand image is captured by a standard camera. After, the hand is segmented using background subtraction, and then the segmented hand gesture is input in the trained neural network for gesture recognition. Finally, hand movements are tracked and recorded using a convex hull algorithm. The corresponding written character is passed to a trained neural network. The proposed architecture is tested and the experimental results are compared with other methods, which showed that the proposed method performed better than traditional methods and achieved 96.12% accuracy, achieved accuracy is overall better than existing methods.
WITHDRAWN: An optimal SLA based task scheduling aid of hybrid fuzzy TOPSIS-PSO algorithm in cloud environment

Cloud computing is an important technology for bulk data storage, resource mobilization and online access to computer services. In the cloud computing platform there are plenty of resources, but the foremost challenge is to allocate tasks. The vital issue in scheduling is how the entire task could be allocated with maximized resource utilization to a resultant virtual machine, existing scheduling algorithms mainly focused on minimization of the task execution time while ignoring the SLA (Service Level Agreement) and QoS (Quality of Service) assurance. This paper thrives to accomplish the total task with eminent resource utilization, minimum migration cost, and minimum utilization of energy. For attaining these objectives, the proposed method utilizes the hybrid algorithm such as Fuzzy-TOPSIS and particle swarm optimization (PSO) approach. Initially the available task and the no. of VMs (virtual machines) are optimized by PSO algorithm. In the cloud, the multi-objective SLA based task scheduling problem is solved by the Fuzzy TOPSIS which uses the weighted sum of energy, cost and execution time as an objective function. Fuzzy TOPSIS method is work positively for many applications and impacts liberally on real world decision making concerns. The proposed approach is investigated based on the distinct evaluation metrics. Our proposed algorithm outperforms the existing approaches and achieves better results in terms of QoS parameters. The implementation had done using JAVA with CloudSim simulator.
FPGA based neural network accelerators

Machine learning (ML) and artificial intelligence (AI) technology are revolutionizing many different fields of study in computer science as well as a wide range of industry sectors such as information technology, mobile communication, automotive, and manufacturing. As more people are using the technology in their everyday life, the demand for new hardware that enables faster and more energy-efficient AI processing is ever increasing. Over the last few years, traditional hardware makers such as Intel and Nvidia as well as start-up companies such as Graphcore and Habana Labs were trying to offer the best computing platform for complex AI workloads. Although GPU still remains the most preferred platform due to its generic programming interface, it is certainly not suitable for mobile/edge applications due to its low hardware utilization and huge power consumption. On the other hand, FPGA is a promising hardware platform for accelerating deep neural networks (DNNs) thanks to its re-programmability and power efficiency. In this chapter, we review essential computations in latest DNN models and their algorithmic optimizations. We then investigate various accelerator architectures based on FPGAs and design automation frameworks. Finally, we discuss the device's strengths and weaknesses over other types of hardware platforms and conclude with future research directions.
Exciting-Inhibition Network for Person Reidentification in Internet of Things

Person reidentification (re-ID), which aims at recognizing the pedestrians captured by multiple nonoverlapping cameras, has attracted more interest due to its significant and potential application in the Internet of Things like intelligent visual surveillance. However, person reID is still a challenging problem in the situations of various pose, similar appearances, partial occlusion, etc. To handle these obstacles, in this article, we investigate an innovative exciting-inhibition network (EINet) that is a two-branch network composed of the exciting branch and the inhibition branch. The channel-spatial attention block that recalibrates the relationship between channels and highlights features at different spatial positions is used in the exciting branch. A novel Soft Batch DropBlock that randomly selects a continuous region of the intermediate feature maps at the same location is applied in the inhibition branch to inhibit the trivial by an inhibitive mask and reinforce learning the remaining regions. We integrate the comprehensive features from both branches for evaluation and show the performance of EINet intuitively using the visualization method. Abundant experiments demonstrate the state-of-the-art performance by comparing with the previous methods on three popular person re-ID benchmarks. For example, our method obtains 95.64% Rank-1 and 88.75% mean average precision (mAP) on Market-1501, and 77.00% Rank-1 and 74.51% mAP on CUHK03-Detect in the single query mode, respectively.
Optimized Deep Reinforcement Learning Approach for Dynamic System

