Reviewerly/exHarmony huggingface dataset:
Auto-Associative Features with Non-Iterative Learning Based Technique for Image Classification

Image classification is a fundamental task that attempts to apprehend an entire image by assigning a specific class or label. Accurate feature extraction techniques are the main building blocks of any image classification and prediction application. Most of these feature extraction techniques are manual (hand-crafted), hence they contain tedious, challenging, and erroneous tasks. In this paper, a new automatic technique for image classification is presented in which a novel concept to extract auto-associative features along with a non-iterative learning is investigated. The proposed technique incorporates an auto-associative neural network for most accurate feature extraction and a noniterative classifier. The proposed technique has been evaluated on benchmark datasets. The experimental results have demonstrated that the proposed technique can achieve same or slightly higher accuracy than the standard and formula-based techniques.
Bibliometric Study and Systematic Literature Review of Blockchain Technology in Vehicle Industry

The application of blockchain technology to the vehicle industry is one of the implementations applied to improve the company system performance with customer relationship management (CRM) and supply chain management (SCM). Research in this field is scarce, so this research aims to analyze and synthesize research topic trends, a part of research related to the advancement of blockchain technology in the vehicle industry. The method used in this research is a systematic literature review and bibliometric using Publish and Perish software and analysis with VOSviewer software. The keywords used are blockchain technology, vehicle, automotive, and the vehicle industry. Based on the findings, there are 40 relevant papers, and there are trends that occur in research in this field, starting from 2017 to the present. Trending topics is data, application, supply chain, feasibility, transparency, traceability, integrity, car, privacy, business, ledger, information, smart contracts, internet, and manufacturing. This research provides perspective and trending topics on blockchain in the vehicle industry.
Classifying Code Commits with Convolutional Neural Networks

Developers change software programs for various purposes (e.g., bug fixes, feature additions, and code refactorings), but the intents of code changes are often not recorded or are poorly documented. To automatically infer the change intent of each program commit (i.e., a set of code changes), existing work classifies commits based on commit messages and/or the sheer counts of edited files, lines, or abstract syntax tree (AST) nodes. However, none of these tools reason about the syntactic or semantic dependencies between co-applied changes, neither do they adopt any deep learning method. To better characterize program commits, in this paper, we present CClassifier—a new approach that classifies commits by (1) using advanced static program analysis to comprehend relationship between co-applied edits, (2) representing edits and their relationship via graphs, and (3) applying convolutional neural networks (CNN) to classify those graphs. Compared with prior work, CClassifier extracts a richer set of features from program changes; it is the first to classify program commits using CNN. For evaluation, we prepared a benchmark that contains 7,414 code changes from 5 open-source Java projects. On this benchmark, we empirically compared CClassifier and the state-of-the-art approach with five-fold cross validation. On average, when predicting bug-fixing commits within the same projects, CClassifier improved the prediction accuracy from 70% to 72%. More importantly, prior work seldom identifies feature-addition commits; CClassifier can successfully identify such commits in a lot more scenarios. Our evaluation shows that CClassifier outperforms prior work due to its usage of advanced program analysis and CNN.
Digital archaeology and ‘D’ transforms?

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BIB-MKS: Post-Quantum Secure Biometric Identity-Based Multi-Keyword Search over Encrypted Data in Cloud Storage Systems

Cloud computing technologies rely on powerful storage services to maintain massive data for users. Sensitive data are encrypted before outsourcing, but this limits the availability of data. Public-key encryption with keyword search (PEKS) contributes to searching target encrypted data with keywords. However, existing PEKS mechanisms require to manage certificates, they are also vulnerable to adversaries equipped with quantum-computing devices. In this paper, we devise a biometric identity-based multi-keyword search (BIB-MKS) mechanism from lattices over encrypted outsourced data, which inherently resists quantum-computing attacks. Each user in BIB-MKS is identified with her/his biometric information, which could be envisioned as the public key, thereby avoiding complex certificate managements. Particularly, BIB-MKS enables a data owner to produce an index associated with a biometric identity <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$BID'$</tex></formula> , such that a user with a biometric identity <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$BID$</tex></formula> issues multiple keywords in a single search query and retrieves corresponding encrypted data, if and only if <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$BID$</tex></formula> and <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$BID'$</tex></formula> are within a certain distance of each other as judged by some metric. BIB-MKS narrows down the search scope, and improves users search experience significantly. We define the formal security model of BIB-MKS, and prove the security of BIB-MKS under this model. The performance evaluation demonstrates that BIB-MKS is practical.
ADA: Adaptive Depth Attention Model for Click - Through Rate Prediction

Click-through rate (CTR) prediction plays a critical role in recommender systems, in which the task is to forecast the probability of the user clicking on recommended items. Many models have been proposed in this field, such as logistic regression, factorization machine based models and deep learning based models. However, many current works calculate the feature interactions in a simple way such as inner product. They care less about the different importance and computational requirements of different feature interactions. The general idea is that some complex feature interactions might require more computations to produce a final result, while some simple or unimportant feature interactions might require less. In this paper, we propose adaptive depth attention (ADA) model, a new model that automatically learns the high-order feature interactions of raw data. The core of ADA is a multi-head self-attention neural network that learns feature interaction and a network depth control module that controls the network depth required for interaction of different feature fields. We conduct extensive experiments on two realworld datasets and results demonstrate the superior predictive performance of ADA against the state-of-the-art methods.
Morphological Preprocessing for Low-Resolution Face Recognition using Common Space

There are many researches on face recognition, but most have not produced satisfactory results on very low-resolution images. This study proposes the use of morphological preprocessing to improve the performance of common space approach for face recognition on low-resolution images. The morphological preprocessing consists of Top-Hat and Bottom-Hat Transformations, which capable of extracting small elements and handling uneven lighting on images. The k-Nearest Neighbor is used to recognize the face by measuring the distance of deep CNN features of low and high-resolution images in the common space. Experiment on the Yale Face dataset shows that the use of Morphological Preprocessing can increase the face recognition accuracy by 14.59%, 1.00%, and 2.50% for low-resolution images with sizes 24x24, 36x35, and 56x56, respectively.
Recommendation Model Based on Dynamic Interest Group Identification and Data Compensation

With the increasing network service content, innovative methods are required for developing optimized network service for e-commerce companies. Accordingly, this study focuses on designing a framework containing personalization, interest group identification, and recommendation mechanisms. The primary contribution of this paper is to propose a recommendation model based on data compensation and dynamic user interest grouping. First, to address the problem of sparse user rating data, homeostasis compensation is performed on native data to more realistically restore the preference relationship between users and items by introducing the advantages of generative adversarial network in learning data distribution and enhancing data samples. Second, to address the problem of user interest generalization, information entropy is introduced to measure the user interest feature space. In addition, the time window marking method is used to further quantify the users’ dynamic interest group around the users’ interest drift. Finally, considering tensor decomposition characteristics in data dimension transformation and data compression, a score prediction model based on the “user-item-interest group” tensor decomposition is constructed. Simultaneously, a time decay function is introduced in the construction of the tensor to dynamically fit the user behavior and further improve prediction accuracy. Experiments show that the proposed framework can effectively improve the recommendation accuracies resulting from both sparse scoring data and dynamic user interest division.
Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views

We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects. Because the LiDAR point clouds are partial, directly fitting bounding boxes to the point clouds is meaningless. Instead, we suggest that obtaining good results requires sharing information between all objects in the dataset jointly, over multiple frames. We then make three improvements to the baseline. First, we address ambiguities in predicting the object rotations via direct optimization in this space while still backpropagating rotation prediction through the model. Second, we explicitly model outliers and task the network with learning their typical patterns, thus better discounting them. Third, we enforce temporal consistency when video data is available. With these contributions, our method significantly outperforms previous work despite the fact that those methods use significantly more complex pipelines, 3D models and additional human-annotated external sources of prior information.
A Multi-Task Cross-Task Learning Architecture for Ad Hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation

Medical image segmentation has significantly benefitted thanks to deep learning architectures. Furthermore, semi-supervised learning (SSL) has recently been a growing trend for improving a model's overall performance by leveraging abundant unlabeled data. Moreover, learning multiple tasks within the same model further improves model generalizability. To generate smooth and accurate segmentation masks from 3D cardiac MR images, we present a Multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justifies the effectiveness of our model for the segmentation of the left atrial cavity from Gadolinium-enhanced magnetic resonance (GE-MR) images. With the incorporation of uncertainty estimates to detect failures in the segmentation masks generated by CNNs, our study further showcases the potential of our model to flag low-quality segmentation from a given model.
Fact-Finding Knowledge-Aware Search Engine

Search engine based on knowledge graphs has become increasingly popular and demanding. One of the most popular aspects is being relations between entities. What is needed is a smarter and apt enterprise search which helps to provide all the right answers using knowledge graph and NLP. Fact-finding signifies that apart from getting a document returned by search engine, with the in-place knowledge graph, it helps in finding facts on user-specific query when it finds a match in the knowledge graph. In this paper, we present an approach using scalable, open-source approach which takes unstructured data and helps in creating a search platform that refines large text for search and a knowledge graph with question–answer system. It helps in getting related document(s) based on the searched query. The complete application can be used and integrated into many use-cases since search is the integral part of most (almost all) applications.
Semi – Automated Software Requirement Specification (SRS) Document Generator: The Guideline to Novice System Analyst

Studies have emphasized that software requirements act as skeleton foundation of the digital project. This would help in the basis of detailed-design, development, test apps and maintenance. The Software Requirement Specifications (SRS) reflect functional and technical requirements that being used for the design and development process. There is, in any case, no impressive research between duplication requirement information and automated requirement tools to summed up issues in producing quality SRS documentation during requirement engineering phases. Issues such duplicate or redundant textual requirement information need to be heads up. This study is to investigate the quality of SRS document and factors of common problems during requirement engineering phases specially to solve the redundant and duplicate textual, analyze on how SRS is generated and the challenge on using requirement tools. The requirement may be missing, questionable, conflicting, unimplementable such the factor of project constraints. Thus, using semi-automated tool on generate SRS document can help in terms of feasible, relevant, clear, comprehensive, and accepted by all stakeholders as the deliverables. This is part of involvement in development process. Through evaluation and comparison with existing tool, the rule and guideline are adopted for this study with a proposed methodology. It purposely to deduce the necessity for examining the quality of SRS and an approach for evaluating the quality of the requirements in projects. As project more innovative, complex, and multi-disciplinary, it is essential to define and ensure the quality of the requirements at the preliminary stages. The findings of this study will distribute as guidelines for System Analyst as a factor of project success.
Dual Learning for Joint Facial Landmark Detection and Action Unit Recognition

Facial landmark detection and action unit (AU) recognition are two essential tasks in facial analysis. Previous works rarely consider the relationship between these complementary tasks. In this article, we introduce a novel multi-task dual learning framework to exploit the relationship between facial landmark detection and AU recognition while simultaneously addressing both tasks. When both tasks share middle-level features, common patterns can be exploited and middle- and high-level features can be used to perform facial landmark detection and AU recognition, respectively. In addition, a dual learning mechanism is designed to convert the predicted landmarks and AUs of the label space to the corresponding facial image of the image space, further exploring the strong correlations between the tasks. By jointly training the proposed method at both the feature and label levels, each task improves the other. Experiments on two benchmark databases demonstrate that the proposed method can leverage dependencies to boost the generalization of both tasks.
GAN-Based Adversarial Patch for Malware C2 Traffic to Bypass DL Detector

The constantly evolving malware brings great challenges to network security defense. Fortunately, deep learning (DL)-based system achieved good performance in the malware command and control (C2) traffic detection field due to its excellent representation capabilities. However, DL models have been shown to be vulnerable to evasion attacks, that is, DL models can easily be misled by adding subtle perturbations to the original samples. In this paper, we propose a GAN-based evasion method, which can help malware C2 traffic bypass the DL detector. Our main contributions contain: (1) directly generate adversarial traffic that can implement malicious functions by inserting additional adversarial patches in the original flow; (2) adaptively imitating victim’s normal traffic by training GAN in victim environment, and introducing transfer learning to reduce the additional victim resource usage caused by GAN training. Results show that the adversarial patch generated by GAN can prevent malware C2 traffic from being detected with 51.4% success rate. The higher time efficiency and smaller malware impact make our method more suitable for real attacks.
Twitter-Based Text Classification Using SVM for Weather Information System

