Reviewerly/exHarmony huggingface dataset:
Assertion Ranking Using RTL Source Code Analysis

We present a systematic and efficient ranking method to quantify the goodness of an assertion. We model dependencies among design variables as a directed graph called a variable dependency graph. We define assertion importance and assertion complexity metrics and use the dependency graph to algorithmically compute those two metrics. We repurpose an assertion coverage algorithm from the literature to form a statement-coverage-based ranking as our baseline. We compare our assertion ranking both qualitatively and quantitatively to this baseline. We demonstrate that our ranking is computationally more efficient than statement-coverage-based ranking and takes up to 4366× less computation time. We identify the potential design intents that each ranking prioritizes. We also discuss at length the effect of those prioritizations on the rank agreement and the bug detection ability of the top-ranked assertions according to the two rankings. Finally, we provide a comprehensive ranking for a set of assertions by combining our ranking and the statement-coverage-based ranking.
Non-Boolean classical relevant logics I

Relevant logics have traditionally been viewed as paraconsistent. This paper shows that this view of relevant logics is wrong. It does so by showing forth a logic which extends classical logic, yet satisfies the Entailment Theorem as well as the variable sharing property. In addition it has the same S4-type modal feature as the original relevant logic E as well as the same enthymematical deduction theorem. The variable sharing property was only ever regarded as a necessary property for a logic to have in order for it to not validate the so-called paradoxes of implication. The Entailment Theorem on the other hand was regarded as both necessary and sufficient. This paper shows that the latter theorem also holds for classical logic, and so cannot be regarded as a sufficient property for blocking the paradoxes. The concept of suppression is taken up, but shown to be properly weaker than that of variable sharing.
The Future of Public Libraries as Convivial Spaces

The proliferation of information and communication technologies (ICT) along with pressures of global capitalism has called in to question the role of public libraries in the future. At the same time, communities continue to rely on public libraries for social and civic functions. In this paper, the authors use research through design (RTD) and design fiction methods to explore alternative conceptions of public libraries and the role of ICTs in them The authors propose a design fiction in the form of a call for grant proposals issued by the Institute of Museum and Library Services. The design fiction focuses on the concept of conviviality to propose future design spaces for new kinds of interactions between library patrons and the world around them.
Keystroke based User Identification with XGBoost

User identification or recognition based on keystrokes is the ability of the system to recognise or identify the persons or users who type the username, passphrase or password with a specific keystroke behaviour patterns. Therefore, user identification problem based on the keystroke behaviour patterns is posed as a multi-class classification problem, where some standard multi-class machine learning techniques identify multiple users. In this paper, we employed Extreme Gradient Boosting (XGBoost) technique for user identification in the context of behavioral biometrics using the keystroke dynamics features. Multiple users from CMU's keystroke dynamics dataset are analysed using Machine Learning (ML) Techniques such as the XGBoost, Multinomial Logistic Regression, Random Forest, Probabilistic Neural Network, Decision Tree, Multinomial Naive Bayes and a Multilayer Perceptron. The performance measure utilised for the user identification problem is accuracy. We observed that XGBoost produced the highest accuracy for user identification. We also performed statistical paired t-Test at 1% level of significance on the techniques employed for the user identification task. Therefore, our results were corroborated statistically too.
Improving Neural Machine Translation by Filtering Synthetic Parallel Data

Synthetic data has been shown to be effective in training state-of-the-art neural machine translation (NMT) systems. Because the synthetic data is often generated by back-translating monolingual data from the target language into the source language, it potentially contains a lot of noise—weakly paired sentences or translation errors. In this paper, we propose a novel approach to filter this noise from synthetic data. For each sentence pair of the synthetic data, we compute a semantic similarity score using bilingual word embeddings. By selecting sentence pairs according to these scores, we obtain better synthetic parallel data. Experimental results on the IWSLT 2017 Korean→English translation task show that despite using much less data, our method outperforms the baseline NMT system with back-translation by up to 0.72 and 0.62 Bleu points for tst2016 and tst2017, respectively.
Source Camera Identification Based on Coupling Coding and Adaptive Filter

