Reviewerly/exHarmony huggingface dataset:
Visually Realistic Rain Modeling Optimization for VR Application

Simulation purposes are to create an event in the world as real as possible, and by using Virtual Reality Technologies, it can achieve a higher level of realism and immersion. By using particle Systems from Unity can be used to producing a phenomenon that involving a small moving object on a big scale such as smoke, fire, or rain. However, when trying to use the particle system, sometimes the performance got reduced because there are too many particles working at the same time and result in a frame rate drop that causing discomfort to the player. For this reason, it's essential to optimize the performance by limiting objects that are needed to draw to lighten up the GPU process. In this paper, we discussed how to produces rain using the particle system in unity and find the best way to simulating rain with the best frame rate. We will show how the lifetime of the particle affecting the cycle of the particle system and the impact on the particle emission. We concluded that it's essential for developers to focus on the balance for creating realistic and detailed simulation and the stability of the simulation performance so the player can be more comfortable and immersive.
Language Detection using Convolutional Neural Network

Language detection is an essential pre-processing step in the implementations of many multilingual document-processing solutions, such as Optical Character Recognition (OCR) and machine translation. Specifically, language detection research for Bangla is very rare, with only a handful of solutions ever reported in the literature. In this paper, we present a lightweight, small footprint convolutional neural network, which detects Bangla and English languages-directly from scanned mixed-language document images. The proposed model achieves 99.98% recognition accuracy for this specific two-language classification problem.
Finding Human–Computer Interaction Outdoors

Human–computer interaction, as a discipline, has shifted from an examination of computers in controlled indoor environments to the study of technology use in a broader collection of settings, including the outdoors. This chapter seeks to explore the evolution of HCI outdoors as an area of study, seeking to identify the considerations that make it an important and unique domain. We begin by examining the tensions and opportunities that have long existed between technology and the outdoors, pointing to the unique position of HCI as a discipline for probing these issues. Using HCI as a lens, we then seek to understand what is meant by the outdoors, and how humans, computers, and their interaction together have evolved to a point that they can address issues of outdoor use. We introduce several seminal efforts related to the study of technology use in outdoor settings, including a collection of recent workshops and events that helped identify and bring together important ideas in the field. We conclude with a categorization and introduction of the chapters in this book on HCI outdoors. The chapters in this book present new theory, design, methods, and applications that will shape the emerging field of HCI outdoors.
Hyperspectral Image Classification Using Comprehensive Evaluation Model of Extreme Learning Machine Based on Cumulative Variation Weights

In order to improve the classification of hyperspectral image(HSI), we propose a novel hyperspectral image classification method based on the comprehensive evaluation model of extreme learning machine(ELM) with the cumulative variation weights(CVW), referred to as ELM with the cumulative variation weights and comprehensive evaluation (CVW-CEELM). To be specific, the cumulative variation value is proposed as a new metric. The inefficient bands are eliminated by the cumulative variation quotient values based on the cumulative variation values. The cumulative variation weights based on the cumulative variation values are used to determine the contribution of each weak ELM classifier to the hyperspectral image classification algorithm. The remaining effective bands are divided by grouping strategy. In each group of the effective bands, the different numbers of bands are selected to reduce the dimension of the hyperspectral image dataset by the weighted random-selecting-based method. After dimensionality reduction, the spatial-spectral features of each pixel are extracted and multiple weak ELM classifiers are trained by the training samples. Then, the results of several weak classifiers are synthetically evaluated by the cumulative variation weights to get the final classification results. Experimental results on the typical hyperspectral image datasets illustrate that the proposed CVW-CEELM has few adjustable parameters to make the operation simple, and outperforms a variety of the image classification counterparts in terms of the calculation cost and classification accuracy.
Enhancing Semantic Interoperability on MIDAS with Similar DaaS Parameters

