Reviewerly/exHarmony huggingface dataset:
Strategies to Map Play Time in Steam Platform Dataset into 5-Star Ratings

This paper presents strategies to map play time in Steam dataset into 5-Star rating.Our objective is to check whether we could use the created explicit feedback in recommender system.In this study we calculated z-score for each users' duration of playtime and using the score we applied 3 strategies to map duration of playtime into explicit feedback.These strategies are Same Size Bin (SSB), Breakpoint Based Bin Sized (BBS) and Hybrid Based Bin Size (HBS).According to RSME value and Hit Rate, we found that the best strategies to map duration of playtime into explicit feedback is using HBS strategies.We checked the feasibility of using the created explicit feedback by feeding it to KNN algorithm.We compare RSME from others researches that used the same algorithm and found the RSME value that we get are in the same range as the researches.Based on this observation, it can be concluded that it is feasible to use created explicit feedback in Recommender System.
THE EFFECTIVENESS OF USING JOLLY PHONICS BLENDING PHONEMES TO YEAR 3 ENGLISH LANGUAGE CLASSROOM

Poor word identifying skill has become an issue among weak students in the education system. Students with good skill of able to identifying words at glance are able to develop and improve their overall reading performance across subjects in the school compared to the weak students who has poor word identifying skill. This problem has not only brought a temporary halt to teachers to teach but also to students to understand any information or tasks transmitted through the teaching and learning process. In the process of word identifying skill, students must be able to recognize the sounds of letters which are phonemes that form a word. For that reason, the purpose of this study is to evaluate the effectiveness of using Jolly Phonics Blending Phonemes to 30 pupils of the Year 3 EL learners. This study uses the mix-mode method that uses the quantitative and qualitative research method to analysed the effect of the Jolly Phonics Blending Phonemes to the experimental group and the control group in the same school in six months. The findings have discovered that the experimental group had showed an overall increase in scores and interest towards the phonics teaching compared the control group that uses the traditional method of teaching phonics. The introduction of Jolly Phonics Blending Phonemes does affect the performance among the poor learners in the Year 3 EL classroom.
Color Shifting-Aware Image Dehazing

Built upon an image formation model for a single hazy image, existing image dehazing methods typically restore hazed pixels by estimating the unknown transmission map and global ambient light via exploiting image priors. They often produce visually unpleasing results when hazy images are with unwanted color shifts due to inaccurate estimation about the actual ambient light of hazy images with color shifts. To address the problem, we propose a novel color shifting-aware image dehazing model that explicitly disentangles the inference of the image formation model. Specifically, our model attempts to calibrate color fading and shifting first, and then restores the hazed pixels via the scene depth based gamma correction using the color-corrected image as the guidance. Extensive experiments show that the proposed dehazing model significantly outperforms existing dehazing methods and achieves superior dehazing results on challenging cases with unwanted color casts.
Detection and Time Series Variation of Latent Topic from Diary in Northern and Southern Courts Period of Japan

In the study, we detected the latent topics by Latent Dirichlet Allocation(LDA) and analyzed the texts through the analysis of the topics, with the text of “後愚昧言己” (Gogumai-ki) which is an old diary written in Northern and Southern courts period of Japan. We analyzed time-series variations of the topics and considered transitions of the topics in the text. In addition, we have tracked time series variations of the topics.
Identifying the Strength of Emotions in Relation with the Topic of Text Using Word Space

Emotion analysis from text is a topic of growing interest over recent years.It is because of the growth and availability of internet.The emotion analysis from text and the strength of the emotions in text plays an important role in understanding and predicting the future events.In this paper, we are identifying the relationship between the strength of emotions and the topic of the text.In our methodology, we extend the concept of Word space that we proposed in our previous works for analyzing emotions.In Word space distances between the words and their occurrences are measures.The emotion carry words with relatively high frequency and less distance between their occurrences are strong emotions.Whereas less frequent emotions that occurs far from, each other are considered less intense emotions in the text.We also made a comparison between the changes in intensity of emotions on same topics over period.The paper is divided into different section.In the methodology and conclusion section, the results of our research mentioned in detail.
Review of Different Strategies for Coordinative Planning of Multi-agent Systems

