Reviewerly/exHarmony huggingface dataset:
TRANSFORMING BIG DATA INTO KNOWLEDGE USING SEMANTIC STREAM PROCESSING TECHNOLOGY: CHALLENGES AND EARLY PROGRESS

In the Big Data era, the volume of streaming data generated from heterogeneous data sources such as social media, smart devices or sensor networks is overwhelming but enables human users and applications to better understand the world around us.Understanding the context, situation or the environment where the data has been produced is a challenging task due the dynamic and fast nature of the streaming data.Modelling the big data using RDF model and applying continuous SPARQL queries over it using Semantic Web tools helps create machine-understandable meanings and valuable insights of the underlying data.This paper provides an overview of the Big Data theoretical foundation, challenges and stages that need to be taken into consideration when building a Big Data pipeline architecture.Also, the research efforts that have been made in the past years using Semantic Stream Processing techniques are described.
RANCANG BANGUN SISTEM INFORMASI KEPEGAWAIAN DI KECAMATAN CIPARAY KABUPATEN BANDUNG

Management of personnel data that are running in the Public and Civil Service Sub District of Ciparay still semi computerization is using Microsoft Word and Microsoft Excel. The situation with the Public and Civil Service Sub Division that one function of implementing the collection, processing, storage and maintenance of data, as well as documentation of personnel, takes a long time in its data management processes. Of these problems led to the idea to create a personnel information system design. This design is expected to help the developer at the development stage of the system. The design includes process management and staffing data storage that consists of employee personal data, benefits information and personnel data as well as the reporting process and mail (SKUM and SK). The study was conducted using data collection methods, and methods of approach and development of the system. System approach used is object-oriented systems approach, using tools Unified Modeling Language (UML), a method using a model system development Rapid Application Development (RAD), which consists of the Business Modeling, Data Modeling, Application Generation, Testing and Turnover. Language programming using PHP Framework Laravel based Model View Controller (MVC), the database processing using MySQL and web server XAMPP, validation data on the level from can ensure validation of input data well, implementation of the system based on MVC makes the programming process became more organized and orderly, and the use of software testing with Black-Box Testing focused on the functional requirements of the software, as well as based on the level of form validation. With the design is expected to be a reference for the developer and can proceed to the next stage which is the stage of implementation by the developer. Keywords : SIMPEG, Laravel, MVC, Personnel, RAD, UML
Consistent Scale Normalization for Object Recognition.

Scale variation remains a challenge problem for object detection. Commonparadigms usually adopt multi-scale training & testing (image pyramid) or FPN(feature pyramid network) to process objects in wide scale range. However,multi-scale methods aggravate more variation of scale that even deepconvolution neural networks with FPN cannot handle well. In this work, wepropose an innovative paradigm called Consistent Scale Normalization (CSN) toresolve above problem. CSN compresses the scale space of objects into aconsistent range (CSN range), in both training and testing phase. Thisreassures problem of scale variation fundamentally, and reduces the difficultyfor network learning. Experiments show that CSN surpasses multi-scalecounterpart significantly for object detection, instance segmentation andmulti-task human pose estimation, on several architectures. On COCO test-dev,our single model based on CSN achieves 46.5 mAP with a ResNet-101 backbone,which is among the state-of-the-art (SOTA) candidates for object detection.
PROFESSIONAL INTERESTS OF SCHOOLCHILDREN AS A FACTOR IN FORECASTING THE LABOR MARKET DEVELOPMENT OF AN INDUSTRIAL CITY

Introduction. The article deals with the problems of professional self-determination of high school students in a large industrial city. It is shown that among the factors of forecasting the development of the labor market the professional interests of the employee’s personality are not singled out as a separate factor. On the part of the city, there is a request for an “educational” model of vocational guidance, in which the problem of a person’s choice between “want” and “need” is decided in favor of society. At the same time, the results of diagnostics and vocational counseling for schoolchildren show that professional choice is made primarily on the basis of the professional interests of the individual, which does not always correspond to the needs of the regional labor market in professional staff to ensure the effectiveness of industrial growth. The purpose of the article is to present the results of the professional interests study in high school students within the framework of forecasting the development of a regional labor market in an industrial city. The Methodology of the research is based on the subjectival and activity approaches to the process of a personality’s professional formation as well as the analysis of scientific literature. The research was carried out in the form of testing. The”Proforientator” (Moscow) computer complex was used to perform the testing. Statistical data processing was conducted making use of non-parametric chi-square test for one selection. Results. Based on a theoretical analysis of the literature, the role of the professional interests of schoolchildren as a factor in forecasting the regional labor market is determined. The dominant professional interests of high school students, indicating the presence of a contradiction between the needs of the individual and the regional labor market, were revealed Conclusions. It is concluded that it is necessary to develop a set of measures to eliminate the sharp contradiction between the current needs of the labor market and the professional interests of high school students.
The Global Workspace Needs Metacognition

