Reviewerly/exHarmony huggingface dataset:
Use of Augmented Reality for Computational Thinking Stimulation through Virtual

Studies with augmented reality show that while the technology helps the student learn, it may end up disturbing other points, showing that more research is needed with the use of augmented reality. Also, a topic that is growing within education is Computational Thinking, which says that the thinking like computer scientists is necessary for the 21st century and should be disseminated mainly in schools during basic education. In the face of this, this work created an augmented reality android application that tracks physical tiles and creates 3D maps for the user, also giving a character that can be programmed to walk on this created map, simulating a programming toy. Finally, tests carried out with the application have shown that augmented reality currently has performance problems when multiple targets are used, so, devices with low processing have problems at map tracking. These issues affected the tests with the children, which made it difficult for them to track the map like they wanted, but they still liked the app even after it. That shows that the acceptance of the technology by the children is good, which can help developing Computational Thinking, but the technology limitation is still a problem. These obstacles while currently hindering the use of augmented reality in education may in the future be irrelevant due to the constant advancement of technology. This paper's main contribution is showing one possibility of using AR with Computational Thinking and what needs to evolve for it to be used by the usable by the children.
Integrated Genetic Algorithm and Artificial Neural Network

Genetic algorithm is used to randomly search the feasible region in global optimization problems. The search mechanism of genetic algorithm needs multiple iterations to evolve current solutions to better solutions for the objective function. This paper explores the possibility of integrating artificial neural network with genetic algorithm to reduce the computation time and improve the performance of this meta-heuristic. In the proposed technique, artificial neural network performs as a brain for the search engine that tries to learn the importance and relations between input and output variables and guide the genetic algorithm for the better solutions. This is implemented by modifying two best solutions of each population using the information stored in artificial neural network. These new solutions are replaced with two worst solutions and get chance to participate in crossover process. The proposed technique is tested using common benchmark problems and the results showed that this hybrid algorithm can find the better solutions in shorter time compared to the traditional genetic algorithm.
A CSA-Secure Multi-Secret Sharing Scheme in the Standard Model

In a multi-stage secret sharing (MSSS) scheme, a dealer D share l secrets among the participants in such a way that secrets are reconstructed one by one in different stages. Recently, we proposed a secure MSSS in the standard model. In this paper, we present another MSSS scheme that has shorter public outputs and better security relation with the underlying symmetric encryption in the standard model.
Performance analysis of an in-situ data centre

The present study aims to carry out a performance analysis of a newly designed and built data centre and further identify/explore the energy conservation opportunities and operational issues. Investigations are carried out based on complete one-year data of 2017 captured from Data Centre Infrastructure Management (DCIM). It comprises of capturing energy consumptions in various subsystems/systems, temperature, humidity, air/water flow rates etc. For each parameter, data for every 5 min is collected resulting in 105,120 number of points. The data is cleaned, processed and analysed based on MATLAB codes, developed for this purpose. The variation in total DC facility energy consumption throughout the year is mainly due to cooling load energy consumption which increases from winter to summer. DC runs 63% time of the year on mechanical mode of operation when PUE goes above 1.4 up to a maximum of 1.6. Remaining time of the year DC runs at lower PUE values up to a minimum of 1.1 due to free-cooling mode of operation. The annualized PUE of the data centre is 1.38. The current data shows scope of improvement of rack inlet temperatures for further efficient operation of system.
An Attack Vector Evaluation Method for Smart City Security Protection

In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.
Low-cost, high-efficiency hardware implementation of smart traffic light system

The following topics are dealt with: invertors; switching convertors; machine control; convolutional neural nets; learning (artificial intelligence); stators; DC-DC power convertors; circuit simulation; Internet of Things; torque.
End-to-end information flow security for web services orchestration