Agent-based systems have been widely examined in the literature for various type of tasks.Within this examination, various strategies and modeling have been employed.Several surveys and reviews have been depicted in the literature regarding agent-based systems.However, minimal efforts have been made in the context of feature extraction and feature selection.This paper aims to review the strategies used for feature extraction and selection agentbased systems.In terms of the nature of agent communications, this paper tackles two types, centralized and decentralized.In terms of the workflow, this paper tackles three types, including coordinative, collaborative and emergent-based systems.Finally, a discussion is presented comparing the strategies and the frequent use of the strategies in the literature.Based on this review, most of feature extraction agent-based systems rely on either coordinating or emergent-based strategies, while feature selection agent-based systems rely on collaborative strategies.However, there are several aspects that we can consider to be classify agent-based strategies.This review develops a classification scheme for systems used for specific tasks, including feature extraction and feature selection.
Towards Practical GGM-Based PRF from (Module-)Learning-with-Rounding

We investigate the efficiency of a $$\mathsf {(module}\text {-}\mathsf {)LWR}$$-based PRF built using the GGM design. Our construction enjoys the security proof of the GGM construction and the $$\mathsf {(module}\text {-}\mathsf {)LWR}$$ hardness assumption which is believed to be post-quantum secure. We propose GGM-based PRFs from PRGs with larger ratio of output to input. This reduces the number of PRG invocations which improves the PRF performance and reduces the security loss in the GGM security reduction. Our construction bridges the gap between practical and provably secure PRFs. We demonstrate the efficiency of our construction by providing parameters achieving at least 128-bit post-quantum security and optimized implementations utilizing AVX2 vector instructions. Our PRF requires, on average, only 39.4 cycles per output byte.
Developing the Parallelization Methods for Finding the All-Pairs Shortest Paths in Distributed Memory Architecture

The All-Pairs Shortest Paths (APSP) is a fundamental graph problem aiming to find the shortest path between any two nodes in a graph. In this paper, a new method is presented to solve the APSP problem for big graphs on distributed systems. In this method, a graph is partitioned judiciously and then processed in parallel. In particular, the graph is first pre-processed to prepare the partition in the computation stages. After the graph is partitioned into smaller sub-graphs, a traditional shortest path algorithm, such as the Floyd-Warshall algorithm or the Dijkstra's algorithm, can be used to find the APSP in each sub-graph. Finally, through the common nodes between the sub-graphs, the local results in each sub-graph are combined to establish the APSP for the entire graph. Our method is implemented with MPI. Two different communication patterns among partitions (and processes) are proposed to achieve the parallelization and the combination of the local results. We then conducted extensive experiments on a high performance cluster. The experimental results show that comparing with the existing solution, our method is able to accelerate the solving of the APSP problem significantly.
Polynomials of degree 4 over finite fields representing quadratic residues

It is proved that in a finite field F of prime order p, where p is not one of finitely many exceptions, for every polynomial f(x) ∈ F[x] of degree 4 that has a nonzero constant term and is not of the form αg(x)2 there exists a primitive root β ∈ F such that f(β) is a quadratic residue in F. This refines a result of Madden and Vélez from 1982 about polynomials that represent quadratic residues at primitive roots.
Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling

Sepsis is a life-threatening response to infection that can lead to tissue damage, organ failure and death. The early prediction of sepsis is important, as it reduces undesirable patient outcomes associated with late-stage septic shock. However, effective early prediction is challenging, because the data is often heavily imbalanced against positive sepsis diagnosis. If the class imbalance is not addressed, models trained will tend to overfit in favour of the majority class, leading to degraded performance on the minority class. In this paper, we suggest a two-step method which consists of a mutual information based downsampling algorithm and a Synthetic Minority Over-sampling Technique (SMOTE), in order to effectively perform early prediction of sepsis. Our team, Kent Ridge AI (ranked 77th), obtained a utility score of -0.164 on the full test set by using the proposed two-step method. Additionally, we report crossvalidation results and identify several methods to improve performance.
FPGA-Based Moving Object Detection with Interferences

Visual interferences caused by bad weather conditions can have a negative impact on the performance of a visual surveillance system. How can we detect the moving object in a quickly and accurately manner is a very important step for further analysis, e.g., object recognition, object tracking, event detection, or behavior analysis, in a visual surveillance system. However most of the moving object object detection systems are based on a high-level micro-processor for its highly complex algorithm. Moreover, the detection accuracy is often affected by the environment of the system used. In this paper, we proposes using an FPGA (Field Programmable Gate Array) to realize a visual surveillance system so that the real-time requirement can be achieved. Besides, the proposed system can adapted itself to a variety of environmental situations, especially for the condition of light changes, such as the existence of a shadow, raindrop, etc. As we will see in the experiment that a very good performance in terms of power consumption, memory usage, throughput, as well as the capability of adapt to interference can be achieved in the proposed system.
LUMINOUS PICTURES AS A FORM OF THE CONTEMPORARY VISUAL ART

