Reviewerly/exHarmony huggingface dataset:
Research and Analysis in Fine-grained Sentiment of Film Reviews Based on Deep Learning

Abstract Films are one of the main entertainment ways for people. By exploring, analyzing and summarizing the reviews that people published on the internet, audiences can make better viewing choices, while investors can get a more convenient way to understand the audience’s feedback. The text proposes a method of deep learning to perform fine-grained sentiment analysis on the film reviews, and restores the user’s real emotion as much as possible. The method first preprocesses the data, and converts the words into vectors using the Word2Vec model, then inputs the word sequence in the Long Short-Term Memory network (LSTM) to learn the semantic dependence. Finally, the logical regression classifier is used to classify. Compared with Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Back Propagation Neural Network (BPNN), and Convolutional Neural Network (CNN) models, the method achieves 83.6% accuracy in the binary classification, and 76.1% and 51.2% accuracy in the positive and negative fine-grained emotional classification, which has achieved the best results proved by experiments.
Adaptive Instructional Systems and Digital Tutoring

This paper discusses the application of machine (or artificial) intelligence intended to accelerate the development of technical expertise by novices. It provides evidence from various sources of the instructional effectiveness of this approach. It also discusses and provides examples of the monetary and operational value of individualizing instruction beyond that obtainable in classroom instruction and points to a future where learning and/or performance aiding is provided anytime, anywhere through a combination of human guidance and tutorial dialogues with computers.
OnkoGan: Bangla Handwritten Digit Generation with Deep Convolutional Generative Adversarial Networks

From a very early age human achieve a precious skill that is a handwriting. After this invention, the ardor of it changed day by day. And every human has a different style of handwriting. So, facsimile anyone's handwriting is a difficult task and it needs the strong ability of brain and practice. This paper is about this mimicry where an artificial system will do this by using Generative Adversarial Networks (GANs) [1]. GANs used in unsupervised machine learning that is implemented by two neural networks. GANs has a generator which generates fake images and a discriminator which make a difference between a real image and a fake image. We trained our proposed DCGAN [2] (Deep convolutional generative adversarial networks) to achieve our goal by using the three most popular Bangla handwritten datasets CMATERdb [3], BanglaLekha-Isolated [4], ISI [5] and our own dataset Ekush [6]. The proposed DCGAN successfully generate Bangla digits which makes it a robust model to generate Bangla handwritten digits from random noise. All code and datasets are freely available on https://github.com/SadekaHaque/BanglaGan .
Determination of Optimum Tilt Angle for Fixed Photovoltaic Modules in Iasi, Romania

The energy output of a photovoltaic system is significantly affected by the variability of the incident solar radiation. Thus, increasing the incident solar radiation is one of the most important solution which can be implemented to increase the output of such PV systems. This can be achieved by installing the PV panels on sun tracking systems. However, these trackers are expensive and most of the photovoltaic power plants are using fixed PV modules. In this paper the optimum tilt angle of the fixed PV panels in analyzed as noticeable gains in terms of energy output can be achieved by using a variable tilt angle over the year.
Augmented Reality Application to Emergency Systems with Usage of SLAM

Nowadays modern devices and new technologies are applied in a wide sphere of human activities. The purpose of this paper is to consider the possibilities of smart phone usage when some emergency situation occurs to help people to escape the building. Therefore, it is suggested to use such technology as augmented reality. In current study analysis of the available technologies is presented, sample application for providing experiments was developed.
RFThermometer: A Temperature Estimation System with Commercial UHF RFID Tags

RFID-based sensors have attracted great interest due to the wide employment of RFID tags, ease of deployment, and low cost. However, most RFID-based sensors rely on hardware modification, which increases the cost and hampers the deployment. In this paper, we propose RFThermometer, a temperature estimation system with commercial UHF RFID tags. We first study the effect of temperature on RFID phase. To mitigate the problem caused by missing phase measurements, we propose a tensor completion method to restore missing phases and a Gaussian Process (GP) model for building phase-temperature maps in the offline stage. A dynamic time warping based greedy method is proposed to estimate the unknown temperature in the online stage. Experimental results verify the performance of RFThermometer.
Image Enhancement by Jetson TX2 Embedded AI Computing Device

We show how library function used for image enhancement can be tailored and optimized to use the full capacity of NVIDIA Jetson TX2 embedded AI computing device. We use parallel processing on CPU and GPU devices to achieve real-time video enhancement. We also outlined further improvement of image enhancement process by machine learning implementation.
Implementation of Smoothing Image Filtering in the Residue Number System

