Reviewerly/exHarmony huggingface dataset:
INVESTIGATION ON THE CURRENT SITUATION AND COUNTERMEASURES OF EXTRACURRICULAR READING FOR PRIMARY SCHOOL STUDENTS: A CASE STUDY OF ZHEJIANG, CHINA

Based on a survey of 697 students in primary schools of Hangzhou, Ningbo, and Jiaxing in Zhejiang Province, this paper found that: (1) 25% of parents support their children to read reference books and regular parent-child reading, but most of the parents are afraid it may negatively influence school curriculum so they limit or against extracurricular reading and do not carry out the parent-child reading; only 27% of teachers often assign extracurricular reading tasks, most rarely or never, parents and schools currently pay little attention to extracurricular reading. (2) Eighty one percent of children spent 2 hours or more on homework or reviewing lessons every day, 88% of them watched about 1 hour of TV every day, and 68% of them played video games for about 1 hour. Except eating, sleeping and commuting, they have an average of 4-5 hours of free time outside school, so more than 50% of primary school students spend less than 0.5 hour reading Chinese books every day. (3) Nearly 80% of children read 2-5 Chinese books every month and spend around ￥500 on books every year; in addition, more than 60% of the respondents read paper books, and the reading of extracurricular English books is close to zero, which caused their limited reading volume, narrow scope of knowledge, restricted international vision, and inadequate reading habits. The situation extracurricular reading is not ideal, which may seriously affect the future academic and career development. To improve the unfavorable situation, this paper put forward the following suggestions: (1) all levels of government should allocate fund to establish and improve bookshelves in school classroom and community library, promote home - school - community cooperation reading plan, let the children at school can have more 0.5-1 hours of reading at noon, and can approach in the community library for reading on weekend and holidays; (2) distribute free books to low-income families and advocate encouraging parents to spend half an hour reading with their children; (3) the school attaches great importance to the extracurricular reading education, organize pupils in class after school every afternoon 1 hour or so of intensive reading, storytelling and drama class PK activities such as English, both can effectively improve the student's reading interest, also solved the question which is many parents unable to pick up their kids during work at 3:30 p.m.; (4) promote digital reading, reduce the cost of reading, and correctly understand the impact of digital reading on eyesight. Article visualizations:
Essays Written by Primary School Students at Grade Six in Tabanan Regency, Bali Province: A Syntactic Approach

This study aimed to describe types, unity, and structures of sentences contained in the essays written by primary schools students at grade six in Tabanan regency, Bali province.The method used to collect the data was document recording and analyzed by a descriptive analysis technique.The results showed that (1) there were 199 declarative sentences, 79 simple sentences, 120 compound sentences, 176 verbal sentences, 23 nominal sentences, 180 active sentences, and 19 passive sentences.(2) The unity of the sentence was indicated by the existence of Subject and Predicate.a) There were 185 sentences categorized as having a clear unity of ideas; b) 7 sentences did not have a clear unity of ideas; c) 4 sentences without a subject, and (d) 3 sentences without a predicate.(3) According to the structure of sentences, there were simple and multiple sentences in the essays and they were quite varied.
Classification of Geometric Primitives in Point Clouds

As introduced in Section 1.1 classification of geometric primitives in point clouds is an integral part of the reverse engineering pipeline. It is also a vital part in the particular case of separating surface structure from base geometry. Compared to object recognition or object retrieval tasks the classification of geometric primitives is somewhat harder.
Text Detection Using Maximally Stable External Regions and Stroke Width Variation

There is a proverb “an image is worth, than ten thousand words”. So it is very difficult to explain a single image. But what happens if a single image itself contains some information in the form of text. Though it is easy to extract text from the structured image, it is difficult to retrieve it from unstructured image. Thus in this paper, we are providing an efficient and concrete algorithm to solve this problem. This algorithm consists of detecting candidate text region using maximally stable external regions (MSER). Then it removes false region based on basic geometric properties. Now, again removing false region based on stroke width variation (SWV) and finally merging all text regions for detection of the result. At last, recognition of detected text with the help of optical character recognition (OCR). All these methods are combined to give high performance of the proposed algorithm.
Lexicon-based sentiment analysis of web discussion posts using SentiWordNet