Reinforcement learning methods provide significant and impressive improvements in artificial intelligence studies in recent years, especially in Atari and Go games. This development attracts the attention of scientists who try to understand how people learn. In addition, the biggest advantage of reinforcement learning over other learning algorithms is that it does not require any prior data. This feature distinguishes Reinforcement Learning from others. Reinforcement Learning is an approach in which smart programs work in a certain or uncertain environment to constantly adapt and learn based on scoring. The feedback process is also known as a reward or called a penalty. Given Agents and environment, it is determined which action to take. In this study, we apply the success of Deep Q-Learning (DQL) algorithm, one of the model-free based deep reinforcement learning algorithms used in the literature, on the CartPole problem. We also offer a different method to improve the agent's success during the training phase. We present an optimized DQL algorithm using a function that updates the weights of the neural network at every step.
Keeping Up with the Trends: Analyzing the Dynamics of Online Learning and Hiring Platforms in the Software Programming Domain

Abstract The Fourth Industrial Revolution has considerably sped up the pace of skill changes in many professional domains, with scores of new skills emerging and many old skills moving towards obsolescence. For these domains, identifying the new necessary skills in a timely manner is a difficult task, where existing methods are inadequate. Understanding the process, by which these new skills and technologies appear in and diffuse through a professional domain, could give training providers more time to identify these new skills and react. For this purpose, in the present work, we look at the dynamics between online learning platforms and online hiring platforms in the software programming profession, a rapidly evolving domain. To do so, we fuse four data sources together: Stack Overflow, an online community questions and answers (Q&amp;A) platform; Google Trends, which provides online search trends from Google; Udemy, a platform offering skill-based Massively Open Online Courses (MOOCs) where anyone can create courses; and Stack Overflow Jobs, a job ad platform. We place these platforms along two axes: i) how much expertise it takes, on average, to create content on them, and ii) whether, in general, the decision to create content on them is made by individuals or by groups. Our results show that the topics under study have a systematic tendency to appear earlier on platforms where content creation requires (on average) less expertise and is done more individually, rather than by groups: Stack Overflow is found to be more agile than Udemy, which is itself more agile than Stack Overflow Jobs (Google Trends did not prove usable due to extreme data sparsity). However, our results also show that this tendency is not present for all new skills, and that the software programming profession as a whole is remarkably agile: there are usually only a few months between the first Stack Overflow appearance of a new topic, and its first appearance on Udemy or Stack Overflow Jobs. In addition, we find that Udemy’s agility has dramatically increased over time. Our novel methodology is able to provide valuable insights into the dynamics between online education and job ad platforms, enabling training program creators to look at said dynamics for various topics and to understand the pace of change. This allows them to maintain better awareness of the trends and to prioritize their attention, both on the right topics and on the right platforms.
Preordering Encoding on Transformer for Translation

The difference in word orders between source and target languages is a serious hurdle for machine translation. Preordering methods, which reorder the words in a source sentence before translation to obtain a similar word ordering with a target language, significantly improve the quality in statistical machine translation. While the information on the preordering position improved the translation quality in recurrent neural network-based models, questions such as how to use preordering information and whether it is helpful for the Transformer model remain unaddressed. In this article, we successfully employed preordering techniques in the Transformer-based neural machine translation. Specifically, we proposed a novel preordering encoding that exploits the reordering information of the source and target sentences as positional encoding in the Transformer model. Experimental results on ASPEC Japanese-English and WMT 2015 English-German, English-Czech, and English-Russian translation tasks confirmed that the proposed method significantly improved the translation quality evaluated by the BLEU scores of the Transformer model by 1.34 points in the Japanese-to-English task, 2.19 points in the English-to-German task, 0.15 points in the Czech-to-English task, and 1.48 points in the English-to-Russian task.
Bringing Empty Rooms to Life for Casual Visitors Using an AR Adventure Game

