Reviewerly/exHarmony huggingface dataset:
The Tilted Self: Visuo-Graviceptive Mismatch in the Full-Body Illusion

The bodily self is a fundamental part of human self-consciousness and relies on online multimodal information and prior beliefs about one's own body. While the contribution of the vestibular system in this process remains under-investigated, it has been theorized to be important. The present experiment investigates the influence of conflicting gravity-related visual and bodily information on the sense of a body and, vice versa, the influence of altered embodiment on verticality and own-body orientation perception. In a full-body illusion setup, participants saw in a head-mounted display a projection of their own body 2 m in front of them, on which they saw a tactile stimulation on their back displayed either synchronously or asynchronously. By tilting the seen body to one side, an additional visuo-graviceptive conflict about the body orientation was created. Self-identification with the seen body was measured explicitly with a questionnaire and implicitly with skin temperature. As measures of orientation with respect to gravity, we assessed subjective haptic vertical and the haptic body orientation. Finally, we measured the individual visual field dependence using the rod-and-frame test. The results show a decrease in self-identification during the additional visuo-graviceptive conflict, but no modulation of perceived verticality or subjective body orientation. Furthermore, explorative analyses suggest a stimulation-dependent modulation of the perceived body orientation in individuals with a strong visual field dependence only. The results suggest a mutual interaction of graviceptive and other sensory signals and the individual's weighting style in defining our sense of a bodily self.
Detection of Dangers in Human Health with IoT Devices in the Cloud and on the Edge

Smart bands are wearable devices that are frequently used in monitoring people’s activity, fitness, and health state. They can be also used in early detection of possibly dangerous health-related problems. The increasing number of wearable devices frequently transmitting data to scalable monitoring centers located in the Cloud may raise the Big Data challenge and cause network congestion. In this paper, we focus on the storage space consumed while monitoring people with smart IoT devices and performing classification of their health state and detecting possibly dangerous situations with the use of machine learning models in the Cloud and on the Edge. We also test two different repositories for storing sensor data in the Cloud monitoring center – a relational Azure SQL Database and the Cosmos DB document store.
LONG WAVE INFRARED IMAGE COLORIZATION FOR PERSON RE-IDENTIFICATION

Abstract. Person re-identification (ReID) in color and thermal images require matching of the object color and its temperature. While thermal cameras increase the performance of ReID systems during the night-time, identification of corresponding features in the visible and the long-wave infrared range is challenging. The biggest challenge arises from the multimodal relationship between an object’s color and its temperature. Modern ReID methods provide state-of-the-art results in person matching in the visible range. Hence, it is possible to perform multimodal matching by translation of a thermal probe image to the color domain. After that, the synthetic color probe image is matched with images from the real color gallery set. This paper is focused on the development of the ThermalReID multispectral person ReID framework. The framework performs matching in two steps. Firstly, it colorizes the input thermal probe image using a Generative Adversarial Network (GAN). Secondly, it matches images in the color domain using color histograms and MSCR features. We evaluate the ThermalReID framework using RegDB and ThermalWorld datasets. The results of the evaluation are twofold. Firstly, the developed GAN performs realistic colorization of thermal images. Secondly, the ThermalReID framework provides matching of persons in color and thermal images that compete with and surpass the state-of-the-art. The developed ThermalReID framework can be used in video surveillance systems for effective person ReID during the nighttime.
ANALISIS PERBANDINGAN DETECTION TRAFFIC ANOMALY DENGAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM)

Intrusion Detection System (IDS) is a software or hardware that can be used to detect any abnormal activity in the network. Situations often arise from various network access in the form of information or data that can cause problems. Detection is a system for detecting activities that are disturbing data access in information. IDS has two methods of doing detection, namely Rule Based (Signature Based) and Behavior-Based. Anomaly traffic can detect an increase in the number of user access and at any time there will be an attack from another party on the network. This study uses 2 algorithm methods are Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes results through the Distributions and Radviz graph data samples have a probability value of 0.1 and the highest probability value is 0.8. Support Vector Machine (SVM) produces a graph that has greater accuracy.
User Story based Information Visualization Type Recommendation System