Source Camera Identification (SCI) has been playing an important role in the security field for decades. With the development of Deep Learning, the performance of SCI has been noteworthily improved. However, most of the proposed methods are forensic only for a single camera identification category, e.g., the camera model identification. For exploiting the coupling between different camera categories, we present a new coding method. That is, we apply the multi-task training method to regress the categories, namely, to classify brands, models and devices synchronously in a single network. Different from the common multi-task method, we obtain the multi-class classification result by just one single label classification. To be specific, we classify the categories in a progressive way that the parent category classification result will be used in the child category classification (a detailed explanation will be given later in the main context). Also, by appropriately increasing the redundancy of the coding method for classifying new camera categories, the training time can be greatly reduced. To better extract camera attributes, we propose an adaptive filter. Additionally, we propose an auxiliary classifier that only focuses on the camera model re-classification, due to the low performance of the main classifier on certain models. Lastly, the extensive experiments show that our methods have a better performance than other existing methods.
Stemming Effects on Sentiment Analysis using Large Arabic Multi-Domain Resources

Sentiment analysis is an area of great interest in research because of its importance and advantages in many different domains. Different approaches and techniques are used to classify the sentiment of texts, and there are different algorithms proposed to improve the performance through text preprocessing. Stemming is one of preprocessing step that is used in many research to enhance the performance of sentiment classification. In this research, we provide new comparative experiments on the impacts and effects of using two of the most commonly used stemmers in the Arabic language; light stemmer and Khoja stemmer in the preprocessing phase of sentiment analysis. We used large Arabic multi-domain datasets that include positive and negative reviews across multiple domains. Five classifiers are used; Naïve Bayes, support vector machines, k-nearest neighbors, decision trees, and logistic regression. The results indicate that Khoja stemmer outperformed that light stemmer Khoja stemmer in terms of precision, recall, f-measure, and accuracy, and it has an advantage in minimizing training time.
SP-MIOV: A novel framework of shadow proxy based medical image online visualization in computing and storage resource restrained environments

The rapid development of Internet and various mobile communication media initiate the demands for access to medical image visualization systems. Medical image reading and interpretation at any time, any place and any device become an urgent need for radiologists. The current medical image online visualization methods have disadvantages in computing and storage resource restrained environments. This study presents a novel framework of medical image online visualization based on shadow proxy, which makes applications have across platform ability and universal environmental adaptability especial for devices with restricted running resources. The framework can be adapted in multiple client architectures including the pure web applications, mobile applications or regular desktop applications. It is easy to be integrated into third party software and there are no restrictions of the communication protocols between the client and server side due to two innovations of the framework that are shadow proxy mechanism and shadow data. The shadow proxy just does lightweight tasks on shadow data and the ultimate processing of computing tasks are moved to the server side to complete. The size of shadow data is small enough for shadow proxy that speeds up local display and processing tasks. Finally, the framework takes advantage of high performance on server side to render high quality image results. The performance of proposed work is evaluated in a web based medical image visualization system, and the results show that the framework in this paper allows the system to have smooth and quasi-real-time interaction performance. Therefore, this study ensures the local client operations fluency and fast while the quality of the visualization is still not lost that gives the best user experience.
Nonlinear Double-Image Encryption in Cylindrical Diffraction-Based Scheme by Aid of Position Multiplexing

Conventional phase-truncation-based double-image encryption (PT-DIE) scheme suffers from the phase retrieval algorithm-based attack, and information disclosure issue. In this paper, we introduce cylindrical diffraction and position multiplexing into the PT-DIE scheme. Through position multiplexing, double-image are integrated together. Then the integrated image is encoded into a real-valued cyphertext and two phase-only keys (POKs) by cylindrical diffraction. The decrypted images are acquired with two POKs by inverse cylindrical diffraction, and because of that, the phase retrieval algorithm-based attack and the information disclosure issue have been thoroughly conquered from a theoretical and experimental point of view. Furthermore, the proposed scheme can be extended to encode multi-image without changing original system architecture, and incurring additional computing burden. Simulation results have been manifested the validity and the security of the proposed scheme.
Blockchain Based Secure Package Delivery via Ridesharing

Delivery service via ridesharing is a promising service to share travel costs and improve vehicle occupancy.Existing ridesharing systems require participating vehicles to periodically report individual private information (e.g., identity and location) to a central controller, which is a potential central point of failure, resulting in possible data leakage or tampering in case of controller break down or under attack.In this paper, we propose a Blockchain secured ridesharing delivery system, where the immutability and distributed architecture of the Blockchain can effectively prevent data tampering.However, such tamperresistance property comes at the cost of a long confirmation delay caused by the consensus process.A Hash-oriented Practical Byzantine Fault Tolerance (PBFT) based consensus algorithm is proposed to improve the Blockchain efficiency and reduce the transaction confirmation delay from 10 minutes to 15 seconds.The Hash-oriented PBFT effectively avoids the double-spending attack and Sybil attack.Security analysis and simulation results demonstrate that the proposed Blockchain secured ridesharing delivery system offers strong security guarantees and satisfies the quality of delivery service in terms of confirmation delay and transaction throughput.
An alternative Strategy for Increasing Indonesian Student Digital Literacy Skills through Interactive Game