The popularity of social media amplified the amount of text data that is used to enrich text classification research with machine learning approach. The modernization in weather information system is needed to produce real-time weather information using the involvement of Twitter users. GetOldTweets was used to cover data acquisition from Twitter. Support Vector Machine (SVM) was deployed as a machine learning model for text classification. The result was delivered in Esri Maps features. It facilitated a social sensing for Indonesia weather information system by using Twitter posts from Indonesian users with 93% accuracy.
Generalizing Complete Semantics to Bipolar Argumentation Frameworks

Computational models of argumentation are an interesting tool to represent decision processes. Bipolar abstract argumentation studies the question of which arguments a rational agent can accept given attack and support relationships between them. We present a generalization of the fundamental complete semantics from attack-only graphs to bipolar graphs. As opposed to previous semantics, the new bi-complete semantics treats attacks and supports symmetrically and directly leads to natural generalizations of grounded, preferred, semi-stable and stable semantics. We show, in particular, that bipolar graphs are strictly more expressive than attack-only graphs under bi-complete semantics. That is, the meaning of support edges cannot be encoded by attack edges like in deductive support frameworks.
Fusing Various Document Representations for Comparative Text Identification from Product Reviews

Identifying reviews that convey comparative opinions is a preliminary and important task for comparative opinion mining. However, prior studies are time- and cost-consuming, present weak generalization capability and weak robustness, and are semantic-unaware. To address these issues and considering that document representations are differentially informative for different samples, we propose a novel neural network architecture named DRFA (Document Representation Fusion with Attention) for identifying comparative text from large amounts of product reviews on E-commerce platforms. Our developed method adopts the attention mechanism to fuse various representations derived by applying LSTM, CNN, and BERT. A series of experiments verified the effectiveness of our proposed DRFA in terms of improving predictive performance and enhancing robustness. This study has implications for both researchers and practitioners.
Someone or Something to Play With?

Children are raised with technologies that are able to respond to them in natural language. This not only makes it easy to communicate but also to connect with them socially. While communication abilities might have benefits (e.g., for learning), it might also raise concerns among parents as the technologies are not necessarily designed to facilitate the children's social, emotional, and cognitive developments and serve as a model for the construction of a social world among humans. First technologies children can talk to differ in their embodiment (e.g., robots and voice assistants), which could affect central variables, such as social presence, trust, and privacy concerns. The present study aimed to investigate how parents conceptualize socially appropriate interactions between children and technologies. The results underline the parents' emphasis on embodiment and privacy protection. The study underlines the importance of incorporating the parental perspective to meet the expectations of responsible interactions between children and technologies.
Exploring automatic liver tumor segmentation using deep learning

The segmentation of liver tumors is crucial for diagnosis, treatment planning and treatment evaluation. Due to the setbacks that the manual segmentation brings, automatic segmentation has recently gained a lot of attention. In this work, we explore various deep learning based approaches to address automatic liver tumor segmentation. We use the data from the Liver Tumor Segmentation challenge (LiTS). In particular, the considered models here are UNet-based architectures. In addition, we investigate the influence of incorporating extra elements to the pipeline such as attention mechanisms, model ensemble, test-time inference as well as an additional model to reject false positives, over the final performance. The obtained results show that the 3D-UNet architecture, together with ensemble learning methods, performs more accurate predictions than the other examined approaches.
Perspectives and Views of Flexible Query Answering

Flexible Query Answering Systems (FQAS) is a multidisciplinary research field at the crossroad of Information retrieval, databases, data mining, multimedia and geographic information systems, knowledge based systems, social networks, Natural Language Processing (NLP) and semantic web technologies, whose aim is to improve human ability to retrieve relevant information from databases, libraries, heterogeneous archives, the Web and the Web 2.0. The present paper has the objective to outline the perspectives and challenges offered by the heterogeneous panorama of current techniques.