Reviewerly/exHarmony huggingface dataset:
Investigating Best Approaches for Activity Classification in a Fully Instrumented Smarthome Environment

Analyzing human behavior in smart environments is an important research area dealing with a multitude of issues related to ubiquitous computing, machine learning and ambient assisted living.With recent advancements in sensing technologies, it is henceforth possible to build computational models that select relevant sensors, and apply statistical models for accurate detection of residents' activities in smarthomes.To this end, we choose to work with the "Orange4home" dataset which represents one of the latest dataset in this research field.The main contribution of this paper is (1) to perform accurate detection of resident activities (extracted from the "Orange4Home" dataset) by proposing relevant preprocessing and machine learning approaches, and (2) enhance previous classification results already published on the same dataset.Thus, our methodology in this paper is to explore the whole process from data preprocessing to classification metrics.Indeed, a carefully designed and original preprocessing algorithm is proposed in order to properly prepare data to the training phase.Then, to perform relevant exploration of the feature space, many strategies for features selection and reduction (based on Univariate feature selection and Principal Component Analysis) were proposed.For the activities classification task, many well-chosen discriminative models (SVMs, Decision Trees, Random Forests) were explored.Our main results outperform previously published results on the same dataset.Moreover, comparing all proposed classifiers, Random Forests outperform other classifiers and shows that the optimal accuracy rate (95%) was obtained thanks to a smart choice of a limited number of sensors rather the use of the full feature space (i.e.data from all installed sensors).Based on our results, many recommendations (for building optimal smarthomes and activity classification models) were emphasized in the end of this paper.
Perkembangan Bahasa Pragmatik Pada Anak Attention Deficit Hyperactivity Disorder (ADHD): Kajian Neurolinguistik

The purpose of this study is to describe the pragmatic development of ADHD childdren. Every child who was born, over time experiencing various development, such as motor development, cognitive development, emotional development, up to language development. Children who have Attention Deficit Hiperactivity Disorder (ADHD) have constraints in some of their development, one of them is pragmatic language development. This research utilizes neurolinguistics theory that explain about language and brain. The result that found in this study are ADHD children can’t survive in a conversation because their focus is easily broken, then they tends to make themselves as a center of a communication. ADHD children also have a difficult in formulating speech and tend to be impulsive and always in a hury. Brain disorder, although not in a language area, still affect the pragmatic language development of ADHD children. By knowing the problem about ADHD children’s pragmatic problem, we can knowing the solution for handle it.
Automatic Routing Study of Spacecraft Cable based on A-star Algorithm

Design of 3D cable network is one of the main bottlenecks that affect spacecraft development progress, cause of its tedium and repetition. In this paper, a minimum spanning tree method is proposed to describe the cable connection relationship of spacecraft, which takes the frequency of connectors as the main investigation point. And an improved A-star algorithm is proposed, which added two new node lists (POOL and PATH table) to store the inspected nodes and generated path respectively, without the need to maintain a large list of nodes to be inspected (OPEN table) in real time. On the basis above, the automatic generation of 3D cable network design model is realized successfully and effectively.
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning

Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded symbolic action models and hand-coded heuristic function generators for efficiency. Learned heuristics for such problems have been of limited utility as they are difficult to apply to problems with objects and object quantities that are significantly different from those in the training data. This paper develops a new approach for learning generalized heuristics in the absence of symbolic action models using deep neural networks that utilize an input predicate vocabulary but are agnostic to object names and quantities. It uses an abstract state representation to facilitate data-efficient, generalizable learning. Empirical evaluation on a range of benchmark domains shows that in contrast to prior approaches, generalized heuristics computed by this method can be transferred easily to problems with different objects and with object quantities much larger than those in the training data.
A Differential Evolution Approach for Camera Space Manipulation

The following topics are dealt with: optimisation; pattern clustering; learning (artificial intelligence); convolutional neural nets; data mining; computational complexity; neural nets; wireless LAN; feature extraction; and image retrieval.
Prosody Generation Using Back Propagation Neural Networks for Sindhi Speech Processing Applications

