Reviewerly/exHarmony huggingface dataset:
Alexa, what's on my shopping list? Transforming customer experience with digital voice assistants

Artificial intelligence is disrupting the retail industry. Digital voice assistants as one of the most popular AI technologies are poised to revolutionize consumers’ shopping journeys yet we have a sparse understanding of their role in fostering customer experience. The present study seeks to address this issue by proposing and validating a research model encompassing human-like attributes (i.e., perceived anthropomorphism, perceived animacy, and perceived intelligence), technology attributes (i.e., performance expectancy, effort expectancy, and perceived security), and contextual factors (i.e., social influence and facilitating conditions) as the antecedents to continuance usage of digital voice assistants to shop. The effects are facilitated by the formation of several perceptual-based outcomes such as parasocial interactions, smart-shopping perception, and AI-enabled customer experience. Data (n = 411) was collected through an online questionnaire-based survey and analysed using Partial Least Squares Structural Equation Modelling. The results indicated (i) all human-like and technology attributes, except effort expectancy, have a significant impact on parasocial interactions, (ii) perceived intelligence, perceived security, and performance expectancy significantly influence smart-shopping perception, (iii) parasocial interactions and smart-shopping perception foster AI-enabled customer experience, and (iv) AI-enabled customer experience and social influence determine the continuance intention to shop using digital voice assistants. Theoretical and practical implications are discussed.
Lattice-based proxy-oriented identity-based encryption with keyword search for cloud storage

Public-key encryption with keyword search (PEKS) enables users to search over encrypted data and retrieve target data efficiently. However, most of existing PEKS schemes are vulnerable to adversaries equipped with quantum computers in the near future, and even incur complex certificate management procedures due to the public key infrastructure (PKI). To this end, we propose a proxy-oriented identity-based encryption with keyword search (PO-IBEKS) scheme from lattices for cloud storage, which is post-quantum secure. In PO-IBEKS, an original data owner authorizes a proxy to encrypt sensitive data as well as corresponding keywords and upload ciphertexts to clouds, which alleviates the data processing burden on the original data owner. Besides, PO-IBEKS can resist inside keyword guessing attacks (IKGA) from misbehaved cloud servers by integrating the learning with errors (LWE) encryption and preimage sampleable function. Each entity in PO-IBEKS is identified with her/his recognizable information, thereby eliminating managing certificates. Formal security analysis proves that PO-IBEKS can achieve ciphertext indistinguishability, existential unforgeability, and delegation security. Experimental results demonstrate PO-IBEKS is much more practical when compared with existing schemes.
Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.
Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmott’s (WI), Legates & McCabe’s (E1) and Nash–Sutcliffe coefficients (ENS) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent.
Robots, Artificial Intelligence, and Service Automation in Restaurants

Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance. The absence of trustworthy human supervision over the data collection process exposes organizations to security vulnerabilities; training data can be manipulated to control and degrade the downstream behaviors of learned models. The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these threats, and an array of open problems in this space.
Machine learning based methods for software fault prediction: A survey

Several prediction approaches are contained in the arena of software engineering such as prediction of effort, security, quality, fault, cost, and re-usability. All these prediction approaches are still in the rudimentary phase. Experiments and research are conducting to build a robust model. Software Fault Prediction (SFP) is the process to develop the model which can be utilized by software practitioners to detect faulty classes/module before the testing phase. Prediction of defective modules before the testing phase will help the software development team leader to allocate resources more optimally and it reduces the testing effort. In this article, we present a Systematic Literature Review (SLR) of various studies from 1990 to June 2019 towards applying machine learning and statistical method over software fault prediction. We have cited 208 research articles, in which we studied 154 relevant articles. We investigated the competence of machine learning in existing datasets and research projects. To the best of our knowledge, the existing SLR considered only a few parameters over SFP’s performance, and they partially examined the various threats and challenges of SFP techniques. In this article, we aggregated those parameters and analyzed them accordingly, and we also illustrate the different challenges in the SFP domain. We also compared the performance between machine learning and statistical techniques based on SFP models. Our empirical study and analysis demonstrate that the prediction ability of machine learning techniques for classifying class/module as fault/non-fault prone is better than classical statistical models. The performance of machine learning-based SFP methods over fault susceptibility is better than conventional statistical purposes. The empirical evidence of our survey reports that the machine learning techniques have the capability, which can be used to identify fault proneness, and able to form well-generalized result. We have also investigated a few challenges in fault prediction discipline, i.e., quality of data, over-fitting of models, and class imbalance problem. We have also summarized 154 articles in a tabular form for quick identification.
Motion Representations for Articulated Animation

