Reviewerly/exHarmony huggingface dataset:
LPGNet

Classification tasks on labeled graph-structured data have many important applications ranging from social recommendation to financial modeling. Deep neural networks are increasingly being used for node classification on graphs, wherein nodes with similar features have to be given the same label. Graph convolutional networks (GCNs) are one such widely studied neural network architecture that perform well on this task. However, powerful link-stealing attacks on GCNs have recently shown that even with black-box access to the trained model, inferring which links (or edges) are present in the training graph is practical. In this paper, we present a new neural network architecture called LPGNet for training on graphs with privacy-sensitive edges. LPGNet provides differential privacy (DP) guarantees for edges using a novel design for how graph edge structure is used during training. We empirically show that LPGNet models often lie in the sweet spot between providing privacy and utility: They can offer better utility than "trivially" private architectures which use no edge information (e.g., vanilla MLPs) and better resilience against existing link-stealing attacks than vanilla GCNs which use the full edge structure. LPGNet also offers consistently better privacy-utility tradeoffs than DPGCN, which is the state-of-the-art mechanism for retrofitting differential privacy into conventional GCNs, in most of our evaluated datasets.
Generating realistic vulnerabilities via neural code editing: an empirical study

The availability of large-scale, realistic vulnerability datasets is essential both for benchmarking existing techniques and for developing effective new data-driven approaches for software security. Yet such datasets are critically lacking. A promising solution is to generate such datasets by injecting vulnerabilities into real-world programs, which are richly available. Thus, in this paper, we explore the feasibility of vulnerability injection through neural code editing. With a synthetic dataset and a real-world one, we investigate the potential and gaps of three state-of-the-art neural code editors for vulnerability injection. We find that the studied editors have critical limitations on the real-world dataset, where the best accuracy is only 10.03%, versus 79.40% on the synthetic dataset. While the graph-based editors are more effective (successfully injecting vulnerabilities in up to 34.93% of real-world testing samples) than the sequence-based one (0 success), they still suffer from complex code structures and fall short for long edits due to their insufficient designs of the preprocessing and deep learning (DL) models. We reveal the promise of neural code editing for generating realistic vulnerable samples, as they help boost the effectiveness of DL-based vulnerability detectors by up to 49.51% in terms of F1 score. We also provide insights into the gaps in current editors (e.g., they are good at deleting but not at replacing code) and actionable suggestions for addressing them (e.g., designing effective editing primitives).
CORMS: a GitHub and Gerrit based hybrid code reviewer recommendation approach for modern code review

Modern Code review (MCR) techniques are widely adopted in both open-source software platforms and organizations to ensure the quality of their software products. However, the selection of reviewers for code review is cumbersome with the increasing size of development teams. The recommendation of inappropriate reviewers for code review can take more time and effort to complete the task effectively. In this paper, we extended the baseline of reviewers' recommendation framework - RevFinder - to handle issues with newly created files, retired reviewers, the external validity of results, and the accuracies of the state-of-the-art RevFinder. Our proposed hybrid approach, CORMS, works on similarity analysis to compute similarities among file paths, projects/sub-projects, author information, and prediction models to recommend reviewers based on the subject of the change. We conducted a detailed analysis on the widely used 20 projects of both Gerrit and GitHub to compare our results with RevFinder. Our results reveal that on average, CORMS, can achieve top-1, top-3, top-5, and top-10 accuracies, and Mean Reciprocal Rank (MRR) of 45.1%, 67.5%, 74.6%, 79.9% and 0.58 for the 20 projects, consequently improves the RevFinder approach by 44.9%, 34.4%, 20.8%, 12.3% and 18.4%, respectively.
How to formulate specific how-to questions in software development?

Developers often ask how-to questions using search engines, technical Q&A communities, and interactive Q&A systems to seek help for specific programming tasks. However, they often do not formulate the questions in a specific way, making it hard for the systems to return the best answers. We propose an approach (TaskKG4Q) that interactively helps developers formulate a programming related how-to question. TaskKG4Q is using a programming task knowledge graph (task KG in short) mined from Stack Overflow questions, which provides a hierarchical conceptual structure for tasks in terms of [actions], [objects], and [constraints]. An empirical evaluation of the intrinsic quality of the task KG revealed that 75.0% of the annotated questions in the task KG are correct. The comparison between TaskKG4Q and two baselines revealed that TaskKG4Q can help developers formulate more specific how-to questions. More so, an empirical study with novice programmers revealed that they write more effective questions for finding answers to their programming tasks on Stack Overflow.
AntMan

Recent works on interactive zero-knowledge (ZK) protocols provide a new paradigm with high efficiency and scalability. However, these protocols suffer from high communication overhead, often linear to the circuit size. In this paper, we proposed two new ZK protocols with communication sublinear to the circuit size, while maintaining a similar level of computational efficiency.
When Frodo Flips

In this work, we recover the private key material of the FrodoKEM key exchange mechanism as submitted to the NIST Post Quantum Cryptography (PQC) standardization process.
Are Attribute Inference Attacks Just Imputation?

Models can expose sensitive information about their training data. In an attribute inference attack, an adversary has partial knowledge of some training records and access to a model trained on those records, and infers the unknown values of a sensitive feature of those records. We study a fine-grained variant of attribute inference we call sensitive value inference, where the adversary's goal is to identify with high confidence some records from a candidate set where the unknown attribute has a particular sensitive value. We explicitly compare attribute inference with data imputation that captures the training distribution statistics, under various assumptions about the training data available to the adversary. Our main conclusions are: (1) previous attribute inference methods do not reveal more about the training data from the model than can be inferred by an adversary without access to the trained model, but with the same knowledge of the underlying distribution as needed to train the attribute inference attack; (2) black-box attribute inference attacks rarely learn anything that cannot be learned without the model; but (3) white-box attacks, which we introduce and evaluate in the paper, can reliably identify some records with the sensitive value attribute that would not be predicted without having access to the model. Furthermore, we show that proposed defenses such as differentially private training and removing vulnerable records from training do not mitigate this privacy risk. The code for our experiments is available at https://github.com/bargavj/EvaluatingDPML.
Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning

In recent years, people have tended to use online social platforms, such as Twitter and Facebook, to communicate with families and friends, read the latest news, and discuss social issues. As a result, spam content can easily spread across them. Spam detection is considered one of the important tasks in text analysis. Previous spam detection research focused on English content, with less attention to other languages, such as Arabic, where labeled data are often hard to obtain. In this paper, an integrated framework for Twitter spam detection is proposed to overcome this problem. This framework integrates data augmentation, natural language processing, and supervised machine learning algorithms to overcome the problems of detection of Arabic spam on the Twitter platform. The word embedding technique is employed to augment the data using pre-trained word embedding vectors. Different machine learning techniques were applied, such as SVM, Naive Bayes, and Logistic Regression for spam detection. To prove the effectiveness of this model, a real-life data set for Arabic tweets have been collected and labeled. The results show that an overall improvement in the use of data augmentation increased the macro F1 score from 58% to 89%, with an overall accuracy of 92%, which outperform the current state of the art.
Fuzzy-driven periodic frequent pattern mining

Frequent pattern mining (FM) has a wide range of applications in the real world. But FM sometimes discovers many uninteresting patterns at the same time. Constraint-based FM, especially periodic constraint FM, becomes of interest, and it reduces redundant patterns. Most of the state-of-the-art algorithms for mining periodic frequent patterns (PFPs) are designed to find PFPs only in binary temporal databases. However, vast information in real situations is more suitable to be modelized as quantitative databases, in which most existing methods are inapplicable. Patterns obtained by those methods may change due to a tiny perturbation in the database, which is not expected in many situations. In this paper, we propose an algorithm (FP2M) to mine fuzzy-driven periodic frequent patterns and an algorithm (SFP2M) to find stable fuzzy-driven periodic frequent patterns in quantitative temporal databases. Fuzzy sets are used in our algorithms to deal with quantitative data since they provide a relaxed interval segmentation and are easy to implement. Novel pruning strategies and a new structure called Estimated Period Co-occurrence Structure (EPCS) are designed to speed up the mining process and improve efficiency. An improved method (SFP2M) is also presented by using the lability measurement to find stable patterns. Experimental evaluations on both real and synthetic datasets show good performance of the designed algorithms.
Scrutinizing IoT applicability in green warehouse inventory management system based on Mamdani fuzzy inference system: a case study of an automotive semiconductors industrial firm

Over the last three decades, sustainability has been increasingly essential and it is a crucial facilitator for building resilient warehouse inventory systems. Uncertainty influences qualitative criteria for evaluating Green Warehouse Inventory Management performance. According to researchers, technological innovations such as the Internet of Things could be used to help warehouse inventory operations achieve long-term sustainability. An Internet of things-based fuzzy set theory model is developed in this paper for dealing with language inaccuracy and uncertainty in human judgment. It is also the first to use the Mamdani Fuzzy Inference System to assess a company’s Green Warehouse Inventory Management performance in terms of green metrics. The suggested model reveals that the green delivery dimension has the greatest impact on firm performance, based on data gathered from a case company. Numerous defuzzification approaches have been used to demonstrate the resilience of the proposed FIS model.
Continual learning with attentive recurrent neural networks for temporal data classification