To help users to determine the most appropriate visualization type is a useful feature of business visualization tools.Existing systems often give preliminary suggestions based on data sources but usually cannot make practical final decision.User stories are generalizations of user requirements.To recommend visualization type based on user stories can make better use of human experience to achieve automated decision making.One approach discussed in the paper is using machine learning techniques to model existing visualization types with corresponding user stories, and then use this model to predict recommended visualization type for new user story.This paper designs and implements a recommendation system prototype ReViz to verify the feasibility of this approach.As a typical web application, Modeling, Input Processing and Predicting components of ReViz are programmed using Python with Flask framework and Anaconda package set, and user interface is implemented using HTML, JavaScript and CSS with Bootstrap front-end library.The evaluation results show that ReViz can give recommended visualization type based on user story keywords.As a data-based intelligent software development technology achievement, visualization type recommendation system can also be integrated into larger business information management systems.
Hiding Malware On Distributed Storage

The Blockchain technology has been one of the major innovations in recent years. Crypto-currencies and decentralized applications are increasingly adopted. Their principles led the foundation for new ideas underlying others distributed ledger technologies (DLTs). In particular, decentralized storage employs non-recursive functions and similar algorithms compared to the Blockchain technology. Furthermore, digital signatures procedures have been used for storage validation. This paper summarizes IPFS and Swarm technologies and exposes how we managed to hide our malware on distributed storage. Our demonstration’s results illustrate the possibility to hide malicious content by segregating it and reveal that decentralized storages have many concerns and lack security verification procedures.
Statistical multidirectional line dark channel for single‐image dehazing

Outdoor scenes often contain atmospheric degradation, such as fog or haze, which deteriorate the performance of tracking, autonomous driving and surveillance systems, among others, making dehazing methods an area of considerable interest. However, some dehazing techniques are computationally demanding, generating a trade-off between time-consumption and restoration quality. A new method is proposed for improving outdoor images taken with haze effects while making them less time-consuming. The proposed method was inspired by the Radon transform and tailored for dehazing images by computing the dark channel, in addition to using statistical computations and a heuristic approach to avoid saturated areas. The results obtained were subjected to a reduced-reference image quality dehazing assessment, a full-reference metrics Structural SIMilarity (SSIM) index and peak signal-to-noise ratio (PSNR) over real-world and synthetic outdoor images. The results demonstrate that the proposed method presents an adequate balance between new visible edges, increased gradient and saturated pixels, in addition to obtaining at least a 5% increase in the SSIM index and, a 16% increase in the PSNR index, as well as being 5.37 times faster than the four dehazing methods recently introduced in the literature.
Penerapan Model Pembelajaran Circuit Learning Berbantuan Media Microsoft Power Point untuk Meningkatkan Hasil Belajar IPS pada Peserta Didik Kelas IV SDN 3 Palangka Tahun Pelajaran 2016/2017

This study aims to: (1) To describe the learning activities of fourth grade students during Social Studies learning by applying the Circuit Learning learning model assisted by Microsoft PowerPoint media and (2) To improve the learning outcomes of fourth grade students during IPS learning by applying the model learning Circuit Learning supported by Microsoft PowerPoint media. The type of research used in this study was Classroom Action Research (PTK) with research subjects all of the fourth-grade students of Palangka Elementary School-3 which numbered 35 students. Data collection techniques in this study used observation and tests. Data analysis using qualitative, quantitative data analysis. The results of this study indicate that: (1) Activity of Grade IV students in Palangka Elementary School-3 of 2016/2017 Academic Year in Social Studies learning after applying the Circuit Learning model assisted by Microsoft PowerPoint media to be very good because students are active and enthusiastic in following the process learning. The observations of teacher activities on teaching and learning activities carried out by observer I and Observer II during the social studies learning the process in the first cycle were 3.73 in the outstanding category, and the universal aspect of the students was 3.57 in the very well typed. (2) There is an increase in social studies learning outcomes by implementing Microsoft PowerPoint Circuit Assisted learning model on social studies subjects in grade IV of the 2016/2017 SDN-3 Academic Year. This is indicated by the learning outcomes seen through the class average in cycle I which is equal to 80 and the completeness percentage of students in classics namely 100%, from the predetermined provisions namely for KKM of 65 and completeness criteria in classical 85% with this then It can be concluded that there is an increase in social studies learning outcomes after applying the Learning Learning model assisted by Microsoft PowerPoint
Content-based Dwell Time Engagement Prediction Model for News Articles

Heidar Davoudi, Aijun An, Gordon Edall. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Code Reuse between Java and Android Applications

