Reviewerly/exHarmony huggingface dataset:
Predictive Engagement: An Efficient Metric for Automatic Evaluation of Open-Domain Dialogue Systems

User engagement is a critical metric for evaluating the quality of open-domain dialogue systems. Prior work has focused on conversation-level engagement by using heuristically constructed features such as the number of turns and the total time of the conversation. In this paper, we investigate the possibility and efficacy of estimating utterance-level engagement and define a novel metric, predictive engagement, for automatic evaluation of open-domain dialogue systems. Our experiments demonstrate that (1) human annotators have high agreement on assessing utterance-level engagement scores; (2) conversation-level engagement scores can be predicted from properly aggregated utterance-level engagement scores. Furthermore, we show that the utterance-level engagement scores can be learned from data. These scores can be incorporated into automatic evaluation metrics for open-domain dialogue systems to improve the correlation with human judgements. This suggests that predictive engagement can be used as a real-time feedback for training better dialogue models.
Query-efficient label-only attacks against black-box machine learning models

Recent studies have shown that machine learning algorithms are susceptible to imperceptible perturbations. These studies focus on the laboratory environment, where the attacker has knowledge about internal information of the victim model or feedbacks such as class probabilities. There is still a gap between theory and physical world, the risk of adversarial attacks under the more extreme and realistic condition needs to be figured out. Here we propose a knowledge restricted black-box attack model where the attacker can only get the final predict label. In the meantime, we model the attacker as a resource-restricted one, such as query-limited. The limitations of knowledge level and resources make previous work unable to be directly applied. For this problem, the current state-of-the-art method is boundary attack, however it requires large a number of queries. In this paper, we make several contributions to investigate the vulnerability of machine learning models in more realistic scenarios. First, we reconstruct the optimization problem, measure the quality of the sample points by L2 distance. Second, we provide a more effective algorithm, using cutting plane method and local optimization. Third, we propose two effective dynamic defense strategy, which is easy to implement. At last, we conduct an experimental evaluation on MNIST, Fashion-MNIST and malware detection datasets. The results show that (1) compared with state-of-the-art method, our cutting plane method reduces the number of queries while ensuring the attack efficiency; (2) Dynamic defense strategy is effective against label-only adversarial attacks, the rate of attack success dropped from nearly 100% to 23%, with a considerable classification accuracy; (3) Improved defense strategy guarantees the effectiveness of defense and improves the stability of the whole model.
Some Brief Reflections on Digital Technologies and Economic Development

Abstract Among the many disruptions caused by artificial intelligence (AI) and other digital technologies (including automation, cyberwarfare, surveillance, loss of privacy, fake news, infrastructure vulnerability), the effects on development pathways are likely to be significant and complex. AI will enable low-income countries to leapfrog in several sectors, including e-governance, e-finance, e-health, and e-education. Yet AI will also lead to automation, reducing the demand for labor, especially unskilled labor. Labor-intensive sectors such as apparel will provide fewer jobs, and lower export earnings. Development strategies will need to adjust accordingly.
Optimizing spontaneous parametric down-conversion sources for boson sampling

An important step for photonic quantum technologies is the demonstration of a quantum advantage through boson sampling. In order to prevent classical simulability of boson sampling, the photons need to be almost perfectly identical and almost without losses. These two requirements are connected through spectral filtering: improving one leads to a decrease of the other. A proven method of generating single photons is spontaneous parametric down-conversion (SPDC). We explore the fundamental limits of several important SPDC sources and show that an optimal tradeoff between indistinguishability and losses can always be found. We conclude that a 50-photon scattershot boson-sampling experiment using SPDC sources is possible from a computational complexity point of view. To this end, we numerically optimize SPDC sources under the regime of weak pumping and with a single spatial mode.
Numerical Evaluation of Job Finish Time Under MTD Environment

Moving target defense (MTD) has recently emerged as a game-changer in the confrontation between cyberattack and defense. MTD mechanism constantly and randomly changes the system configurations to create uncertainty of the attack surface against cyber-adversaries. To date, researches on the evaluation of MTD techniques either focused on analyzing the effectiveness of MTD or studying the system performance loss due to the use of MTD. The impact on job/service running on the protected system is always ignored. In this paper, we propose an SRN (Stochastic reward net) based analytical modeling approach to investigate how MTD techniques influence the job running on protected system from the perspective of job finish time. The SRN model developed in this paper captures the behaviors of both the adversary and the job execution process. Furthermore, we carry out numerical analysis to study the impact of different system parameters on job finish time and other evaluation metrics. The results in this paper can help defenders choose a better MTD configuration to complete the job execution as soon as possible.
Multi-context aware user–item embedding for recommendation

Real recommender systems usually contain various auxiliary information. Some of the most recent works make meaningful exploration of incorporating auxiliary information into the representation model for competitive recommendation. However, learning user and item representations still faces two challenges: (1) existing works do not well address the problem of integrating multi-type auxiliary information; (2) learning representations for inactive users is still challenging due to the high sparsity of explicit user-item associations. In order to tackle these problems, in this paper, the attributed heterogeneous network and bipartite interaction network are employed to incorporate various auxiliary information and user-item associations. A joint objective function and an efficient algorithm are devised for the representation learning. Experimental results show that the proposed algorithm has significant advantages over the state-of-the-art baselines. What is remarkable is that our proposed method is demonstrated to be especially useful for dealing with low-active users in the system.
Improved Reference Image Encryption Methods Based on 2<i> <sup>K</sup> </i> Correction in the Integer Wavelet Domain

Abstract Many visually meaningful image encryption (VMIE) methods have been proposed in the literature using reference encryption. However, the most important problems of these methods are low visual quality and blindness. Owing to the low visual quality, the pre-encrypted image can be analyzed simply from the reference image and, in order to decrypt nonblind methods, users should use original reference images. In this paper, two novel reference image encryption methods based on the integer DWT (discrete wavelet transform) using 2 k correction are proposed. These methods are blind and have high visual quality, as well as short execution times. The main aim of the proposed methods is to solve the problem of the three VMIE methods existing in the literature. The proposed methods mainly consist of the integer DWT, pre-encrypted image embedding by kLSBs ( k least significant bits) and 2 k correction. In the decryption phase, the integer DWT and pre-encrypted image extraction with the mod operator are used. Peak signal-to-noise ratio (PSNR) measures the performances of the proposed methods. Experimental results clearly illustrate that the proposed methods improve the visual quality of the reference image encryption methods. Overall, 2 k correction and kLSBs provide high visual quality and blindness.
Stand-alone renewable power system scheduling for a green data center using integer linear programming

In light of the rapid growth of data centers around the world and their huge energy consumption, several researchers have focused on the task scheduling and resource allocation problem in order to minimize the energy consumed by the data center. Other initiatives focus on the implementation of green energy sources in order to minimize the consumption of fossil fuels and their emission of CO $$_{2}$$ . As part of the ANR DATAZERO project (Pierson et al. in IEEE Access 7, 2019. https://doi.org/10.1109/ACCESS.2019.2930368 ), several research teams have engaged in efforts at defining the main concepts of a full green data center, powered only by renewable energy. Achieving this goal necessitates a focus on the efficient management of an autonomous hybrid power system consisting of solar panels, wind turbines, batteries, and fuel cell systems. The purpose of this work is not to show that a stand-alone data center is economically viable, but rather is feasible. This paper proposes a set of models based on mixed integer linear programs capable of managing the energy commitment to address data center power demand. The approach takes the season and weather forecasts into account at the time of optimization.
Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.
EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.
Image Super-Resolution via Residual Block Attention Networks