Continual learning is an emerging research branch of deep learning, which aims to learn a model for a series of tasks continually without forgetting knowledge obtained from previous tasks. Despite receiving a lot of attention in the research community, temporal-based continual learning techniques are still underutilized. In this paper, we address the problem of temporal-based continual learning by allowing a model to continuously learn on temporal data. To solve the catastrophic forgetting problem of learning temporal data in task incremental scenarios, in this research, we propose a novel method based on attentive recurrent neural networks, called Temporal Teacher Distillation (TTD). TTD solves the catastrophic forgetting problem in an attentive recurrent neural network based on three hypotheses, namely Rotation Hypothesis, Redundant Hypothesis, and Recover Hypothesis. Rotation Hypothesis and Redundant hypotheses could cause the attention shift phenomenon, which degrades the model performance on the learned tasks. Moreover, not considering the Recover Hypothesis increases extra memory usage in continuously training different tasks. Therefore, the proposed TTD based on the above hypotheses complements the inadequacy of the existing methods for temporal-based continual learning. For evaluating the performance of our proposed method in task incremental setting, we use a public dataset, WIreless Sensor Data Mining (WISDM), and a synthetic dataset, Split-QuickDraw-100. According to experimental results, the proposed TTD significantly outperforms state-of-the-art methods by up to 14.6% and 45.1% in terms of accuracy and forgetting measures, respectively. To the best of our knowledge, this is the first work that studies continual learning in real-world incremental categories for temporal data classification with attentive recurrent neural networks and provides the proper application-oriented scenario.
A comprehensive review of content-based image retrieval systems using deep learning and hand-crafted features in medical imaging: Research challenges and future directions

Computer-based medical image retrieval (CBMIR) system helps practitioners to enhance their diagnostic abilities, speeds up accurate diagnosis, and minimizes intra-and inter-observer variability. We investigate in-depth insights on content-based image retrieval systems over the past 20 years, to enable improvements with an imperative feature set to enhance the performance of the proposed CBMIR. After a comprehensive survey, we have made some inferences from the literature survey which is beneficial for researchers and medical practitioners. Existing studies, consider patch sizes of 32 only where 16 and 64 are ignored. We conduct experiments using 16, 32, and 64 patch sizes and two retrieval approaches i.e. simple distance-based retrieval (SDR) and customized query-based approach (CQA). SDR exhibits high inter-class ambiguity, in which some of the retrieved images are from different classes posing similar feature values, which negatively impacts the retrieval performance. To tackle this, a framework has been proposed based on CQA-CBMIR which utilizes wavelet-based Riesz features and improves the retrieval outcomes. The proposed framework is compared with the state-of-the-art (SOTA) methods and obtained f1-score and accuracy i.e. 86.5% and 86.8% respectively. Furthermore, this study illustrates some of the unresolved issues in the literature that demand future researchers' utmost attention to investigate new pathways in this discipline.
Enhanced Elman spike neural network based sentiment analysis of online product recommendation

The major task of Natural Language Processing (NLP) is Sentiments Analysis (SA) or Opinion Mining (OM). It captures the opinion, trust, and feelings of every user’s about the related products to find whether the attitude of users is positive, negative, or neutral. By this, companies can make necessary changes in the product for customer satisfaction. The majority of the existing approaches on sentiment analysis have inaccurate and takes more time. Therefore, in this manuscript, an Enhanced Elman Spike Neural Network based Sentiment Analysis of Online Product recommendation is proposed (EESNN-SA-OPR). Here, filtering Collaborative (FC) and product to product (P–P) similarity are used as new recommendation systems. The aim of Collaborative Filtering is “to predict the best shops and product–product​ similarity is to predict the best products”. Initially, the datas are taken from Amazon reviews database. Then the input data is pre-processed. The trilateral smoothing filtering (TRSF) is used to remove the content which is no more needed and texts related filtering. After that, the features like manufacturing price (MRP), Manufacturing date (MFD), discounts, offers, ratings in qualities are extracted by using Dominant Gradient Local Ternary Pattern descriptor (DGLTPD) technique. At last, enhanced Elman spike neural network classifies the product recommendation as excellent, good, very good, bad and very bad. The proposed method is executed in MATLAB and its performance is analyzed under some performance metrics, like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), accuracy, F-Score, recall and precision. The proposed EESNN-SA-OPR method provides 23.14%, 15.96%, 31.54% higher accuracy and 12.33%, 21.31%, 41.09% lower mean absolute error compared with the existing techniques, like Sentiment analysis of online product reviews utilizing DLMNN and future prediction of online product utilizing IANFIS (DLMNN-SA-OPR), a machine learning-based sentiment analysis of online product reviews with new term weighting along feature selection method (LSIBA-ENN-SA-OPR) and Sentiment Analysis on the Reviews of Online Product utilizing Optimized RNN-LSTM along Support Vector Machine (RNN-LSTM-SA-OPR) respectively.
Cognitive load in immersive media settings: the role of spatial presence and cybersickness