The vast amount of social data and the extensive use of smart devices have enabled different formats of DaaS (Data as a Service). Cloud consumers are encouraged to access such DaaS from a SaaS (Software as a Service) directly. However, DaaS can evolve and modify some of its parameters, thus avoiding a SaaS to catch all such adjustments. Thus, it is important to have a middleware to bind the request from SaaS applications to data from DaaS. One such middleware is MIDAS (Middleware for DaaS and SaaS), which provides a way from SaaS to DaaS seamlessly. Although there are many advantages to publishing data as a DaaS format, the major challenge is the dynamicity of DaaS parameters, which can change and evolve. Such changes can affect SaaS applications, even providing runtime errors. Thus, our approach overcomes this problem by ensuring a transparent mechanism to DaaS parameters when modifications have occurred. Our work aims to recognize each similar attribute seamlessly, increasing availability to interoperate SaaS and DaaS through MIDAS. We evaluate the distance measures thought 22 parameters from 11 DaaS providers into five scenarios on parameters change. Two of them were the most outstanding measures (Cosine and Jaccard), and we included them in our approach to better obtain the similarity of two parameters. Each parameter was carried out toward WordNet thesaurus, and a second evaluation was performed analyzing our approach into MIDAS through three criteria: overhead, execution time, and correctness. Our experiments have shown that we are in a good direction to provide interoperability into SaaS and DaaS.
An Attention Based Approach for Sentiment Analysis of Food Review Dataset

Sentiment Analysis is a technique related to text analysis and natural language processing used to detect various types of insights or information from a portion of text. Over the past few years, researchers have done many works regarding this. In Bangladesh, many online services like-e-com become very popular day by day. One of them is online food delivery services. We can order various foods of our choice from online and sometimes people gives reviews based on that food. Those reviews are usually discarded as unstructured data which of them have no work in further. In this piece of research focus primarily on those unstructured data to analyze them in a correct manner to find insight into customers' behavior and their reactions on those online platforms. To do this experiment first we collect data from websites. Later deep learning-based techniques applied here. For baseline structure, we have used both CNN and LSTM models. Then for improving the model accuracy an attention mechanism applied followed by CNN which gives us 98.45% accuracy. We've also evaluated our model performances with some evaluation metrics also. From them, CNN based attention model gives a higher f1-score of 0.93.
American Sign Language Identification Using Hand Trackpoint Analysis

Sign language helps people with speaking and hearing disabilities communicate with others efficiently. Sign language identification is a challenging area in the field of computer vision and recent developments have been able to achieve near-perfect results for the task, though some challenges are yet to be solved. In this paper, we propose a novel machine learning-based pipeline for American sign language identification using hand trackpoints. We convert a hand gesture into a series of hand trackpoint coordinates that serve as an input to our system. In order to make the solution more efficient, we experimented with 28 different combinations of pre-processing techniques, each run on three different machine learning algorithms, namely, k-Nearest Neighbours, Random Forests and a Neural Network. Their performance was contrasted to determine the best pre-processing scheme and algorithm pair. Our system achieved an accuracy of 95.66% to identify American sign language gestures.
Time-varying wavelet estimation and deconvolution for nonstationary data based on a FWE function

• We present a nonstationary convolution model constituted of the reflectivity series and a time-varying wavelet matrix. • We propose a novel wavelet estimation method toextract time-varying wavelet at every instant for nonstationary seismic data . • We achieve blind deconvolution without assumptions about factor Q value and the source wavelet.
Research on Crowdsourcing Mode of Internet + Rural Logistics Based on Blockchain

Blockchain is a new internet database technology, which ensures the authenticity and security of data through distributed supervision technology. It has excellent characteristics such as decentralization, non-tampering, traceability, high transparency and so on. The combination of blockchain and internet can help solve the trust problem that has always restricted the application of "internet plus", and thus speeds up the sharing economy. This paper first gives the concept of farmers' deep participation in the "internet + rural logistics" mode, then the crowdsourcing logistics operation mode based on farmers' deep participation is established by using blockchain technology. And the operation process is presented, the logical framework of smart contract under two scenarios of self-built platform and third-party platform is provided, which provides technical support and trust guarantee for the healthy development of "internet plus rural logistics" crowdsourcing mode.
Spam Mail Classification Using Ensemble and Non-Ensemble Machine Learning Algorithms

Spam in emails has been a prevalent issue ever since the inception of the email service. However, the use of ensemble (aggregate) and non-ensemble algorithms for the detection and filtering of spam has been less explored. In this paper, we develop certain ensemble and non-ensemble machine learning (ML) algorithms for classifying emails as spam or ham (i.e., not spam). Using the Enron-SMS dataset from the UCI ML repository and an 80 and 20% training and test split, we develop and calibrate non-ensemble ML algorithms like KNN, Naïve Bayes, and Support Vector Machine. Also, we develop and calibrate ensemble ML algorithms containing the non-ensemble algorithms via voting, bagging, and boosting methods. Results reveal that the non-ensemble Support Vector Machine performed the best with 98.47% accuracy on test data and it was followed by the ensemble voting algorithm with 96.80% accuracy on test data. We highlight the implications of using non-ensemble and ensemble methods for spam classification in the real world.
Transparent Integration of a Dynamic FPGA Database Acceleration System

