Reviewerly/exHarmony huggingface dataset:
Software Engineering for AI-Based Systems: A Survey

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms

Text-based chatbots are being touted as a disruptive innovation with unprecedented business potential. However, frequent failures in human–chatbot conversations have led to consumer pushback. This study investigates the response of consumers to chatbots in terms of their intention to switch to human agents. Drawing upon the stimulus–organism–response (SOR) framework, focus is placed on how the anthropomorphic attributes of chatbots influence consumers’ perceived trust in chatbots and its implications for switching intention. Further, the moderating role of relationship norms in the relationships between the anthropomorphic attributes and trust in chatbots is examined. A mixed-methods approach is used; the qualitative analysis reveals three main anthropomorphic attributes of chatbots, two types of relationship norms and the specific response to chatbots. The quantitative results suggest that the anthropomorphic attributes of perceived warmth and perceived competence positively affect consumers’ perceived trust in chatbots, whereas communication delay negatively affects it. Relationship norms are found to moderate some of these effects such that exchange relationships strengthen the importance of perceived competence on trust, although communal relationships do not moderate the effects of perceived warmth on trust. Trust in chatbots negatively affects consumers’ intention to switch to a human agent. Theoretical and managerial implications are also discussed for scholars and practitioners in ways to improve the design and maximize the utility of chatbots.
Realization of quantum secure direct communication over 100 km fiber with time-bin and phase quantum states

Rapid progress has been made in quantum secure direct communication in recent years. For practical application, it is important to improve the performances, such as the secure information rate and the communication distance. In this paper, we report an elaborate physical system design and protocol with much enhanced performance. This design increased the secrecy capacity greatly by achieving an ultra-low quantum bit error rate of <0.1%, one order of magnitude smaller than that of existing systems. Compared to previous systems, the proposed scheme uses photonic time-bin and phase states, operating at 50 MHz of repetition rate, which can be easily upgraded to over 1 GHz using current on-the-shelf technology. The results of our experimentation demonstrate that the proposed system can tolerate more channel loss, from 5.1 dB, which is about 28.3 km in fiber in the previous scheme, to 18.4 dB, which corresponds to fiber length of 102.2 km. Thus, the experiment shows that intercity quantum secure direct communication through fiber is feasible with present-day technology.
Optimization of an auto drum fashioned brake using the elite opposition-based learning and chaotic k-best gravitational search strategy based grey wolf optimizer algorithm

Highly non-linear optimization problems are widely found in many real-world engineering applications. To tackle these problems, a novel assisted optimization strategy, named elite opposition-based learning and chaotic k-best gravitational search strategy (EOCS), is proposed for the grey wolf optimizer (GWO) algorithm. In the EOCS based grey wolf optimizer (EOCSGWO) algorithm, the elite opposition-based learning strategy (EOBLS) is proposed to take full advantage of better-performing particles for optimization in the next generations. A chaotic k-best gravitational search strategy (CKGSS) is proposed to obtain the adaptive step to improve the global exploratory ability. The performance of the EOCSGWO is verified and compared with those of other seven meta-heuristic optimization algorithms using ten popular benchmark functions. Results show that the EOCSGWO is more competitive in accuracy and robustness, and obtains the first in ranking among the six optimization algorithms. Further, the EOCSGWO is employed to optimize the design of an auto drum fashioned brake. The results show that the braking efficiency factor can be improved by 28.412% compared with the initial design.
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstraction level, the motion of the pose becomes more stable, which benefits pose prediction too. In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. The GCNs are used to extract features from fine to coarse scale and then from coarse to fine scale. The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses. Intermediate supervisions are imposed on all the predicted poses, which enforces the network to learn more representative features. Our proposed approach is evaluated on two standard benchmark datasets, i.e., the Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches. Code and pre-trained models are available at https://github.com/Droliven/MSRGCN.
Video Summarization by Learning Deep Side Semantic Embedding

With the rapid growth of video content, video summarization, which focuses on automatically selecting important and informative parts from videos, is becoming increasingly crucial. However, the problem is challenging due to its subjectiveness. Previous research, which predominantly relies on manually designed criteria or resourcefully expensive human annotations, often fails to achieve satisfying results. We observe that the side information associated with a video (e.g., surrounding text such as titles, queries, descriptions, comments, and so on) represents a kind of human-curated semantics of video content. This side information, although valuable for video summarization, is overlooked in existing approaches. In this paper, we present a novel deep side semantic embedding (DSSE) model to generate video summaries by leveraging the freely available side information. The DSSE constructs a latent subspace by correlating the hidden layers of the two uni-modal autoencoders, which embed the video frames and side information, respectively. Specifically, by interactively minimizing the semantic relevance loss and the feature reconstruction loss of the two uni-modal autoencoders, the comparable common information between video frames and side information can be more completely learned. Therefore, their semantic relevance can be more effectively measured. Finally, semantically meaningful segments are selected from videos by minimizing their distances to the side information in the constructed latent subspace. We conduct experiments on two datasets (Thumb1K and TVSum50) and demonstrate the superior performance of DSSE to the several state-of-the-art approaches to video summarization.
Reliable Filtering of Nonlinear Markovian Jump Systems: The Continuous-Time Case

