Reviewerly/exHarmony huggingface dataset:
Toward an Automated Auction Framework for Wireless Federated Learning Services Market

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.
Human-like communication in conversational agents: a literature review and research agenda

Purpose Conversational agents (chatbots, avatars and robots) are increasingly substituting human employees in service encounters. Their presence offers many potential benefits, but customers are reluctant to engage with them. A possible explanation is that conversational agents do not make optimal use of communicative behaviors that enhance relational outcomes. The purpose of this paper is to identify which human-like communicative behaviors used by conversational agents have positive effects on relational outcomes and which additional behaviors could be investigated in future research. Design/methodology/approach This paper presents a systematic review of 61 articles that investigated the effects of communicative behaviors used by conversational agents on relational outcomes. A taxonomy is created of all behaviors investigated in these studies, and a research agenda is constructed on the basis of an analysis of their effects and a comparison with the literature on human-to-human service encounters. Findings The communicative behaviors can be classified along two dimensions: modality (verbal, nonverbal, appearance) and footing (similarity, responsiveness). Regarding the research agenda, it is noteworthy that some categories of behaviors show mixed results and some behaviors that are effective in human-to-human interactions have not yet been investigated in conversational agents. Practical implications By identifying potentially effective communicative behaviors in conversational agents, this study assists managers in optimizing encounters between conversational agents and customers. Originality/value This is the first study that develops a taxonomy of communicative behaviors in conversational agents and uses it to identify avenues for future research.
A Scalable Protocol for Driving Trust Management in Internet of Vehicles With Blockchain

Recent developments in IoT have facilitated advancements in the Internet of Vehicles (IoV) with autonomous vehicles and roadside infrastructure as its key components. IoV aims to provide new innovative services for different modes of transport with adaptive traffic management and enables vehicles to broadcast messages to improve traffic safety and efficiency. However, due to nontrusted environments, it is difficult for vehicles to evaluate the credibility of the messages that they receive. Therefore, trust establishment in IoV is a key security concern that is constantly limited by scalability challenges. This article proposes a blockchain-based protocol for IoV using smart contracts, physical unclonable functions (PUFs), certificates, and a dynamic Proof-of-Work (dPoW) consensus algorithm. The blockchain, in conjunction with contracts, provides a secure framework for registering trusted vehicles and blocking malicious ones. PUFs are used to assign a unique identity to each vehicle via which trust is established. Certificates are issued by roadside units that preserve the privacy of vehicles, whereas the dPoW consensus allows the protocol to scale according to the incoming traffic generated by the vehicles. To demonstrate the feasibility and scalability of the proposed protocol, security and performance analyses are presented. A case study is also discussed along with a comparative analysis, which confirms that our protocol can provide superior decentralized trust management for IoV.
PPO-CPQ: A Privacy-Preserving Optimization of Clinical Pathway Query for E-Healthcare Systems

With the help of patients' health data, e-healthcare providers can offer reliable data services for better medical treatment. For example, the clinical pathway provides optimal detailed guidance for the clinical treatment. However, the e-healthcare providers are incompetent with the huge volumes of e-healthcare data, and a popular and feasible solution is used to outsource the medical data to powerful cloud servers (CSs). Because the medical data are very sensitive yet the outsourced servers are not fully trusted, the straightforward execution of clinical pathway query service will inevitably bring huge privacy risks to patients' data. Apart from the privacy issues, the efficiency issues also need to be taken into consideration, such as the communication overhead and computational cost between servers and providers. Considering the above issues, this article proposes a privacy-preserving optimization of clinical pathway query scheme (PPO-CPQ) to achieve the secure clinical pathway query under e-healthcare CSs without revealing neither the private information of patients, such as name, gender, age, and physical index, nor the sensitive information of hospitals, such as treatment, medication, and expense. In our proposed scheme, it first designs secure and privacy-preserving several subprotocols, such as privacy-preserving comparison, privacy-preserving clinical comparison, privacy-preserving stage selection, and privacy-preserving stage update protocol, to ensure privacy in the e-healthcare system, then it adopts the greedy algorithm in a secure manner to perform the query and the min-heap technology to improve efficiency. The experimental result shows that our scheme is practical and efficient in terms of computational cost and communication overhead.
A New Chaotic Map With Dynamic Analysis and Encryption Application in Internet of Health Things

