Reviewerly/exHarmony huggingface dataset:
Neural Co-training for Sentiment Classification with Product Attributes

Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose neural co-training to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.
The Intel Realsense Depth-Camera Performance for Real-Time Customer Satisfaction Analysis using Facial Expression Detection

Customer satisfaction evaluation is an important process in business development. Unfortunately, doing it manually by using survey or by phone will take up the time and resources that can actually be reallocated. In this research, we are going to make an automated customer satisfaction analysis tool by using Intel RealSense. The depth sensor and its emotion recognition API is enable real-time 3-D customer emotion recognition monitoring accurately. Emotions such as anger, disgust, fear, joy, sadness, and surprise that is expressed by the customer is logged. Then, the captured emotions are processed and analyzed by the computer. Finally, the summarized survey presents an interactive data in form of charts that can show the customer service's performance. As a result, the data that is produced can be used to improve the performance of the customer service representative. In this research, experiments show that while Intel RealSense can capture customer's emotions well most of the time, there are still some cases in which it couldn't present the accurate result due to the limitation in the API. Therefore, further testing need to be done that also takes several factors e.g. head accessories, glasses into account to increase the credibility of the output from the program.
Click Prediction in Digital Advertisements: A Fuzzy Approach to Model Selection

The fuzzy logic theorem is inherently used effectively in expressing current life problems. So, using fuzzy logic in machine learning is getting popular. In machine learning problems, especially using digital advertisement data, products/objects are being trained and predicted together, but this can cause worse prediction performance. A significant commitment of our research is, we propose a new approach for ensembling prediction with fuzzy clustering in this study. This approach aims to solve this problem. It also enables flexible clustering for the objects which have more than one cluster’s characteristics. On the other hand, our approach allows us ensembling boosting algorithms which are different types of ensembling and very popular in machine learning because of their successful performance in the literature. For testing our approach, we used an online travel agency’s digital advertisements data for predicting each hotel’s next day click amount, which is crucial for predicting marketing cost. The results show that ensembling the algorithms with a fuzzy approach has better performance result than applying algorithms individually.
Short Paper - Taming the Shape Shifter: Detecting Anti-fingerprinting Browsers

When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.
Adaptive Puzzle Generation for Computational Thinking

This paper describes a system to generate puzzles with a difficulty degree that adapts to the player. The puzzle is designed with the objective of being used by young pupils, and it is mainly a planning/sequencing task, which is considered one of the aspects of computational thinking. The system is powered by a constrained multi-objective algorithm (NSFI-2Pop) – which evolves the sequences of actions necessary to solve the puzzle – combined with a stochastic algorithm that translates the sequences in playable levels. We also present a pilot evaluation of the system, which seems to indicate that the levels presented to the player are perceived as having an increasing difficulty.
Hermes: A Language for Light-Weight Encryption

Hermes is a domain-specific language for writing light-weight encryption algorithms: It is reversible, so it is not necessary to write separate encryption and decryption procedures, and it avoids several types of side-channel attacks, both by ensuring no secret values are left in memory and by ensuring that operations on secret data spend time independent of the value of this data, thus preventing timing-based attacks. We show a complete formal specification of Hermes, argue absence of timing-based attacks (under reasonable assumptions), and compare implementations of well-known light-weight encryption algorithms in Hermes and C.
One-Handed Character Input Method for Smart Glasses

A character input method optimized for smart glasses is presented. The user grabs the input device with one hand and taps or strokes with the thumb. The entered text is displayed on the smart glass. A dynamic input guide is also provided to help users enter characters. The feature of this method is that the user can enter characters without looking at the fingertip. This allows the user to enter text while paying attention to the surroundings. Characters are selected by the movement of the thumb, so when a user enters text on a crowded street, people around him/her will not notice. Of course, the entered text will not be peeped. Japanese hiragana and alphanumeric characters can be entered in each mode. One character is entered in two steps. In the first step, a group of 5 characters is selected by slide-in. This is a stroke from outside to inside the input area. In the second step, one character in the group is selected by a tap or a stroke. In experiments, the average input speed for beginners was 21.4 [CPM] after using 15.5 min. The error rate was about 6%, but many misoperations occurred. These misoperations were mainly caused by determining the position of the fingertip by the feel of the touch. To reduce them, we shown an alternative to slide-in strokes. In addition, we have presented how to apply the proposed method to European languages.
Services Task Model Based Dialogue Scenarios Design Towards L2 WTC Support Oriented Dialogues Authoring Tool