This article reports on a heritage engagement project called Skullduggery at Old Government House. It offers an approach to augmented reality design based on adventure games as a solution to the problem of “empty rooms” encountered by casual visitors to cultural heritage sites such as historic buildings. Many cultural heritage sites depend on didactic panels and perhaps a few tangible items to communicate the history of a heritage location to casual visitors, however, providing a sense of the “spirit” of a site is harder to present to visitors who may only be passing through. The project is a response to this problem in Old Government House, Brisbane. It is inspired by a theatrical tour of the house and the potential of narrative games to re-create the tour's experiential aspects and engagement with a sense of history and place for casual visitors.
Clifford Geometric Algebra-Based Approach for 3D Modeling of Agricultural Images Acquired by UAVs

Three-dimensional image modeling is essential in many scientific disciplines, including computer vision and precision agriculture. So far, various methods of creating three-dimensional (3D) models have been considered. However, the processing of transformation matrices of each input image data is not controlled. Site-specific crop mapping is essential because it helps farmers determine yield, biodiversity, energy, crop coverage, etc. Clifford Geometric Algebraic understanding of signaling and image processing has become increasingly important in recent years. Geometric Algebraic treats multi-dimensional signals in a holistic way to maintain relationship between side sizes and prevent loss of information. This article has used agricultural images acquired by unmanned aerial vehicles (UAVs) to construct three-dimensional models using Clifford geometric algebra. The qualitative and quantitative performance evaluation results show that Clifford geometric algebra can generate a three-dimensional geometric statistical model directly from drones' RGB images. Through peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual comparison, the proposed algorithm's performance is compared with latest algorithms. Experimental results show that proposed algorithm is better than other leading 3D modeling algorithms.
Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.
Interpreting Video Features: A Comparison of 3D Convolutional Networks and Convolutional LSTM Networks

A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what the networks have based their classification on. However, interpretability for deep video architectures is still in its infancy and we do not yet have a clear concept of how to decode spatiotemporal features. In this paper, we present a study comparing how 3D convolutional networks and convolutional LSTM networks learn features across temporally dependent frames. This is the first comparison of two video models that both convolve to learn spatial features but have principally different methods of modeling time. Additionally, we extend the concept of meaningful perturbation introduced by [] to the temporal dimension, to identify the temporal part of a sequence most meaningful to the network for a classification decision. Our findings indicate that the 3D convolutional model concentrates on shorter events in the input sequence, and places its spatial focus on fewer, contiguous areas.
A Model for Classifying Network Traffic Using Reinforcment Learning

Recently, more attention has been paid to the task of traffic classification. This is due to the fact that, in the era of active development of digital systems, it is necessary to pay particular attention to the issues of application security and the protection of user data). There are many different algorithms and approaches with their own advantages and disadvantages, which differ from each other in processing speed, field of applicability and accuracy of the final results. One of the most actively developing areas at the moment is the use of various machine learning algorithms. This direction also has some disadvantages. In particular, there is a problem in the classification of completely new traffic, which was not during training, the need for periodic retraining when changing the characteristics of network protocols or other important parameters for any particular task. This article describes a network traffic classification model using reinforcement learning methods.
Secure Regenerating Codes for Reducing Storage and Bootstrap Costs in Sharded Blockchains

Blockchain is a distributed ledger with wide applications. Due to the increasing storage requirement for blockchains, the computation can be afforded by only a few miners. Sharding has been proposed to scale blockchains so that storage and transaction efficiency of the blockchain improves at the cost of security guarantee. This paper aims to consider a new protocol, Secure-Repair-Blockchain (SRB), which aims to decrease the storage cost at the miners. In addition, SRB also decreases the bootstrapping cost, which allows for new miners to easily join a sharded blockchain. In order to reduce storage, coding-theoretic techniques are used in SRB. In order to decrease the amount of data that is transferred to the new node joining a shard, the concept of exact repair secure regenerating codes is used. The proposed blockchain protocol achieves lower storage than those that do not use coding, and achieves lower bootstrapping cost as compared to the different baselines.
Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records

Extensive data available in electronic health records (EHRs) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can be accomplished only when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent.Our aims were to create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies and also to examine the presence and absence of these data in relation to patient characteristics.We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity.The asthma severity computable phenotype performs as expected in comparison with national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present.We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs.