Reviewerly/exHarmony huggingface dataset:
Improving Short Text Classification using Information from DBpedia Ontology

With the emergence of social networks and micro-blogs, a huge amount of short textual documents are generated on a daily basis, for which effective tools for organization and classification are needed. These short text documents have extremely sparse representation, which is the main cause for the poor classification performance. We propose a new approach, where we identify relevant concepts in short text documents with the use of the DBpedia Spotlight framework and enrich the text with information derived from DBpedia ontology, which reduces the sparseness. We have developed six variants of text enrichment methods and tested them on four short text datasets using seven classification algorithms. The obtained results were compared to those of the baseline approach, among themselves, and also to some state-of-the-art text classification methods. Beside classification performance, the influence of the concepts similarity threshold and the size of the training data were also evaluated. The results show that the proposed text enrichment approach significantly improves classification of short texts and is robust with respect to different input sources, domains, and sizes of available training data. The proposed text enrichment methods proved to be beneficial for classification of short text documents, especially when only a small amount of documents are available for training.
A Clothing Recommendation Dataset for Online Shopping

In this work, we constructed a clothing dataset which contains more than one hundred thousand images labeled with brand, category, color, material and price. In recent years, Deep Neural Network makes various of computer vision tasks, such as image retrieval, age estimation [6], [11] and multimedia quality assessment [5], [12], reach impressive performance. However, some problem comes, training data plays an important role in the result of the deep neural networks. Generally speaking, high quality images can lead to a better outcome, and the amount of training data has huge influence on the results. So, with the rise of neural networks, more people are committed to the establishment of a database. We collected hundreds of thousands of images with higher resolution than other clothing datasets, and we provide their corresponding brands and price as attributes. Finally, we compared our dataset with some other clothing datasets.
Adversarial Learning for Depth and Viewpoint Estimation From a Single Image

Estimating a depth map and, at the same time, predicting the 3D pose of an object from a single 2D color image is a very challenging task. Depth estimation is typically performed through stereo vision by following several time-consuming stages, such as epipolar geometry, rectification and matching. Alternatively, when stereo vision is not useful or applicable, depth relations can be inferred from a single image as studied in this paper. More precisely, deep learning is applied in order to solve the problem of estimating a depth map from a single image. Then, that map is used for predicting the 3D pose of the main object depicted in the image. The proposed model consists of two successive neural networks. The first network is based on a Generative Adversarial Neural network (GAN). It estimates a dense depth map from the given color image. A Convolutional Neural Network (CNN) is then used to predict the 3D pose from the generated depth map through regression. The main difficulty to jointly estimate depth maps and 3D poses using deep networks is the lack of training data with both depth and viewpoint annotations. This contribution assumes a cross-domain training procedure with 3D CAD models corresponding to objects appearing in real images in order to render depth images from different viewpoints. These rendered images are then used to guide the GAN network to learn the mapping from the image domain to the depth domain. By exploiting the dataset as a source of training data, the proposed model outperforms state-of-the-art models on the PASCAL 3D+ dataset. The code of the proposed model is publicly available at https://github.com/SaddamAbdulrhman/Depth-and-Viewpoint-Estimation/tree/master.
RETRACTED: Estimation of the optimum tilt angle of solar collectors in Coatzacoalcos, Veracruz

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of Editor-in-Chief, because it contains a number of discrepancies such as: 1.Almost all numbers presented in the paper differ from reality by an order of magnitude of one million. 2.Although the paper is about the optimal angles for a specific location (Veracruz) only beam radiation is considered ignoring the diffuse and ground reflected components of radiation, which according to the specific weather conditions of a location can significantly affect the results. 3.No real weather data were used in the analysis which can lead to errors of the order of 30%. The Authors apologize for the inconvenience caused by these mistakes and agree with the retraction. The Journal apologizes that these errors were not detected during peer review.
ARMv8-A System Semantics: Instruction Fetch in Relaxed Architectures

Abstract Computing relies on architecture specifications to decouple hardware and software development. Historically these have been prose documents, with all the problems that entails, but research over the last ten years has developed rigorous and executable-as-test-oracle specifications of mainstream architecture instruction sets and “user-mode” concurrency, clarifying architectures and bringing them into the scope of programming-language semantics and verification. However, the system semantics , of instruction-fetch and cache maintenance, exceptions and interrupts, and address translation, remains obscure, leaving us without a solid foundation for verification of security-critical systems software. In this paper we establish a robust model for one aspect of system semantics: instruction fetch and cache maintenance for ARMv8-A. Systems code relies on executing instructions that were written by data writes, e.g. in program loading, dynamic linking, JIT compilation, debugging, and OS configuration, but hardware implementations are often highly optimised, e.g. with instruction caches, linefill buffers, out-of-order fetching, branch prediction, and instruction prefetching, which can affect programmer-observable behaviour. It is essential, both for programming and verification, to abstract from such microarchitectural details as much as possible, but no more. We explore the key architecture design questions with a series of examples, discussed in detail with senior Arm staff; capture the architectural intent in operational and axiomatic semantic models, extending previous work on “user-mode” concurrency; make these models executable as test oracles for small examples; and experimentally validate them against hardware behaviour (finding a bug in one hardware device). We thereby bring these subtle issues into the mathematical domain, clarifying the architecture and enabling future work on system software verification.
Quantification of the morphology of gold grains in 3D using X-ray microscopy and SEM photogrammetry

ABSTRACT The shape of gold is widely used in mineral exploration and in sedimentology to estimate the distance of transport from the source to the site of deposition. However, estimation of the morphology is based on qualitative observations or on the quantification of shape in 2D. The 3D analysis of grain shape is useful for accurate morphometric quantification and to evaluate its volume, which is related to particle size. This study compares X-ray 3D microscope and 3D SEM photogrammetry to reconstruct the shape of gold particles. These new methods are exploited to quantify the shape of gold grains 85 to 300 μm in size. The shape parameters, such as axial lengths, surface area, volume, diameter of curvature of all corners, and diameter of the largest inscribed sphere and smallest circumscribed sphere are measured on a particle in order to estimate shape factors such as flatness ratios, shape indices, sphericity, and roundness. Most shape parameters and shape factors estimated on the same gold grain with simple geometry are similar between the two approaches. This result validates these methods for the 3D description of gold particles with simple morphology, while providing a methodology for describing grains with more complex geometry.
A Low-Cost VLSI Architecture of the Bilateral Filter for Real-Time Image Denoising

In this paper, a low-cost hardware architecture of the bilateral filter for real-time image processing is proposed. Based on the techniques of distance-oriented grouping and hardware resource sharing, the usage of multipliers can decrease 48% as compared to the previous approach. Besides, an efficient quantization method is applied to reduce the size of required look up tables. The experimental results show that the proposed architecture is cost efficient while maintaining the same image quality, frame rate and working clock frequency.
What and where: A context-based recommendation system for object insertion

Abstract We propose a novel problem revolving around two tasks: (i) given a scene, recommend objects to insert, and (ii) given an object category, retrieve suitable background scenes. A bounding box for the inserted object is predicted in both tasks, which helps downstream applications such as semiautomated advertising and video composition. The major challenge lies in the fact that the target object is neither present nor localized in the input, and furthermore, available datasets only provide scenes with existing objects. To tackle this problem, we build an unsupervised algorithm based on object-level contexts, which explicitly models the joint probability distribution of object categories and bounding boxes using a Gaussian mixture model. Experiments on our own annotated test set demonstrate that our system outperforms existing baselines on all sub-tasks, and does so using a unified framework. Future extensions and applications are suggested.
Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks

Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly or emergency room visits are instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs. This study evaluates previous anomaly detection machine learning models and proposes an unsupervised framework to identify anomalies using a Generative Adversarial Network (GANs) model. The GANs anomaly detection (GAN-AD) model was applied on two different healthcare provider data sets. The anomalous healthcare providers were further analysed through the application of classification models with the logistic regression and extreme gradient boosting models showing good performance. Results from the SHapley Additive exPlanation (SHAP) also signifies that the predictors used explain the anomalous healthcare providers.
Lightdet: A Lightweight and Accurate Object Detection Network

The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone that is able to capture rich low-level features by the proposed Detail-Preserving Module. To effectively aggregate bottom and top-down features, we introduce an efficient Feature-Preserving and Refinement Module. A lightweight prediction head is employed to further reduce the entire network complexity. Experimental results show that our LightDet achieves 75.5% mAP on PASCAL VOC 2007 at the speed of 250 FPS and 24.0% mAP on MS COCO dataset.
Factors influencing cellular device purchase decisions in Jordan

