Reviewerly/exHarmony huggingface dataset:
On the design of biometric-based user authentication protocol in smart city environment

Among the security services, like authentication, access control, key management and intrusion detection, user authentication is very much needed for a smart city environment because an external authorized user may require the real time data to be accessed directly from the deployed Internet of Things (IoT) enabled smart devices. Using the established session key between the user and an access smart device though mutual authentication and key agreement process, the real time data can be securely accessed. To deal with this issue, we propose a new user authentication scheme in smart city environment using three factors of a legal registered user (mobile device, password and biometrics). The proposed scheme is shown to be robust against a number of potential attacks needed in an IoT-based smart city deployment. The simulation study for formal security verification using the widely-accepted “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool demonstrates that the proposed scheme is also secure. Furthermore, experiments on various cryptographic primitives have been carried out using “MIRACL Cryptographic SDK: Multiprecision Integer and Rational Arithmetic Cryptographic Library” under both server and Raspberry PI 3 settings. Finally, a comprehensive comparative analysis shows the effectiveness and better security of the proposed scheme as compared with other state of art user authentication schemes.
Graph Structural-topic Neural Network

Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on graph structures within the neighborhood, especially higher-order structural patterns. However, such local structural patterns are shown to be indicative of node properties in numerous fields. In addition, it is not just single patterns, but the distribution over all these patterns matter, because networks are complex and the neighborhood of each node consists of a mixture of various nodes and structural patterns. Correspondingly, in this paper, we propose Graph Structural topic Neural Network, abbreviated GraphSTONE 1, a GCN model that utilizes topic models of graphs, such that the structural topics capture indicative graph structures broadly from a probabilistic aspect rather than merely a few structures. Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently. In addition, we design multi-view GCNs to unify node features and structural topic features and utilize structural topics to guide the aggregation. We evaluate our model through both quantitative and qualitative experiments, where our model exhibits promising performance, high efficiency, and clear interpretability.
Service robots in online reviews: Online robotic discourse

• Online reviews used to track customers' discourse about hospitality service robots. • 50,000 TripAdvisor online reviews from 19 international hotels are analysed. • Trend analyses show different levels of robotic discourse across the hotels. • On average online reviews covering robots increase over time. • Reviews covering robots show different and distinctive features.
TRBACF: Learning temporal regularized correlation filters for high performance online visual object tracking

Correlation filter-based trackers (CFTs) have recently shown remarkable performance in the field of visual object tracking. The advantage of these trackers originates from their ability to convert time-domain calculations into frequency domain calculations. However, a significant problem of these CFTs is that the model is insufficiently robust when the tracking scenarios are too complicated, meaning that the ideal tracking performance cannot be acquired. Recent work has attempted to resolve this problem by reducing the boundary effects from modeling the foreground and background of the object target effectively (e.g., CFLB, BACF, and CACF). Although these methods have demonstrated reasonable performance, they are often affected by occlusion, deformation, scale variation, and other challenging scenes. In this study, considering the relationship between the current frame and the previous frame of a moving object target in a time series, we propose a temporal regularization strategy to improve the BACF tracker (denoted as TRBACF), a typical representative of the aforementioned trackers. The TRBACF tracker can efficiently adjust the model to adapt the change of the tracking scenes, thereby enhancing its robustness and accuracy. Moreover, the objective function of our TRBACF tracker can be solved by an improved alternating direction method of multipliers, which can speed up the calculation in the Fourier domain. Extensive experimental results demonstrate that the proposed TRBACF tracker achieves competitive tracking performance compared with state-of-the-art trackers.
Explicit aspects extraction in sentiment analysis using optimal rules combination

Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and informal texts, because customer reviews are a mix of formal and informal texts. These 126 rules include certain dependency-based rules and pattern-based rules from previous studies, in addition to newly developed rules intended to overcome prior rules’ weaknesses. In addition, many aspect extraction rules have remained unexplored by previous studies. However, many of these 126 rules are irrelevant and should be removed. Thus, a prober selection of the included rules is required. Therefore, in this study we also improved the Whale Optimization Algorithm (WOA) to address rules selection problem with an improved algorithm called improved WOA (IWOA). Two major improvements were included into IWOA. The first improvement is the development of a new update equation based on Cauchy mutation to improve WOA population diversity. The second improvement is the development of a new local search algorithm (LSA) to solve WOA local optima. The IWOA algorithm is applied on the full set of rules to select best rules subset and remove low quality rules. Finally, a new pruning algorithm (PA) has been developed to remove incorrect aspects and retain correct aspects. The Results on seven benchmark datasets demonstrate that IWOA+PA outperforms all other state-of-the-art baseline works and most recent works.
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction

Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats. To mitigate this limitation, therefore, we borrow two notable ideas (i.e., "explanation by intervention" from causality and "explanation are contrastive" from philosophy) and propose a novel solution, named as GRACE, that better explains neural network models' predictions for tabular datasets. In particular, given a model's prediction as label X, GRACE intervenes and generates a minimally-modified contrastive sample to be classified as Y, with an intuitive textual explanation, answering the question of "Why X rather than Y?" We carry out comprehensive experiments using eleven public datasets of different scales and domains (e.g., # of features ranges from 5 to 216) and compare GRACE with competing baselines on different measures: fidelity, conciseness, info-gain, and influence. The user-studies show that our generated explanation is not only more intuitive and easy-to-understand but also facilitates end-users to make as much as 60% more accurate post-explanation decisions than that of Lime.
Anomaly Detection in Videos for Video Surveillance Applications using Neural Networks

Security is always a main concern in every domain, due to a rise in crime rate in the crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, the needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. Anomaly detection is a technique used to distinguish various patterns and identify unusual patterns with a minimal period, this pattern is called outliers. Surveillance videos can capture a variety of realistic anomalies. Anomaly detection in video surveillance involves breaking down the whole process into three layers, which are video labelers, image processing, and activity detection. Hence, anomaly detection in videos for video surveillance application gives assured results in regards to real-time scenarios. In this paper, we anomaly was detected in images and videos with an accuracy of 98.5 %.
Realtime mobile bandwidth prediction using LSTM neural network and Bayesian fusion

With the increasing popularity of mobile Internet and the higher bandwidth requirement of mobile applications, user Quality of Experience (QoE) is particularly important. For applications requiring high bandwidth and low delay, such as video streaming, video conferencing, and online gaming, etc., if the future bandwidth can be estimated in advance, applications can leverage the estimation to adjust their data transmission strategies and significantly improve the user QoE. In this paper, we focus on accurate bandwidth prediction to improve user QoE. Specifically, We study realtime mobile bandwidth prediction in various mobile networking scenarios, such as subway and bus rides along different routes. The primary method used is Long Short Term Memory (LSTM) recurrent neural network. In individual scenarios, LSTM significantly improves the prediction accuracy of state-of-the-art prediction algorithms, such as Recursive Least Squares (RLS) by 12% in Root Mean Square Error (RMSE) and by 17% in Mean Average Error (MAE). We further developed Multi-Scale Entropy (MSE) to analyze the bandwidth patterns in different mobility scenarios and discuss its connection to the achieved accuracy. For practical applications, we developed Model Switching and Bayes Model Fusion to use pre-trained LSTM models for online realtime bandwidth prediction.
Deep Learning Network Architecture based Kannada Handwritten Character Recognition

In this work, a novel model for recognition of handwritten Kannada characters using transfer learning from Devanagari handwritten recognition system is presented. The objective is to use the knowledge of large data corpus of Devanagari recognition system as training data to perform the recognition of handwritten Kannada characters that has a smaller data corpus. The transfer of knowledge for recognition is carried out using deep learning network architecture to VGG19 NET. VGG19 NET is defined of five blocks of hidden layers, two dense fully connected layers and an output layer. Each block (except block1) consists of four convolution layers along with a max pooling layer. In proposed classification framework, Devanagari character set consists of totally 92000 images with 46 classes and Kannada character set is built with 81654 for training and 9401 for testing, for about 188 classes with each class comprising of 200-500 sample image. A total of 1,23,654 data samples is employed for training with VGG19 NET. For experimentation 9401 samples of about 188 classes built of about 40-100 samples in each classes is used and for which accuracy close to 90% is achieved. Validated accuracy after evaluation in 10 epochs with VGG19 NET, it has recorded an accuracy of 73.51% with a loss of 16.18%.
Adaptive Thermal Sensor Array Placement for Human Segmentation and Occupancy Estimation

Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a fixed sensor location to make the human-sensor distance and the human presence shape fixed. However, placing this sensor in different locations and new indoor environments can pose a significant challenge. In this paper, a novel framework based on a deep convolutional encoder-decoder network is proposed to address this challenge in real-life deployment. The framework presents a semantic segmentation of the human presence and estimates the occupancy in indoor-environment. It is also capable to segment the human presence and counts the number of people from different sensor locations, indoor environments, and human to sensor distance. Furthermore, the impact of the distance on the human presence using the TSA is investigated. The framework is evaluated to estimate the occupancy in different sensor locations, the number of occupants, environments, and human distance with classification and regression machine learning approaches. This paper shows that the classification approach using the adaptive boosting algorithm is an accurate approach which has achieves an accuracy of 98.43% and 100% from vertical and overhead sensor locations respectively.
Blockchain Oracles: A Framework for Blockchain-Based Applications

Oracles support the access, validation, and transmission of data from external sources to blockchain systems. They are important components of blockchain-based architectures. However, there exists no guidance on how oracles could be used when designing blockchain-based applications. In this paper, based on the results of a systematic literature review, we propose a framework to explain blockchain oracles and their relationships to blockchain-based applications. More specifically, the blockchain oracle framework addresses the origin of data, oracle properties, encryption method, oracle data source, validation procedures, and the integration of oracles to blockchain-based applications. Potentially, this framework can guide developers when incorporating oracles to blockchain-based applications.
Intersectionality in HCI

In this forum we explore different perspectives for how to apply intersectionality as a critical framework for design across multiple contexts. --- Yolanda A. Rankin and Jakita O. Thomas, Editors
All Quantum Resources Provide an Advantage in Exclusion Tasks

A key ingredient in quantum resource theories is a notion of measure. Such as a measure should have a number of fundamental properties, and desirably also a clear operational meaning. Here we show that a natural measure known as the convex weight, which quantifies the resource cost of a quantum device, has all the desired properties. In particular, the convex weight of any quantum resource corresponds exactly to the relative advantage it offers in an exclusion task. After presenting the general result, we show how the construction works for state assemblages, sets of measurements and sets of transformations. Moreover, in order to bound the convex weight analytically, we give a complete characterisation of the convex components and corresponding weights of such devices.
A Systematic Comparison of search-Based approaches for LDA hyperparameter tuning

Context:Latent Dirichlet Allocation (LDA) has been successfully used in the literature to extract topics from software documents and support developers in various software engineering tasks. While LDA has been mostly used with default settings, previous studies showed that default hyperparameter values generate sub-optimal topics from software documents. Objective: Recent studies applied meta-heuristic search (mostly evolutionary algorithms) to configure LDA in an unsupervised and automated fashion. However, previous work advocated for different meta-heuristics and surrogate metrics to optimize. The objective of this paper is to shed light on the influence of these two factors when tuning LDA for SE tasks. Method:We empirically evaluated and compared seven state-of-the-art meta-heuristics and three alternative surrogate metrics (i.e., fitness functions) to solve the problem of identifying duplicate bug reports with LDA. The benchmark consists of ten real-world and open-source projects from the Bench4BL dataset. Results:Our results indicate that (1) meta-heuristics are mostly comparable to one another (except for random search and CMA-ES), and (2) the choice of the surrogate metric impacts the quality of the generated topics and the tuning overhead. Furthermore, calibrating LDA helps identify twice as many duplicates than untuned LDA when inspecting the top five past similar reports. Conclusion:No meta-heuristic and/or fitness function outperforms all the others, as advocated in prior studies. However, we can make recommendations for some combinations of meta-heuristics and fitness functions over others for practical use. Future work should focus on improving the surrogate metrics used to calibrate/tune LDA in an unsupervised fashion.
The Influence of Professional Subculture on Information Security Policy Violations: A Field Study in a Healthcare Context

The Influence of Professional Subculture on Information Security Policy Violations: A Field Study in a Healthcare Context
Online classification of user activities using machine learning on network traffic