In this paper, we report an effective cryptosystem aimed at securing the transmission of medical images in an Internet of Healthcare Things (IoHT) environment. This contribution investigates the dynamics of a 2-D trigonometric map designed using some well-known maps: Logistic-sine-cosine maps. Stability analysis reveals that the map has an infinite number of solutions. Lyapunov exponent, bifurcation diagram, and phase portrait are used to demonstrate the complex dynamic of the map. The sequences of the map are utilized to construct a robust cryptosystem. First, three sets of key streams are generated from the newly designed trigonometric map and are used jointly with the image components (R, G, B) for hamming distance calculation. The output distance-vector, corresponding to each component, is then Bit-XORed with each of the key streams. The output is saved for further processing. The decomposed components are again Bit-XORed with key streams to produce an output, which is then fed into the conditional shift algorithm. The Mandelbrot Set is used as the input to the conditional shift algorithm so that the algorithm efficiently applies confusion operation (complete shuffling of pixels). The resultant shuffled vectors are then Bit-XORed (Diffusion) with the saved outputs from the early stage, and eventually, the image vectors are combined to produce the encrypted image. Performance analyses of the proposed cryptosystem indicate high security and can be effectively incorporated in an IoHT framework for secure medical image transmission.
Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks

The primary neural networks’ decision-making units are activation functions. Moreover, they evaluate the output of networks neural node; thus, they are essential for the performance of the whole network. Hence, it is critical to choose the most appropriate activation function in neural networks calculation. Acharya et al. (2018) suggest that numerous recipes have been formulated over the years, though some of them are considered deprecated these days since they are unable to operate properly under some conditions. These functions have a variety of characteristics, which are deemed essential to successfully learning. Their monotonicity, individual derivatives, and finite of their range are some of these characteristics. This research paper will evaluate the commonly used additive functions, such as swish, ReLU, Sigmoid, and so forth. This will be followed by their properties, own cons and pros, and particular formula application recommendations.
"Phantom Friend" or "Just a Box with Information"

As voice-based conversational agents such as Amazon Alexa and Google Assistant move into our homes, researchers have studied the corresponding privacy implications, embeddedness in these complex social environments, and use by specific user groups. Yet it is unknown how users categorize these devices: are they thought of as just another object, like a toaster? As a social companion? Though past work hints to human-like attributes that are ported onto these devices, the anthropomorphization of voice assistants has not been studied in depth. Through a study deploying Amazon Echo Dot Devices in the homes of older adults, we provide a preliminary assessment of how individuals 1) perceive having social interactions with the voice agent, and 2) ontologically categorize the voice assistants. Our discussion contributes to an understanding of how well-developed theories of anthropomorphism apply to voice assistants, such as how the socioemotional context of the user (e.g., loneliness) drives increased anthropomorphism. We conclude with recommendations for designing voice assistants with the ontological category in mind, as well as implications for the design of technologies for social companionship for older adults.
The relation between in-service teachers' digital competence and personal and contextual factors: What matters most?

The objective of this article is twofold: (i) to provide a valid and reliable instrument to measure teachers' digital competence on the basis of the European Framework for the Digital Competence of Educators (also referred to as DigCompEdu) and (ii) to examine the relation between in-service teachers' digital competence and personal and contextual factors. For this purpose, a study was conducted with 1071 in-service teachers. The instrument was validated with respect to its factorial structure, and the relation between teachers' digital competence and personal and contextual factors was analysed using three different analyses: (i) simple linear regression, (ii) multiple linear regression, and (iii) machine learning. The results show that all the analyses conducted confirm the prevalence of personal factors over contextual ones, as well as their stronger predictive capacity. Gender and age differences were found, but the number of tools used for teaching and learning was the strongest predictor of teachers' digital competence, followed by ease of use, confidence in using digital technology, and openness to new technology. The article shows the soundness of the DigCompEdu framework and provides knowledge that could benefit teacher training programmes and inform policy and practice.
The impact of chatbot conversational skill on engagement and perceived humanness