Prior studies have demonstrated that embodied conversational agents, which provide opportunities to simulate realistic daily conversation situations, could be effective in motivating second language learners to communicate in the target language. However, implementing such systems in various situations requires significant knowledge-engineering effort, which limits their widespread adoption and deployment. In this paper, we present a conceptual framework for dialogue scenario design based on an ontological model of service process. We provide an overview of a scenario authoring workflow and reduced number of activities that should be conducted by scenario designers to specify both the properties of the dialogue task structure and dialogue flow logic in the targeted conversation situation. We also discuss the benefits of embedding such a common service task structure in terms of reducing the authoring load of dialogue scenarios in various task-oriented service situations.
ArSign: Toward a Mobile Based Arabic Sign Language Translator Using LMC

Communication connects people by allowing them to exchange messages, to express their feelings either verbally or non-verbally. To communicate with their surroundings, hearing impaired people or deaf use sign language. Unfortunately, practicing sign language is not common among society witch make barriers between peoples. In this work we propose a new system to convert Arabic sign language from gesture to written text. To do that we propose a system based on three main components: 1) The Leap motion controller as gesture recognition device to capture hand and finger movements. 2) A processing module to convert recognized gesture to alphabet using a comparison algorithm working on a predefined dataset of gesture. 3) A user interface to display the text or to convert it to speech through text to speech engine. The proposed system is implemented as a mobile application running into an android based device.
Explainable Agents as Static Web Pages: UAV Simulation Example

Motivated by the apparent societal need to design complex autonomous systems whose decisions and actions are humanly intelligible, the study of explainable artificial intelligence, and with it, research on explainable autonomous agents has gained increased attention from the research community. One important objective of research on explainable agents is the evaluation of explanation approaches in human-computer interaction studies. In this demonstration paper, we present a way to facilitate such studies by implementing explainable agents and multi-agent systems that i) can be deployed as static files, not requiring the execution of server-side code, which minimizes administration and operation overhead, and ii) can be embedded into web front ends and other JavaScript-enabled user interfaces, hence increasing the ability to reach a broad range of users. We then demonstrate the approach with the help of an application that was designed to assess the effect of different explainability approaches on the human intelligibility of an unmanned aerial vehicle simulation.
Reduction of False Positives in Identification of Masses in Mammograms

Breast cancer has become one of the prevalent diseases in this era among women. Although breast cancer is a fatal disease, patients still have high chances of survival if detected at an early stage. The use of computer aided diagnosis (CAD) systems could provide an objective solution compared to qualitative solutions by radiologists. Many cases are getting resulted as false positives that the patients without cancer are sent to diagnostic mammography, on the other hand, patients with breast cancer are sent back saying that there is no cancer growth due to the inaccuracy which results in cancer enormously. In order to overcome this problem, a machine learning-based system is designed that can analyze the mammogram output and can predict whether the patient is developing cancer or not. To this end, a unique descriptor, namely Local Neighbourhood Intensity pattern (LNIP) is proposed for suspicious region representation. Experiments on DDSM database employing 2000 suspicious regions with support vector machine (SVM) classifier, demonstrate the efficiency of proposed system. The obtained result significantly reduces false positives achieving an accuracy of 97.5%.
Multi-instance Multi-label Text Categorization Algorithm Based on Multi-quadric Function Radial Basis Network Model

The activation function of the hidden layer in MIMLRBF algorithm is a radial basis function which is always a Gaussian function, but the performance of algorithm depends on the center, the width of radical basis function and weight values. In this paper, the radial basis is set as multi-Quadric function which is widely used in interpolation. The parameters in experimental design and multi-Quadric function are determined by Hamming Loss, Ranking Loss, One Error and Coverage. The experimental results in text classification show that the method is highly competitive to other MIML algorithms.
Remote Monitoring and Propagation Modeling of EM Side-Channel Signals for IoT Device Security