Technology development is one of the significant steps fulfill the early childhood needs. The need to get information, remote communication and facilitate interaction in the learning process is an element that could be met by technology. The purpose of this study was to determine the Interactive Game Influence on Digital Literacy Ability in Kindergarten at Padang, Indonesia. This research is quasi-experimental study using interactive games for experimental class, and non-interactive game for control class. The digital literacy rate is measured using Digital Literacy for Early Childhood Inventory. Analysis of experimental data shows the digital literacy ability of children in experimental class by using interactive games show higher results than non-interactive game. Data analyses conclude that interactive games affect the digital literacy ability in kindergarten.
A unified framework of source camera identification based on features

Source camera identification, which aims at identifying the source camera of an image, has attracted a wide range of attention in the field of digital image forensics recently. Many approaches to source camera identification have been proposed by extracting some image features. However, most of these methods only focused on extracting features from the single artifact of the camera left on the captured images and ignored other artifacts that may help improve final accuracy. Therefore, in this paper, we propose a feature-based framework for source camera identification, which first captures various pure camera-specific artifacts through preprocessing and residual calculation, then extracts discriminative features through image transform, and finally reduces the algorithm complexity through feature reduction. Based on the framework, a novel source camera identification method is proposed, which can identify different camera brands, models and individuals with high accuracy. A large number of comparative experiments show that the proposed method outperforms the state-of-the-art methods.
Tweets Emotion Prediction by Using Fuzzy Logic System

The social media world is growing day by day; people are using social media platforms to express their feelings. Leading companies are deploying those platforms in measuring customer's satisfaction with their products and services. The huge amount of data produced by such platforms can be analyzed to help those companies in improving their businesses. This paper introduces a new contribution in the field of emotion classification for Tweets by using Fuzzy Logic intellection that classify each tweet to an emotion with different degrees of intensity. We achieved that by developing two fuzzy classification systems, the first one inspects the text and referred to as (TCFL), while the second one inspects emojis associated with tweet's text and referred to as (ECFL). Our approaches classify tweets into eight different emotion categories (Joy, Sadness, Anger, Disgust, Trust, Fear, Surprise, and Anticipation) with seven emotion degrees (Extremely High, Very High, High, Medium, Low, Very Low, and Extremely Low). After comparing the developed two systems with human-based classification, TCFL outperformed ECFL with 48.96% match as compared to 32.54% match for ECFL.
A Proposed Method Using the Semantic Similarity of WordNet 3.1 to Handle the Ambiguity to Apply in Social Media Text

The semantic similarity between two concepts is widely used in natural language processing. In this article, we propose a method using WordNet 3.1 to determine the similarity based on feature combinations. This work focuses on overcoming the ambiguity in social media text via the selection of informative features to improve semantic representation. In addition, this research uses social media as its research domain used in this work, and the study is only limited to the politic dataset. A feature-based method is applied to predict the outcome and improve the performance of the proposed method depending on factors related to the fidelity, continuity, and balance of knowledge sources in WordNet 3.1. Semantic similarity measurements among words are insufficient and unbalanced features. However, this study presents a semantic similarity measure of a feature-based method in WordNet 3.1 to determine the similarity between two concepts/words depending on the selected features used to measure their similarity, which is also known as a “noun” and “is-a” relations-based method. We evaluate our proposed method using the data set in Agirre [1] (AG203) and compare our results of our new method as which three of methods taxonomy relation, non-taxonomy and Glosses with those of related studies. The correlation with human judgments is subjective and low based on our results was a better. Experimental results show that our new method significantly outperforms other existing computational methods with the following results: r = 0.73%, p = 0.69%, m = 0.71% and nonzero = 0.95%.
P-Stemmer or NLTK Stemmer for Arabic Text Classification?

