Reviewerly/exHarmony huggingface dataset:
Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

Abstract Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest and application in clinical practices. First, the interpretability of the diagnostic/prognostic results will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. Furthermore, from the clinical aspect, when the datasets are imbalanced in diagnostic categories, the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it is desirable to have a method that could produce explicit transparent and interpretable results in decision-making, even for data with imbalanced groups. Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients, we present our new method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small and succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even for small and rare groups. Results Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover fewer patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discover all patterns implanted in the data, display them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel explainable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.
Congratulations! JACIII Best Paper, Young Researcher, &amp; Best Review Paper Awards 2019

We are pleased to announce that the JACIII Awards of 2019 have been decided by the JACIII Editorial Board. This year, the award winning papers were severely and fairly selected among 399 papers published in JACIII Vols. 20 (2016) to 22 (2018). The awarding achievements of 2019 are as follows: JACIII BEST PAPER AWARD 2019 Title: Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation Authors: Yasufumi Takama, Yu-Sheng Chen, Ryori Misawa, and Hiroshi Ishikawa &lt;a href="https://www.fujipress.jp/jaciii/jc/jacii002200040506/" rel="noopener noreferrer" target="_blank"&gt;J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.4, pp. 506-513, July 2018&lt;/a&gt; JACIII YOUNG RESEARCHER AWARD 2019 Title: Characteristics of Rough Set C-Means Clustering Authors: Seiki Ubukata, Keisuke Umado, Akira Notsu, and Katsuhiro Honda &lt;a href="https://www.fujipress.jp/jaciii/jc/jacii002200040551/" rel="noopener noreferrer" target="_blank"&gt;J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.4, pp. 551-564, July 2018&lt;/a&gt; JACIII BEST REVIEW PAPER AWARD 2019 Title: A Survey of Video-Based Crowd Anomaly Detection in Dense Scenes Authors: Junjie Ma, Yaping Dai, and Kaoru Hirota &lt;a href="https://www.fujipress.jp//jaciii/jc/jacii002100020235/" rel="noopener noreferrer" target="_blank"&gt;J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.2, pp. 235-246, March 2017&lt;/a&gt;
A Systematic Methodology on Class Imbalanced Problems involved in the Classification of Real-World Datasets

Current generation real-world data sets processed through machine learning are imbalanced by nature. This imbalanced data enables the researchers with a challenging scenario in the context of perdition for both the machine learning and data mining algorithms. It is observed from the past research studies most of the imbalanced data sets consists of the major classes and minor classes and the major class leads the minor class. Several standards and hybrid prediction algorithms are proposed in various application domains but in most of the real-time data sets analyzed in the studies are imbalanced by nature thereby affecting the accuracy of the prediction. This paper presents a systematic survey of the past research studies to analyze intrinsic data characteristics and techniques utilized for handling class-imbalanced data. In addition, this study reveals the research gaps, trends and patterns in existing studies and discusses briefly on future research directions.
Interpretation of Formal Semantics from Hand Gesture to Text using Proficient Contour Tracing Technique

As an assorted nation with numerous religion tongues, India has attempted to embrace an official, institutionalized gesture based communication. Where as in Indo-Pakistani communication through signing, is viewed as the prevalent sort utilized in South Asia. Many who are deaf or hard of hearing rely on sign language, to communicate. However the estimation of sign language are very unsophisticated and definitions of what counts as proficiency that varies depends on many factors. There are many existing systems which use shape parameters like orientation, palm centroid ,data gloves with 5 accelerometer sensors , and optical markers which reflect infrared light to recognise hand gestures of sign language. Background subtraction techniques used in these systems are K-means clustering ,boundary counters, Eigen backgrounds using Eigen values and wireless technology and bluetooth for connecting software for transmitting recognised hand gesture signals. They are not cost effective but, the accuracy is not met to the need. Whereas, In our proposed system we concentrate mainly to convert hand gestures to text using contour tracing technique to recognise hand gestures using normal webcam. The semantics are classified by support vector machine with trained datasets. The recognised hand gestures are displayed as text. Our main objective is to resolve the problem of facing interviewer for vocally impaired individuals. This helps them to build their confidence and eradicate their inferiority complex compared to other methods
Movie Sentiment Analysis u sing Feature Dictionary and Multiview Light Semi Supervised Convolution Neural Network

Emotional information in film commentary is very important for emotional analysis. An emotional analysis that focuses on classifying opinions into positive and negative classes according to an emotional glossary is a study. Most existing research focuses on word synthesis and user evaluation, while users' attitudes toward feedback are ignored. To consider this point, this paper uses an emotional analysis and in-depth learning approach to examine the relationship between online film reviews, and this point is used for movie box revenue efficiency. In this paper, this work present a 11 different types of Feature Dictionary. It is modeled with information from sentences (i.e., reviews) and aspects simultaneously. First, Feature Dictionary is created with all aspects of the sentence. After obtaining the aspects, it utilize all data in the source domain and the target domain for training Multiview Light Semi Supervised Convolution Neural Network (MLSSCNN) classifier. To understand the predictive performance of this approach several performance metrics are used. The experimental result shows that the MLSSCNN provides a superior predictive effect than other classifier.
Cover

Meteoritics & Planetary ScienceVolume 54, Issue 2 p. i-i CoverFree Access Cover First published: 07 February 2019 https://doi.org/10.1111/maps.13150AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Volume54, Issue2February 2019Pages i-i RelatedInformation
Domestic Service Corps™

The Domestic Service CorpsTM (DSC) is an organization built upon a technology platform to link members together dynamically in order to brainstorm potential solutions to problems. These problems are posed by government employee clients (federal, state, local) of theoretical or applied significance. Principles shown to be successful at distributing problems solvable by individuals will be adopted, such as human intelligence tests with Mechanical Turk and protein conformations with fold.it. Successful strategies used in matchmaking algorithms such as eHarmony and the medical residents' match will be used to dynamically create compatible working groups among DSC members more diverse than is typically possible. Time spent by any member on the platform will be limited in order to retain high-quality engagement, extend interactions over longer periods to engage more creative processes, and protect members from overuse. Participation is expected to aid members as well as clients, engaging Americans not typically able to serve full-time or in-person and imbuing them with a sense of purpose expected to deliver advantages consistent with known mental benefits of service to others.
The Impacts of Experiential Factors on Satisfaction and Behavioral Intention: An Empirical Study in the 6 th Industry

As South Korea has made rapid growth until the 2000s, the implementation of the five-day workweek and the five-day school week system has been made in a full scale, which leads to an increase in leisure time and an improvement in the level of household consumption.Now, the consumption of leisure leads to active consumption of other cultures beyond simple rest or recharging in the past, and the desire for experiential leisure activities is increasing by directly participating rather than passive leisure activities.This environmental change brought new challenges to rural areas, which is called the sixth industry.Because the 6th industry is relatively recently implemented in Korea, it has been slowly developed than other industries.Though the government makes a lot of administrative and monetary support in policy, its effectiveness is not significant.Moreover, most previous studies relevant to the 6th industry were useful for proposing policy alternatives for the more activated 6th industry, but most of them tended to focus on some factors of activation of the industry.Therefore, this study conducted both literature research and empirical research to achieve the purpose of the study.For the empirical analysis, a survey was conducted for 28 days from February 1, 2020 to February 28, 2020.The subjects of the survey were those who visited the 6th industrial experience center in South and North Gyeongsang Province.As for data distribution and collection, questionnaire method was used, and the collected data were analyzed using SPSS Ver.25 which is a statistical package program.Prior to the hypothesis verification, frequency analysis, reliability analysis, validity analysis, and correlation analysis were conducted to determine whether the data is appropriate.Multiple regression analysis was also conducted to verify the hypothesis through which the results of the study were derived.Based on the results of this study, the conclusions and implications of this study and future research directions were presented.The results out of the empirical analysis showed that cognitive, emotional, social factors, and the factors that induce the experience of the 6th industry presented in this study have a positive effect on the experience satisfaction.In
Computer Graphics in Games

This chapter discusses the specific considerations that apply to graphics in game development, from the platforms on which games run to the game production process. The graphics methods selected for a given game have a profound effect, not only on the game engine code, but also on the art asset creation, and even sometimes on the gameplay, or core game mechanics. The graphics methods selected for a given game have a profound effect, only on the game engine code, but also on the art asset creation, and even sometimes on the gameplay, or core game mechanics. In some sense, multiplatform development is the hardest of all, since the multiplatform game developer has neither the assurance of a fixed platform or the convenience of a single high-level API. Games distributed through this service must use a virtual machine platform provided by Microsoft for security reasons.
An Integrated Strategy for Data Mining Based on Identifying Important and Contradicting Variables for Breast Cancer Recurrence Research

Cancer leads to most deaths worldwide, and breast cancer isa leading disease that causes death among women. This disease is unique in the way that once treated, can recur in some cases. Individuals are unable to identify their condition before it becomes dangerous. Extracting significant predictive features of breast cancer is an important and risky job for further study. Researchers have applied data mining techniques in medical science. Several authors suggest that a single method doesn't resolve issues in diagnosing problems, and a hybrid model is desirable. In this paper, the authors propose an integrated approachto avoid Type 1 and 2 errors in predicting recurrence. They identify important and contradicting variables and consider them for inclusion and exclusion, respectively, to revise the dataset. The evaluation of findings of crucial methods, using original and revised datasets, widens the choice of identifying the technique with higher accuracy. The results show that the accuracy improves with the selection of variables restricted to the ones identified as relatively significant, and the dataset revised after elimination of contradicting variables.
Problems in Setting Teaching Objectives and Countermeasures to Work out Teaching Objectives in College English Class

The professional competence of college English teachers in setting teaching goal has a direct impact on their teaching design level. Enhancing college teachers’ability in setting classroom teaching goal facilitates the improvement of their design level. By combining with the teaching design scheme of normal class, this paper defines the meaning of College English classroom teaching objectives,analyzes the problems that the college English teachers have in setting teaching objectives, and puts forward corresponding countermeasures to develop the college English teachers’ capability of setting teaching objectives, which are expected to promote college English teachers’ ability to design the activities for their class.
Appositeness and usage of Visual Cryptography: An Exploration

Visual cryptography is a cryptographic strategy which enables visual data to be scrambled so that unscrambling turns into a mechanical activity that does not require a PC. In this paper, we expect to think about the extraordinary application zones of Visual Cryptography. Visual Cryptography is a wide zone of research utilized in information stowing away, anchoring pictures, shading imaging, media and other such fields. Visual Cryptography comes in the field of information stowing away utilized in cybercrime, record positions and so on. This paper centers around the application zones of visual cryptography from four distinctive research papers/diaries which talk about the most critical application regions of visual cryptography.
Ranking of Academic Programs using Fuzzy Modified Topsis

Academic program ranking is very important for education providers to gauge the competitiveness and robustness of programs offered at their institutions.Identification of the level of competitiveness and robustness of academic programs involve several criteria and is very subjective to human judgements.Thus, a multi-criteria decision method with fuzzy environment is applied to solve the ranking problem.This paper highlights the ranking of 11 academic programs offered at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Shah Alam based on 7 criteria with 12 decision makers.The methodology consists of 2phases.First, determining the importance weightage of 7 criteria using Fuzzy Analytical Hierarchy Process (FAHP) method.Second, ranking of the 11 academic programs using modified Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (MFTOPSIS) method.The results show, future job demand and percentage of graduate employed six months upon graduation are the two most important criteria to be used in ranking the academic programs.While, program Bachelor of
HAK ATAS KEKAYAAN INTELEKTUAL DALAM DUNIA TEKNOLOGY BERBASIS REVOLUSI INDUSTRI 4.0

Academic achievement is one of the major subjects in the educational researches.Academic achievement is defined as the outcomes of the education. Preventingstudents from failure is related with identifying the factors affecting the achievement.By defining the important factors affecting the students’ achievement, failure can beunder controlled [Ozguven, 1974]. Education is a complex process and many factorsdirectly or indirectly may affect achievement. Besides, it is difficult to properly definethe major factors influencing students' achievement. Although researchers haveidentified several factors affecting the student achievement, there are still somearguments about them. Some researchers attribute the student’s achievement to theschool; others indicate that the school makes little impact on academic outcome. Otherresearchers say that the effective teacher is the only one who can play the main role interms of student progress. All the factors such as teacher, school context, classroomcontext and the community around the school contribute or impact student’sachievement somehow [Harris, 1999]. In all these thoughts, we can see that there arethree common factors affecting the students’ achievement. Factors related withteachers, students and lastly environment. All these factors are affective on thestudents’ grade more or less. In our research, we handle the students’ perspective andsearch for the factors affecting the students’ achievement i n their demographic itemssuch as age, sex, and their academic background such as high school type, grade,University Entrance score, their families’ social and economic status, income, housing,computer knowledge skills and social behaviors and mannerism. The aim of this studyis to find out what kinds of factors have effects on students’ exam success among thestudents at School of Transportation and Logistics by using the path analysis.
A new analog-to-digital method for forming and using of cartographic visualization of the geo-space with the use of multimedia means

The article discusses the development directions of cartographic methods to provide users with geoinformation using modern digital devices. Explains the need to review the substantive essence, the system of symbols, use, information content and the peculiarity of the perception of cartographic information by users. A new method of cartographic support of the needs of the economy and society, which is based on a combination of the capabilities of analog traditional maps, mobile and multimedia cartography, is substantiated and proposed. Various ways of increasing the volume of cartographic information using traditional maps with special markers on them, multimedia files and mobile devices, introducing an interactive mode of working with a map, and also taking into account the peculiarities of the perception of digital mobile maps are considered. The authors proposed option for combining sources of geoinformation to increase the information capacity of a map by combining a traditional map and a set of markers that either store the necessary information in encoded form or represent a link to external sources. We analyzed the features of QR codes and highlighted the basic possibilities of using them when creating a new type of interactive map, and also performed statistical studies of this option regarding a quantitative increase in the amount of stored information. Five methods of creating sources of geospatial information and using it interactively, including in the field and in the absence of communication for the transmission of digital data, are proposed and a summary table of the advantages and disadvantages of the methods of analog-to-digital representation of spatial information is compiled. The results of experimental work are presented, including samples of new types of maps compiled.
Evaluation of High-Resolution Satellite-Derived Solar Radiation Data for PV Performance Simulation in East Africa

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.
Design Analysis of Turbo Decoder Based on One MAP Decoder Using High Level Synthesis Tool

High Level Synthesis (HLS) tool does not only simplify the designing operation and rapid prototyping but also allows the designers to explore large number of design's techniques such as parallelism, pipeline, memory partitioning and many other techniques.Turbo decoder based on Maximum APosterior Probability (MAP) algorithm is designed in this work using Vivado HLS.The normal turbo decoder with two MAP decoders were implemented with and without parallelism and proposed a new design of turbo decoder with one MAP decoder and it was designed with and without parallelism using different window technique in HLS tool which it is not explored previously.These designs were implemented for different frame size in this work.A comp-arison in latency and resource utilization where done and how a tradeoff done between these two parameters to reach the specific design that we need.The new design produces better results.
Pixel Classification

An important task in image analysis is to assign each pixel in an image to a predefined class of objects. This class can be a specific anatomy like the liver in a computed tomography image. One commonly used step is pixel classification where each pixel is assigned a class-based purely on its value and independently of the pixel neighbors. In this chapter, we introduce basic pixel classification approaches like minimum distance classification and parametric classification, where the pixel value distributions are approximated using Gaussian kernels. Finally, a brief overview of Bayesian classification is given.