Analysis and synthesis of speech to be automated still require more research efforts in general and for the development of speech processing applications based on Arabic Script like Sindh Textto-Speech in particular.To achieve the required results from the speech processing applications prosodic features must be exercised extremely as the prosody is highly linked with the information of sounds having different characteristics like linguistic rules, complications and variations of expressions.Objectives: This study aims to generate and analyze the prosodic information specifically pitch and duration from the recorded Sindhi sounds using the back propagation neural network.Methods: Two methods are used to obtain the prosodic information of Sindhi sounds, PRAAT speech analyser is used to obtain the results and for the validation a back propagation neural network model is implemented.From the four districts of Sindh 228 speakers were chosen and the sound of different descriptive sentences was recorded for the experiments.Finding: After the experiments with a neural network model with multiple layers on the collected sound, 98.8% a highly acceptable level of accuracy achieved at the 18 th epoch among the 100 epochs.Application and improvements: The generated Sindhi prosodic information and adopted research methodology will be supportive to the scholars of Sindhi speech processing applications.This research work can be considered as the first step as no work for generating Sindhi prosody is found yet.
Compyle: a Python package for parallel computing

Compyle allows users to execute a restricted subset of Python on a variety of HPC platforms.It is an embedded domain-specific language (eDSL) for parallel computing.It currently supports multi-core execution using Cython, and OpenCL and CUDA for GPU devices.Users write code in a restricted subset of Python that is automatically transpiled to high-performance Cython or C. Compyle also provides a few very general purpose and useful parallel algorithms that allow users to write code once and have them run on a variety of HPC platforms.In this article, we show how to implement a simple two-dimensional molecular dynamics (MD) simulation package in pure Python using Compyle.The result is a fully parallel program that is relatively easy to implement and solves a non-trivial problem.The code transparently executes on multi-core CPUs and GPGPUs allowing simulations with millions of particles.A 3D MD code is also provided and compares very favorably with a well known, open source, molecular dynamics package.
Implementasi Dempster Shafer Dalam Diagnosa Penyakit Impetigo Pada Balita

The diagnosis of impetigo by using an expert system that uses the source of expertise aims to obtain accurate information in accordance with the symptoms suffered by the patient, this expert system analysis uses the dempster shafer method with mathematical theory for proof based on belief function and plausible reasoning it makes sense to combine separate pieces of information (evidence) to calculate the likelihood of an event that can produce information from experts about the symptoms and solutions of impetigo symptoms accurately and build an expert application system program that is able to diagnose early impetigo in infants and provide solutions and information quickly
Getting Insights from a Large Corpus of Scientific Papers on Specialisted Comprehensive Topics - the Case of COVID-19

COVID-19 is one of the most important topics these days, specifically on search engines and news. While fake news is easily shared, scientific papers are reliable sources where information can be extracted. With about 24,000 scientific publications on COVID-19 and related research on PubMed, automatic computer-assisted analysis is required. In this paper, we develop two methodologies to get insights on specific sub-topics of interest and latest research sub-topics. These rely on natural language processing and graph-based visualizations. We run these methodologies on two cases: the virus origin and the uses of existing drugs.
An efficient color image compression technique

We present a new image compression method to improve visual perception of the decompressed images and achieve higher image compression ratio. This method balances between the compression rate and image quality by compressing the essential parts of the image-edges. The key subject/edge is of more significance than background/non-edge image. Taking into consideration the value of image components and the effect of smoothness in image compression, this method classifies the image components as edge or non-edge. Low-quality lossy compression is applied to non-edge components whereas high-quality lossy compression is applied to edge components. Outcomes show that our suggested method is efficient in terms of compression ratio, bits per-pixel and peak signal to noise ratio.
The Influence of Knowledge Sharing, Affective Commitment, and Meaningful Work on Employee‘s Performance

Purpose:The research aimed to test the influence of knowledge sharing, affective commitment, and meaningful work on the performance of the employee.This study was conducted in Indonesia, specifically in the banking sector.Design/Methodology/Approach: Data collection of this study used administered questionnaires among employees.Thus, 219 questionnaires were returned and analyzed using SmartPLS 3 software.Findings: The study revealed that knowledge sharing and meaningful work significantly affect the performance of the employee.In contrast, affective commitment did not influence the performance of the employee.This study further showed that meaningful work mediated the correlation between knowledge sharing and the performance of employees.Practical implications: This study's findings contribute to the literature review advancement related to the associations among knowledge sharing, affective commitment, meaningful work, and the performance of employees.Originality/value: This paper is the first empirical study to uncover the impact of knowledge sharing, affective commitment, and meaningful work on the performance of employees in the Indonesian banking sector.
Analysing Large-Scale Scrum Practices with Respect to Quality Requirements Challenges