Multi-party interactions in Web Service (WS) composition are hard to managed and difficult to design and verify, especially while end-to-end information flow security (IFS) must be respected. Usually, IFS is guaranteed for a data originator and ultimate recipient WS, however inter-organizational service compositions involve partners that do not necessarily share the same level of security, especially for data they do not generate nor process themselves. In this paper, we focus on the non-interference property and we present a correct-by-construction approach to build orchestrated WSs with multi-party interactions. A key ingredient of this approach is to present the system composition at an abstract level as a component-based model where the IFS verification and the system configuration are considered at early stage. When this configuration is validated, that is, no security leak is detected, we automatically generate orchestrator components that handle IFS. Afterwards, we generate accordingly BPEL processes where the IFS constraints are enforced as security WS-policies in the BPEL description of services. We develop a set of tools that automate the approach and we validate the effectiveness of our approach with well known Web service use cases.
Using Part of Speech Tagging for Improving Word2vec Model

Word2vec is an efficient word embedding model that convert words to vectors by considering syntax and semantic relationship between words. In this paper, an extension of the two approaches of word2vec model is proposed. The proposed model considers part of speech tagging of the words when exploring the probability of prediction output word given the input. Considering part of speech tagging provides deeper semantic meaning for words when training the model. Thus, the quality of the generated vectors becomes high and more representative. In addition, the proposed model equips the user with the ability to query about the words and their part of speech tagging. In this case, the words that have the same surface but different meaning will have different vector representations. This model can be used in all languages including English and Arabic languages. The focus of this paper is on Arabic language. The experiments are performed on OSAC datasets which consists of 22,429 documents. Several pre-processing stages include using Farasa stemmer take place. Moreover, part of speech tagging of the word is determined using Farasa toolkit.
A Constraint-Based Model for the Frequent Itemset Hiding Problem

This paper introduces a novel constraint-based hiding model to drastically reduce the preprocessing overhead that is incurred by border-based techniques in the hiding of sensitive frequent itemsets. The proposed model is solved by an efficient constraint-based mining algorithm that pushes a conjunction of antimonotone constraints into an Apriori-like algorithm, for inducing the support theory of non-sensitive frequent itemsets along with its negative border. The patterns induced by the constraint-based mining algorithm can be used in border-based hiding algorithms to construct a sanitized version of the original database, where the sensitive knowledge is concealed. The efficiency of the constraint-based mining algorithm is evaluated on real and synthetic datasets.
The Comparison Between 3D Studio Max and Blender Based on Software Qualities

The purpose of this article is to determine the comparison of 3D Studio Max and Blender animation software in terms of software quality in accordance with ISO 9126. The quality matrix compared in this study is usability and efficiency. This type of research is descriptive comparative. The research subjects were taken by purposive sampling, consist of 3D Studio Max 3-dimensional users and Blender animation software, as well as experienced animation lecturers. Data collection techniques used was questionnaires, interviews, and documentation. Data analysis uses the ISO 9126 matrix based on the research stages and ends with a checking technique for processed questionnaire data. This study found that: 3D Studio Max animation software, although widely used by animators, the quality is still below Blender, which is still rarely used by animators. This is because the use of Blender animation software itself is very easy to facilitate its users and Blender's tools and facilities are more complete than 3D Studio Max. But in the rendering process, Blender still takes a long time, while 3D Studio Max does not require a long time in the rendering process. This is what makes 3D Studio Max still popular for use among animators.
An Urban Pigeon-Inspired Optimiser for Unconstrained Continuous Domains

A new metaheuristic is proposed for optimisation of unconstrained continuous domain cost functions, taking inspiration in the foraging behaviour of populations of urban pigeons in the public parks of large cities. We discuss the ideas underlying the computational model of the resulting optimiser, particularly its exploration vs exploitation strategies, and verified the applicability of the algorithm on a number of benchmark functions, obtaining error deviations from the optimum smaller than 0.1%. Despite the simplicity of the model, its effectiveness is comparable to other recently proposed swarm bird-inspired techniques. Besides, we discuss ideas to enhance the model towards the context of non-stationary function optimisation, a natural challenge pigeons undertake during their daily-basis food discovering journeys.
Situation‐centered goal reinforcement of activities of daily living in smart home environments