The two leading cognitive accounts of consciousness currently available con-cern global workspace (a form of working memory) and metacognition. There isrelatively little interaction between these two approaches and it has even beensuggested that the two accounts are rival and separable alternatives. Here, weargue that the successful function of a global workspace critically requires thatthe broadcast representations include a metacognitive component.
The Twist In The Tale

We are surrounded by & constantly engaging with stories whether we like it or not. Stories which come in the form of advertising, news, information, entertainment, make believe & some even impart knowledge. But are we able to engage with all of them or do we even wish to engage with all of them? Is it possible to completely comprehend every story in its entirety? Are we able to sieve one from the other or is it all a strange complex mix of narratives running parallel and confusingly intertwined? What do we make of this information overload considering our attention spans these days are 20 seconds at best because as a society we all seem to be suffering from collective Attention Deficit/Hyperactivity Disorder popularly known as ADHD. Despite all this we all do engage, believe & invest our time & effort in stories. We are constantly buying into ideas & information sometimes with cynicism, sometimes with complete faith & often times with a total & absolute suspension of our disbelief especially when we want Amitabh Bachchan to beat a tiger in hand to hand combat for instance. And if you ask Why the answer is simple we all love to be entertained! There is nothing like entering a world of make believe, to live a fantasy, be transported to La La Land, go on impossible adventures, be heroic, sing, dance, vanquish & romance with gay abandon & live the story thus escaping from the boring, repetitive drudgery of our mundane lives. Although this may seem a cliche, it is true. However interesting our lives maybe living someone else’s vicariously seems more interesting always. And if we talk about the future of storytelling I believe we are living that Future, Future is Today & as I explore the narrative form & content I believe the future of the narrative structure especially dramatic narrative shall be determined by how we consume those narratives / stories, henceforth.
The Fokker-Planck equation in estimation and control

In this work it is shown how the Fokker-Planck equation can be used to address the solution of problems in a variety of fields in which a set of dynamical agents is concerned, including continuous discrete filters, adaptive control, and tracking groups of targets.
A PERFORMANCE EVALUATION OF A MULTIFACETED ELECTRONIC VOTING FRAMEWORK

This paper, we present a performance evaluation of a multifaceted electronic voting framework.The electronic voting model that was developed on the framework is capable of handling electronic ballots with multiple scopes simultaneously via three different electronic voting means.The model catered for probity of an election process in terms of generic and functional requirements.The performance evaluation detailed the degree to which the framework meets the generic and functional requirements of electronic voting systems using a five-point psychometric scale.The results of the quantitative analysis showed that the framework is capable of ensuring voters' privacy and authenticity while the integrity, accuracy and verifiability of ballots casted are guaranteed.
Deep learning for the automatic classification of tissue types in breast biopsies

Breast biopsies are crucial in the process of detecting a wide range of diseases such as breast cancer.The evaluation of these biopsies is performed by trained pathologists that are often overworked due to the increasing number of pathologies requested.Automatic tumour detection techniques have been developed, achieving very good results.In this work, we propose to classify breast biopsies in all the different types of tissue present in them.The tissue types were identified by hand-labeling them following the indications of an expert pathologist.Afterward, they were trained with different convolutional neural networks such as GoogleNet [12], AlexNet [4], SqueezeNet [5] and DenseNet [4].Out of these four networks, GoogleNet outperformed all of them achieving 95.4% of accuracy.Finally, we tried to identify why the networks were underperforming while also suggesting how results could be improved.
LEARNING STRATEGIES THAT CONTRIBUTE TO ACADEMIC EFFICIENCY IN RELATION TO THE BUSINESS SCHOOL STUDENT'S LEARNING STYLES