Sentiment Analysis is the computational study of opinions that are expressed in text (e.g. blogs, comments and discussion forums). Web discussion forums provide a means to express and share user views on different topics. Such content is usually rich in opinion which if mined can provide useful insight into the interaction. Although several contextual sentiment analysis techniques have been proposed and tested in the domain of product reviews, little of such techniques have been tested in a web discussion setting which fundamentally differs from reviews. This research presents the result of accounting for lexical and non-lexical contextual valence shifters to the problem of sentiment classification of web discussion posts using SentiWordNet lexicon. The approach comprises the extraction of prior polarity scores from the lexicon and modification of such scores based on context. An evaluation on three discussion forums data sets shows that SentiWordNet together with the proposed approach could be used as important resource for sentiment classification of web discussion posts.Keywords: sentiment analysis, contextual polarity, genre-adaptation
Computational Organizational Cognition

Computational Organizational Cognition presents simulations to clearly assess the advantages of agent-based computational organizational cognition
A blockchain based approach to the sharing of pet healthcare data

Pets healthcare data would be stored in scattered manner due to the changes in the pet owner, service agency or other reasons, which result in a large number of repeated examinations in healthcare service process and even medical negligence. Therefore, we proposed a blockchain-based pet healthcare data sharing approach so that the pet's healthcare history information can be presented in a complete manner when needed and prevent data from being attacked intensively, moreover, data integrity and accountability in healthcare service processes can be fulfilled. The ciphertext-policy attribute-based encryption(CP-ABE) is used to safeguard the privacy of the pet owner and the confidentiality of the healthcare data of the pets stored in different healthcare service parties. The smart contract is applied to ensure the data interaction between users and the normal acquisition of data after the change of the pet owner. In addition, we provide security analysis and performance evaluation.
Image caption generation method based on adaptive attention mechanism

An image caption generation model with adaptive attention mechanism is proposed for dealing with the weakness of the image description model by the local image features. Under the framework of encoder and decoder architecture, the local and global features of images are extracted by using inception V3 and VGG19 network models at the encoder. Since the adaptive attention mechanism proposed in this paper can automatically identify and acquire the importance of local and global image information, the decoder can generate sentences describing the image more intuitively and accurately. The proposed model is trained and tested on Microsoft COCO dataset. The experimental results show that the proposed method can extract more abundant and complete information from the image and generate more accurate sentences, compared with the image caption model based on local features.
Co-occurrence relationship encoding via channel merging for vehicle part recognition

Vehicle part recognition aims to determine the subcategories of each vehicle part. Existing algorithms consider to recognize each category as independent classification tasks, which ignore the potential co-occurrence relationship between vehicle parts. In addition, it remains challenges to obtain satisfactory results due to the small intra- class difference. In this paper, we propose a part-pair recognition method based on deep learning by utilizing the co-occurrence relationship. Specifically, we construct a deep neural network for vehicle part recognition, which can use the co-occurrence relationship and recognize two vehicle part simultaneously. We also propose a massive dataset of vehicle parts with fully annotated labels for training and testing. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art vehicle recognition algorithms.
Increment adaptive correlation filter for visual tracking

Currently, the correlation filter is widely used in visual tracking because of its effectiveness and efficiency. To adapt the representation to changing target appearances, a linear interpolation is used to update tracking models according to a manually designed learning rate. However, The limitation of manually tricks make methods only apply to some special scenes because the threshold parameters are sensitive to different response maps in complex scenes. In this paper, to overcome this problem, an adaptive increment correlation filter based tracker is proposed. Different from traditional linear interpolation depending on a manual learning rate, the increment is learned by linear regression based on the history tracking model and the current training samples. Experimentally, we show that our algorithm can outperform state-of-the-art key point-based trackers.
An Efficient Approach to Implement Multiplier using Vedic Maths Squaring Technique

In this paper, the authors have implemented a new approach to the squaring technique proposed by Vedic mathematics to make basic calculations easier and the circuits more efficient. Vedic mathematics contain many sutras, sub sutras and their corollaries in which the selected one is Dwandwa yoga. The technique is used to perform the squaring of a two digits number. Xilinx Integrated Synthesis Environment design suite (14.7) was used to design the schematic and the results were compared with the conventional multiplier, through waveforms and simulation data. The time taken for squaring operation was reduced by employing the Vedic algorithms. The proposed Vedic multiplier architecture exhibited lesser component requirements and speed improvements.
Enhancing Web-based Students’ Results and Transcripts Computation in a Hybrid System