Abstract Faced with the ongoing diversification and commercial success of highly immersive media technologies (e.g., VR headsets), both content producers and scientific scholars have become highly invested in understanding the psychological consequences of experiencing media in these new and lifelike ways. While many studies underscore positive effects of high media immersivity—such as increased enjoyment or persuasive success—others warn about the intense cognitive load that technologies such as VR might put on their users. In a laboratory experiment with N = 121 participants, we compare the cognitive load experienced while watching a 360° video on a laptop screen or via an immersive VR head-mounted display. Furthermore, we scrutinize two prominent explanations for the additional cognitive load in immersive media settings, i.e., the role of spatial presence and cybersickness. As expected, the VR condition results in higher cognitive load, spatial presence, and cybersickness than the 2D condition. However, by means of a parallel mediation model, we observe that only cybersickness emerges as a meaningful mediator of participants’ strained cognitive capacity; spatial presence, on the other hand, remains statistically irrelevant in this regard. We discuss our findings considering implications for media producers and future research.
Multi-object detection at night for traffic investigations based on improved SSD framework

Despite significant progress in vision-based detection methods, the task of detecting traffic objects at night remains challenging. Visual information of medium and small stationary objects is deteriorated due to poor lighting conditions. And the visual information is important for traffic investigations. For meeting the needs of night traffic investigations, this study focuses on presenting a nighttime multi-object detection framework based on Single Shot MultiBox Detector (SSD). Considering the need of traffic investigations, the applicable detection framework is presented for detecting traffic objects, especially medium and small stationary objects. In the framework, the Dense Convolutional Network (DenseNet) and deconvolutional layers are introduced to enhance the feature reuse, and the effectiveness of the optimization is finally verified. In this paper, qualitative and quantitative experiments are presented. The results show that our presented framework has better detection performance for medium and small stationary objects. Moreover, the results show that presented framework has better performance for nighttime traffic investigations at intersections.
Investigating the effectiveness of immersive VR skill training and its link to physiological arousal

This paper details the motivations, design, and analysis of a study using a fine motor skill training task in both VR and physical conditions. The objective of this between-subjects study was to (a) investigate the effectiveness of immersive virtual reality for training participants in the 'buzz-wire' fine motor skill task compared to physical training and (b) investigate the link between participants' arousal with their improvements in task performance. Physiological arousal levels in the form of electro-dermal activity (EDA) and ECG (Electrocardiogram) data were collected from 87 participants, randomly distributed across the two conditions. Results indicated that VR training is as good as, or even slightly better than, training in physical training in improving task performance. Moreover, the participants in the VR condition reported an increase in self-efficacy and immersion, while marginally significant differences were observed in the presence and the temporal demand (retrieved from NASA-TLX measurements). Participants in the VR condition showed on average less arousal than those in the physical condition. Though correlation analyses between performance metrics and arousal levels did not depict any statistically significant results, a closer examination of EDA values revealed that participants with lower arousal levels during training, across conditions, demonstrated better improvements in performance than those with higher arousal. These findings demonstrate the effectiveness of VR in training and the potential of using arousal and training performance data for designing adaptive VR training systems. This paper also discusses implications for researchers who consider using biosensors and VR for motor skill experiments.The online version contains supplementary material available at 10.1007/s10055-022-00699-3.
PSDCE: Physiological signal-based double chaotic encryption for instantaneous E-healthcare services

As fundamental technical support to instantaneous E-healthcare services, Wireless Body Area Networks (WBANs) have attracted huge attention in the scientific and industrial community in recent years, which defines a unique wireless communication protocol for medical and healthcare circumstances. However, the concerns of the network and data security in WBANs have become an obstacle to the rapid development of it. In this paper, a double chaotic encryption method containing the natural randomness by adding physiological signal, electroencephalogram (EEG), is proposed. Initially, the three-dimensional eigenvalues contained deep multi-domain features are extracted as the initial factors of the chaos-based pseudo-random number generator to ensure the natural randomness of the initial values. Then, a hybrid pseudo-random number generator based on two chaotic maps, Piecewise linear Map (PLCM) and Coupled Logistic-Tent map (LTM), is proposed, which has the advantages of high randomness and low hardware complexity. Finally, the double chaotic encryption method based on the EEG eigenvalue for WBANs is presented. Various types of evaluations are performed to verify the efficiency and robustness of the proposed solution.