Reviewerly/exHarmony huggingface dataset:
Deep Neural Network-Based Click-Through Rate Prediction using Multimodal Features of Online Banners

As the online advertisement industry continues to grow, by 2020, it will account for over 40% of global advertisement spending. Thus, predicting the click-through rates (CTRs) of various advertisements is increasingly crucial for companies. Many studies have addressed CTR prediction. How-ever, most tried to solve the problem using only metadata and excluded information such as advertisement images or texts. Using deep learning techniques, we propose a method to predict CTRs for online banners, a popular form of online advertisements, using all these features. We show that multimedia features of advertisements are useful for the task at hand. The proposed learning architecture outperforms a previous method that uses the three features mentioned above. We also present an attention-based model, which enables visualization of contributions of each feature to the prediction. We analyze how each feature affects CTR prediction with visualization and detailed studies.
Reducing the Bandwidth of Block Propagation in Bitcoin Network With Erasure Coding

With the popularity of Bitcoin, there is a greater demand for the scalability of the Bitcoin blockchain, which is susceptible to the efficiency of block propagation. In the Bitcoin blockchain, efficient block propagation approach can reduce the computing power and the risk of forks. Meanwhile, larger blocks help to improve the throughput of transactions. Thus, the block propagation is a major issue of the scalability of the Bitcoin network. This paper introduces a method to reduce the required bandwidth of block propagation with erasure coding. To begin with, the network nodes are classified into several clusters. When a node wants to propagate a block, the node does not need to propagate the whole information of the block. Instead, the node can only transmit the transaction IDs and the coded information to each cluster. The simulation shows that the proposed method can significantly ease the network traffic among these clusters.
CV-C3D: Action Recognition on Compressed Videos with Convolutional 3D Networks

Action recognition in videos has gained substantial attention from the computer vision community due to the wide range of possible applications. Recent works have addressed this problem with deep learning methods. The main limitation of existing approaches is their difficulty to learn temporal dynamics due to the high computational load demanded for processing huge amounts of data required to train a model. To overcome this problem, we propose a Compressed Video Convolutional 3D network (CV-C3D). It exploits information from the compressed representation of a video in order to avoid the high computational cost for fully decoding the video stream. The speed up of the computation enables our network to use 3D convolutions for capturing the temporal context efficiently. Our network has the lowest computational complexity among all the compared approaches. Results of our approach in the task of action recognition on two public benchmarks, UCF-101 and HMDB-51, were comparable to the baselines, with the advantage of running at faster inference speed.
Intelligent Tutoring System Trends 2006-2018: A Literature Review

This research examined the longitudinal trends of intelligent tutoring systems (ITS) research using text mining techniques in a more comprehensive manner. Two hundred and thirty-one (231) refereed journal articles were retrieved and analyzed from the Web of Science database from the top six major educational technology-based journals, which are based on the Google Scholar metrics and Baidu Scholar in the period from January 2006 to December 2018. Content analysis was implemented for further analysis based on (a) category of research purpose, (b) disciplines domains, (c) sample group, (d) context utilization, (e) research design, (f) category of learning, (g) learning outcome, (h) periodic journal, (i) country, (j) publisher. This review research of ITS presented findings, which could be a layover platform and guidance for researchers, educators, policymakers or even journal publisher for the future research or reference in the realm of ITS regarding the latest trends.
Learning to Identify Security-Related Issues Using Convolutional Neural Networks

Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.
Web service discovery with incorporation of web services clustering

With the rapid growth and adoption of web-based technologies, more and more service providers offer various services on the web. This can facilitate the organizations to develop business applications using web services composition over the Internet according to their business requirements. The primary requirement for developing composite web service is to discover the most relevant web service for realizing each function of the required composite web service. Finding the most relevant web service is still a challenging task. In this paper, we propose web service discovery approach that incorporates web services clustering. Clustering technique combines latent Dirichlet allocation(LDA) and k-Medoids and reduces search space for web service discovery. Web service discovery uses a match-making algorithm that utilizes WordNet database and Hungarian method to compute the semantic similarity score for a web service based on the requested query. Experimental results show that the proposed approach performs better than LDA-based approach.
Occlusion Pattern Discovery for Object Detection and Occlusion Reasoning

Despite recent progress of object category detection in real scenes, detecting objects that are partially or heavily occluded remains a challenging problem due to the uncertainty and diversity of occlusion situations which could cause large intra-category appearance variance. To learn these occlusion situations, we propose a novel approach to discover occlusion patterns that cannot only boost occluded object detection but also provide occlusion reasoning. Our approach is based on a classic deformable part model (DPM) trained on fully observed object examples. Each occlusion pattern contains only a subset of visible parts, thus the total number of occlusion patterns are exponential to the number of parts, i.e., m parts will generate 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> occlusion patterns to compose an occlusion pattern pool. From this occlusion pattern pool, we look for a small group of occlusion patterns that are: (1) representative patterns that can well explain training examples and (2) discriminative patterns that have high detection performance individually. To select such occlusion patterns, we formulate occlusion pattern discovery as a facility location problem, which can be solved effectively by greedy search. The discovered occlusion patterns are themselves DPMs and can be used as object detectors when properly tuned. They can also be combined with the state-of-the-art detectors (e.g. Faster R-CNN) for improving detection performance and achieving part-level occlusion reasoning. The effectiveness of the proposed approach is validated on Pascal VOC2007 and VOC2010 datasets.
Combining feature selection, feature learning and ensemble learning for software fault prediction

This paper studies a combination of feature selection and ensemble learning to address the feature redundancy and class imbalance problems in software fault prediction. Also, a deep learning model is used to generate deep representation from defect data to improve the performance of fault prediction models. The proposed method, GFsSDAEsTSE, is evaluated on 12 NASA datasets, and the results show that GFsSDAEsTSE outperforms state-of-the-art methods in both small and large datasets.
An ensemble of fine-tuned fully convolutional neural networks for pleural effusion cell nuclei segmentation

Pleural effusion (PE) is a problem commonly encountered in patients with malignant neoplasms, especially carcinomas of the breast and lung. Nuclei segmentation is a prerequisite step of a computer-aided diagnosis (CAD) system for pleural cancer. We propose an ensemble of fully convolutional neural networks (FCNN) for PE cell nuclei segmentation. A two-stage method, fine-tuning of deep FCNN and an ensemble network, is designed. It involves, first, fine-tuning the well-known deep learning-based segmentation networks such as fully convolutional network (FCN), SegNet, and U-Net, and then employing an ensemble network that uses the features from fine-tuned networks to improve the efficiency. The proposed ensemble method achieves a Jaccard index of 90.82%, a sensitivity of 97.15%, and a specificity of 99.80% on the new dataset consisting of 120 PE cytology images. These experimental results have not only contributed to the pleural cancer diagnosis but also demonstrated the effectiveness of an ensemble of FCNN.
Buyers of ‘lemons’: How can a blockchain platform address buyers’ needs in the market for ‘lemons’?

The second-hand automotive market is one with the least trust from consumers. Customers on the second-hand car market suffer from such problems as the car being in worse condition than initially indicated, accident damage that is not disclosed, fraud, etc. Akerlof, described the market for used cars as an example of the problem of information asymmetries and resulting quality uncertainty. In order to cope with quality uncertainties, used car buyers actively engage themselves in information seeking. Blockchain technology promises to automatize the tracking of cars through their lifecycles and provide reliable information at any point in time it is needed. In our study, we investigate the problems car buyers face during information seeking and propose requirements for the design of a blockchain-based system to address these.
Sentiment analysis of Turkish Twitter data

In this paper, we present a mechanism to predict the sentiment on Turkish tweets by adopting two methods based on polarity lexicon (PL) and artificial intelligence (AI). The method of PL introduces a dictionary of words and matches the words to those in the harvested tweets. The tweets are then classified to be either positive, negative, or neutral based on the result found after matching them to the words in the dictionary. The method of AI uses support vector machine (SVM) and random forest (RF) classifiers to classify the tweets as either positive, negative or neutral. Experimental results show that SVM performs better on stemmed data by achieving an accuracy of 76%, whereas RF performs better on raw data with an accuracy of 88%. The performance of PL method increases continuously from 45% to 57% as data are being modified from a raw data to a stemmed data.
SOTER: A Playbook for Cybersecurity Incident Management

SOTER, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> a cybersecurity incident management playbook, is developed to provide a comprehensive model to manage cybersecurity incidents, particularly for the cybersecurity operations center. The proposed playbook is adaptive, cross-sectorial, and process driven. Each key components of the incident management playbook are outlined and discussed. Furthermore, a lexicon based on equivalence mapping is developed and used to map existing cybersecurity incident vocabulary and taxonomy into a common and consistent lexicon to aid understanding among incident management stakeholder communities—national, government, and private sectors. A versatile workbook model has been explored, which proves to be adaptable to serve a wide range of cases for successfully managing government and private sector security operations center. Cybersecurity incident sharing partnership, formalism for metric and measurements of cybersecurity incident parameters, and cybersecurity incident classification and prioritization schemes are presented, and finally, cybersecurity incident “plays” and playbook templates are discussed.
Generalized elastic net Lp-norm nonparallel support vector machine

Generalized eigenvalue proximal support vector machine (GEPSVM) is the first nonparallel support vector machine. Compared to standard support vector machine (SVM), GEPSVM coped with the “Xor” problem well. In this paper, by defining a generalized elastic net regularization which is the combination of the L2-norm and Lq-norm, we propose a generalized elastic net Lp-norm nonparallel proximal support vector machine (GLpNPSVM), where p,q>0. GLpNPSVM measures the distance of a sample to each hyperplane by the Lp-norm, and hence can achieve desired performance by choosing appropriate p. In addition, the generalized elastic net regularization makes GLpNPSVM own good generalization ability. GLpNPSVM is a generalized formulation, and GEPSVM and some of its improvements are special cases of GLpNPSVM. A simple but effective iterative technique is introduced to solve GLpNPSVM, and we prove its convergence for certain p,q>0. Experimental results on different types of contaminated data sets show the effectiveness of GLpNPSVM.
Passive classification of source printer using text-line-level geometric distortion signatures from scanned images of printed documents

In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a severe threat. Ability to efficiently and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters' locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions. Their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of 14 printers demonstrate that the proposed system achieves performance of the state of the art system based on geometric distortion and gives much higher accuracy under small training size constraint. A classifier trained using 1 page/printer/font with 3 different fonts and 14 printers achieves 98.85% average classification accuracy.
Recognizing Facial Expressions of Occluded Faces Using Convolutional Neural Networks

In this paper, we present an approach based on convolutional neural networks (CNNs) for facial expression recognition in a difficult setting with severe occlusions. More specifically, our task is to recognize the facial expression of a person wearing a virtual reality (VR) headset which essentially occludes the upper part of the face. In order to accurately train neural networks for this setting, in which faces are severely occluded, we modify the training examples by intentionally occluding the upper half of the face. This forces the neural networks to focus on the lower part of the face and to obtain better accuracy rates than models trained on the entire faces. Our empirical results on two benchmark data sets, FER+ and AffectNet, show that our CNN models' predictions on lower-half faces are up to 13% higher than the baseline CNN models trained on entire faces, proving their suitability for the VR setting. Furthermore, our models' predictions on lower-half faces are no more than 10% under the baseline models' predictions on full faces, proving that there are enough clues in the lower part of the face to accurately predict facial expressions.
Lightweight and Privacy-Preserving ID-as-a-Service Provisioning in Vehicular Cloud Computing

Vehicular cloud computing (VCC) is composed of multiple distributed vehicular clouds (VCs), which are formed on-the-fly by dynamically integrating underutilized vehicular resources including computing power, storage, and so on. Existing proposals for identity-as-a-service (IDaaS) are not suitable for use in VCC due to limited computing resources and storage capacity of onboard vehicle devices. In this paper, we first propose an improved ciphertext-policy attribute-based encryption (CP-ABE) scheme. Utilizing the improved CP-ABE scheme and the permissioned blockchain technology, we propose a lightweight and privacy-preserving IDaaS architecture for VCC named IDaaSoVCC. It realizes lightweight and privacy-preserving access control of vehicles' personally identifiable information (PII) in a large distributed vehicular cloud system. Security analysis demonstrates the security features of IDaaSoVCC, most notably forward secrecy, confidentiality and identity information privacy. Meanwhile, we verify that IDaaSoVCC is feasible and practical in a large distributed VC system through extensive simulations.
Mixed Reality Tabletop Gameplay: Social Interaction With a Virtual Human Capable of Physical Influence