An automated computerized examination results management system for tertiary Institution use for end semester examination and generation of transcripts as student's records processing in a hybrid system is design to overwhelmed the manual or computerized method used for students' end of semester academic result processing for some institution which was found to be complex and tedium , especially when apply for a large number of students or hybrid system in term of different program.The processing of result in term of grade collection on different courses may involves the entire process time-consuming, error prone and inability to carry out real time control system.The system to be designed is meant to perform student result administration of all facet.The method to be employ to achieve this approach is top -down hierarchical approach.The expected result show that result and transcript generation was achieved on real time and the monitoring of students with outstanding subject are transfer to a particular partition in the database, where they are monitored on the duration of academic studies before transfer to the first, second and third probations and finally terminated.The system presents a single platform that will be used to manage the processing of all examination records both in part time and full time within the institution.The data used for testing was obtained from the Department of Academic Planning (DAP) Lagos State Polytechnic, Lagos State.In conclusion, the deployment of academic student processing result and transcripts has demonstrated high sense of efficiency, reliability, error free and real time actualization of student position and good standing.
Em-Droid: A Visual m-Learning Platform to Teach and Learn Embedded Systems in Introductory University Stages

Recently, the m-learning has gained an important place in the educational scenario due to the mediation between the technology, the learning and the student. Furthermore, some important reports in education as the NMC Horizon Report: 2017 Higher Education Edition states that the m-learning is a key methodology that could foster the access to the education around the world. In this context, this paper describes to Em-Droid, a m-learning platform to learn and teach embedded systems focused on introductory courses in electronic engineering or electrical engineering. Em-Droid uses algorithm visualizations (AVs) through Blockly, a real-time C language converter and a proposed development board with a microcontroller (ATmega328p) in order to the students design and implement their algorithms in hands-on activities. Also, the students can see the C language code for their algorithms in real-time. In this way, the platform allows to improve the algorithmic thinking and the motivation of the students in the mentioned area. Currently, the platform is being tested in our introductory courses in the program of Technology in Electronics at Uniminuto university.
Object Detection in Real Time Images using Saliency Mapping Technique

As indicated by the World Health Organization (WHO), 285 million individuals are evaluated to have vision problems and among these 39 million are visually impaired and 246 million have low vision. Visual saliency is the ability of a vision system (human or machine) to select a certain subset of visual information. Whereas an accurate localization of the salient object from an image is a difficult a problem, when the saliency map is noisy and incomplete. The objective of this research is to build up a framework, which utilizes constant recordings to recognize the articles around and direct the visually impaired individuals to explore and evade obstacle impact. Here an effective algorithm is proposed, which detects the objects and obstacles in salient regions by decomposing the input video into the background and residual videos in much lesser time without sacrificing the accuracy of the decomposition. Here after extracting the feature vectors the corresponding saliency map is constructed. A lot of saliency map is processed at various scales for each element and the best saliency map for each element channel are intensity channels, color channels and motion channels. Further, the classification of saliency map is undertaken, wherein objects detected are grouped accordingly. Finally, the optimal visual saliency of a corresponding visual scene is to calculated, which helps the user to understand the characteristics of the respective object. This is a literature review paper of proposed research.
Graphical Patterning-Platform of Software Malfunction for Power Profile-based Side-Channel Analysis

Recently, many defects that are not detected in a software system have been found. This flaw is dangerous because the system cannot detect it even if it is attacked by a fault. To detect these defects, we have focused on the unique characteristic of the software system, the power consumption pattern. By comparing this unique normal operation pattern with the realtime operation pattern, we thought that we could fully detect the malfunction. This paper proposes 2-dimension graphical string patterning platform and matcher using side-channel analysis (SCA). We designed the monitoring block which is composed of matcher and pattern generator using Chipwhisperer that is open-source platform for SCA. The patterns are generated in run-time and compared with normal operation pattern. By this proposed method, we were enable to detect malfunction in runtime.
Accelerating Image and Sensor Data Computation in Smartphones

Artificial intelligence (AI) is the next big thing in the world of technology which will revolutionize the way in which technology is perceived by society. Till date smartphone is the most widely adopted consumer electronic and will hence provide the pervasive platform for AI and its adoption. AI algorithms require copious amounts of data to learn and train the models. Cloud computing has played a pivotal role in making AI possible on smartphones. But cloud computing has its restraints such as latency, privacy risks, and network dependency. An advanced approach is required for AI to become truly ubiquitous. On-device AI, employing dedicated AI chipsets can solve these issues by accelerating computing and improving the user experience simultaneously. This paper mainly focuses on the improvements that can be made to handling image and sensor data, especially in the area of smartphone photography and computing power of smartphones through the use of Digital Signal Processors (DSP), Image Processing Units (IPU) and Neural Processing Units (NPU) which employ heterogeneous computing architecture for improved execution of AI-based operations in smartphones.
THE CORRELATION AMONG THE ELEVENTH GRADE STUDENTS’ LEARNING STRATEGIES, READING ATTITUDE, AND READING ACHIEVEMENT AT STATE HIGH SCHOOL 22 OF PALEMBANG