We propose novel motion representations for animating articulated objects consisting of distinct parts. In a completely unsupervised manner, our method identifies object parts, tracks them in a driving video, and infers their motions by considering their principal axes. In contrast to the previous keypoint-based works, our method extracts meaningful and consistent regions, describing locations, shape, and pose. The regions correspond to semantically relevant and distinct object parts, that are more easily detected in frames of the driving video. To force decoupling of foreground from background, we model non-object related global motion with an additional affine transformation. To facilitate animation and prevent the leakage of the shape of the driving object, we disentangle shape and pose of objects in the region space. Our model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> can animate a variety of objects, surpassing previous methods by a large margin on existing benchmarks. We present a challenging new benchmark with high-resolution videos and show that the improvement is particularly pronounced when articulated objects are considered, reaching 96.6% user preference vs. the state of the art.
A novel pixel-split image encryption scheme based on 2D Salomon map

Images serve as a valuable information carrier for their properties of visualization and holding a large amount of messages. This paper presents a novel chaos-based encryption scheme to protect images from being captured or attacked. Firstly a two-dimensional Salomon map inspired by the Salomon function is proposed and there is a comprehensive comparison with other recent counterparts to validate the effectiveness of this chaotic system. Compared with traditional maps, the results show that 2D Salomon map has larger Lyapunov exponent, Kolmogorov entropy, Correlation dimension and Sample entropy. Especially the values of Lyapunov exponent can reach 11, which indicates that the chaotic sequence generated by Salomon map has strong randomness. Then a pixel-split image encryption scheme supported by 2D Salomom map is presented, itselectively exchanges the high and low bits of the image and globally spreads the altered pixels to random positions, resulting in that the cipher image contains no useful information any more and can only be recovered with a unique key. Ultimately, we have conducted comprehensive tests on the proposed algorithm, including various simulated attack, randomness tests and decorrelation analysis on cipher images, the overall results show that the scheme has excellent cryptographic effect and security.
Focal and Global Knowledge Distillation for Detectors

Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have failed on it. In this paper, we point out that in object detection, the features of the teacher and student vary greatly in different areas, especially in the foreground and background. If we distill them equally, the uneven differences between feature maps will negatively affect the distillation. Thus, we propose Focal and Global Distillation (FGD). Focal distillation separates the foreground and background, forcing the student to focus on the teacher's critical pixels and channels. Global distillation rebuilds the relation between different pixels and transfers it from teachers to students, compensating for missing global information in focal distillation. As our method only needs to calculate the loss on the feature map, FGD can be applied to various detectors. We experiment on various detectors with different backbones and the results show that the student detector achieves excellent mAP improvement. For example, ResNet-50 based RetinaNet, Faster RCNN, RepPoints and Mask RCNN with our distillation method achieve 40.7%, 42.0%, 42.0% and 42.1% mAP on COCO2017, which are 3.3, 3.6, 3.4 and 2.9 higher than the baseline, respectively. Our codes are available at https://github.com/yzd-v/FGD.
Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine

This study aims to propose an efficient evaluation model for Sino foreign cooperative education projects, which can offer a reasonable reference for universities to deepen reform
COVID-19 Contact Trace App Deployments: Learnings From Australia and Singapore