In this paper, we propose a new method for image smoothing using the Residue Number System (RNS). The essence of the approach is to replace the computationally complex division operation in RNS with the multiplication of all fractional numbers by a power of two and rounding off. As a result of these actions, all subsequent calculations are performed only in fixed-point numbers. Theoretical and practical studies have shown that when a certain accuracy of calculations is achieved, the error that occurs during rounding does not significantly affect the result of filtering an image. It opens the possibility of effective hardware implementation on FPGA and ASIC.
From analytical purposes to data visualizations: a decision process guided by a conceptual framework and eye tracking

Data visualizations are versatile tools for gaining cognitive access to large amounts of data and for making complex relationships in data understandable. This paper proposes a method for assessing data visualizations according to the purposes they fulfill in domain-specific data analysis settings. We introduce a framework that gets configured for a given analysis domain and allows to choose data visualizations in a methodically justified way, based on analysis questions that address different aspects of data to be analyzed. Based on the concepts addressed by the analysis questions, the framework provides systematic guidance for determining which data visualizations are able to serve which conceptual analysis interests. In a second step of the method, we propose to follow a data-driven approach and to experimentally compare alternative data visualizations for a particular analytical purpose. More specifically, we propose to use eye tracking to support justified decisions about which of the data visualizations selected with the help of the framework are most suitable for assessing the analysis domain in a cognitively efficient way. We demonstrate our approach of how to come from analytical purposes to data visualizations using the example domain of Process Modeling Behavior Analysis. The analyses are performed on the background of representative analysis questions from this domain.
Designing Hand Pose Aware Virtual Keyboard With Hand Drift Tolerance

An unintentional hand drift adversely affects the typing performance of conventional virtual keyboards. To overcome this, we proposed to utilize the typing patterns of skilled typists. First, as most typists enter the keys in the same column with a predetermined finger only, we restricted these keys to be typed by their corresponding fingers. Second, our investigation of skilled typists demonstrated that hand poses vary when the typists touch different keys. Thus, rather than locating the touch point as in the case of existing virtual keyboards, we attempted to use unique hand poses to infer the target key. Based on these two techniques, we implemented a novel hand poses aware virtual keyboard that is tolerant of hand drift. Our experimental studies yielded the following results: 1) most of the QWERTY-familiar typists who have varying typing habits were easily adaptable to the proposed keyboard design and 2) the proposed keyboard outperformed existing virtual keyboards in terms of typing speed and several error rates, and eventually achieved a typing speed of approximately 56 WPM.
Matrix regression-based classification with block-norm

In this paper, we consider the robust face recognition problem via matrix regression-based classification with block-norm(MRC-block). Specifically, we propose an inner-class sparse representation classification approach in which images are expressed as matrices instead of vectors and adopted to encode more discriminative information than other regression-based methods. In the regression step, a block-norm based matrix regression model is proposed, which can efficiently depress the effect of occlusion in probe images. Accordingly, an efficient algorithm is derived to optimize the proposed objective function. Comprehensive experiments on representative datasets demonstrate that MRC-block is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face image with occlusion.
Gray Matter Segmentation and Delineation from Positron Emission Tomography (PET) Image

Gray matter segmentation and delineation from positron emission tomography (PET) are a very essential requirement in medical applications due to low spatial resolution, presence of noise, and larger variability in the shape as well as in texture. PET images provide the functional details of the brain which are very much essential to detect brain disorders. The diagnosis of dementia, particularly in the early stages, are very much helpful with PET image processing. In this present work, we implement three essential steps, which significantly help during dementia analysis. The first step is skull stripping from PET image based on K-means and connected component analysis, the second step is to segment gray matter from skull-stripped PET image using fuzzy possibilistic C-means (FPCM), and the last step is to delineate the gray matter based on cellular automata. The Indian PET dataset collected from Apollo Gleneagles Hospitals, Kolkata, India, has been used in our experiment as a standard dataset. Experimental results show that the proposed method has achieved promising results for automatic segmentation and delineation of gray matter on PET brain images.
Research on Opinion Spam Detection by Time Series Anomaly Detection