The study aimed to examine the brand which is among the main dimensions for making the final purchasing decision among Jordanian consumers when selecting a cellular device which is an index for them and when purchasing in particular.Within this frame, the study aimed to investigate the factors influencing purchasing decision of Jordanian consumers when selecting the brand of cellular devices.The study followed the analytic descriptive approach in data collection where a questionnaire was distributed on a random sample of 240 participants from the study population.The study concluded that there was a positive relationship between the quality of cellular devices and the selection of brand and this relationship was attributed to the variables of gender, age, and educational culture.In light of the results revealed, the study recommended the producers of cellular devices of the various brands to offer high quality products with appropriate prices that take into accounts the purchasing power for consumers based on the targeted markets.
ASPLe: A methodology to develop self-adaptive software systems with systematic reuse

More than two decades of research have demonstrated an increasing need for software systems to be self-adaptive. Self-adaptation manages runtime dynamics, which are difficult to predict before deployment. A vast body of knowledge to develop Self-Adaptive Software Systems (SASS) has been established. However, we discovered a lack of process support to develop self-adaptive systems with reuse. The lack of process support may hinder knowledge transfer and quality design. To that end, we propose a domain-engineering based methodology, Autonomic Software Product Lines engineering (ASPLe), which provides step-by-step guidelines for developing families of SASS with systematic reuse. The evaluation results from a case study show positive effects on quality and reuse for self-adaptive systems designed using the ASPLe compared to state-of-the-art engineering practices.
Deep Convolutional Network for Stereo Depth Mapping in Binocular Endoscopy

Depth mapping from binocular endoscopy images plays an important role in stereoscopic surgical treatment.Owing to the development of deep convolutional neural networks (CNNs), binocular depth estimation models have achieved many exciting results in the fields of autonomous driving and machine vision.However, the application of these methods to endoscopic imaging is greatly limited by the fact that binocular endoscopic images not only are rare, but also have unsatisfying features such as no texture, no ground truth, bad contrast, and high gloss.Aiming at solving the above-mentioned problems, we have built a precise gastrointestinal environment by the open-source software blender to simulate abundant binocular endoscopy data and proposed a 23-layer deep CNNs method to generate real-time stereo depth mapping.An efficient scale-invariant loss function is introduced in this paper to accommodate the characteristics of endoscope images, which improves the accuracy of achieved depth mapping results.Regarding the considerable training data for typical CNNs, our method requires only a few images (960 × 720 resolution) at 45 frames per second on an NVIDIA GTX 1080 GPU module, then the depth mapping information is generated in real-time with satisfactory accuracy.The effectiveness of the developed method is validated by comparing with state-of-the-art methods on processing the same datasets, demonstrating a faster and more accurate performance than other model frames.
Organizational Information Security Management for Sustainable Information Systems: An Unethical Employee Information Security Behavior Perspective

This article examines the occurrences of four types of unethical employee information security behavior—misbehavior in networks/applications, dangerous Web use, omissive security behavior, and poor access control—and their relationships with employees’ information security management efforts to maintain sustainable information systems in the workplace. In terms of theoretical contributions, this article identifies and develops reliable and valid instruments to measure different types of unethical employee information security behavior. In addition, it investigates factors affecting different types of such behavior and how such behavior can be used to predict employees’ willingness to report information security incidents. In terms of managerial contributions, the article suggests that information security awareness programs and perceived punishment have differential effects on the four types of unethical behavior and that certain types of unethical information security behavior exert negative effects on employees’ willingness to report information security incidents. The findings will help managers to derive better security rules and policies, which are important for business continuity.
Digital image correlation applied to in situ evaluation of surface cracks upon curing of MgO-containing refractory castables

MgO particles are added to high-alumina castables to provide in situ spinel formation at high temperatures. However, the MgO hydration upon curing may damage the material because of localized volumetric expansion. Usually, damage is evaluated by the ex situ overall measurement of Young's modulus changes during processing via Impulse Excitation Techniques. In this paper, an experimental setup was designed to use Digital Image Correlation (DIC) as an in situ alternative to evaluate damage. Tomographic scans highlighted that all cracks initiated on the sample surfaces, and propagated in the bulk in an intergranular mode. Crack initiation and growth were assessed, for different temperatures, via Surface Crack Density (SCD) measurements, and Mean Crack Opening Displacement (MCOD) fields. They provided important insights into heterogeneous expansion phenomena and crack network quantification; for example, the SCD flattened while the overall damage was still increasing. The results attested the usefulness of DIC for in-situ quantification of ceramics cracking during processing.
Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples

Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigations have increasingly shown DNNs to be highly vulnerable when adversarial examples are used as input. Here, we present a comprehensive defense framework to protect DNNs against adversarial examples. First, we present statistical and minor alteration detectors to filter out adversarial examples contaminated by noticeable and unnoticeable perturbations, respectively. Then, we ensemble the detectors, a deep Residual Generative Network (ResGN), and an adversarially trained targeted network, to construct a complete defense framework. In this framework, the ResGN is our previously proposed network which is used to remove adversarial perturbations, and the adversarially trained targeted network is a network that is learned through adversarial training. Specifically, once the detectors determine an input example to be adversarial, it is cleaned by ResGN and then classified by the adversarially trained targeted network; otherwise, it is directly classified by this network. We empirically evaluate the proposed complete defense on ImageNet dataset. The results confirm the robustness against current representative attacking methods including fast gradient sign method, randomized fast gradient sign method, basic iterative method, universal adversarial perturbations, DeepFool method, and Carlini &amp; Wagner method.
Transformer-based End-to-End Question Generation.

Question Generation (QG) is an important task in Natural Language Processing (NLP) that involves generating questions automatically when given a context paragraph. While many techniques exist for the task of QG, they employ complex model architectures, extensive features, and additional mechanisms to boost model performance. In this work, we show that transformer-based finetuning techniques can be used to create robust question generation systems using only a single pretrained language model, without the use of additional mechanisms, answer metadata, and extensive features. Our best model outperforms previous more complex RNN-based Seq2Seq models, with an 8.62 and a 14.27 increase in METEOR and ROUGE_L scores, respectively. We show that it also performs on par with Seq2Seq models that employ answer-awareness and other special mechanisms, despite being only a single-model system. We analyze how various factors affect the model's performance, such as input data formatting, the length of the context paragraphs, and the use of answer-awareness. In addition, we also look into the modes of failure that the model experiences and identify the reasons why it fails.
A quantile-based approach for hyperparameter transfer learning

Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance objectives of the same algorithm across multiple datasets. In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}. The main idea is to regress the mapping from hyperparameter to objective quantiles with a semi-parametric Gaussian Copula distribution, which provides robustness against different scales or outliers that can occur in different tasks. We introduce two methods to leverage this mapping: a Thompson sampling strategy as well as a Gaussian Copula process using such quantile estimate as a prior. We show that these strategies can combine the estimation of multiple objectives such as latency and accuracy, steering the hyperparameters optimization toward faster predictions for the same level of accuracy. Extensive experiments demonstrate significant improvements over state-of-the-art methods for both hyperparameter optimization and neural architecture search.
Predicting the performance of big data applications on the cloud

Data science applications have become widespread as a means to extract knowledge from large datasets. Such applications are often characterized by highly heterogeneous and irregular data access patterns, thus often being referred to as big data applications. Such characteristics make the application execution quite challenging for existing software and hardware infrastructures to meet their resource demands. The cloud computing paradigm, in turn, offers a natural hosting solution to such applications since its on-demand pricing model allows allocating effectively computing resources according to application’s needs. However, these properties impose extra challenge to the accurate performance prediction of cloud-based applications, which is a key step to adequate capacity planning and managing of the hosting infrastructure. In this article, we tackle this challenge by exploring three modeling approaches for predicting the performance of big data applications running on the cloud. We evaluate two queuing-based analytical models and dagSim, a fast ad-hoc simulator, in various scenarios based on different applications and infrastructure setups. The considered approaches are compared in terms of prediction accuracy and execution time. Our results indicate that our two best approaches, one analytical model and dagSim, can predict average application execution times with only up to a $$7\%$$ relative error, on average. Moreover, a comparison with the widely used event-based simulator available with the Java Modeling Tool (JMT) suite demonstrates that both the analytical model and dagSim run very fast, requiring at least two orders of magnitude lower execution time than JMT while providing slightly better accuracy, being thus practical for online prediction.