Reviewerly/exHarmony huggingface dataset:
Compression of Large-Scale Image Dataset using Principal Component Analysis and K-means Clustering

Digital images, being on the verge of its utmost popularity encompasses plenty of applications and as such are generated at an unprecedented rate. These digital form of data are often found with redundant information. Applications that require a bulk amount of images to be processed, turn out to be high regarding computational complexity. Needless to say, it leads to inefficient storage utilization. In this paper, a hybrid approach is applied to compress a large-scale image data-set by combining two popular algorithms: Principal Component Analysis (PCA) and K-means. This paper works with a view to diminishing the redundant information by implementing dimensionality reduction followed by color quantization. The PCA is used to project the data onto a lower dimensional space with retaining as maximum variance as possible. The K-means algorithm is used to restrict the distinct number of colors to represent an image by means of clustering the data together. The results obtained from the proposed method is compared with the results obtained from implementing PCA and K-means clustering algorithms independently, where the proposed method provides with a better compression ratio.
Edmodo as a media for history learning in the digital era

This study aims to analyze the critical thinking abilities of students in history learning using Edmodo media. This research is a survey research involving 165 students and 5 educators in upper secondary schools in Jember. The instruments used were questionnaires, interview guides and observations using quantitative and qualitative data analysis. Analysis of data using SPSS 23. The results of the questionnaire showed that the level of cognitive and psychomotor abilities in history learning showed a result of 32.1%. The results of the questionnaire distributed to educators showed that 80% had not used Edmodo as a learning media in the current digital era. Interviews and observations show that educators only use media such as power points. Based on these results, the availability of learning media is still in a low level stage, while the application of the 2013 Curriculum guides students to open the horizons of knowledge and skills with communicative, collaborative, critical thinking, and creative processes. Therefore, it is necessary to develop historical learning media using Edmodo, which can be accessed anytime and anywhere according to the needs of students.
Need of e- integrated science teaching material developed using 4S TMD model for science learning and teaching in junior high school

In this article, we explain the needs of E-integrated science teaching materials in junior high school. As technology develops, integrated science learning also needs to be supported by electronic-based teaching materials. Electronic teaching materials included in this research are the materials such as e-books, e-magazine, e-news, e-journal, CD / DVD interactive multimedia flash models, interactive slides and e-learning. we use a qualitative descriptive research. The instruments of data collection used in this study were structured questions and interview. This study was conducted in junior high schools 29 of Bandung with 4 science teachers and 25 seven-grade students. The teachers and students' structure question responses were analyzed descriptively for each item. Most students wanted more interesting, colorful, pictorial, video loading teaching materials. However, some teachers were not very good at using computers so they have not been able to fulfill students' wishes regarding electronic-based teaching materials. The research findings indicate the need to develop electronic-based teaching materials to meet the shortcomings of interesting teaching materials in integrated science learning. The results of this study are the basis for us in developing integrated electronic-based science teaching materials that can be used in science learning and teaching in junior high schools.
A corpus for argument analysis of academic writing: argumentative paragraph detection

Academic writing is a complex task which requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill difficult to master. Aiming to contribute to develop this skill, we present a freely available annotated corpus to support research in argumentation in Spanish. To build it, we elaborated an annotation guide to identify argumentation in paragraphs. The guide also specified how to determine segments of sentences as a claim or premise, and to indicate relations (support or attack) between such segments. Then, an annotated corpus of 300 sections was created. After its construction, the corpus was used to perform an exploratory analysis which aimed to identify and present the amount of argumentation in each section, as well as resulting patterns for argument identification. Hence, we also report an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. The results of the experiments to evaluate argumentative paragraph detection were encouraging. In addition, we discuss a web-based prototype for argument detection in paragraphs to reach the broader academic community of students, instructors and researchers.
Semantic Service Gateway for ECHONET based Smart Homes

With the advent of the IoT, semantic is a potential candidate to enhance the interoperability between heterogeneous devices and applications in smart homes. Additionally, many Ambient Assisted Living (AAL) solutions are being proposed to deal with the population ageing makes the support of semantic for smart homes becomes even more important. This paper proposes a semantic service gateway for the ECHONET Lite, a leading protocol for smart homes in Japan to integrate with the universAAL, a semantic supported AAL platform in Europe. The proposed service gateway was implemented and the feasibility is verified by testing with not only simulator, but also with commercial devices.
Pedestrian counting estimation based on fractal dimension

