Reviewerly/exHarmony huggingface dataset:
Increasing the Efficiency of GPU Bitmap Index Query Processing

Once exotic, computational accelerators are now commonly available in many computing systems. Graphics processing units (GPUs) are perhaps the most frequently encountered computational accelerators. Recent work has shown that GPUs are beneficial when analyzing massive data sets. Specifically related to this study, it has been demonstrated that GPUs can significantly reduce the query processing time of database bitmap index queries. Bitmap indices are typically used for large, read-only data sets and are often compressed using some form of hybrid run-length compression.In this paper, we present three GPU algorithm enhancement strategies for executing queries of bitmap indices compressed using Word Aligned Hybrid compression: 1) data structure reuse 2) metadata creation with various type alignment and 3) a preallocated memory pool. The data structure reuse greatly reduces the number of costly memory system calls. The use of metadata exploits the immutable nature of bitmaps to pre-calculate and store necessary intermediate processing results. This metadata reduces the number of required query-time processing steps. Preallocating a memory pool can reduce or entirely remove the overhead of memory operations during query processing. Our empirical study showed that performing a combination of these strategies can achieve 33\(\times \) to 113\(\times \) speedup over the unenhanced implementation.
Tutorial: Feature Engineering for Recommender Systems

The selection of features and proper preparation of data for deep learning or machine learning models plays a significant role in the performance of recommender systems. To address this we propose a tutorial highlighting best practices and optimization techniques for feature engineering and preprocessing of recommender system datasets. The tutorial will explore feature engineering using pandas and Dask, and will also cover acceleration on the GPU using open source libraries like RAPIDS and NVTabular. Proposed length is 180min. We’ve designed the tutorial as a combination of a lecture covering the mathematical and theoretical background and an interactive session based on jupyter notebooks. Participants will practice the discussed features by writing their own implementation in Python. NVIDIA will host the tutorial on their infrastructure, providing dataset, jupyter notebooks and GPUs. Participants will be able to easily attend the tutorial via their web browsers, avoiding any complicated setup. Beginner to intermediate users are the target audience, which should have prior knowledge in python programming using libraries, such as pandas and NumPy. In addition, they should have a basic understanding of recommender systems, decision trees and feed forward neural networks.
CNN-based Classification of Degraded Images

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.
Revealing Implicit Relations in Russian Legal Texts

In this paper, new methods for detecting implicit links between legal documents are proposed. Those methods are based on approaches for building vector representations of words, such as Word2Vec, FastText, as well as vector representations of texts and sentences: BERT and Doc2Vec. In addition, as part of this study, we propose an approach to create a dataset for the detection of implicit links and provide such a dataset. The dataset contains more than 36K Russian legal documents. The experiments on that dataset show applicability of the proposed methods. Namely, BERT-based fine-tuned models show the best performance; however, they have the highest demand for memory and computational resources.
Python Programming in Jupyter Notebook

In an earlier chapter, we learned all about the Python programming language. We studied its advantages, compared it with some other languages, and understood how its features make it stand out as a dependable language for machine learning.
Illuminating Vehicles With Motion Priors For Surveillance Vehicle Detection

Vehicle detection in traffic surveillance videos is a special subtask in object detection, where desired objects are vehicles moving on the road while the background is still within a sequence. The disparity of speed within each frame, i.e. moving and static, is consistent with the vehicle and background semantic to some extent, thus motions can be extracted to enhance the appearance of foreground. In this paper, we propose a motion prior embedded parallel architecture for vehicle detection, aiming at illuminating vehicles and suppressing false positives in the background. We further implement extensive experiments on the UA-DETRAC dataset to validate the effectiveness of our approach, and achieve promising performance in both accuracy and speed.
Model Integrated Decomposition and Assisted Specification (MIDAS)