This paper presents results from an investigation into long-range detection and monitoring of Electromagnetic (EM) side-channel signals leaked from Internet-of-Things (IoT) and Field Programmable Gate Array (FPGA) devices. Our work shows that operational information and program activities of the IoT and FPGA modules can be garnered at distances excess of 25 m in an indoor Line-Of-Sight (LOS) environment, while at about 10 m in an indoor (through the wall) Non-Line-Of Sight (NLOS) scenario. We provide a propagation model that can be used to predict the received power (and corresponding variation i.e., shadowing gain) of leaked EM side-channel signals at various distances and scenarios. A standard benchmark program bitcount used in the performance evaluation of ARMbased microprocessors and a microbenchmark SAVAT running on an IoT device were detected and monitored remotely in our work.
Surveying global verifiability

We explore global verifiability; discovering that voting systems vulnerable to attack can be proven to satisfy that security notion, whereas many secure systems cannot. We conclude that current definitions are unsuitable for the analysis of voting systems, fuelling the exploration for a suitable definition.
Predicting Clinical Re-admission using Discharge Summaries (PCRUDS)

Before days in hospitals used to prescribe medicines in paper format. As the technology increasing in most of the fields to make the process easy handling & robust. The hospital clinical notes are digitalized in electronic medical/health record (EMR) format. This notes which is being very helpful for analyzing for future predictions. The data is created by combing the covid-19 issues and MIMIC-I to predict the re-admission cases through NLP (Natural language processing) techniques. In this paper implemented covid-19 possibility of re-admitting due to some symptoms like fever, cold etc. in histogram form. Used MIMIC-III database which consists of free text in unstructured form and converting in to structured form using natural language processing (NLP) technique and python flask to make a web-app which identifies important keywords and probable diagnosis from the data.
A New, Fast Method for Image Denoising Using KPE Vector Quantization & Its Comparison with LBG

Almost all recent techniques for image denoising are iterative and complex. This paper proposes a new faster and improved method for image denoising using KPE Vector Quantization (VQ). The main idea of using vector quantization for image denoising is that as approximate points are selected in quantization from noisy image, and the noise gets reduced because noise is generally a different value than image intensities. The proposed technique utilizes proportionate error while calculating clusters and therefore the results are improved as compared to constant error. The proposed technique is compared with existing LBG algorithm and proved better in terms of PSNR and SSIM objective criteria. PSNR is improved on average by 0.56% by LBG while that by using KPE is improved by 0.77% as compared to noisy images for gray images. Even if codebook size is reduced to 32 this method gives better PSNR and SSIM so it is a very fast method for image denoising. The complexity is reduced very much by codebook size 32 compared to existing iterative algorithms. KPE is a recent technique for vector quantization and this paper explores KPE for denoising for the first time.
Baguamarsh: An Immersive Narrative Visualization for Conveying Subjective Experience

As ubiquitous sensing becomes embedded in our everyday world, we can easily obtain multimodal sensor data from our physical surroundings. Objectivity is a core value in the visualization research community. However, personal experiences are characterized not only by objective facts, but by personal emotions. In this paper, we explore immersive data visualization for conveying personal subjective perception and experience by using multidimensional data and multimedia. We introduce a framework to describe narrative structures in immersive data visualization, and provide an example project Baguamarsh as a proof of theme-based creative methods for designing correlations between different information. We hope this study may offer outline opportunities for future research in narrative visualization.
Impression Estimation Model for Clothing Patterns Using Neural Style Features

In product design, the impressions evoked by surface properties of objects attract attention. These impressions are called affective texture and demand is growing for technologies that quantify, index, and model it. Even in the fashion industry, the diversification of user needs necessitates the customization and personalization of products. Consequently, there has been a focus on custom-made services. However, enormous amounts of time and human costs are needed to find clothing patterns to suit one’s own preferences and ideas from among the countless patterns available. This research focused on affective texture related to visual impressions, and we proposed a method for automatically estimating the affective texture evoked by clothing patterns. To this end, we conducted the following steps: (1) quantified the visual impressions for patterns; (2) extracted style features as physical characteristics; and (3) modeled the relationships between visual impressions and physical characteristics. Afterward, based on the obtained models, we estimated the impressions for unlabeled patterns. Then, we verified their validity through relative evaluation and absolute evaluation, and we confirmed that our models estimated the impressions corresponded to the impressions that people actually felt. In addition, we implemented a system to enable users to intuitively search for patterns.
Electromagnetic shower generation with Graph Neural Networks