empirical research using agile practitioners' perceptions indicated several important Quality Requirements (QRs) challenges experienced in agile large-scale distributed projects.It also indicated that a popular solution approach to those challenges is to inject some heavyweight practices into agile, for example adding documentation or roles of authorities for QRs.At the same time, agile methodologists proposed several scaled agile frameworks to specifically serve agile organizations working on large and distributed projects.How do these frameworks address QRs? Do they put forward any heavyweight practices as a solution to QRs challenges, or do they invent new agile practices fully aligned with the values of the Agile Manifesto?Currently, very little is known about the extent to which the QRs issues are accounted for in the design of these frameworks as proposed by agile methodologists.This paper attempts to narrow this gap of knowledge.It analyses Large-Scale Scrum (LeSS), a prominent scaled framework, from the perspective of QRs engineering and the Agile Manifesto's values.To this end, we first applied the 4-Dimensional Analytical Tool to evaluate the degree of agility of the practices of the LeSS framework.We then analysed these practices and evaluated their applicability to mitigate the QRs challenges reported in previous work.a
Scaffolding in Teaching Writing

As one of the prominent skills in English language learning, mastering writing is a must for students at various educational levels, yet many of them sometimes have difficulties in generating ideas, using grammar and vocabularies and maintaining unified ideas in their writing. Considering the problems faced by the students and practices needed by students to improve their writing skills, the teachers can use scaffolding strategy derived from Vygostky’s ZPD theory. It is believed that students can complete the difficult task that is a bit higher than their ability through guidance from more expert others. The purpose of the study was to figure out how scaffolding can be used to improve students’ writing skills. The results of the study show that scaffolding can be used as teacher’s strategy to improve students’ writing skills by integrating the writing process in the genre-based approach representing the scaffolding. It shows how the teacher gradually removes his assistance as soon as students have the experience for completing the task. Through scaffolding, students will learn how to experience the strategy, rather than merely to complete the task and it leads them to be autonomous and successful writers. Keywords: Scaffolding, writing, and ZPD
Measurement of National Education Standards: (Analysis of Achievement of Accreditation Results and Improvement Strategy)

Setting standards in the education becomes a necessity in an effort to guarantee the implementation of quality education.In Indonesia, education standards are formulated in a law governing national education standards consisting of eight minimum standards that must be met.The research was a qualitative descriptive research with a case study method in senior high schools in Indonesia that studies the measurement of the achievement of national education standards from the aspects of obstacles encountered and their improvement strategies.Research data were collected through interviews, observations and documentation studies.The data were analyzed by data reduction, data presentation and conclusion drawing.The results of the study provide information that in general national education standards have been met, but in some aspects still need further improvement efforts.The efforts to improve can be done through participatory leadership, making standards compliance as a culture and building commitment.
Identity Identification and Management in the Internet of Things

Henceforth, users agreed on the necessity of continuous Internet connection independently of the place, the manner, and the time. Nowadays, several elite services are accessible by people over the Internet of Things (IoT), which is a heterogeneous network defined by machine-to-machine communication. Despite the fact that the devices are used to establish the communication, the users can be considered as the actual producers of input data and consumers of the output data. Consequently, the users should be viewed as a smart object in IoT; therefore, user identification, authentication, authorization are required. However, the user identification process is too complicated because the users are worried to share their confidential and private data. on the other hand, this private data should be used by some of their devices. Accordingly, an equitable mechanism to identify users and manage their identities is necessary. In addition, the user plays an extreme important role in the establishment of rules needed for identity identification and in ensuring the continuity of receptive services.The main purpose of this paper is to develop a new framework for Identity Management System (IdMS) for IoT. The primary contributions of this paper are: the proposition of a device recognition algorithm for user identification, the proposition of a new format for the identifier, and a theoretical framework for IdMS
A CRITICAL DISCOURSE ANALYSIS OF MOHAMAD NASIR’S SPEECH

ABSTRACT&#x0D; Discourse is a unit of language in which its form is longer than a sentence including the vast majority of everyday communication; in addition, it ussually employes specific kinds of language as well as infromation structures to deliver specific purposes. In order to identify the structure and purpose of a discourse, the present research employed Critical Discourse Analysis (CDA) to analyze an educational speech in May 2nd, 2016 delivered by the Indonesian Ministry of Research, Technology, and Higher Education, Mohamad Nasir in the celebration of the National Education Day of Indonesia. The result of the analysis showed how the speech was constructed in form of macrostructure, superstructure, and microstructure revealed how the social power was used by symbolic elites to invite all parties in educational field to join together in reforming educational system of Indonesia to be better than what had been conducted by previous partisans in the past. &#x0D; Keywords: Critical Discourse Analysis (CDA); Indonesian Ministry’s speech; educational speech; National Education Day
Design and Implementation of SQL Injection Vulnerability Scanning Tool

Abstract According to the “Top Ten Security Vulnerabilities List” (OWASPTop 10) released by OWASP in 2017, SQL injection attacks are still at the top of the list, and there are many ways of SQL injection attacks, which cause great harm. Although there are many vulnerability scanning tools, there is still a high rate of false negatives. Aiming at the current problems of SQL injection vulnerability detection, this paper proposes a scanning tool for SQL injection vulnerabilities. First, use the crawler framework scrapy to obtain the URL associated with the form and the a tag, and segment the URL based on the improved simhash algorithm. Deduplicate the link, then analyze the injection point to modify the URL parameter value injection test, and determine whether there is a vulnerability based on the response result of the server. The experimental results show that the detection method achieves a 96.50% URL deduplication rate in the crawler module, which greatly reduces the rate of false negatives. It is more suitable for detecting whether a website has a SQL injection vulnerability.
Content-based Image Retrieval System using Second-Order Statistics

The development of a Content-Based Image Retrieval System (CBIRS) is presented.The second-order statistics were adopted as image features by the system as a means of distinguishing between images.The numbers of cooccurrences of pairs of gray values in an image are recorded in the Gray Level Co-occurrence Matrix (GLCM).Five of the second-order statistics which usually have values greater than 1 were selected; Contrast, Dissimilarity, Entropy, Mean (µ), and Standard Deviation (σ).Thus, fifteen features were recorded for each image from the Horizontal GLCM, Vertical GLCM, and Diagonal GLCM.During Database querying, features of the Query Image are computed and compared with those of the Database images, and Euclidean distance is computed as a similarity measure.The system displays the Query Image, the Retrieved Image (if any), the Best Match Image, and Eight Close Images with their Euclidean distances from the Query Image.Columbia Object Image Library image collection of 7,200 images was selected as the test Database.The developed CBIRS system accurately detects and retrieves Exact Match Images to Query Images with Euclidean distance of the Best Match Image being zero.The system also accurately identifies Query Images which are not in the Database with Euclidean distance of the Best Match Image being greater than zero.The system recorded 100% Recall ratio and 100% Precision ratio.
A PRIMER ON GENERATIVE ADVERSARIAL NETWORKS

Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data.They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.This paper will introduce the core components of GANs.This will take you through how every part function and the significant ideas and innovation behind GANs.It will likewise give a short outline of the advantages and downsides of utilizing GANs, comparison of architectures of various GANs and knowledge into certain true applications.
Analysis of Artificial Neural Network Point Forecasting Models and Prediction Intervals for Solar Irradiance Estimation

An accurate knowledge on solar irradiance prediction is particularly required for proper development and planning of Photovoltaic (PV) energy systems.The main purpose of the present research is to assess the accuracy of Artificial Neural Networks (ANN) short-term forecast of univariate solar irradiance time series, with conventional point prediction and Prediction Intervals (PIs), comparing models.The Lower Upper Bound Estimation trained with Particle Swarm Optimization (PSO-LUBE) was used for PIs estimation.Solar irradiance data collected from a station in Amazon region in Brazil was used to train and test the models.Results demonstrate that all ANN models yield good accuracy in terms of prediction error: 8.1-8.5% for normalized root Mean Square Error (nRMSE), 5.8-6.0%for normalized Mean Absolute Error (nMAE) and 94-95% for determination coefficient (R 2 ).However, due to the accuracy of PI information (Coverage Probability = 94.94% and PI Normalized Average Width = 32.50%),PSO-LUBE was the best method tested for decision-making.