With the continuous development of e-commerce, buying online product has become a more desirable shopping option for modern people, but the product reviews which people need to consult when they buy goods are easily become the root cause of misleading consumption. Black profit chains of online sales make a lot of spam opinion makers flood the network who guide consumer’s choice by writing deceptive and misleading spam opinions. In order to eliminate the impact of this black interest chain and safeguard the interests of the mass consumers, we propose a new model by using the time series anomaly detection, reducing the time complexity of spam opinion detection found by time series analysis, while improve the performance of spam detection. Based on time series anomaly detection, we analyze the content of reviews and the characteristics of reviewers and take experiments on real dataset that demonstrate advantages and effectiveness of the proposed approach.
Facilitating Data Association In Particle Tracking Using Autoencoding And Score Matching

A crucial aspect of automated particle tracking in time-lapse fluorescence microscopy images is the linking or association of detected objects between frames. Recent evaluation studies have shown that the best results are achieved by making use of accurate motion models of the underlying particle dynamics. However, existing approaches often employ rather simple motion models which may be inappropriate for a given application, and even if complex models are used they all require careful user-parameter tuning. To alleviate these problems we propose a novel method based on autoencoding and score matching which can learn the dynamics from the data. Results on both synthetic and real data show the method performs comparable to state-of-the-art linking methods.
Global‐ and Local‐Scale High‐Resolution Event Catalogs for Algorithm Testing

ABSTRACT During the development of new seismic data processing methods, the verification of potential events and associated signals can present a nontrivial obstacle to the assessment of algorithm performance, especially as detection thresholds are lowered, resulting in the inclusion of significantly more anthropogenic signals. Here, we present two 14 day seismic event catalogs, a local‐scale catalog developed using data from the University of Utah Seismograph Stations network, and a global‐scale catalog developed using data from the International Monitoring System. Each catalog was built manually to comprehensively identify events from all sources that were locatable using phase arrival timing and directional information from seismic network stations, resulting in significant increases compared to existing catalogs. The new catalogs additionally contain challenging event sequences (prolific aftershocks and small events at the detection and location threshold) and novel event types and sources (e.g., infrasound only events and long‐wall mining events) that make them useful for algorithm testing and development, as well as valuable for the unique tectonic and anthropogenic event sequences they contain.
A SVM Algorithm for Falling Detection in an IoTs-Based System

Falling of elderly people is one of main reasons causing serious injuries or the risk of early death. However, this may be reduced by using an IoTs-based fall detection system, in which a SVM algorithm and PCA features are applied. In addition, datasets collected from tri-axial accelerometer sensors and/or Kinect camera systems are transferred to a central Hub via Zigbee interface and are updated continuously to a cloud server for processing and detecting fall states. In addition, fall messages can be sent to relatives through smartphones and/or healthcare centers for alert and supporting soon. Experimental results show to illustrate the effectiveness of the proposed system.
“Why Did You Do That?”

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations can also increase trust in these uncertain systems. In our paper we present RICE, a method for generating explanations of the behaviour of black box models by (1) probing a model to extract model output examples using sensitivity analysis; (2) applying CNPInduce, a method for inductive logic program synthesis, to generate logic programs based on critical input-output pairs; and (3) interpreting the target program as a human-readable explanation. We demonstrate the application of our method by generating explanations of an artificial neural network trained to follow simple traffic rules in a hypothetical self-driving car simulation. We conclude with a discussion on the scalability and usability of our approach and its potential applications to explanation-critical scenarios.
Incentivizing Secure Block Verification by Contract Theory in Blockchain-Enabled Vehicular Networks

The burgeoning vehicular networks generate a huge amount of sensing data. Data sharing among vehicles enables a number of valuable vehicular applications to improve driving safety and enhance vehicular services. To ensure security and traceability of data sharing, an efficient Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized to establish Blockchain-Enabled VEhicular Networks (BEVENs). Miners in DPoS include active miners and standby miners. The active miners are responsible for block generation and block verification. However, due to the limited number of active miners in DPoS, the compromised active miners may collude with each other to generate maliciously manipulated results of block verification. To prevent the internal collusion among the active miners, a newly generated block can be further verified and audited by the standby miners. To incentivize the participation of the miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where both block verification security and delay are taken into consideration. Numerical results demonstrate the security and efficiency of our schemes for data sharing in BEVENs.
Segmenting The Left Ventricle In Cardiac In Cardiac MRI: From Handcrafted To Deep Region Based Descriptors