& AS THE COVID-19 pandemic has gripped nation states around the world, technological fixes based on smartphone solutions have been proposed as a means to mitigate the risk posed to humans, prevent further economic loss, and overcome societal and business challenges.If health authorities and government agencies can track and trace the movement of the citizen at the most granular level, verify with whom they have had contact over a given period, then they can respond immediately by placing confirmed cases and suspected contacts in quarantine.By doing so, it is theoretically possible to minimize the level of exposure to the disease, particularly in high-density areas, before a potential outbreak happens, limiting the spread of a virus. 1 THERE IS AN APP FOR THATThe location-based technologies proposed in response to COVID-19 are built primarily on the foundations of mobile commerce, encompassing a range of applications that can potentially utilize proximity details, identity, location data, and other condition information in the provision of value-added services to the end-user. 2These technologies leverage a range of telecommunication standards, protocols, and features depending on the required functionality.That is, the technologies at work depend on the specific application or context within which they are implemented.For instance, location services can be instituted at varying levels of detail across the mobile value chain 3 : at the network level (determined by the mobile network provider), at the device level (edge device like a FitBit 4 ), at the operating system level (Android or iOS), at the app level (deployed by the local health authority or government agency).
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

Word Sense Disambiguation (WSD) is a longstanding but open problem in Natural Language Processing (NLP).WSD corpora are typically small in size, owing to an expensive annotation process.Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training.This leads to poor performance on rare and unseen senses.To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zeroshot learning.To obtain target sense embeddings, EWISE utilizes sense definitions.EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method.We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings.EWISE achieves new stateof-the-art WSD performance.
Augmented reality for dental implantology: a pilot clinical report of two cases

Despite the limited number of articles dedicated to its use, augmented reality (AR) is an emerging technology that has shown to have increasing applications in multiple different medical sectors. These include, but are not limited to, the Maxillo-facial and Dentistry disciplines of medicine. In these medical specialties, the focus of AR technology is to achieve a more visible surgical field during an operation. Currently, this goal is brought about by an accurate display of either static or dynamic diagnostic images via the use of a visor or specific glasses. The objective of this study is to evaluate the feasibility of using a virtual display for dynamic navigation via AR. The secondary outcome is to evaluate if the use of this technology could affect the accuracy of dynamic navigation. Two patients, both needing implant rehabilitation in the upper premolar area, were treated with flapless surgery. Prior to the procedure itself, the position of the implant was virtually planned and placed for each of the patients using their previous scans. This placement preparation contributed to a dynamic navigation system that was displayed on AR glasses. This, in turn, allowed for the use of a computer-aided/image-guided procedure to occur. Dedicated software for surface superimposition was then used to match the planned position of the implant and the real one obtained from the postoperative scan. Accuracies, using this procedure were evaluated by way of measuring the deviation between real and planned positions of the implants. For both surgeries it was possible to proceed using the AR technology as planned. The deviations for the first implant were 0.53 mm at the entry point and 0.50 mm at the apical point and for the second implant were 0.46 mm at the entry point and 0.48 mm at the apical point. The angular deviations were respectively 3.05° and 2.19°. From the results of this pilot study, it seems that AR can be useful in dental implantology for displaying dynamic navigation systems. While this technology did not seem to noticeably affect the accuracy of the procedure, specific software applications should further optimize the results.
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint

The effectiveness of model-based versus model-free methods is a long-standing question in reinforcement learning (RL). Motivated by recent empirical success of RL on continuous control tasks, we study the sample complexity of popular model-based and model-free algorithms on the Linear Quadratic Regulator (LQR). We show that for policy evaluation, a simple model-based plugin method requires asymptotically less samples than the classical least-squares temporal difference (LSTD) estimator to reach the same quality of solution; the sample complexity gap between the two methods can be at least a factor of state dimension. For policy evaluation, we study a simple family of problem instances and show that nominal (certainty equivalence principle) control also requires several factors of state and input dimension fewer samples than the policy gradient method to reach the same level of control performance on these instances. Furthermore, the gap persists even when employing commonly used baselines. To the best of our knowledge, this is the first theoretical result which demonstrates a separation in the sample complexity between model-based and model-free methods on a continuous control task.
Unsupervised State Representation Learning in Atari

State representation learning, or the ability to capture latent generative factors of an environment is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations in an unsupervised manner without supervision from rewards is an open problem. We introduce a method that tries to learn better state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods.