Reviewerly/exHarmony huggingface dataset:
Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making

Abstract The current research demonstrates how conversational robo advisors as opposed to static, non-conversational robo advisors alter perceptions of trust, the evaluation of a financial services firm, and consumer financial decision making. We develop and empirically test a novel conceptualization of conversational robo advisors building on prior work in human-to-human communication and interpersonal psychology, showing that conversational robo advisors cause greater levels of affective trust compared to non-conversational robo advisors and evoke a more benevolent evaluation of a financial services firm. We demonstrate that this increase in affective trust not only affects firm perception (in terms of benevolence attributions or a more positively-valenced onboarding experience), but has important implications for investor behavior, such as greater recommendation acceptance and an increase in asset allocation toward conversational robo advisors. These findings have important implications for research on trust formation between humans and machines, the effective design of conversational robo advisors, and public policy in the digital economy.
Every Pixel Matters: Center-Aware Feature Alignment for Domain Adaptive Object Detector

A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds. Most existing methods adopt feature alignment either on the image level or instance level. However, image-level alignment on global features may tangle foreground/background pixels at the same time, while instance-level alignment using proposals may suffer from the background noise. Different from existing solutions, we propose a domain adaptation framework that accounts for each pixel via predicting pixel-wise objectness and centerness. Specifically, the proposed method carries out center-aware alignment by paying more attention to foreground pixels, hence achieving better adaptation across domains. We demonstrate our method on numerous adaptation settings with extensive experimental results and show favorable performance against existing state-of-the-art algorithms. Source codes and models are available at https://github.com/chengchunhsu/EveryPixelMatters .
New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM

In actual industrial production, the historical data sets used for bearing fault diagnosis are generally limited and imbalanced and consist of multiple classes. These problems present challenges in the field of bearing fault diagnosis, for which traditional fault diagnosis methods (e.g., multi-class least squares support vector machine (multi-class LS-SVM)) are not very effective. Therefore, we propose a new multi-class imbalanced fault diagnosis method based on Sample-characteristic Oversampling Technique (SCOTE) and multi-class LS-SVM, where SCOTE is a new oversampling method proposed by us. SCOTE transforms multi-class imbalanced problems into multiple binary imbalanced problems. In each binary imbalanced problem, first, SCOTE uses the k-nearest neighbours (knn) noise processing method to filter out noisy points. Second, samples are trained by LS-SVM, and minority samples are sorted by importance according to the misclassification error of the minority classes in the training sets. Moreover, based on the importance sorting of minority samples, SCOTE performs a sample synthesis method based on the k* information nearest neighbours (k*inn) to address the binary imbalanced problems. Thus, when all the binary imbalance problems are addressed, the multi-class imbalanced problem will also be addressed. The 20 fault diagnosis examples represented by Case Western Reserve University (CWRU) bearing data and Intelligent Maintenance Systems (IMS) bearing data show that the proposed method has higher fault diagnosis recognition rates and algorithm robustness than 8 oversampling algorithms and 8 multi-class imbalanced algorithms.
On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.
Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.
Cybersecurity in logistics and supply chain management: An overview and future research directions

Technological applications have increasingly improved the quality of services in the logistics industry. However, the adoption of internet-based technologies has increased the attack surface in the logistics environment. Such attacks could negatively impact both logistics and overall supply chain performance. This paper reviews studies on measures that enhance cybersecurity in logistics and supply chain management. Several key findings and potential research directions are presented in relation to research on cybersecurity in logistics and supply chain management. These findings are: (1) Existing studies rarely use real cybersecurity data; (2) Studies focusing on cybersecurity in logistics are scarce although logistics plays an important role in supply chains; (3) There is only a limited number of papers adopting quantitative research approaches to study cybersecurity in logistics and supply chain management; (4) While a few studies focus on real-time recovery and aftermath measures, most studies focus on precautionary measures; (5) Blockchain technologies are still in their infancy in the transport and logistics sector; (6) Most studies use one-way encryption schemes that overlook the potential threats in a future dominated by quantum computing techniques; (7) Studies on information security and digital forensic investigation are scarce.
Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead

Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. However, they are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy. These threats get aggravated in emerging edge ML devices that have stringent constraints in terms of resources (e.g., compute, memory, power/energy), and that therefore cannot employ costly security and reliability measures. Security, reliability, and vulnerability mitigation techniques span from network security measures to hardware protection, with an increased interest towards formal verification of trained ML models. This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities, both at the cloud (i.e., during the ML training phase) and edge (i.e., during the ML inference stage), discusses the implications of a resource-constrained design on the reliability and security of the system, identifies verification methodologies to ensure correct system behavior, and describes open research challenges for building secure and reliable ML systems at both the edge and the cloud.
Credit card fraud detection using artificial neural network

Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
A Blockchain-Based Multi-Cloud Storage Data Auditing Scheme to Locate Faults

Network storage services have benefited countless users worldwide due to the notable features of convenience, economy and high availability. Since a single service provider is not always reliable enough, more complex multi-cloud storage systems are developed for mitigating the data corruption risk. While a data auditing scheme is still needed in multi-cloud storage to help users confirm the integrity of their outsourced data. Unfortunately, most of the corresponding schemes rely on trusted institutions such as the centralized third-party auditor (TPA) and the cloud service organizer, and it is difficult to identify malicious service providers after service disputes. Therefore, we present a blockchain-based multi-cloud storage data auditing scheme to protect data integrity and accurately arbitrate service disputes. We not only introduce the blockchain to record the interactions among users, service providers, and organizers in data auditing process as evidence, but also employ the smart contract to detect service dispute, so as to enforce the untrusted organizer to honestly identify malicious service providers. We also use the blockchain network and homomorphic verifiable tags to achieve the low-cost batch verification without TPA. Theoretical analyses and experiments reveal that the scheme is effective in multi-cloud environments and the cost is acceptable.
The COVID-19 pandemic: The mitigating role of government and hotel support of hotel employees in Egypt

We investigated the effect of government support of hotels on hotels' employee support (namely, health support, staff retention, and staff training) and consequently on employee job satisfaction and organizational commitment, through the moderating role of perceived overall organizational justice and ethical climate, during the COVID-19 pandemic. Using a quantitative approach and a framework that drew on the stakeholder and organizational support theories, we collected data from 669 employees in Egyptian hotels through a web-based survey. The results support the proposed framework and show a positive effect of government support through the strengthened perception of perceived overall organizational justice. Surprisingly, findings indicated that the association between job satisfaction and organizational commitment is significantly and negatively influenced by hotel ethical climate. Furthermore, job satisfaction partially mediates the association between hotels' support of employees and organizational commitment. The study holds important implications for both theory and practice.
Hybrid MPSO-CNN: Multi-level Particle Swarm optimized hyperparameters of Convolutional Neural Network

Recent advances in swarm inspired optimization algorithms have shown its extensive acceptance in solving a wide range of different real-world problems. Particle Swarm Optimization (PSO) is one of the most explored nature-inspired population-based stochastic optimization algorithm. In this paper, a Multi-level Particle Swarm Optimization (MPSO) algorithm is proposed to find the architecture and hyperparameters of the Convolutional Neural Network (CNN) simultaneously. This automated learning will reduce the overhead of human experts to find these parameters through manual analysis and experiments. The proposed solution uses multiple swarms at two levels. The initial swarm at level-1 optimizes architecture and multiple swarms at level-2 optimize hyperparameters. The proposed method has used sigmoid like inertia weight to adjust the exploration and exploitation property of particles and avoid the PSO algorithm to prematurely converge into a local optimum solution. In this paper, we have explored an approach to suggest the best well-conditioned CNN architecture and its hyperparameters using MPSO in a specified search space. The complexity and performance of MPSO-CNN will depend on the dimension of the search space. The experimental results on 5 benchmark datasets of MNIST, CIFAR-10, CIFAR-100, Convex Sets, and MDRBI have demonstrated one more effective application of PSO in learning a deep neural architecture.
A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data

The algorithm of C4.5 decision tree has the advantages of high classification accuracy, fast calculation speed and comprehensible classification rules, so it is widely used for medical data analysis. However, for imbalanced medical data, the classification accuracy of decision trees-based models is not ideal. Therefore, this paper proposes a cluster-based oversampling algorithm (KNSMOTE) combining Synthetic minority oversampling technique (SMOTE) and k-means algorithm. The sample classes clustered by k-means and the original sample classes are calculated to select the ‘‘safe samples” whose sample classes have not been changed. The ‘‘safe samples” are linearly interpolated to synthesize the new samples. The improved SMOTE sets the oversampling ratio according to the imbalance ratio of the original samples, which is used to synthesize the samples whose number is the same as that of the original samples. Compared with other oversampling algorithms on 8 UCI datasets, our algorithm has achieved significant advantages. Our algorithm was applied to the medical datasets, and the average values of the Sensitivity and Specificity indexes of the Random forest (RF) algorithm were 99.84% and 99.56%, respectively.
A Survey of Incentive Mechanism Design for Federated Learning