Abstract MIDAS (Model Integrated Decomposition and Assisted Specification) is an innovative new approach to requirement specification. By integrating textual and intent‐based model‐assisted requirement flows, MIDAS presents a paradigm shift from prior model‐based and traditional textual requirement processes. Such a change is necessary to address the full intent of design assurance. The goal of MIDAS is to address the many challenges we have observed in applying the current model‐based certification guidance. The principal issue with interpreting the current guidance is a misplaced focus on the separation of graphical vs. textual specification rather than a clean and clear separation of design from intent. Hence, this aspect forms the core of our proposed MIDAS approach. Our intent‐based focus also mitigates the often implementation‐centric perspective of model‐based requirement flows. Our hybrid model‐based constrained language flow also addresses the weakness of informal natural language‐based specifications. Finally, MIDAS facilitates the integration of knowledge management with requirement engineering, allowing easier visualization of the full requirement context. By writing this paper, we share our observations and ideas openly to encourage feedback and collaboration with potential development partners.
Live Demonstration: Comparison of Three FIR Digital Filter Sharpening Techniques

Digital filter sharpening aims to improve filtering by cascading structures employing multiple use of an original prototype's unchanged coefficient set. Starting with a visitor-designed FIR prototype, we demonstrate the operation of three different sharpening methods, comparing their various advantages and shortcomings. Two tools are employed: a Simulink model, facilitating visual examination of virtual instrument displays, and a MATLAB package that provides a compact equivalent filter model of the sharpened resultant.
Remote sensing for exploring heavy mineral deposits

The coastal tracts of India consist of beach sands showing good concentration of strategic heavy minerals. In the present study, the mineralogy of important coastal placer deposits in India like Chavara deposits in Kerala and Manavalakurichi deposits in Tamilnadu were studied using Landsat multispectral satellite data for deriving potential targets of these strategic minerals. The ilmenite mineral predominantly exists in these areas, followed by monazite, sillimanite, rutile, zircon, garnet, leucoxene, and kyanite. The distribution of minerals was studied using advanced hyperspectral techniques like minimum noise fraction transformation, pixel purity index, and n-dimensional visualizer followed by spectral angle mapper (SAM) classification. Two end members of ilmenite mineral and quartz were identified from the Landsat imagery and successfully mapped using SAM algorithms. Detailed investigation of the mineralogy of beach sediments using advanced hyperspectral remote sensing techniques helps to derive potential targets for the ecofriendly exploration of minerals.
Polarity Detection in a Cross-Lingual Sentiment Analysis using spaCy

This paper presents a comparison of sentiment analyses performed on a dataset of French tweets and it's machine translated version (in English) using Google Translate. In recent years, a fairly new Python library called spaCy has gained significant traction in performing sentiment analyses in languages other than English due to its multilingual support. There still haven't been major publications on evaluating its usage for the said purpose. In this research, TFIDF features are extracted from three different N-grams (Unigrams, Bigrams and Trigrams) in the corpus after preprocessing to remove irrelevant details. These are then trained and tested using three machine learning algorithms - Logistic Regression, Naïve Bayes Algorithm and Stochastic Gradient Descent. A comparative study then put forth will help future researchers in the following three areas - the capability of performing sentiment analyses in languages other than English, reliability of machine translation tools in performing cross-lingual sentiment analyses and the evaluation of Python's library, spaCy for performing multilingual sentiment analyses.
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-Quality Data for Training

Generative adversarial network (GAN) can greatly improve the quality of unsupervised image generation. Previous GAN-based methods often require a large amount of high-quality training data. This work aims to reduce the use of high-quality data in training, meanwhile scaling up GANs to thousands of classes. We propose an image generation method based on conditional transferring features, which can capture pixel-level semantic changes when transforming low-quality images into high-quality ones. Self-supervision learning is then integrated into our GAN architecture to provide more label-free semantic supervisory information observed from the training data. As such, training our GAN architecture requires much fewer high-quality images with a small number of additional low-quality images. Experiments show that even removing 30% high-quality images from the training set, our method can still achieve better image synthesis quality on CIFAR-10, STL-10, ImageNet, and CASIA-HWDB1.0, compared to previous competitive methods. Experiments on ImageNet with 1,000 classes of images and CASIA-HWDB1.0 with 3,755 classes of Chinese handwriting characters also validate the scalability of our method on object classes. Ablation studies further validate the contribution of our conditional transferring features and self-supervision learning to the quality of our synthesized images.
Security-as-a-service: a literature review

Purpose This study aims to identify the level of security from existing work, analyze categories of security as a service (SECaaS) and classify them into a meaningful set of groups. Further, the report will advise commercial applications and advice of SECaaS as an extended context to help firms make decisions. Design/methodology/approach This paper compares the SECaaS categories in Cloud Security Alliance (CSA) with the security clauses in ISO/IEC 27002:2013 to give a comprehensive analysis of those SECaaS categories. Reviewed from a number of related literature, this paper analyzes and categorizes SECaaS into three major groups including protective, detective and reactive based on security control perspectives. This study has discussed the three groups and their interplay to identify the key characteristics and problems that they aim to address. Findings This paper also adds new evidence to support a better understanding of the current and future challenges and directions for SECaaS. Also, the study reveals both the positive and negative aspects of SECaaS along with business cases. It advises on various sizes and domains of organizations to consider SECaaS as one of their potential security approaches. Originality/value SECaaS has been demonstrated to be one of the increasingly popular ways to address security problems in Cloud computing. As a new concept, SECaaS could be treated as integrated security means and delivered as a service module in the Cloud. However, it is still in infancy and not very widely investigated. Recent studies suggest that SECaaS is an efficient solution for Cloud and real industries. However, shortcomings of SECaaS have not been well-studied and documented. Moreover, reviewing the existing research, researchers did not classify the SECaaS-related categories.
Valutare per includere Il valore dei repertori linguistici plurali

This contribution illustrates a proposal for an inclusive plurilingual assessment, analysing the construct of plurilingual competence and the concept of intercomprehension between Romance languages. It also presents data from the first examination sessions of an innovative plurilingual test, developed within the EVAL-IC Erasmus+ project to assess intercomprehension competence among the five Romance languages of the project (Portuguese, Spanish, Italian, French and Romanian). It focuses on the written receptive dimension of intercomprehension, proving that a single test can highlight the value of the pluralistic repertoire of individuals.
"Miss understandable"

This study examines the appropriation and usage of voice assistants like Google Assistant or Amazon Alexa in private households. Our research is based on 10 in-depth interviews with users of voice assistants and a follow-up evaluation of their interaction histories. Our results illustrate situations in which the voice assistants were used at home, what strategies the users adopted to interact with them, how the interactions were performed, and what difficulties users experienced. A focus of our study is on misunderstandings, situations where interactions failed partially or completely. Our study shows that the full potential of voice assistants is often not utilized, as complex interactions are often subject to failures and users revert to simple use cases. Our participants used their voice assistant mostly for simple commands, often not even trying new functions. An analysis of their appropriation strategies resulted in implications for the design of supportive tools as well as the further development and optimization of voice interfaces.
Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.
Various Pre-processing Strategies for Domain-Based Sentiment Analysis of Unbalanced Large-Scale Reviews

User reviews are important resources for many processes such as recommender systems and decision-making programs. Sentiment analysis is one of the processes that is very useful for extracting the valuable information from these reviews. Data preprocessing step is of importance in the sentiment analysis process, in which suitable preprocessing methods are necessary. Most of the available research that study the effect of preprocessing methods focus on balanced small-sized dataset. In this research, we apply different preprocessing methods for building a domain lexicon for unbalanced big-sized reviews. The applied preprocessing methods study the effects of stopwords, negation words and the number of word’s occurrence. Followed by applying different preprocessing methods to determine the words that have high sentiment orientations in calculating the total review sentiment score. Two main experiments with five cases are tested on the Amazon dataset for the movie domain. The best suitable preprocessing method is then selected for building the domain lexicon as well as calculating the total review sentiment score using the generated lexicon. Finally, we evaluate the proposed lexicon by comparing it with the general-based lexicon. The proposed lexicon outperforms the general lexicon in calculating the total review sentiment score in term of accuracy and F1-measure. Furthermore, the results prove that sentiment words are not restricted to adjectives and adverbs only (as commonly claimed); nouns and verbs also contribute to the sentiment score and thus effects in the sentiment analysis process. Moreover, the results also show that negation words have positive effects in the sentiment analysis process.
Text Input in Virtual Reality Using a Tracked Drawing Tablet