Natural Language Processing (NLP) is a branch of computer science that focuses on developing systems that allow computers to communicate with people using everyday language. NLP tools are Devoted to making computers understand statements written in human language. Indexing, text retrieval and word processing are considered as challenges in the classification process. Hence, Arabic Natural Language Processing ANLP tools are needed to achieve the aforementioned tasks. ANLP includes preprocessing such as Stemming, Normalization, Stop-word Removal, Part of speech POS and other processes. In this work, we collected 1,000 news articles from Alghad.com newspaper, then we classified our dataset using SVM and NB algorithms using NLTK tool. We compared the results of two stemmers; P-Stemmer and NLTK stemmer using the mentioned classification process. The results of the classification for the P-Stemmer was better than the NLTK stemmer and for the two classifiers.
MoBVQA: A Modality based Medical Image Visual Question Answering System

This paper discusses about the work on medical image visual question answering done on the ImageCLEF 2019 medical VQA dataset. Visual question answering is a task where an image and a related question is given as input to the machine and we get a correct answer to the question as output. In our problem, both the input image and question are from medical domain. In medical imaging, VQA has applications like providing a second opinion to radiologists about their analysis of the image. It can also be used by the patients for getting a basic information about the image without consulting the doctor. We have considered the problem of answering modality based questions for medical images like X-ray, Computed Tomography(CT), ultra sound(US), magnetic resonance imaging(MRI) etc. The approach used here is to use a Convolutional Neural Network(CNN) to classify the input image to its modality class and thus generate the answer according to the CNN output. The proposed model shows a testing accuracy of 83.8% which is comparable with state of the art.
Image-Enhancement-Based Data Augmentation for Improving Deep Learning in Image Classification Problem

In this paper, we propose a novel data augmentation method based on image enhancement. When training CNN models for image classification, it is required to prepare sufficient training data taken in various conditions. However, traditional data augmentation methods are limited to physical transformation, and shooting conditions of images, so exposure conditions lack for data augmentation. Therefore, we utilize an image enhancement method to generate images with different exposures, although enhanced methods are generally used for generating high quality images. Experimental results show that the proposed method improves the classification accuracy of a CNN model. The results also demonstrate that combining the proposed method with other existing data augmentation methods provides further improvement of the classification accuracy.
Architecture and optimization of data mining modeling for visualization of knowledge extraction: Patient safety care

Visualization of the knowledge extraction process is a front line to reveal the detail process and data structure, which is an advanced technique for the presentation of data modeling. However, the mechanisms for healthcare are challenging and dynamic processes to gain a clear insight or understanding of patient care. In this paper, we proposed a new approach of architecture and optimization of data mining modeling for visualization of knowledge extraction by analyzing clinical data sets to define the determinant attributes through modeling techniques. Therefore, architecture for the visualization of the knowledge extraction process is a systematic approach to support users to the best of their knowledge of the issues over the challenge of visualizing techniques. The proposed approach is capable and dynamic to handle and analyze large-scale data in its dimension and context. Such a variable is defined using various techniques to characterize them towards the detection of determinant variables as its influential circumstance. We focused on modeling based visualization as model representation, factor's interaction and integration. The detection process experimented in a different approach and justification as discussed in section five. The finding showed a deep understandability for an advanced and dynamic data mining modeling techniques to integrate applications with domain contexts for the optimal and understandable decision process. The strength of this approach is the depth for visualization towards the knowledge extraction process and its understandability for users as per their background and circumstances. It is also essential to inference for architecture based modeling and visualization for large scale data. Researchers, physicians, experts, and other users are the potentials to refer to these novel ideas and findings.
Millimeter-Wave Radar and RGB-D Camera Sensor Fusion for Real-Time People Detection and Tracking

One of the key aspects of modern-day robotics research is the development of perceptual capabilities of agents. Robots need to understand their surroundings in order to reason or infer about a given situation. Chief among the areas of perception is that of detection and tracking of people. In this work we employ millimeter-wave radar and imaging sensor fusion approach to pedestrian detection and tracking. We perform experiments in a variety of settings (single and multi- target, varying illumination, varying distances and fields of view, dense and light clutter, and through-the-wall tracking). Our results show that our fusion and tracking architecture is far superior to camera only systems in terms of accuracy and added functionality. Our implementation mitigates the effects of occlusions (including wooden walls), blurry images, obscured lens, and field of view limitations.
Special issue on hybrid pedagogies editorial

An increasing awareness of the potential biases and problematic impacts of digital technologies is driving a renewed focus on social responsibility and ethical considerations within the fields of engineering and the computer and data sciences. Similarly, a renewed sense of complicity in our socio-technical environments has encouraged scholars from a range of design, humanities, and social science disciplines to engage more directly in public-facing work, often through prototyping, exhibitions, and hands-on educational activities. This special issue on hybrid pedagogies contributes to this developing and ongoing conversation in a resolutely interdisciplinary way.