A substantial amount of recent work has been conducted on accelerating different database operators or database management systems (DBMS) as a whole, both in proprietary and open-source services. The missing piece of work in this field is to transparently accelerate a widely-used open-source database on an FPGA without substantial changes to the core database code. This PhD project aims to use a partially reconfigurable stream processing architecture to compose different database operators into a pipeline orchestrated automatically by the database query optimizer and query planner. This way, the database users do not have to change any of the existing database management system code while allowing a runtime system to optimize queries with information known only during runtime.
A Categorization of Cloud-Based Services and their Security Analysis in the Healthcare Sector

The contribution of cloud-based services in the healthcare environments is a vital issue in the 21st century. In this paper, presenting its benefits and tools in hospitals, clinics as well as diagnostic centers, the already existing applications and services are separated in categories, which basically concern: data storage, computing power, network, PaaS, SaaS, data analytics, business intelligence and project management. Then, some security and risk assessment issues in cloud-based services are analyzed thoroughly together with some case studies. Furthermore, a risk analysis with a comparative diagram between secured and non-secured cloud systems in health is listed. Finally, conclusions with the suggested future work are presented.
Variability realization in UML/SysML models

Motivated by experiences from different industrial settings, the tutorial reveals the increasing need for guidance and decision support on how to handle variants and variability in SysML and UML models. While a substantial amount of variability realization approaches has already been discussed on the level of source code, there is little guidance for practitioners on the model level. With this, there is major uncertainty in dealing with concurrent changes and parallel modeling of similar system variants.
FairCVtest Demo

With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest. The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Aditionally, the demo includes a new algorithm (SensitiveNets) for discrimination-aware learning which eliminates sensitive information in our multimodal AI framework.
Çift Elli Durağan Parmak Türk İşaret Dili Alfabesi Tanıma : Recognition of Two-Handed Posture Finger Turkish Sign Language Alphabet

The importance of hearing-impaired individuals to communicate easily with other person of the society is indisputable. In this study, the recognition of two-handed posture finger alphabet of Turkish sign language was studied by using a device based on the sensor called Leap Motion. In addition to collecting data, the study consists of four stages: pretreatment, feature extraction, dimension reduction and classification. Recognition success was analyzed using traditional classifier which is one of the machine learning methods. In this analysis, the recognition model was tested using k-fold cross-validation and the results obtained were compared. In the analyses, performance obtained with the features selected by using the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) feature selection algorithms independently and in a hybrid structure, and all the features of the original data were compared in terms of recognition success. The results of detailed analyses showed that a more successful recognition is achieved with the features selected using the PCA and LDA algorithms in a hybrid structure.
How Initialization is Related to Deep Neural Networks Generalization Capability: Experimental Study

Deep learning has lately received significant research effort, considering its capabilities followed by the advantages in many domains. The focus of this paper is on improving the generalization ability, which is a key for successful implementation of deep neural networks. In order to answer the question how initialization is related to the deep neural networks' generalization capability, an experimental study is done. The study consists of several experiments, which belong to the data-independent initialization techniques. Using different approaches, the change of generalization gap is presented.
Rethinking the Role of a Mobile Computing in Recreational Hiking

Mobile computing devices, especially smartphones, are part of the recreational hiking experience in the United States. In our survey of over a thousand people in the United States in 2017, about 95% of respondents reported that they prefer to bring a smartphone when they go hiking. A smartphone used during hiking is simply a tool. That tool can improve or worsen the quality of a hiking experience. In this chapter, we propose a vision of interactive mobile computing design for hiking which may improve the quality of the hiking experience. Our vision of interactive computing and hiking is built on three principles: time spent outdoors is good for individuals, computing can play a positive role in outdoor recreation, and human–nature interaction is more important than human–computer interaction. We illustrate our approach using an extended scenario.
A network performance view of a biobanking system for diagnostic images