Recently, deep convolutional neural networks (CNNs) have been widely used in image super-resolution (SR). Most state-of-the-art CNN-based SR methods focus on improving the performance by designing deeper and wider networks. However, 1) using deeper networks makes the network difficult to train; 2) the relationships of features have not been thoroughly explored, therefore hindering the representational power of CNNs. In this paper, we investigate an effective end-to-end neural structure for more powerful feature expression and feature correlation learning. Specifically, we propose a residual block attention networks (RBAN) framework, which consists of two types of attention modules to efficiently exploit the feature correlations in spatial and channel dimensions for stronger feature expression. The proposed RBAN framework is constituted of a series of residual attention groups, which is further composed of several repeated residual block attention block to not only fully exploit the hierarchical features from different convolutional layers but also efficiently capture the contextual information and interdependencies among channels. Experimental results demonstrate the superiority of our RBAN network over state-of-the-art SR methods in terms of both quantitive and visual quality.
Design and Implementation of a Computer Aided Intelligent Examination System

Developing a computer based system for examinations are the substitute for the current examination system based on paper. In recent days, the e-learning has become more popular because of its adaptability, integrity and user friendliness. In terms of the paper based examinations, major challenge is the proctoring techniques used. In this paper, a novel method to avoid the presence of a proctor throughout the examination is proposed by an intelligent based examination system. This method is proposed to improve the e-learning using intelligent question bank and examination system. The system is designed with different complexity levels among the questions and also it acts as a tool to assessing the understanding of student from the teaching materials. This system can be timesaving and more efficient with an adequate level of security. The proposed methodology can be classified in to two main phases such as the design of question bank along with its database, design of the Artificial Intelligence (AI) based system for examination and its evaluation in order to improve the e-learning. Future works in this system can be the addition of theory-based questions and the integration of biometric based systems for enhancing the level of security.
Leakage-Resilient Authenticated Key Exchange for Edge Artificial Intelligence

Edge Artificial Intelligence (AI) is a timely complement of cloud-based AI. By introducing intelligence to the edge, it alleviates privacy concerns of streaming and storing data to the cloud, enables real-time operations where milliseconds matter, and brings AI services to remote areas with poor networking infrastructures. Security is a significant problem in Edge AI applications such as self-driving cars and intelligent healthcare. Since the edge devices are empowered to process data and take actions, attacking and compromising them can cause serious damage. However, the wide deployment of computationally limited devices in edge environments and the increasing happening of side-channel (or leakage) attacks pose critical challenges to security. This article thereby aims to enhance the security for Edge AI by designing and developing lightweight and leakage-resilient authenticated key exchange (LRAKE) protocols. Compared with available LRAKE protocols, the proposed protocols in this article can be effortless applied in some mainstreaming security and communication standards. Moreover, this article realizes prototypes and presents implementation details; and a use case of applying the proposed protocol in Bluetooth 5.0 is illustrated. The theoretical design and implementation details will provide a guidance of applying the LRAKE protocols in Edge AI applications.
Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work

Sentiment analysis (SA) is a study where people's opinions and emotions are automatically extracted in the form of sentiments from the natural language text.In social media monitoring, it is very useful because it allows user to gain an overall picture of the extensive public opinion behind many topics.Most works on SA are for the English text.Only a few works focus on the Malay language.Currently, a review on SA for the Malay language only focus on the SA approaches and the dataset.Some major issues such as the pre-processing techniques used to normalize the noisy text, the most employed performance measures for Malay SA, and the challenges for Malay SA has not been reviewed.Malaysians tend not to fully follow any abbreviations rules when writing on social media.Thus, a lot of noisy text can be found in social media sites like Facebook and Twitter which create some issues to SA process.Hence, the aim of this study is to investigate the state of the art, challenges and future works of SA for Malay social media text.This study provides a review on various approaches, datasets, performance measures, and pre-processing techniques used in the previous works on SA of the Malay text.More than 700 articles from journals and conference proceedings have been identified using the search keywords, however, only 17 relevant articles published from year 2013 to 2018 were reviewed.The findings from this review focus on three commonly used SA approaches which are lexicon-based, machine learning, and hybrid.
Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