Federated learning is promising in enabling large-scale machine learning by massive clients without exposing their raw data. It can not only enable the clients to preserve the privacy information, but also achieve high learning performance. Existing works of federated learning mainly focus on improving learning performance in terms of model accuracy and learning task completion time. However, in practice, clients are reluctant to participate in the learning process without receiving compensation. Therefore, how to effectively motivate the clients to actively and reliably participate in federated learning is paramount. As compared to the current incentive mechanism design in other fields, such as crowdsourcing, cloud computing, smart grid, etc., the incentive mechanism for federated learning is more challenging. First, it is hard to evaluate the training data value of each client. Second, it is difficult to model the learning performance of different federated learning algorithms. In this paper, we survey the incentive mechanism design for federated learning. In particular, we present a taxonomy of existing incentive mechanisms for federated learning, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, some future directions of how to incentivize clients in federated learning have been discussed.
Limits and security of free-space quantum communications

The study of free-space quantum communications requires tools from quantum information theory, optics and turbulence theory. Here we combine these tools to bound the ultimate rates for key and entanglement distribution through a free-space link, where the propagation of quantum systems is generally affected by diffraction, atmospheric extinction, turbulence, pointing errors, and background noise. Besides establishing ultimate limits, we also show that the composable secret-key rate achievable by a suitable (pilot-guided and post-selected) coherent-state protocol is sufficiently close to these limits, therefore showing the suitability of free-space channels for high-rate quantum key distribution. Our work provides analytical tools for assessing the composable finite-size security of coherent-state protocols in general conditions, from the standard assumption of a stable communication channel (as is typical in fiber-based connections) to the more challenging scenario of a fading channel (as is typical in free-space links).
Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration. We propose Recovery RL, an algorithm which navigates this tradeoff by (1) leveraging offline data to learn about constraint violating zones <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">before</i> policy learning and (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">separating</i> the goals of improving task performance and constraint satisfaction across two policies: a task policy that only optimizes the task reward and a recovery policy that guides the agent to safety when constraint violation is likely. We evaluate Recovery RL on 6 simulation domains, including two contact-rich manipulation tasks and an image-based navigation task, and an image-based obstacle avoidance task on a physical robot. We compare Recovery RL to 5 prior safe RL methods which jointly optimize for task performance and safety via constrained optimization or reward shaping and find that Recovery RL outperforms the next best prior method across all domains. Results suggest that Recovery RL trades off constraint violations and task successes 2–20 times more efficiently in simulation domains and 3 times more efficiently in physical experiments. See <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://tinyurl.com/rl-recovery</uri> for videos and supplementary material.
Sentiment Analysis and Topic Recognition in Video Transcriptions

Nowadays, videos are an integral modality for information sharing on the World Wide Web. However, systems able to automatically understand the content and sentiment of a video are still in their infancy. Linguistic information transported in spoken parts of a video is known to convey valuable properties in regards to context and emotions. In this article, we explore a lexical knowledge-based extraction approach to obtain such understanding from the video transcriptions of a large-scale multimodal dataset (MuSe-CAR). To this end, we use SenticNet to extract natural language concepts and fine-tune several feature types on a subset of MuSe-CAR. With these features, we explore the content of a video as well as learning to predict emotional valence, arousal, and speaker topic classes. Our best model improves the linguistic baseline from the MuSe-Topic 2020 subchallenge by almost 3% (absolute) for the prediction of valence on the predefined challenge metric and outperforms a variety of baseline systems that require much higher computational power than the one proposed herein.
Probabilistic Embeddings for Cross-Modal Retrieval

Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their captions, the multiplicity of the correspondences makes the task particularly challenging. Given an image (respectively a caption), there are multiple captions (respectively images) that equally make sense. In this paper, we argue that deterministic functions are not sufficiently powerful to capture such one-to-many correspondences. Instead, we propose to use Probabilistic Cross-Modal Embedding (PCME), where samples from the different modalities are represented as probabilistic distributions in the common embedding space. Since common benchmarks such as COCO suffer from non-exhaustive annotations for cross-modal matches, we propose to additionally evaluate retrieval on the CUB dataset, a smaller yet clean database where all possible image-caption pairs are annotated. We extensively ablate PCME and demonstrate that it not only improves the retrieval performance over its deterministic counterpart but also provides uncertainty estimates that render the embeddings more interpretable. Code is available at https://github.com/naver-ai/pcme.