A significant contribution of ICT to healthcare is constituted by systems automating and enhancing the management of research and clinical data. More specifically, PACS (Picture Archiving ad Communication Systems) have improved the efficiency of diagnostic images and clinical data management. Their evolution (image biobanks) are now enabling new collaborations and analysis possibilities similarly to—and beyond—the biobanks (their biologic samples analogous and complement). In this work we describe and evaluate the network performance of a biobanking system for diagnostic images, based on the XNAT open source platform, as implemented and operated by Bio Check Up Srl. The point of view of the user is adopted, in assessing the performance in three setups: local (virtual machines communicating in a single host), LAN (organization-local access), and VPN (remote secure access through the Internet). Both upload and download usage cases are considered, with both a medium-sized and big-sized set of diagnostic images. Several metrics are extracted from traffic traces captured in the experimental campaign, and discussed. Results show that the current setup is well provisioned for satisfying the planned number of concurrent users, and point to further experimental campaigns.
NIH Training and Education for Biomedical Data Science

In 2012, at the National Institutes of Health (NIH), the Working Group on Data and Informatics of the Advisory Committee to the NIH Director made recommendations about changes needed in NIH investments and procedures to assure that the research potential of biomedical big data would be fully realized. With its "Big Data to Knowledge" initiative as the pilot program, NIH leaders developed a strategic plan for data science, and tasked hundreds of staff with the assignment to develop implementation plans for its five goals. The plan, issued in 2018, has the overall goal of assuring that research, training and the products of research supported by the public funds adhere to the FAIR principles, meaning that the data be Findable, Accessible, Interoperable, and Reusable.
Invasive Ductal Carcinoma Detection in Histopathology Images Using Machine Learning Techniques Without Deploying Feature Extraction

Breast cancer is one of the common malignant diseases in female all over the world. Microscopic investigation of tissues in breast is essential for analysis of breast cancer. For detection of breast cancer, pathologist uses various magnificent stages for obtaining accurate diagnosis of biopsy images which is time consuming. Development in digital imaging techniques has helped in assessment of pathology images using machine learning and computerized methods which could computerize a few of the pathology stages in the diagnosis of breast cancer. This kind of automation can be helpful in achieving quick and exact results reducing the observers’ inconsistency, thus increasing the accuracy. In this work, a new method is proposed to categorize breast cancer histopathology images. The objective is to evaluate the robustness and accuracy of a classification system based on machine learning, to automatically identify invasive tumor on digitized images without extracting the features. Here, a new method is presented that employs machine learning classifiers for classification of invasive tumor on whole slide images. The accuracy of different classifiers varies from 80% to 85%, leaving scope for improvement. The aim is to gather different researchers in both machine learning and medical field to proceed toward this Computer Aided Diagnosis (CAD) system for classification of invasive ductal carcinoma (IDC).
Controlling Patent Text Generation by Structural Metadata

The ultimate goal of my long-term project is "Augmented Inventing." This work is a follow-up effort toward the goal. It leverages the structural metadata in patent documents and the text-to-text mappings between metadata. The structural metadata includes patent title, abstract, independent claim, and dependent claim. By using the structural metadata, it is possible to control what kind of patent text to generate. By using the text-to-text mapping, it is possible to let a generative model generate one type of patent text from another type of patent text. Furthermore, through multiple mappings, it is possible to build a text generation flow, for example, generating from a few words to a patent title, from the title to an abstract, from the abstract to an independent claim, and from the independent claim to multiple dependent claims. The text generation flow can also go backward after training with bi-directional mappings. In addition to those above, the contributions of this work include: (1) released four generative models trained with patent corpus from scratch, (2) released the sample code to demonstrate how to generate patent text bi-directionally, (3) measuring the performances of the models by ROGUE and Universal Sentence Encoder as preliminary evaluations of text generation quality.
Threats of the Rural: Writing and Designing with Affect

Drawing from the author’s background and fieldwork in the rural West and Midwest, this chapter presents findings that are, at times, evasive and ambiguous. Stylistically, the writing foregrounds the unnamed and subliminal intensities that circulate in and out of the rural and onto the body. These rural affects give rise to our emotions shaped by social culture. This chapter is a provocation toward writing ethnographically inspired work in human–computer interaction (HCI). I suggest that the hallmarks of HCI research—deep analysis and clarity of argument—may unduly simplify the complexity of the rural, particularly its inhabitants. This chapter presents an alternative to normative modes of writing HCI, inviting multiple readings, interpretations, affects, and, indeed, “designs” from readers.
Generating Angry Birds-Like Levels With Domino Effects Using Constrained Novelty Search

This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels - the 100% stability and the perfect-shot rate - are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.