In Web 3.0, semantic data gives machines the ability to understand and process data. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinatorial optimization algorithms, whose objective is to propose to the learner a relevant combination of Learning Object based on its contextual constraints. Our solution guarantees scalability, and high data availability through the use of the principle of replication. The results obtained in the system evaluation experiments, on a large number of servers show the efficiency, scalability, and robustness of our system if the amount of data processed is very large.
Deep Ensemble Reinforcement Learning with Multiple Deep Deterministic Policy Gradient Algorithm

Deep deterministic policy gradient algorithm operating over continuous space of actions has attracted great attention for reinforcement learning. However, the exploration strategy through dynamic programming within the Bayesian belief state space is rather inefficient even for simple systems. Another problem is the sequential and iterative training data with autonomous vehicles subject to the law of causality, which is against the i.i.d. (independent identically distributed) data assumption of the training samples. This usually results in failure of the standard bootstrap when learning an optimal policy. In this paper, we propose a framework of m-out-of-n bootstrapped and aggregated multiple deep deterministic policy gradient to accelerate the training process and increase the performance. Experiment results on the 2D robot arm game show that the reward gained by the aggregated policy is 10%–50% better than those gained by subpolicies. Experiment results on the open racing car simulator (TORCS) demonstrate that the new algorithm can learn successful control policies with less training time by 56.7%. Analysis on convergence is also given from the perspective of probability and statistics. These results verify that the proposed method outperforms the existing algorithms in both efficiency and performance.
Storing and querying fuzzy RDF(S) in HBase databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) have been used for meta-data management on the Web. With the rapid development of Web-based applications, the growth of RDF(S) data is increasing dramatically. In the context of open Web, uncertainty naturally arises in RDF and RDFS (RDF(S) in short). To manage large-scale fuzzy RDF(S) data efficiently and effectively, we propose, in this paper, a fuzzy RDF(S) storage schema with fuzzy HBase databases (FHDBs). On the basis, we investigate how to query in FHDBs. We propose a set of query algorithms. We implement a prototype system to demonstrate the feasibility of our approach.
Precision Medicine and its Imprecise History

sketches a history of precision medicine.Though sometimes portrayed as a field with roots primarily in genetics, he describes the decisive historical role played by statisticians and data scientists, particularly a group of midcentury biostatisticians located at the National Institutes of Health.
NPrSVM: Nonparallel sparse projection support vector machine with efficient algorithm

The recently proposed projection twin support vector machine (PTSVM) is an excellent nonparallel classifier. However, PTSVM employs the least-squares loss function to measure its within-class empirical risk, resulting in several drawbacks, such as non-sparseness for decision, sensitivity to outliers, expensive matrix inversion, and inconsistency in the linear and nonlinear models. To alleviate these issues, in this paper, we propose a novel nonparallel sparse projection support vector machine (NPrSVM). Different from the original PTSVM that squeezes the projected values of within-class instances to its own class center, NPrSVM aims to cluster them as much as possible within an insensitive tube. Specifically, our NPrSVM owns the following attractive merits: (i) Benefiting from the L1-norm symmetric Hinge loss function, NPrSVM not only enjoys sparseness for decision but also improves robustness to outliers. (ii) The elegant formulation of dual problems in NPrSVM no longer involves the matrix inversion during the training procedure. This greatly saves the computing time compared to PTSVM. (iii) While the nonlinear formulation of PTSVM is not the direct extension of linear PTSVM, the linear and nonlinear versions of our NPrSVM are consistent. (iv) An efficient dual coordinate descent algorithm is further designed for NPrSVM to handle large-scale classification. Finally, the feasibility and effectiveness of NPrSVM are validated by extensive experiments on both synthetic and real-world datasets.