The daily deployment of new applications, along with the exponential increase in network traffic, entails a growth in the complexity of network analysis and monitoring. Conversely, the increasing availability and decreasing cost of computational capacity have increased the popularity and usability of machine learning algorithms. In this paper, a system for classifying user activities from network traffic using both supervised and unsupervised learning is proposed. The system uses the behaviour exhibited over the network and classifies the underlying user activity, taking into consideration all of the traffic generated by the user within a given time window. Those windows are characterised with features extracted from the network and transport layer headers in the traffic flows. A three-layer model is proposed to perform the classification task. The first two layers of the model are implemented using K-Means, while the last one uses a Random Forest to obtain the activity labels. An average accuracy of 97.37% is obtained, with values of precision and recall that allow online classification of network traffic for Quality of Service (QoS) and user profiling, outperforming previous proposals.
Indicator element selection and geochemical anomaly mapping using recursive feature elimination and random forest methods in the Jingdezhen region of Jiangxi Province, South China

Determining indicator element association for mineralization can not only improve mineral exploration efficiency but also reduce the cost of unnecessary element analysis during geochemical exploration. This study provides a case study of Zhuxi tungsten-copper deposits and presents a workflow using recursive feature elimination and random forest methods to select the indicator element association for copper and tungsten mineralization in regional geochemical mapping. First, a training dataset containing positive and negative samples was built based on the known mineral deposits and mineral deposit model. Second, a 100-time simulation of recursive feature elimination with cross-validation based on random forest (RFECV-RF) was run to get a robust result of indicator elements by the ranking of variable importance. Third, the random forest (RF) method was used to integrate six indicator elements for mapping geochemical anomaly. The Youden index and prediction-area (P-A) plot were used to determine the threshold value for geochemical anomaly identification. The results demonstrated the hybrid workflow was useful to determine key indicator element for geochemical anomaly identification associated with copper and tungsten mineralization. Bi, Mo, Cu, Cd, W, and As were selected as the key indicator elements for geochemical exploration of Cu–W mineralization. Bi, Mo, W and Cu elements correspond to skarn and altered granite mineralization at depth while Cd and As elements correspond to the hydrothermal-vein mineralization at shallow levels. The result of receiver operating characteristic (ROC) curve showed that geochemical anomaly identified using the hybrid method proposed in this study had the best performance in producing comprehensive geochemical signatures. The six indicator elements also exhibited an excellent performance of identifying geochemical anomaly associated to Cu–W mineralization. This study provides a cost-benefit solution to reduce the cost of unnecessary elements concentration detection by determining a small number of key indicator elements using machine learning methods in the regional geochemical mapping for discovering mineral deposits.
On the time-based conclusion stability of cross-project defect prediction models

Researchers in empirical software engineering often make claims based on observable data such as defect reports. Unfortunately, in many cases, these claims are generalized beyond the data sets that have been evaluated. Will the researcher’s conclusions hold a year from now for the same software projects? Perhaps not. Recent studies show that in the area of Software Analytics, conclusions over different data sets are usually inconsistent. In this article, we empirically investigate whether conclusions in the area of cross-project defect prediction truly exhibit stability throughout time or not. Our investigation applies a time-aware evaluation approach where models are trained only on the past, and evaluations are executed only on the future. Through this time-aware evaluation, we show that depending on which time period we evaluate defect predictors, their performance, in terms of F-Score, the area under the curve (AUC), and Mathews Correlation Coefficient (MCC), varies and their results are not consistent. The next release of a product, which is significantly different from its prior release, may drastically change defect prediction performance. Therefore, without knowing about the conclusion stability, empirical software engineering researchers should limit their claims of performance within the contexts of evaluation, because broad claims about defect prediction performance might be contradicted by the next upcoming release of a product under analysis.
Examining the representativeness of a virtual reality environment for simulation of tennis performance

There has been a growing interest in using virtual reality (VR) for training perceptual-cognitive skill in sport. For VR training to effectively simulate real-world tennis performance, it must recreate the contextual information and movement behaviours present in the real-world environment. It is therefore critical to assess the representativeness of VR prior to implementing skill training interventions. We constructed a VR tennis environment designed for training perceptual-cognitive skill, with the aim of assessing its representativeness and validating its use. Participants movement behaviours were compared when playing tennis in VR and real-world environments. When performing groundstrokes, participants frequently used the same stance in VR as they did in the real-world condition. Participants experienced a high sense of presence in VR, evident through the factors of spatial presence, engagement and ecological validity being high, with minimal negative effects found. We conclude that Tennis VR is sufficiently representative of real-world tennis. Our discussion focuses on the opportunity for training perceptual-cognitive skill and the potential for skill transfer.