Counting the number of pedestrians in urban environments has become an area of interest over the past few years. Its applications include studies to control vehicular traffic lights, urban planning, market studies, and detection of abnormal behaviors. However, these tasks require the use of intelligent algorithms of high computational demand that need to be trained in the environment being studied. This article presents a novel method to estimate pedestrian flow in uncontrolled environments by using the fractal dimension measured through the box-counting algorithm, which does not require the use of image pre-processing and intelligent algorithms. Four scenarios were used to validate the method presented in this article, of which the last scene was a low-light surveillance video, showing experimental results with a mean relative error of 4.92% when counting pedestrians. After comparing the results with other techniques that depend on intelligent algorithms, we can confirm that this method achieves improved performance in the estimation of pedestrian traffic.
An Evaluation Ontology Applied to Connected Vehicle Security Assurance

Abstract Connected vehicles have great potential to benefit society, yet create huge challenges. Vehicles, infrastructure and enterprise activities combine to form massively complex systems of systems (SoSs) that are vulnerable to cyber‐attacks. Security is ill‐defined, making it difficult to achieve a consistent, common understanding of security capabilities across the diverse industries that collaborate to develop connected vehicles. Rigorous evaluation is essential for developing strong security assurance cases. This paper contributes a model‐based systems engineering (MBSE) ontology that enables integrated evaluation processes in enterprise SoSs. The Evaluation Ontology allows diverse types of evaluation to be captured in a single integrated model. A connected vehicle security story is presented to demonstrate the value of the approach. Benefits include enhanced business intelligence that can provide a quantifiable, reportable level of confidence in security‐related processes and technologies. Further work will extend the ontology to develop a customisable suite of enabling patterns for security.
Preliminary Evaluation of an Ontology-Based Contextualized Learning System for Software Security

Learning software security is a big challenging task in the information technology sector due to the vast amount of security knowledge and the difficulties in understanding the practical applications. The traditional teaching and learning materials, which are usually organized topically and security-centric, have fewer linkages with learners' experience and prior knowledge that they bring to the learning sessions. Learners often do not associate vulnerabilities or coding practices with programs similar to what they were writing in their previous time. Consequently, their motivation for learning is not touched by conventional methods. The aim of this paper is the presentation of an ontology-based learning system for software security with contextualized learning approaches, and of the results of an initial evaluation using a controlled quasi-experiment in a university learning environment. This system facilitates the contextual learning process by providing contextualized access to security knowledge via real software application scenarios, in which learners can explore and relate the security knowledge to the context they are already familiar with. The experiment results show that the prototyped system with the proposed learning approach not only yields significant knowledge gain compared to the conventional learning approach but also gains better learning satisfaction of students.
Learning Similarity-specific Dictionary for Zero-shot Fine-grained Recognition

In this paper, we study the problem of zero-shot fine-grained recognition. It aims to distinguish unseen subordinate categories through some other seen categories within an entry-level category. We demonstrate the necessity to learn multiple latent dictionaries through joint training with specific set of instances, human-defined attributes and the class labels. A novel approach that is capable of 1) automatically assigning suitable dictionaries for each instance and 2) learning similarity-specific semantic representations for zero-shot fine-grained recognition is proposed. Experimental results on three benchmark datasets demonstrate that the proposed method achieves superior or comparable performance.
hLGP: A Modified Local Gradient Pattern for Image Classification

For image classification, Local Gradient Pattern (LGP) is an adaptive threshold-based feature descriptor which extracts the changes of intensities locally or globally of an image. This threshold is calculated by using Arithmetic Mean (AM) of gradient values of neighboring pixels. Due to using AM, the threshold value often unable to reduce outlier's effect. Hence some of the elements of an image are not identified properly. As a result, the discrimination capacity of LGP comparatively lower than other descriptors for several applications. Above this issue, we introduce a new gradient-based feature descriptor named as modified Local Gradient Pattern (hLGP) to overcome this problem of LGP. This paper shows the effective performance of hLGP on several applications like scene, flower, aerial, event, object image classifications which belong to some popular datasets and also show the experimental results which exhibited that hLG P performs comparatively better than LGP in those datasets.
Coding-Data Portability in Systematic Literature Reviews