Abstract In this work, we propose an approach for electromagnetic shower generation on a track level. Currently, Monte Carlo simulation occupies 50-70% of total computing resources that are used by physicists experiments worldwide. Thus, speedup of the simulation step allows to reduce simulation cost and accelerate synthetic experiments. In this paper, we suggest dividing the problem of shower generation into two separate issues: graph generation and tracks features generation. Both these problems can be efficiently solved with a cascade of deep autoregressive generative network and graph convolution network. The novelty of the proposed approach lies in the application of graph neural networks to the generation of a complex recursive physical process.
On the Use of <i>m</i>-Probability-Estimation and Imprecise Probabilities in the Naïve Bayes Classifier

Within the field of supervised classification, the naïve Bayes (NB) classifier is a very simple and fast classification method that obtains good results, being even comparable with much more complex models. It has been proved that the NB model is strongly dependent on the estimation of conditional probabilities. In the literature, it had been shown that the classical and Laplace estimations of probabilities have some drawbacks and it was proposed a NB model that takes into account the a priori probabilities in order to estimate the conditional probabilities, which was called m-probability-estimation. With a very scarce experimentation, this approximation based on m-probability-estimation demonstrated to provide better results than NB with classical and Laplace estimations of probabilities. In this research, a new naïve Bayes variation is proposed, which is based on the m-probability-estimation version and takes into account imprecise probabilities in order to calculate the a priori probabilities. An exhaustive experimental research is carried out, with a large number of data sets and different levels of class noise. From this experimentation, we can conclude that the proposed NB model and the m-probability-estimation approach provide better results than NB with classical and Laplace estimation of probabilities. It will be also shown that the proposed NB implies an improvement over the m-probability-estimation model, especially when there is some class noise.
Domain Adaptation With Foreground/Background Cues and Gated Discriminators

Self-driving cars leverage on semantic segmentation to understand an urban scene. However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train segmentation models. Unfortunately, the synthetic to real domain shift causes these models to perform poorly. Prior works use adversarial training to align features of both synthetic and real-world images. We observe that background objects tend to be similar across domains, while foreground objects tend to have more variations. Using this insight, we propose an adaptation method that uses foreground and background cues and adapt them separately. We also propose a mask-aware gated discriminator that learns soft masks from the input foreground and background masks instead of naively performing binary masking that immediately removes information outside of the predicted masks. We evaluate our method on two different datasets and show that our method outperforms several state-of-the-art baselines, which verifies the effectiveness of our approach.
Blind Quality Assessment for Multiply-Distorted Images via Distortion Decomposition

In the real world, digital images are often contaminated by several distortions simultaneously, and the inability to identify the types of these distortions and describe their joint effects makes quality assessment of multiply-distorted images an intractable problem. In this paper, a new no-reference image quality assessment (NR-IQA) approach is proposed by summarizing various distortions into detail loss (DL) and detail redundancy (DR), according to the visual impact caused by the changes of image details. DR is expressed by the features of the absolute error map between the distorted image and its denoised version, while DL is represented by the difference between the denoised image features and the estimated pristine features. Experiment results on multiply-distorted image databases demonstrate that the proposed metric is superior to existing NR-IQA methods in terms of the coherence with human subjective rating.
Event-B formalization of a variability-aware component model patterns framework

In the domain of model-driven engineering, patterns have emerged as a ubiquitous structuring mechanism. Patterns are used for instance at the requirement analysis level, during system design, and during the deployment and code generation phases. We focus on formalizing the operational semantics of pattern application on component-based system designs. More precisely, our ultimate goal is to provide a semantic framework to support the use of patterns for the production of correct-by-construction architectures, i.e., the structural (static) correctness of the architectures obtained through the formal application of patterns. To that end, we propose an Event-B framework for patterns specification, instantiation and application. This model is built incrementally through horizontal refinements which introduce components, ports, and connectors. Patterns with variability are defined, instantiated and can be applied to user models. We show that these operations preserve the structural properties of hierarchical component models.