This paper is concerned with the reliable ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter design problem for the nonlinear continuous-time Markov jump systems based on Takagi-Sugeno fuzzy model. A stochastic variable is introduced to describe the encountered sensor failures, the value of which is dependent on the considered plant mode based on a hidden Markov process. In practice, generally the information on plant modes is not fully accessible to the reliable filter, which results in the nonsynchronous phenomena between the modes of involved plant and filter, and has a negative effect on the system performance. A hidden Markov model is also adopted to depict such kinds of nonsynchronous phenomena. The filtering error systems are called fuzzy dual hidden Markov jump systems. A sufficient condition, associated to the modes of the plant, sensor failures, and the filter are proposed for the filtering error systems to ensure the stochastic stability and guaranteed ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - ℒ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance, based on which the existence condition and explicit design method of a nonsynchronous filter are both given. Finally, two simulation examples illustrate the effectiveness of the proposed approach.
Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume

There is a large literature that analyzes time-specific anomalies in equity markets such as the Monday effect, the January effect and the Halloween effect. This study reports intra-day time-of-day, day-of-week, and month-of-year effects for Bitcoin returns and trading volume. Using more than 15 million observations from seven global and continuously-traded Bitcoin exchanges, we find time-specific anomalies in returns but no persistent effects across time. In contrast, we find persistent differences in trading activity across all exchanges with lower activity during local evening hours and on weekends. The results suggest that both retail and institutional investors are actively trading Bitcoin.
An unsupervised aspect extraction strategy for monitoring real-time reviews stream

One of the most important opinion mining research directions falls in the extraction of polarities referring to specific entities (aspects) contained in the analyzed texts. The detection of such aspects may be very critical especially when documents come from unknown domains. Indeed, while in some contexts it is possible to train domain-specific models for improving the effectiveness of aspects extraction algorithms, in others the most suitable solution is to apply unsupervised techniques by making such algorithms domain-independent and more efficient in a real-time environment. Moreover, an emerging need is to exploit the results of aspect-based analysis for triggering actions based on these data. This led to the necessity of providing solutions supporting both an effective analysis of user-generated content and an efficient and intuitive way of visualizing collected data. In this work, we implemented an opinion monitoring service implementing (i) a set of unsupervised strategies for aspect-based opinion mining together with (ii) a monitoring tool supporting users in visualizing analyzed data. The aspect extraction strategies are based on the use of an open information extraction strategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques.
Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services

Facial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. In this paper, we propose a novel, cost-effective, and energy-efficient framework designed for suspicious activity recognition based on facial expression analysis for smart security in law-enforcement services. The Raspberry Pi camera captures the video stream and detects faces using the Viola Jones algorithm. The face region is pre-processed using Gabor filter and median filter prior to feature extraction. Oriented FAST and Rotated BRIEF (ORB) features are then extracted and the support vector machine (SVM) classifier is trained, which predicts the known emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise). Based on the collective emotions of the faces, we predict the sentiment behind the scene. Using this approach, we predict if a certain situation is hostile and can prevent it prior to its occurrence. The system is tested on three publically available datasets: Cohen Kande (CK+), MMI, and JAFEE. A detailed comparative analysis based on SURF, SIFT, and ORB is also presented. Experimental results verify the efficiency and effectiveness of the proposed system in accurate recognition of suspicious activity compared to state-of-the-art methods and validate its superiority for enhancing security in law enforcement services.
YOLO3D: End-to-End Real-Time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge. In this paper, we build on the success of the one-shot regression meta-architecture in the 2D perspective image space and extend it to generate oriented 3D object bounding boxes from LiDAR point cloud. Our main contribution is in extending the loss function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian coordinates and the height of the box as a direct regression problem. This formulation enables real-time performance, which is essential for automated driving. Our results are showing promising figures on KITTI benchmark, achieving real-time performance (40 fps) on Titan X GPU.
Feed-forward neural network training using sparse representation

The feed-forward neural network (FNN) has drawn great interest in many applications due to its universal approximation capability. In this paper, a novel algorithm for training FNNs is proposed using the concept of sparse representation. The major advantage of the proposed algorithm is that it is capable of training the initial network and optimizing the network structure simultaneously. The proposed algorithm consists of two core stages: structure optimization and weight update. In the structure optimization stage, the sparse representation technique is employed to select important hidden neurons that minimize the residual output error. In the weight update stage, a dictionary learning based method is implemented to update network weights by maximizing the output diversity from hidden neurons. This weight-updating process is designed to improve the performance of the structure optimization. Based on several benchmark classification and regression problems, we present experimental results comparing the proposed algorithm with state-of-the-art methods. Simulation results show that the proposed algorithm offers comparative performance in terms of the final network size and generalization ability.
Unsupervised EEG feature extraction based on echo state network