Systematic Literature Reviews (SLRs) are increasingly popular to categorize and identify research gaps. Their reliability largely depends on the rigour of the attempt to identify, appraise and aggregate evidences through coding, i.e. the process of examining and organizing the data contained in primary studies in order to answer the research questions. Current Qualitative Data Analysis Software (QDAS) lack of a common format. This jeopardizes reuse (i.e. difficult to share coding data among different tools), evolution (i.e. difficult to turn coding data into living documents that evolve as new research is published), and replicability (i.e. difficult for third parties to access and query coding data). Yet, the result of a recent survey indicates that 71,4% of participants (expert SLR reviewers) are ready to share SLR artifacts in a common repository. On the road towards open coding-data repositories, this work looks into W3C's Open Annotation as the way to RDFized those coding data. Benefits include: portability (i.e. W3C's prestige endorses the adoption of this standard among tool vendors); webization (i.e. coding data becomes URL addressable, hence openly reachable), and data linkage (i.e. RDFizing coding data benefit from Web technologies to query, draw inferences and easily link this data with external vocabularies). This paper rephrases coding practices as annotation practices where data is captured as W3C's Open Annotations. Using an open annotation repository (i.e. Hypothes.is), the paper illustrates how this repository can be populated with coding data. Deployability is proven by describing two clients on top of this repository: (1) a write client that populates the repository through a color-coding highlighter, and (2), a read client that obtains a traditional SLR spreadsheets by querying so-populated repositories.
Improving Graph Trend Filtering with Non-convex Penalties

In this paper, we study the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of nonconvex regularizers that exhibit superior recovery performance over existing convex ones. We present theoretical results in the form of asymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposed optimization problem and analyze its convergence. Numerical performance of the proposed framework with non-convex regularizers on both synthetic and real-world data are presented for denoising, support recovery, and semi-supervised classification.
Learning directed acyclic graphs by determination of candidate causes for discrete variables

The aim of this paper is learning directed acyclic graph (DAG) by determination of candidate causes for each discrete variable. Based on the fact that the candidate causes of a variable must be a subset of its potential neighbours, we first estimate the potential neighbours for each variable using L1-regularized Markov blanket. We then introduce a novel scoring function which infers the candidate causes for each variable through its Markov blanket. The lasso regression between each variable (as response variable) and its candidate causes (as predictors) is used to obtain a directed graph. We finally remove the cycles using the simulated annealing (SA) algorithm for achieving a DAG. Experimental results over well-known DAGs indicate that proposed method has higher accuracy and better degree of data matching.
LBAC <sub> <i>Web</i> </sub>

The e-government puts forward the growing security demands on information system, in this context, thin client based solution appears. The thin client provides similar functions but better security by cloud storage and centralized management as rich client. The main technologies are virtual desktop infrastructure (VDI) and Web client by now. Recently the development of Web-based operating system (Web OS) promotes Web client. But these Web OSes mainly aim at mobility and cross-platform, give less confidentiality and integrity support for e-government. Firstly, this paper analyzes four open source Web OSes on their application type, access control policies and their security problems. Then it abstracts the common security model of Web OSes. Secondly, based on this common security model, it constructs a LBACWeb (Lattice-based access control model for Web OS) model, which combining confidential label, integrity label and category set on the basis of lattice structure. Finally, it proposes the least privilege principle on trusted subjects and define a special privileged subject to enhance the flexibility and usability. The analysis and verification are given at last to elaborate the security and applicability of the LBACWeb model.
Teaching Design of Intercultural Discussions in College English Based on ORID Mode

In college English courses, discussion as a way of teaching is commonly used in intercultural teaching.However, in the actual English classroom teaching, discussions often become a mere formality, and the effectiveness is limited.Based on the ORID mode, this paper focuses on the subjects of intercultural discussions and the teaching objectives, and explores the teaching design that has effective ways of intercultural discussion and creative ways of discussion grouping.
Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding

In this paper, we propose a novel video coding scheme to significantly reduce the coding complexity and enhance overall coding efficiency in videos acquired by high mobility devices such as unmanned aerial vehicles (UAVs). In order to reduce the encoded data bits and encoding time to facilitate real-time data transmission, as well as minimize the image distortion caused by the jitter of onboard camera, a sensor-assisted global motion estimation (GMV) algorithm is designed to calculate perspective transformation model and global motion vectors, which are used in both the inter-frame coding to improve the coding efficiency and intra-frame coding to reduce block search complexity. We conducted comprehensive simulation experiments on official HM-16.10 codec and the performance results show the proposed method can achieve faster block search by 50% to 60% speedup and lower bitrate by 15% to 30% compared with standard HEVC coding software.
Noise Benefits in Feedback Machine Learning: Bidirectional Backpropagation

The new bidirectional backpropagation algorithm converts an ordinary feedforward neural network into a simple feedback dynamical system. The algorithm minimizes a joint performance measure so that training in one direction does not overwrite training in the reverse direction. This involves little extra computation. The forward direction gives the usual classification or regression network. The new backward pass approximates the centroids of the input pattern classes in a neural classifier. The bidirectional algorithm can also approximate inverse point mappings in the rare cases where such mappings exist. Carefully injected noise can speed the convergence of the bidirectional backpropagation. This holds because backpropagation is a special case of the expectation-maximization algorithm for maximum likelihood and because such noise can always boost its convergence. The noise also tends to improve accuracy in classification and regression.
A Shift in the Research Design Approach: Reporting on the Complexities of the Application of Human Centred Design and the Co-Design Process in a Library Setting

This paper reports on a practice-based study, re-evaluating the brand and wayfinding design of the Sasolburg Public Library (SPL) through the integration of Human-Centred Design (HCD) thinking, Participatory Action Research (PAR) and the HCD IDEO toolkit. Findings suggest that although HCD is valuable within the realm of research, the integration of HCD processes in practice inhibited the successful finalization of a design outcome, within a library setting. Considering this, tensions are confirmed between the design in practice and the democracy embedded within HCD. By recognizing identified tensions, suggestions are made to better accommodate the divergent roles of the designer and community participant – allowing each to remain a specialist within their designated fields. The findings of this study contribute to the HCD process and its integration within design practice.
Image Compression Using GMM Model Optimization

A Gaussian Mixture Model (GMM)-based framework for image compression is proposed in this paper. The image is predicted using GMM whose parameters are estimated using common Expectation-Maximization (EM) algorithm and encoded with fixed length bits. We introduce a new GMM Model Optimization (GMO) measure to select the optimal number of models and avoid local optimum of EM at the same time. The encoding cost of the residual and parameters are considered in GMO which is demonstrated to be near concave and effective. A parameter dictionary is designed to utilize the correlation of the parameters to improve the coding efficiency. The residual between the original image and the GMM image is encoded using High Efficiency Video Coding (HEVC) intra coding. Experimental results show that our method performs better than HEVC.
Generalized Distributed Dual Coordinate Ascent in a Tree Network for Machine Learning

With explosion of data size and limited storage space at a single location, data are often distributed at different locations. We thus face the challenge of performing large-scale machine learning from these distributed data through communication networks. In this paper, we generalize the distributed dual coordinate ascent in a star network to a general tree structured network, and provide the convergence rate analysis of the general distributed dual coordinate ascent. In numerical experiments, we demonstrate that the performance of the distributed dual coordinate ascent in a tree network can outperform that of the distributed dual coordinate ascent in a star network when a network has a lot of communication delays between the center node and its direct child nodes.
Learning Pose-aware 3D Reconstruction via 2D-3D Self-consistency

3D reconstruction, inferring 3D shape information from a single 2D image, has drawn attention from learning and vision communities. In this paper, we propose a framework for learning pose-aware 3D shape reconstruction. Our proposed model learns deep representation for recovering the 3D object, with the ability to extract camera pose information but without any direct supervision of ground truth camera pose. This is realized by exploitation of 2D-3D self-consistency between 2D masks and 3D voxels. Experiments qualitatively and quantitatively demonstrate the effectiveness and robustness of our model, which performs favorably against state-of-the-art methods.
Formalization of SOA Design Patterns Using Model-Based Specification Technique

Due to the increase in the complexity of the current system, the need for developing solution of recurring design problems becomes more prominent. Nowadays, various analysis and design methodologies are available to resolve the complexity of the modern system. But most of them are informal and semiformal in nature. One of them is design pattern that produces reusable solution for the recurring design problem. Solution based on design pattern is specified by using UML diagrams, which are further analyzed by using formal notation, because UML is semiformal in nature. In this study, the analysis of the behavioral aspect of service-oriented architecture design pattern is presented. For the formalization of pattern notations, object modeling language, i.e., Alloy, has been used. For demonstrating the proposed approach, a case study, i.e., purchase order system, has been taken into consideration.