Abstract Older adults with early‐stage dementia (ED) can experience confusion or lack clarity when performing routine activities of daily living (ADLs). These circumstances predispose the older adult to safety‐critical and often risky situations. A safety‐critical risky situation is one that constitutes a hazard. To support independent living, a sensor‐laden smart environment can be employed to mitigate such hazards. In this paper, we propose a situation‐centered goal reinforcement framework that supports older adults with ED in their decision making, and guides them through their ADL in order to fulfill their goal or intention and avoid hazards. First, we employ an LSTM (Long Short‐Term Memory) model to infer the current goal of the resident, using their previously observed normal ADL patterns. Secondly, we identify potentially risky situations in their currently observed goal path. We then incorporate a situ‐learning agent (SLA) that helps an inhabitant to make the right decision, thus preventing adverse events while guiding her through the task sequence that leads to her goal state. In addition, we use a naïve agent to simulate episodes of confusion similar to those that might be experienced by older adults with ED. We validated our method against an open‐source dementia dataset (Quesada et al., 2015) by considering four types of ADLs as case studies. We achieved an accuracy of 90.1% for our goal inference model, higher than the accuracies reported by related studies. We also reported other metrics including precision, recall and f1‐score for goal inference model. Finally, SLA's action recommendations relevance was evaluated accordingly.
Training a multilayer network with low-memory kernel-and-range projection

Recently, a learning method based on the kernel and the range space projections has been proposed. This method has been applied to learn the multilayer network analytically with interpretable relationships among the weight matrices. However, the bulk matrix based formulation suffers from a high memory demand during network learning. In this study, a low-memory resolution is proposed to address the memory demanding problem. Essentially, the bulk matrix operations are implemented by a low-memory formulation in which only one training sample is processed at a time. Such a formulation is proved to be mathematically equivalent to the original batch learning version. We also point out that the rounding errors in computing systems could hinder the performance of the proposed formulation. This formulation is then robustified by introducing a regularization technique with the cost of an additional but negligible memory usage. Our experiments show that the proposed low-memory resolution can indeed tremendously reduce the memory consumption while maintaining reasonably good performances in both regression and classification tasks.
Genetic Algorithm Based Parallel K-Means Data Clustering Algorithm Using MapReduce Programming Paradigm on Hadoop Environment (GAPKCA)

Data clustering algorithm has been receiving considerable attention in many application areas such as data mining, document retrieval, image processing and pattern classification. A hybrid data clustering algorithm using the combination of genetic algorithm (GA) with a popular variant of K-Means clustering algorithm, parallel k-Means clustering algorithm (PKCA) is proposed in this paper. The objective of the proposed algorithm is to combine the search process of GA to generate new data clusters and apply parallel K-Means to further speed up the quality of the search process during clusters formation. The proposed approach is implemented using the popular MapReduce programming model on Hadoop framework. Experiments were conducted with multiple synthetic datasets to evaluate the performance of the proposed algorithm. Results show that the proposed algorithm was able to speed up document clustering process by 0.54 s on average and outperformed PKCA. Data analysts in marketing and finance, telecommunication and transport companies and researchers in academia can use this algorithm to make sense out of their huge volume of data.
Supervoxel Graph Cuts: An Effective Method for GGO Candidate Regions Extraction on CT Images

In this article, a method to reduce artifacts on temporal difference images is introduced. The proposed method uses a nonrigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local matching, and three-dimensional (3D) elastic matching are performed on the current and previous images, and an initial temporal subtraction image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and use supervoxel and graph cuts to segment region of interest in the 3D images. For each extracted region, a support vector machine is used to reduce the oversegmentation. The voxel matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact.
A batch informed sampling-based algorithm for fast anytime asymptotically-optimal motion planning in cluttered environments

Practical applications favor anytime asymptotically-optimal algorithms that find and improve an initial solution toward the optimal solution as quickly as possible due to the algorithms may be terminated at any time. We present Batch-to-batch Informed Fast Marching Tree (BBI-FMT*), an anytime asymptotically-optimal sampling-based algorithm that is designed for solving complex motion planning problems. The proposed algorithm has the ability to fast find an initial low-cost solution by the batch sampling-based incremental search and the “lazy” optimal search, then it employs the batch informed sampling-based incremental search and the anytime optimal search to quickly improve the tree and achieve the optimal solution. The proposed anytime optimal search strategy integrates the “lazy” and “non-lazy” optimal search to efficiently improve the tree to the minimum-cost spanning tree in cluttered environments. This paper theoretically analyzes the proposed algorithm in depth and evaluates it by numerical experiments under a few challenging scenarios. The experimental results show that BBI-FMT* outperforms the state-of-the-art algorithms in the self-adaptability, robustness, convergence rate, and success rate of the planning. The proposed algorithm can be widely applied to intelligent robots with expert systems to improve the efficiency and stability of the motion planning and navigation modules which are the core modules in the expert systems.
An Emotional Virtual Character: A Deep Learning Approach with Reinforcement Learning