We present an experiment evaluating the effectiveness of a tracked drawing tablet for use in virtual reality (VR) text input. Participants first completed a text input pre-test, entering several phrases using a physical keyboard. Participants then entered text in VR using an HTC Vive, with a tracker mounted on a drawing tablet with a QWERTY soft keyboard overlaid on the virtual tablet. This was similar to text input using stylus-supported mobile devices. Our results indicate that not only did participants prefer the Vive controller, it also offered superior entry speed (16.31 wpm vs. 12.79 wpm with the tablet and stylus) and error rates (4.1% vs. 6.4%). Pre-test scores were also correlated to measured entry speeds, and reveal that user typing speed on physical keyboards provides a modest predictor of VR text input speed (R2 of 0.6 for the Vive controller, 0.45 for the tablet).
An Approach for Sentiment Analysis and Personality Prediction Using Myers Briggs Type Indicator

Due to the rapid development of “Web 5.0”, in the last few years, researchers have started to pay attention to social media using Personality Prediction and Sentiment Analysis. However, due to the high costs and the privacy of these datasets, this paper presents a study on Sentiment Analysis and Personality Prediction through Social Media using Myers–Briggs Type Indicator (MBTI) personality assessment test to analyze textual data with the use of different classification algorithms. The data are collected from Kaggle with approximately 8600 rows of Twitter data. The system is tested using 25% of the dataset and the remaining 75% is for the training set. The results show an average accuracy rate of 78.2% with the use of different classification algorithms, and a 100% accuracy rate using the Random Forest (RF) and Decision Tree classifiers.
Breast Cancer Classification from Histopathological Images with Separable Convolutional Neural Network and Parametric Rectified Linear Unit

The convolutional neural network has achieved great success in the classification of medical imaging including breast cancer classification. Breast cancer is one of the most dangerous cancers impacting women all over the world. In this paper, we propose a deep learning framework. This framework includes the proposed pre-processing phase and the proposed separable convolutional neural network (SCNN) model. Our pre-processing uses patch extraction and data augmentation to enrich the training set and improve the performance. The SCNN model uses separable convolution and parametric rectified linear unit (PRELU) as an activation function. The SCNN shows superior performance and faster than the pre-trained neural network models. The SCNN approach is evaluated using the BACH2018 dataset [1]. We test the performance using 40 random images. The framework achieves accuracy between 97.5% and 100%. The best accuracy is 100% for multi-class and binary class. The framework provides superior classification performance compared to existing approaches.
3D Mobility Models and Analysis for UAVs

We present a flexible family of 3D mobility models suitable for unmanned aerial vehicles (UAV). Based on stochastic differential equations, the models offer a unique property of explicitly incorporating the mobility control mechanism and environmental perturbation, while enabling tractable steady state solutions for properties such as position and connectivity. Specifically, motivated by UAV flight data, for a symmetric mobility model with an arbitrary control mechanism, we derive the steady state distribution of the distance from the target position. We provide closed form expressions for the special cases of the Ornstein-Uhlenbeck (OU) process and on-off control (OC). We extend the model to incorporate imperfect positioning and asymmetric control. For a practically relevant scenario of partial symmetry (such as in the x-y plane), we present steady state position results for the OU control. Building on these results, we derive UAV connectivity probability results based on a SNR criterion in a Rayleigh fading environment.
User Engagement for Collaborative Learning on a Mobile and Desktop Augmented Reality Application

User engagement and information understanding was assessed for a basic electronics tutorial designed using Augmented Reality (AR). The main idea was to compare user performance in collaborative environments between two different versions of the AR application: a desktop version and a mobile version. Preliminary testing of both versions revealed that users were interested in AR technology but did not intuitively understand how to better utilize the system in comparison with a static video. Moreover, in spite there was not a statistically significant difference between information understanding between both conditions, the mobile application was rated significantly lower than the desktop application in user engagement for collaborative environments.