In this article, we investigate the effects of the physical influence of a virtual human (VH) in the context of face-to-face interaction in a mixed reality environment. In Experiment 1, participants played a tabletop game with a VH, in which each player takes a turn and moves their own token along the designated spots on the shared table. We compared two conditions as follows: the VH in the virtual condition moves a virtual token that can only be seen through augmented reality (AR) glasses, while the VH in the physical condition moves a physical token as the participants do; therefore the VH's token can be seen even in the periphery of the AR glasses. For the physical condition, we designed an actuator system underneath the table. The actuator moves a magnet under the table which then moves the VH's physical token over the surface of the table. Our results indicate that participants felt higher co-presence with the VH in the physical condition, and participants assessed the VH as a more physical entity compared to the VH in the virtual condition. We further observed transference effects when participants attributed the VH's ability to move physical objects to other elements in the real world. Also, the VH's physical influence improved participants' overall experience with the VH. In Experiment 2, we further looked into the question how the physical-virtual latency in movements affected the perceived plausibility of the VH's interaction with the real world. Our results indicate that a slight temporal difference between the physical token reacting to the virtual hand's movement increased the perceived realism and causality of the mixed reality interaction. We discuss potential explanations for the findings and implications for future shared mixed reality tabletop setups.
Spectrum Awareness at the Edge: Modulation Classification using Smartphones

As spectrum becomes crowded and spread over wide ranges, there is a growing need for emcient spectrum management techniques that need minimal, or even better, no human intervention. Identifying and classifying wireless signals of interest through deep learning is a first step, albeit with many practical pitfalls in porting laboratory-tested methods into the field. Towards this aim, this paper proposes using Android smartphones with TensorFlow Lite as an edge computing device that can run GPU-trained deep Convolutional Neural Networks (CNNs) for modulation classification. Our approach intelligently identifies the SNR region of the signal with high reliability (over 99%) and chooses grouping of modulation labels that can be predicted with high (over 95%) detection probability. We demonstrate that while there are no significant differences between the GPU and smartphone in terms of classification accuracy, the latter takes much less time (down to 1 870x), memory space (3 1 of the original size), and consumes minimal power, which makes our approach ideal for ubiquitous smartphone-based signal classification.
Observing and Mitigating Micro-Burst Traffic in Data Center Networks

Micro-burst traffic is not uncommon in data centers. It can cause packet dropping, which may result in serious performance degradation (e.g., Incast problem). However, current approaches to mitigate micro-burst is usually ad-hoc and not based on a principled understanding of the underlying behaviors. On the other hand, traditional studies focus on traffic burstiness in a single flow, while micro-burst traffic in the data centers could occur with highly fan-in communication pattern, and its dynamic behavior is still unclear. To this end, in this paper, we re-examine the micro-burst traffic in typical data center scenarios. We find that the evolution of micro-burst is determined by both TCP's self-clocking mechanism and congestion control algorithm. Besides, dynamic behaviors of micro-burst under various scenarios can all be described by the time derivative of queue length evolution.Our observations also implicate that conventional solutions like absorbing and pacing are ineffective to mitigate micro-burst traffic.Instead, senders need to rapidly respond to some explicit signals of the queue buildup caused by the micro-burst traffic rather than independently and ineffectually pacing themselves in isolation. Inspired by the findings and insights from experimental observations, we propose Micro-burst-Aware Transport Control Protocol (MATCP), which leverages characteristic behaviors of micro-burst traffic derived from the time derivative of the queue occupancy. MATCP can suppress the sharp queue length increment by over 2x and reduce the tail query completion time by up to 84.4%.