Achieving robust object segmentations requires the ability to discard outliers (invalid observations) in the segmentation process. When considering an object model, such as an active shape model (ASM), described by a set of points, a one-to-one mapping from one model point to one valid observation would be ideal. However, in general, a one-to-many mapping is necessary to ensure the valid observation is detected and, thus, to have a reliable and robust fitting process. In this work, we compare three observation detectors: two of them based on handcrafted texture features and another based on a deep CNN classifier. Furthermore, to reduce from many detections to just one valid observation, we incorporate the Generalized Expectation-Maximization (GEM) algorithm in the ASM framework. This algorithm is able to neglect outliers by assigning them low weights. The proposed methodology exhibits remarkable accuracy with all the detectors in the context of the segmentation of the left ventricle in two publicly available MRI datasets, for which the proposed approach is competitive with other state-of-the-art methods.
Presenting Your Products in Virtual Reality: Do not Underestimate Cybersickness

For e-commerce retailers it is crucial to present their products both informatively and attractively. Virtual reality (VR) systems represent a new marketing tool that supports customers in their decision-making process and offers an extraordinary product experience. Despite these advantages, the use of this technology for e-commerce retailers is also associated with risks, namely cybersickness. The aim of the study is to investigate the occurrence of cybersickness in the context of the customer's perceived enjoyment and the perceived challenge of a VR product presentation. Based on a conceptual research framework, a laboratory study with 533 participants was conducted to determine the influence of these factors on the occurrence of cybersickness. The results demonstrate that the perceived challenge has a substantially stronger impact on the occurrence of cybersickness, which can only be partially reduced by perceived enjoyment. When realizing VR applications in general and VR product presentations in particular, e-commerce retailers should therefore first minimize possible challenges instead of focusing primarily on entertainment aspects of such applications.
The Behavior Driven Development Applied to the Software Quality Test:

The mechanisms of private investment in Brazilian sport are one of the main sources of resources that athletes hold to promote participation in high-performance competitions. In order to improve this flow, softwares are designed to shorten the path between donors and recipients. These tools, such as “Meu Campeãu” used in this paper, require efficient analysis of the quality of the product offered. Concepts known as agile methodologies have brought a new style to the analysis of the software developed, and one of them has gained considerable prominence, known such as BDD (Behavior Driven Development), so this paper aims to analyze the implementation of Behavior Driven Development in the software quality verification process “Meu Campeãu”.
Collaborative Design of Urban Spaces Uses: From the Citizen Idea to the Educational Virtual Development

The paper is concerned with the design of virtual environments for collaborative design. The interests of citizens are coming to the forefront nowadays with the awareness that a livable city does not only consist of good infrastructure and sustainable energy supply but also citizen input and feedback. The project consists of a transversal research at the intersection between computer science, the teaching of future architects and multimedia engineers and the urban policies of the cities of the future in which citizen participation is fundamental. The hypothesis is based on demonstrating the following two statement: (1) The implementation of virtual gamified strategies in the field of urban design will generate an improvement in citizen participation by being a more dynamic, real and agile collaborative environment thanks to enhanced visual technologies and immersive. (2) Gamified strategies for the understanding of three-dimensional space improve spatial competencies of students generating greater motivation in their use and a degree of satisfaction high. First, we studied the contemporary student profile and better ways of teaching according to it. Second, rehearse and evaluate the teaching of the urban project incorporating collaborative design, immersive ICTs, gamification and citizen participation. Third, the data obtained from the quantitative assessment, exemplify the role and use of technologies, in the educational processes to improve the students' motivation, involvement and way of learning.
Benchmarking of several disparity estimation algorithms for light field processing

A number of high-quality depth imaged-based rendering (DIBR) pipelines have been developed to reconstruct a 3D scene from several images taken from known camera viewpoints. Due to the specific limitations of each technique, their output is prone to artifacts. Therefore, the quality cannot be ensured. To improve the quality of the most critical and challenging image areas, an exhaustive comparison is required. In this paper, we consider three questions of benchmarking the quality performance of eight DIBR techniques on light fields: First, how does the density of original input views affect the quality of the rendered novel views? Second, how does disparity range between adjacent input views impact the quality? Third, how does each technique behave for different object properties? We compared and evaluated the results visually as well as quantitatively (PSNR, SSIM, AD, and VDP2). The results show some techniques outperform others in different disparity ranges. The results also indicate using more views not necessarily results in visually higher quality for all critical image areas. Finally, we have shown a comparison for different scene’s complexity such as non-Lambertian objects.