The article analyses luminous pictures as a form of contemporary visual art.The emergence of light sources (LEDs) with a fundamentally new way of generating luminous flux has directly influenced not only the theory and practice of functional and decorative lighting, but also visual arts, extending their expressive capabilities.Today, the introduction of LEDs has brought artificial lighting beyond the need of energy saving, creating necessary conditions for widespread use of light not only for utilitarian purposes, but also as a powerful tool of influencing the aesthetics of the environment.Therefore, the analysis of modern forms of using light as an artistic tool will help to reveal the peculiarities of the obtained aesthetic consequences which are the result of increased expressiveness of the image.The aforementioned aspects have actualized the study of luminous pictures as a form of contemporary visual art in order to identify the technological features of their construction and to classify the main visual techniques for creating the pictorial component of such works.Taking into account gaining the most adequate impression of the light environment and light objects is possible in the immediate environment, in the framework of the study the author created a series of works (luminous pictures) in order to exper-imentally outline the peculiarities of visual aesthetics of the result of the application of various artistic techniques in combination with interior lighting.The technology of indoor LED lighting has been used for the production of luminous pictures.This was determined by the following specific features of its use: invisibility of light sources for user's direct visual perception, which promotes the interpretation of illumination as an integral component of the picture image; uniform scattering of light by the surface used for drawing the image.In the course of the study a number of scholarly problems have been solved.The place of luminous pictures in the framework of
Relationship Between R&amp;D Outsourcing and Innovation Performance: Based on Resource Based View

This study aims to analyze the relationship between innovation performance and internal and external resources of R&D outsourcing firms. In order to achieve this, logistic regression analysis was conducted using the STEPI “Korean innovation survey for 2016: Manufacturing sector” data, As a result, it was found that the ratio of researcher in employee, the number of patents per employee, and the cooperation partners had a positive effect on product innovation and the utilization of government support had negative effect. In addition, cooperative partners and utilization of government support had a positive effect on process innovation and organizational innovation, and the number of patents per employee and utilization of government support had a positive effect on marketing innovation. Through this study, it is possible to expect improvement of innovation performance and firm’s competitive by using internal and external resources efficiently in carrying out R&D outsourcing strategy, and provide reference data on policy directional for government support in R&D outsourcing.
Low Pixel Rate 3DoF+ Video Compression Via Unpredictable Region Cropping

Enhanced three degrees of freedom (3DoF+) video system enables both rotational and translational movements for viewers within a limited scene. It introduces interactive motion parallax that provides viewers with a more natural and immersive visual experience on head-mounted displays (HMDs). A large set of views are required to support 3DoF+ video system, hence huge data costs a tremendous amount of computation and bandwidth. In this paper, a method based on unpredictable region cropping is proposed to reduce the size of data. The proposed method can be separated into two steps: basic view selection and sub-image cropping. One or several views in the source view set are selected as basic views adaptively. The goal of the basic views is to predict the remaining views, and unpredictable regions are cropped to multiple rectangular sub-images. The basic views and the cropped sub-images, called to-be-coded views, are coded by using High Efficiency Video Coding (HEVC) standard. Experimental results show the proposed method can achieve up to 75.0% pixel-rate saving and improve the quality of rendered views by 1.5dB. The method of basic view selection was adopted by the moving picture experts group (MPEG) video subgroup as a tool for the first version of Test Model of Immersive Video (TMIV).
Fast Tuning of Topic Models: An Application of Rényi Entropy and Renormalization Theory

In practice, the critical step in building machine learning models of big data (BD) is costly in terms of time and the computing resources procedure of parameter tuning with a grid search. Due to the size, BD are comparable to mesoscopic physical systems. Hence, methods of statistical physics could be applied to BD. The paper shows that topic modeling demonstrates self-similar behavior under the condition of a varying number of clusters. Such behavior allows using a renormalization technique. The combination of a renormalization procedure with the Rényi entropy approach allows for fast searching of the optimal number of clusters. In this paper, the renormalization procedure is developed for the Latent Dirichlet Allocation (LDA) model with a variational Expectation-Maximization algorithm. The experiments were conducted on two document collections with a known number of clusters in two languages. The paper presents results for three versions of the renormalization procedure: (1) a renormalization with the random merging of clusters, (2) a renormalization based on minimal values of Kullback–Leibler divergence and (3) a renormalization with merging clusters with minimal values of Rényi entropy. The paper shows that the renormalization procedure allows finding the optimal number of topics 26 times faster than grid search without significant loss of quality.
Unknown On-Body Device Position Detection Based on Ensemble Novelty Detection