We have developed an emotional agent based on reinforcement learning. The agent is a virtual character who lives in a three-dimensional maze world. Thus, we found that an emotional engine can induce the behavior of an agent after training. The training algorithm was the policy based reinforcement learning. Experiments showed that the emotional engine and reinforcement learning produced coherent behaviors. This is an important step towards believable autonomous virtual characters.
An Ultrahigh-Speed Object Detection Method With Projection-Based Position Compensation

A hardware-oriented ultrahigh-speed object detection and measurement method based on the descriptor of histogram of oriented gradient (HOG) and projection information is proposed in this article. The frame rate is up to 10 000 frames/s through a large amount of algorithm optimization and field-programmable gate array implementation. The input parallelism is 64 pixels characterized with 768 bits per clock cycle, and the stride between detection windows is fixed at 16 pixels through $4\times $ up-clocking operation in the existing algorithm proposed by Li et al . The large intervals among adjacent detection areas lead to lower positioning precision with only HOG descriptors. Addressing this problem, the pixel values projection information in both the horizontal and vertical directions within each detection window is used for compensation. The accuracy of object location is improved through a combination of both image features, and the deviation between the measured and actual values of the proposed algorithm is reduced to less than 30% of the previous one. Besides, the rotating speed of a fan is measured as 1150.8 r/min using the detected coordinate values with the proposed method.
ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing

Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by the inconsistent annotation quality. In this paper, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues, which consists of a pre-processing module for data augmentation, a new neural network architecture, ANT-UNet, and a FCCRF inference module. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 4-23% accuracy improvement than other commonly used segmentation methods. Moreover, the proposed architecture is hardware-friendly and can be incorporated with a GPU acceleration flow to reach 24-128× speed-up.
Image Compression Using Hybrid Approach and Adaptive Scanning for Color Images

This paper aims at the design of an algorithm for image compression. For color images RGB components are extracted and converted to YCbCr as a preprocessing step and then block based Hybrid Transform is performed. After this preprocessing step image is quantized and projected lossless scanning is done to ensure the excellent quality of the recovered image. In this paper adaptive scanning approach is anticipated to compress the length of codeword to be transmitted and thus achieving higher compression ratios. Principle behind image compression is to reduce the number of bits required per pixel.
Design Smell Detection and Analysis for Open Source Java Software

Software design smells have gained significant importance in recent years since those directly lead to the increase of design debts and drastically affect software quality. Although the impact of design smells is manifold, techniques to detect design smells using both rule based and data mining approaches have been explored to a limited extent. This research aims to provide a tool which uses software metrics as a guide to detect smells and also deploys Spectral Clustering to mine the software repositories and group similar smells. The tool has been partially implemented till now and initial experiments on 2,59,509 Lines of Code (LoC) covering 3,306 classes of real life open source Java software show 2,220 occurrences of four types of design smells.
ABR streaming with separate audio and video tracks

Adaptive bitrate (ABR) streaming is the predominant approach for video streaming over the Internet. When the audio and video tracks are stored separately (i.e., in demuxed mode), the client needs to dynamically determine which audio and which video track to select for each chunk/playback position. Somewhat surprisingly, there is very little literature on how to best mesh together audio and video adaptation in ABR streaming. In this paper, we first examine the state of the art in the handling of demuxed audio and video tracks in predominant ABR protocols (DASH and HLS), as well as in real ABR client implementations in three popular players covering both browsers and mobile platforms. Combining experimental insights with code analysis, we shed light on a number of limitations in existing practices both in the protocols and the player implementations, which can cause undesirable behaviors such as stalls, selection of potentially undesirable combinations such as very low quality video with very high quality audio, etc. Based on our gained insights, we identify the underlying root causes of these issues, and propose a number of practical design best practices and principles whose collective adoption will help avoid these issues and lead to better QoE.