Advanced analytics such as event detection, pattern recognition, clustering, and classification with electroencephalogram (EEG) data often rely on extracted EEG features. Most of the existing EEG feature extraction approaches are hand-designed with expert knowledge or prior assumptions, which may lead to inferior analytical performances. In this paper, we develop a fully data-driven EEG feature extraction method by applying recurrent autoencoders on multivariate EEG signals. We use an Echo State Network (ESN) to encode EEG signals to EEG features, and then decode them to recover the original EEG signals. Therefore, we name our method feature extraction based on echo state network, or simply FE-ESN. We show that the well-known autoregression-based EEG feature extraction can be seen as a simplified variation of our FE-ESN method. We have conducted experiments on real-world EEG data to evaluate the effectiveness of FE-ESN for both classification tasks and clustering tasks. Experimental results demonstrate the superiority of FE-ESN over the state-of-the-art methods. This paper not only provides a novel EEG feature extraction method but also opens up a new way towards unsupervised EEG feature design.
Joint Image-Text Hashing for Fast Large-Scale Cross-Media Retrieval Using Self-Supervised Deep Learning

Recent years have witnessed the promising future of hashing in the industrial applications for fast similarity retrieval. In this paper, we propose a novel supervised hashing method for large-scale cross-media search, termed self-supervised deep multimodal hashing (SSDMH), which learns unified hash codes as well as deep hash functions for different modalities in a self-supervised manner. With the proposed regularized binary latent model, unified binary codes can be solved directly without relaxation strategy while retaining the neighborhood structures by the graph regularization term. Moreover, we propose a new discrete optimization solution, termed as binary gradient descent, which aims at improving the optimization efficiency toward real-time operation. Extensive experiments on three benchmark data sets demonstrate the superiority of SSDMH over state-of-the-art cross-media hashing approaches.
Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in Permanent Magnet Synchronous Motor

Permanent Magnet Synchronous Motor (PMSM) is widely used in the industrial applications because of its simple construction and its high efficiency. Unfortunately, if a small change has occurred in the system parameters, the PMSM displays an unexpected performance which is known as a chaotic behavior. To control this undesirable behavior, it is crucial to propose a robust tool to extract the model parameters accurately and rapidly. In this work, novel developed optimization techniques called Chaotic Whale Optimization variants (CWOA) are proposed where the standard Whale Optimization Algorithm (WOA) is integrated with different ten chaos maps to tune some of its parameters. Four CWOA variants are introduced, tested and validated mathematically over CEC 2017. In addition, CWOA variants and the standard WOA version are proposed to estimate the parameters of the chaotic behavior in PMSM at both of the off-line and the on-line operating conditions without and with noise. The results of the proposed algorithms are compared with that of the previous techniques in literature to test their efficiency, reliability, and accuracy. An intensive statistical analysis is accomplished for extra validation of the superiority of the proposed variants. The overall results indicate that the CWOA-II variant with the logistic chaos map offers the best performance among all other variants where it provides lower error values between the estimated and the original system performance, higher convergence speed and shorter execution time. This eventually helps to provide an accurate and fast description of the chaotic region to ensure a fast control of the motor as well as a fast protection from damage.
Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence

In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quantities of interest are modeled as linear combinations of multiple parameterized stationary Gaussian processes (GPs), and the hyperparameters of these GPs are estimated from data via optimization. In CoPhIK, we construct a GP representing low-fidelity data using physics-informed Kriging (PhIK), and model the discrepancy between low- and high-fidelity data using a parameterized GP with hyperparameters identified via optimization. We prove that the physical constraints in the form of deterministic linear operators are satisfied up to an error bound. Furthermore, we combine CoPhIK with a greedy active learning algorithm for guiding the selection of additional observation locations. The efficiency and accuracy of CoPhIK are demonstrated for reconstructing the partially observed modified Branin function, reconstructing the sparsely observed state of a steady state heat transport problem, and learning a conservative tracer distribution from sparse tracer concentration measurements.
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation

Action recognition has become a rapidly developing research field within the last decade. But with the increasing demand for large scale data, the need of hand annotated data for the training becomes more and more impractical. One way to avoid frame-based human annotation is the use of action order information to learn the respective action classes. In this context, we propose a hierarchical approach to address the problem of weakly supervised learning of human actions from ordered action labels by structuring recognition in a coarse-to-fine manner. Given a set of videos and an ordered list of the occurring actions, the task is to infer start and end frames of the related action classes within the video and to train the respective action classifiers without any need for hand labeled frame boundaries. We address this problem by combining a framewise RNN model with a coarse probabilistic inference. This combination allows for the temporal alignment of long sequences and thus, for an iterative training of both elements. While this system alone already generates good results, we show that the performance can be further improved by approximating the number of subactions to the characteristics of the different action classes as well as by the introduction of a regularizing length prior. The proposed system is evaluated on two benchmark datasets, the Breakfast and the Hollywood extended dataset, showing a competitive performance on various weak learning tasks such as temporal action segmentation and action alignment.