In the XXI century one of the main challenges facing the education system at the superior level, is to promote education through the creation of flexible teaching environments, which allow raising the level of autonomous learning in students, that is why, in this research, the study of learning styles and their correlation with academic performance is proposed, demonstrating that if it is taught based on the strategies of each learning style, the flow in the teaching-learning process channel is accelerated, which will favor the development of skills and abilities that build meaningful knowledge.The results reveal that among the styles of reflexive, active and pragmatic learning there is a correlation between the variables analyzed, while in the students with emphasis on theoretical styles no significant correlation was found.This information shows the importance of establishing strategies for learning style that contribute to improving academic performance.
Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images

This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of the images were computed to validate the comparison. The image histograms of both methods show that the distribution of red, green, and blue channel is better than the histograms of original images. In terms of the four metrics, the method that uses FA shows higher performance than CS. In terms of image variance and entropy, the method using CS shows better results than FA. These results suggest that when the performance metrics used are MSE, PSNR, MSSIM, and FSIM, the method using FA is more suitable for cancer cells image enhancement and thresholding. However, when the variance and entropy of the images are used as the performance metrics, the method using CS is more suitable for cancer cells image enhancement and thresholding. Both methods will be useful to assist in the analysis of cancer cell images by the experts in the field.
Online quiz application

An Online Quiz Application is the most widely used application nowadays. It is one of the most helpful ways to conduct examinations or to prepare for them in any organization. The programs which help in conducting the exam in an effective way and saves time are Online Quiz Programs. It also helps to get through the tutorials of particular topics. You can put forth any query of yours and you will get the appropriate answer to your question. Therefore, it can be said that an Online Quiz Application helps you to save time in giving exams and provides security. This research paper will describe a Quiz Application that helps the students in a college or in a university to give exams in an effective way.
USING TWA (THINK-WHILE-AFTER) TECHNIQUE TO IMPROVE THE ANALYTICAL EXPOSITION TEXT READING COMPREHENSION OF THE ELEVENTH GRADERS OF SMA NEGERI 2 MAGELANG IN THE SCHOOL YEAR 2018/2019

Reading comprehension is a component of English skill that has to be mastered by the students. However, XI IPS 1 students of SMA Negeri 2 Magelang in the school year 2018/2019 have difficulties in comprehending reading of analytical exposition text. Thus, the writer conducted a research to overcome the problem by applying TWA technique. The objectives of this paper are to know how great is the increase of motivation in learning analytical exposition text reading comprehension of the eleventh graders of SMA Negeri 2 Magelang in the school year 2018/2019 after being taught by using TWA Technique and to know how great is the improvement of the analytical exposition text reading comprehension of the eleventh graders of SMA Negeri 2 Magelang in the school year 2018/2019 after being taught by using TWA Technique. The writer conducted a Classroom Action Research (CAR). The writer collected the data by using non-test technique (observation, questionnaire, and documentation) and test technique. Based on the result, there is increase of students’ motivation in learning reading comprehension of analytical exposition text. The results of observation, questionnaire, and documentation show that most of the students have behavioral and perception change. Moreover, the improvement also show in the test results. The writer concluded that the use of TWA technique could increase the students’ motivation in learning analytical exposition text reading comprehension and improve the students’ analytical exposition text reading comprehension.
Understanding and Improving the Intermediate Features of FCN in Semantic Segmentation

In full convolution networks, the intensive prediction results generated by intermediate features have a disadvantage of low contribution rate. This paper used the visualization tools and quantitative analysis, and proposed a new training strategy using dynamic weighting strategy which was verified by experiments. The results show that the strategy can effectively improve the accuracy of prediction intermediate features results.
ACCLOUD-MAN - Power Efficient Resource Allocation for Heterogeneous Clouds

In this paper we propose ACCLOUD-MAN, a novel resource manager for heterogeneous cloud data centers. In heterogeneous clouds a user request can be satisfied with more than one physical resource alternative. Resource manager must decide which resource alternative will be chosen, along with the decision of the server the request will be assigned to. ACCLOUDMAN's resource management objective is to reduce the power consumption of the cloud. Manager is modeled as an Integer Linear Problem and is implemented on MATLAB, together with a simulation platform. Simulation results show that the solution times are too long for practical applications. Dividing requests into smaller groups is suggested to improve the timings. It is seen that this method improves the timings with a penalty in decision performance.
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference

Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify predictive uncertainty. Our contribution in this work is to propose an uncertainty aware multimodal Bayesian fusion framework for activity recognition. We demonstrate a novel approach that combines deterministic and variational layers to scale Bayesian DNNs to deeper architectures. Our experiments using in- and out-of-distribution samples selected from a subset of Moments-in-Time (MiT) dataset show a more reliable confidence measure as compared to the non-Bayesian baseline and the Monte Carlo dropout (MC dropout) approximate Bayesian inference. We also demonstrate the uncertainty estimates obtained from the proposed framework can identify out-of-distribution data on the UCF101 and MiT datasets. In the multimodal setting, the proposed framework improved precision-recall AUC by 10.2% on the subset of MiT dataset as compared to non-Bayesian baseline.
The Missing Link in Software Development : - A case study of a small software company and the interaction with its clients in joint software projects.

Introduction: The impact of software in today’s society is massive and the market is competitive. The clients are demanding tailor-made solutions, but the development is complex as software project ...
Experimental Study of Wireless Music And Speech Communication with the Use of Technology of Single-Wire Lines

The paper presents the results of experimental studies on the wireless transmission of music and speech using the technology of single-wire lines.The ability to transmit energy, speech and music over a single-wire line allows this technology to be used to create a wireless transmission line.
CECEK IN CULTURAL INTERPRETATION AMONG BALINESE COMMUNITY

The linguistics study is currently developing, and now it also covers elements of cultures. In the study of cultural linguistics, linguistic symbol is always associated with the elements of culture itself, such as religious system, knowledge system, equipment system, livelihood system, social system and the art system. One of linguistic expression in Balinese culture is cecek which is not only considered as animal, but also associated with the religious system of the Hindu community as a symbol of beginning of knowledge. This research will explore further the cultural interpretation of Cecek among Balinese people especially related with the correlation between the study of language and culture. To obtain the data, interview method was used, where the data collected from the informants before drawing a conclusion. Keywords: Cecek, Cultural Interpretation, Balinese Community
Geological Survey of New South Wales: New products and data

The Geological Survey of New South Wales (GSNSW) collects, manages and distributes geological, geophysical, geochemical and geospatial data to inform government, resource industries and the communi...
Architecture Design of the Internet of Things (IOT) based on Cloud Computing

Cloud computing and the Internet of Things (IOT) are two exceptionally diverse advances, both of which are portion of our life presently. The appropriation and utilize of them show up to be indeed more unavoidable and they are getting to be more critical as the most components of long run Web. A case of an unused demonstrate in which cloud frameworks and the Internet of Things (IOT) are blended is known as a calculate which makes it conceivable to actualize a huge volume of the programs. The significance and improvement of the utilize of the Web industry of things chain are discussed. The fundamental thoughts strategies of cloud computing and the Internet of Things (IOT) are portrayed. At the side cloud computing, the Web has coordinates the Question Administration Framework Administration System. The Broker Product System employments an unused stage of cloud computing innovation, which significantly makes strides the working effectiveness of the Internet of Things (IOT). Under the framework of the Internet Commerce Objects, a platform construction, operation plan is provided, can be used as a construction reference to design a platform for the Internet of things.
Sparse Ordinal Regression via Factorization Machines

Most existing ordinal regression methods are adapted from traditional supervised learning algorithms (e.g., support vector machines and neural networks) which have shown to work well mostly on dense data. However, the use of existing ordinal regression methods on sparse data has received less scrutiny. This paper proposes to address the sparsity issue arose in many real-world ordinal regression applications by leveraging the feature interaction modeling techniques. Following the popular threshold methodology in ordinal regression studies, we extend Factorization Machines, an effective solution to modeling pairwise feature interactions in sparse feature space, to ordinal regression. The proposed model, namely Factorization Machines for Ordinal Regression (FMOR), combines the ability of threshold methodology in predicting targets of ordinal scale with the advantages of factorization models in handling high-dimensional sparse data. Through extensive experimental studies, we show that the proposed FMOR is both effective and efficient against state-of-the-art baselines.