Abstract The study is concerned with the correlation among the eleventh grade students’ learning strategies, reading attitude and reading achievement at state high school 22 palembang. In this study, 40 students of the eleventh grade students at state high school 22 of Palembang in academic year 2015/2016 were chosen as samples through cluster random sampling. This study applied correlational research design. The data were collected by using questionnaires and test and were analyzed by using Pearson Product Moment coefficient and multiple regression analysis. The result showed that there was positive correlation between language learning strategies and reading achievement (0.004 < 0.05); between reading attitude and reading achievement (0.028 < 0.05); between language learning strategies and reading attitude (0.006 < 0.05), and among language learning strategies, reading attitude and reading achievement ( 0.004 < 0.05) Key words: Language Learning strategies, Reading Attitude, reading Achievement
A Parallelistic Approach toward Ontology Design to Overcome Systems Nuance in Decision Governance

Artificial intelligence (AI) or machine intelligence (MI) can be faster and more accurate in domain specific decision tasks than human, however, AI's inability to achieve true general human creative cognitive capacity is still a deficiency in the AI or MI decision-making systems. The fundamental problem of decision-making governance, thus, poses a great challenge in advancing human-intelligent and machine-intelligent systems. The purpose of this paper is to present a noble parallelistic approach that has recently been applied to build a top-level ontology for HI-MI decision governance. The objective of this paper thus is threefold: 1) to address the gap in existing knowledge in decision governance for HI-MI systems and as a potential solution to the problem; 2) outline an integrative methodology to detailing the top to application level ontological development, resulting in; 3) a more specific 'parallelistic approach' for the development of a top ontology for human-machine intelligence decision governance.
Usability Evaluation for Digital Library: A Case Study of Library Websites, Mikroskil

Todays, library can be accessed online which so called online library. Online library contains a collection of information and services which can be accessed anywhere and anytime. it has become an important part for a university that should provide flexibility for its academic community. Thus, its usability need to be evaluated to get hints on its performance. There is checklist that can be used as a tool to evaluate the usability of a digital library. This research uses the library website of Mikroskil as a case study. The aim of this research is to evaluate the usability of the digital based library using Mikroskil's library website by using usability index checklist (UIC). Through this research, there are 100 elements need to be assessed, there are only 47 elements that can be discussed and there are 53 remaining elements that are not found on the website being evaluated. The remain 47 elements also have several notes that need attention. This research will provide a base for a new digital library website development for Mikroskil with a higher performance and usability.
Group detection assisted by density map

Group detection is crucial component in intelligent video surveillance, which can capture crowd motion and directly apply to emergency security in complex scenes, thus it has attracted plenty of attention in the related fields. However, the existing works cannot fully utilize the deep and precise features of the crowd. Recently, with the rapid development of deep learning and the promotion of challenging datasets, crowd density estimation has achieved the desired accuracy in single image. Since density maps can provide a high-level semantic information for the crowd, in this paper, a density map assisted scene analysis method is proposed to detect the groups in crowd scenes. The main contributions in this study are threefold: (1) Using density map-based super-pixel segmentation method to obtain the multiple image patches, which are taken as the next research objects; (2) A group detection method based on multi-view clustering is proposed. The density maps are used to construct similar graphs from the aspects of interaction, spatial distribution, motion distribution and motion pattern. (3) A post-processing strategy is designed to combine the groups with higher relevance to determine the final group. The experimental results show that the method can accurately detect the groups in image sequence. Furthermore, compared with the existing methods, the proposed method achieves better performance on the CUHK Crowd Dataset.
THE EFFECT OF USING BEHAVIORISTIC THEORY ON STUDENTS’ SPEAKING SKILL OF RECOUNT TEXT

The aim of this study is to find out the effectiveness of applying Behavioristic Theory on students’ speaking skill of recount text. The research design was experimental by giving pre-test and post-test to the class. It involved 25 participants of SMP N 3 Sungai Lala. The research question was posted in this study in order to find out the significant effect of applying behavioristic theory on students’ speaking skill of recount text. Pretest and posttest were administered in this study and sample t-test was used to analyze the data. The finding shows that there was no significant effect of behavioristic theory on students’ speaking skill. The mean level is 37,33 (for the experimental class) become 49,96 (for the control class). The finding of t-0 is -1.947; while the level of significance of 5% is 2.02 and the level of significant 1% is 2.64. It can be stated that 2.02>-1.947<2.64. So, it can inferred that Behavioristic Theory cannot be applied to the students’ speaking skill in recount text especially at SMP N 3 Sungai Lala.