Java and Android applications can be written in the same programming language. Thus, it is natural to ask how much code can be shared between them. In this paper we perform a case study to measure quantitatively the amount of code that can be shared and reused for a multiplatform application running on the Java platform and the Android platform. We first configure a multiplatform development environment consisting of platform-specific tools. We then propose a general architecture for a multiplatform application under a guiding design principle of having clearly defined interfaces and employing loose coupling to accommodate platform differences and variations. Specifically, we separate our application into two parts, a platform-independent part (PIP) and a platform-dependent part (PDP), and share the PIP between platform-specific versions. Our finding is that 37%–40% of code can be shared and reused between the Java and the Android versions of our application. Interestingly, the Android version requires 8% more code than Java due to platform-specific constraints and concerns. We also learned that the quality of an application can be improved dramatically through multiplatform development.
Accuracy Assessment of Similarity Measures in Collaborative Recommendations Using CF4J Framework

There are various libraries that facilitate the design and development of recommender systems (RSs) research in both the academia and industry.Different libraries provide a different set of functionalities based on their foundational design principles.When new algorithms are proposed, researchers need to compare these against prior algorithms considering many challenges such as reproducibility of results, evaluation metrics, test harnesses, etc.Although many open source RS libraries exist to carry out research experiments and provide a varying degree of features such as extensibility, performance, scalability, flexibility, etc.To that end, this paper describes a recently introduced open-source RS library, Collaborative Filtering for Java (CF4J), which is specially designed for collaborative recommendations.Firstly, the brief internals of the CF4J framework are explained and it has been compared with other related libraries such as LibRec, LensKit, and Apache Mahout based on the recommendation approaches and evaluation tools.Secondly, we have summarized all the state-of-art similarity measures provided by the CF4J library.Finally, in order to determine the accuracy of these similarity measures, several experiments have been conducted using standardized benchmark datasets such as MovieLens-1M, MovieLens-10M, and MovieLens-20M.Empirically obtained results demonstrate that the Jaccard-Mean Squared Difference (JMSD) similarity measure provides better recommendation accuracy among all similarity measures.
Evaluasi Program Pendidikan Pemakai Dengan Model CIPP di Perpustakaan Fakultas Teknik UGM

The optimal use of libraries is through user education, because through this program users get a series of knowledge about information sources. One way to find out the success of a program is to do an assessment. This study aims to evaluate the implementation of user education that has been implemented in the Library of the Faculty of Engineering UGM. CIPP Model (Context, Input, Process &amp; Product) used for evalution with a qualitative approach. Research subjects were members of the UGM Faculty of Engineering who had attended user education programs and library management team. Data collection is done with documentation, interviews, and observations. The results showed (1) the Context evaluation was good, there was a match between the program with the vision and mission of the institution, (2) Evaluation of inputs was good, supporting facilities and facilities were adequate, (3) Process evaluation was sufficient, the implementation schedule needed to be improved, ( 4) Evaluation of the product is good, there is a certificate as a sign of following the activity by achieving the competency that has been set. The factors that support the implementation of the program are competency of human resources, communication, adequate facilities and infrastructure, and good cooperation with stakeholders. The factors that hinder the program are the schedule of activities that are not yet routine, those responsible for the program cannot be delegated to other people, there is no analysis of student needs, and there is no written policy on this program. The recommendation of this research is that it is necessary to improve several inhibiting factors so that the implementation of user education activities can run smoothly in accordance with the stated goals.
Cloud Infrastructure for Storing and Processing EEG and ERP Experimental Data

Current infrastructures for experimental data, results and computational tools make a shift from locally maintained solutions to remote cloud-based infrastructures. It brings a higher availability, sustainability and performance. However, specifics of different research areas require development of customized solutions for individual research domains. For example, electroencephalography and event-related potentials (EEG/ERP) use specific devices, data formats and machine learning workflows. As a solution, a cloud-based system for the EEG/ERP domain containing a distributed data storage, a signal processing method library and a client GUI is presented. The signal processing method library is used for training of classifiers and classifying the data in the cloud-based system controlled by the GUI. The presented system was tested using a machine learning workflow based on the data stored in the system. In the workflow, various classifiers were trained and their parameters stored into the system. Finally, testing data were classified using previously trained classifiers.
Feature based Algorithmic Analysis on American Sign Language Dataset

Physical disability is one of the factor in human beings, which cannot be ignored.A person who can't listen by nature is called deaf person.For the representation of their knowledge, a special language is adopted called 'Sign-Language'.American Sign Language (ASL) is one of the most popular sign language that is used for learning process in deaf persons.For the representation of their knowledge by deaf persons, a special language is adopted 'Sign-Language'.American Sign Language contains a set of digital images of hands in different shapes or hand gestures.In this paper, we present feature based algorithmic analysis to prepare a significant model for recognition of hand gestures of American Sign Language.To make a machine intelligent, this model can be used to learn efficiently.For effective machine learning, we generate a list of useful features from digital images of hand gestures.For feature extraction, we use Matlab 2018a.For training and testing, we use weka-3-9-3 and Rapid Miner 9 1.0.Both application tools are used to build an effective data modeling.Rapid Miner outperforms with 99.9% accuracy in auto model.
Optimization and Security in Information Retrieval, Extraction, Processing, and Presentation on a Cloud Platform

This paper presents the processing steps needed in order to have a fully functional vertical search engine. Four actions are identified (i.e., retrieval, extraction, presentation, and delivery) and are required to crawl websites, get the product information from the retrieved webpages, process that data, and offer the end-user the possibility of looking for various products. The whole application flow is focused on low resource usage, and especially on the delivery action, which consists of a web application that uses cloud resources and is optimized for cost efficiency. Novel methods for representing the crawl and extraction template, for product index optimizations, and for deploying and storing data in the cloud database are identified and explained. In addition, key aspects are discussed regarding ethics and security in the proposed solution. A practical use-case scenario is also presented, where products are extracted from seven online board and card game retailers. Finally, the potential of the proposed solution is discussed in terms of researching new methods for improving various aspects of the proposed solution in order to increase cost efficiency and scalability.
COST-SENSITIVE TOPICAL DATA ACQUISITION FROM THE WEB

The cost of acquiring training data instances for induction of data mining models is one of the main concerns in real-world problems.The web is a comprehensive source for many types of data which can be used for data mining tasks.But the distributed and dynamic nature of web dictates the use of solutions which can handle these characteristics.In this paper, we introduce an automatic method for topical data acquisition from the web.We propose a new type of topical crawlers that use a hybrid link context extraction method for topical crawling to acquire on-topic web pages with minimum bandwidth usage and with the lowest cost.The new link context extraction method which is called Block Text Window (BTW), combines a text window method with a block-based method and overcomes challenges of each of these methods using the advantages of the other one.Experimental results show the predominance of BTW in comparison with state of the art automatic topical web data acquisition methods based on standard metrics.
Solar Power Ramp Event Forewarning with Limited Historical Observations

Timely and accurate forewarning of solar power ramp events (SPREs) is crucial for power system operation. In this paper, a novel forewarning method for SPREs is proposed based on credal network (CN) and imprecise Dirichlet model (IDM). A new expression of SPRE is proposed, which focuses on the power change caused by meteorological fluctuation. Considering that the single-valued probability may not provide convincing results in case of insufficient ramp event records, probability interval is adopted to reflect the ambiguous correlation between SPREs and meteorological conditions. The meteorological evidences are mapped to ramp events directly by using a CN to enhance the sensitivity of SPRE identification. Maximum weight spanning tree and greedy search are applied to build the structure of the CN. Furthermore, an extended IDM is developed to estimate the interval-valued parameters in the CN. Then, a credal classifier is established to output the ramp forewarning grade. The effectiveness of the proposed method is verified through case studies, and obvious improvement on accuracy of ramp forewarning can be seen.
Fluctuations and photon statistics in a quantum metamaterial near a superradiant transition

The analysis of single-mode photon fluctuations and their counting statistics at the superradiant phase transition is presented. The study concerns the equilibrium Dicke model in a regime where the Rabi frequency, related to a coupling of the photon mode with a finite-number qubit environment, plays a role of the transition's control parameter. We use the effective Matsubara action formalism based on the representation of Pauli operators as bilinear forms with complex and Majorana fermions. Then, we address fluctuations of superradiant order parameter and quasiparticles. The average photon number, the fluctuational Ginzburg-Levanyuk region of the phase transition and Fano factor are evaluated. We determine the cumulant generating function which describes a full counting statistics of equilibrium photon number. Exact numerical simulation of the superradiant transition demonstrates quantitative agreement with analytical calculations.
YesElf

Onboarding users to a complex application or a new functionality can be a serious issue, especially for organizations that need to train their new employees. Using a complex application without proper training or guidance can lead to users' confusion and frustration. In this paper, we introduce the onboarding platform YesElf intended for web applications. Its approach to onboarding is to use embedded guides within the application; its novelty lies in the robustness, ease of setup and integration of the YesElf guides into any web-based application. Most importantly, YesElf supports personalized adaptation of user guidance. This, we demonstrate by a novel method for automated recognition of user's confusion in real time that we integrated into YesElf. The information on user's confusion serves as a basis for adaptive display of the guides, when they are needed the most. We evaluated the proposed method on the data collected in a user study with 60 participants and achieved 63% precision which outperforms the state-of-the-art classifier based on the eye tracking data (although, in our case, we used the more readily available mouse movement data).