In recent years, on-body device position recognition has attracted a lot of attention from the ubiquitous computing community with a view to providing reliable services to users.The existing work has focused on the recognition of classes included in a training dataset, but handling a new position that the recognition system does not know is still impossible.The unknown position should be handled in an appropriate way to avoid incorrect behavior and adapt to each user's way of carrying the device.In this article, we propose a new detection method based on the ensemble learning principle, in which the final results are obtained from a collection of judgments by a weak novelty detector.We devise a method of finding a threshold that maximizes overall accuracy, rather than a mere majority vote.This method is evaluated with three datasets and various conditions to confirm the effectiveness of ensemble novelty detection and the threshold estimation method.
Development of Ludo Game for Elementary School Students 'Motion Material

The purpose of this study is to produce a product in the form of a game module that contains ludo games with textbased media for elementary school children, so that it can complement existing learning at this time.This research was conducted at several Private Primary Schools in Medan Johor District, with the number of small groups in this study being students of Class II SD W.R. Supratman 2 and Sriwijaya Elementary School with a total of 12 students who have the characteristics of the age range of 8-9 years.Subjects in a large group in this study were SD W.R. Supratman Medan with a total of 24 students each school.This research uses the Research & Development (R & D) development model.The final result of this development research is to produce a product in the form of a game module that contains ludo games with text-based media for elementary school children.The conclusion of this development research can complement existing learning at this time, namelythat in learning students can be more effective, meaning that it is completeness in increasing effectiveness or convenience for students in mastering locomotor basic motion material, efficiency, meaning that it is the completeness of learning aids, wherein minimal cost and time so that maximum results can be obtained in mastering locomotor basic motion material, interesting, meaning that it is a completeness of learning that has an attraction so that students can be motivated to use it.
An Approach for Analyzing Influence Factors on Performance of Implementations of Standard Algorithms

This paper illustrates the approach for inspecting factors of hardware details and computing problems that impact the performance of typical computing applications and for estimating its influence. The approach extends the idea of supercomputing lists (like TOP500, Graph500, HPCG and others) and combines it with researching algorithms properties (given by AlgoWiki). Possible applications of the approach are described and the main principles of the implementation are given.
Video Surveillance System with Auto Informing Feature

The present document represents a thorough study of the making of an efficient surveillance system along with a feature of automatically informing the owner about the suspicious movement. In this moving world, normally people are suffering from the availability of time, so if any crime has happened at the site, it will take many days of searching for finding the actual presence of criminals, and thus a good chance for those burglars to flee away to protect themselves. For making the task possible, chose Python as the weapon for this battle and used different efficient techniques like COCO dataset for getting labeled and annotated images, LabelImg for making the annotation set of images, TensorFlow, object detection API for object detection and faster RCNN for training as faster RCNN has shown the highest accuracy for the COCO dataset so far. The owner can be informed in two ways: Either send a message to him via mail or phone or call at the time of suspicious image capturing. Here, both of these cases are used: For mail, the task is done via SMTP and for phone calls Twilio is used which provides us registered phone no. and can make both outbound and inbound calls. After using all the mentioned things and making the model in a way described above, it was found that faster RCNN is much more accurate than the other conventional methods. The results have been very well as RCNN show 86.7% accuracy and 100% has come out with the informing module as there simply the mail will be sent to the one whose mail is given in the code and the same is for Twilio calling.
Upaya Meningkatkan Kompetensi Guru Menggunakan Media Pembelajaran ICT melalui Supervisi dengan Teknik Individual di Sekolah Dasar

&lt;p&gt;Abstract. The of this study was to improve teacher competence in SD Negeri &lt;br /&gt;Kalinanas in using ICT learning media. The study was conducted in SD Negeri 1&lt;br /&gt;Kalinanas for ± 5 months starting Friday, July 22, 2016 until Saturday, November 19,&lt;br /&gt;2016. The subjects of this study were 9 teachers. Data collection is done through&lt;br /&gt;interviews, observation and documentation. From the interviews, the results show that it is important to use ICT learning media very well. The study was conducted in 2 cycles. In the pre cycle, the average score was 78 with a percentage of 53% achievement of all teachers in the research indicators for the use of ICT learning media, in the first cycle 88 average values were obtained with 69% achievement of all teachers in the research indicators of ICT learning media use, and in cycle II obtained an average value of 97 with a percentage of 88% achievement of all teachers in the research indicators of the use of ICT learning media. There has been an increase in teacher competency in the use of ICT learning media after being supervised with individual techniques by the principal of SD Negeri 1 Kalinanas.&lt;/p&gt;