Conversational agents (CAs)—frequently operationalized as chatbots—are computer systems that leverage natural language processing to engage in conversations with human users. CAs are often operationalized as chatbots which are used for many applications including technical support, customer service, and digital personal assistants. Despite their widespread use, little research to date has investigated how improving the conversational skill of a CA impacts user perceptions of the agent. To elucidate this relationship, this research uses Social Presence Theory to describe how conversational skill influences perceived social presence and ultimately anthropomorphism of a chatbot. We conducted a series of studies in which 450 participants interacted with CAs exhibiting varying levels of conversational skill. We show that people perceive a more skilled CA to be more socially present and anthropomorphic than a less skilled CA. This research advances the knowledge of computer-human interface in information systems, as CA research to date has largely focused on the technical challenges rather than the behavioral questions of how users interact with CAs.
Learning Spatial Attention for Face Super-Resolution

General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark prediction. However, multi-task learning requires extra manually labeled data. Besides, most of the existing works can only generate relatively low resolution face images (e.g., 128 × 128), and their applications are therefore limited. In this paper, we introduce a novel SPatial Attention Residual Network (SPARNet) built on our newly proposed Face Attention Units (FAUs) for face super-resolution. Specifically, we introduce a spatial attention mechanism to the vanilla residual blocks. This enables the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions. This makes the training more effective and efficient as the key face structures only account for a very small portion of the face image. Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e.g., 16×16). Quantitative comparisons on various kinds of metrics (including PSNR, SSIM, identity similarity, and landmark detection) demonstrate the superiority of our method over current state-of-the-arts. We further extend SPARNet with multi-scale discriminators, named as SPARNetHD, to produce high resolution results (i.e., 512×512). We show that SPARNetHD trained with synthetic data can not only produce high quality and high resolution outputs for synthetically degraded face images, but also show good generalization ability to real world low quality face images. Codes are available at https://github.com/chaofengc/Face-SPARNet.
Cryptocurrency-portfolios in a mean-variance framework

We apply the Markowitz mean-variance framework in order to assess risk-return benefits of cryptocurrency-portfolios. Using daily data of the 500 most capitalized cryptocurrencies for the time span 1/1/2015 to 12/31/2017, we relate risk and return of different mean-variance portfolio strategies to single cryptocurrency investments and two benchmarks, the naively diversified portfolio and the CRIX. In an out-of-sample analysis accounting for transaction cost we find that combining cryptocurrencies enriches the set of ‘low’-risk cryptocurrency investment opportunities. In terms of the Sharpe ratio and certainty equivalent returns, the 1/N-portfolio outperforms single cryptocurrencies and more than 75% of mean-variance optimal portfolios.
A Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Cloud computing has become a widely exploited research area in academia and industry.Cloud computing benefits both cloud services providers (CSPs) and consumers.The security challenges associated with cloud computing have been widely studied in the literature.This systematic literature review (SLR) is aimed to review the existing research studies on cloud computing security, threats, and challenges.This SLR examined the research studies published between 2010 and 2020 within the popular digital libraries.We selected 80 papers after a meticulous screening of published works to answer the proposed research questions.The outcomes of this SLR reported seven major security threats to cloud computing services.The results showed that data tampering and leakage were among the highly discussed topics in the chosen literature.Other identified security risks were associated with the data intrusion and data storage in the cloud computing environment.This SLR's results also indicated that consumers' data outsourcing remains a challenge for both CSPs and cloud users.Our survey paper identified the blockchain as a partnering technology to alleviate security concerns.The SLR findings reveal some suggestions to be carried out in future works to bring data confidentiality, data integrity, and availability.
Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

Global electricity consumption has raised in the last century due to many reasons such as the increase in human population and technological development. To keep up with this increasing trend, the use of fossil resources has increased. But these resources are not environmentally friendly, and for this reason, many countries and governments are encouraging the use of green sources. Among these sources, PV technology is widely promoted and used due to its improved efficiency and lower prices for photovoltaic panels. Therefore, the importance of forecasting power production for these plants is necessary. In this work, two hybrid models were proposed (CNN-LSTM and ConvLSTM) to effectively predict the power production of a self-consumption PV plant. To confirm the efficiency of the proposed models, the LSTM model was used as a baseline for comparison. The three models were trained on two datasets, a univariate dataset containing only the power output of the previous days, while the multivariate dataset contains more features (weather features) that affect the production of the PV plant. The time frames for the forecast ranged from one day to one week ahead of time. The results show that the proposed methods are more accurate than a normal LSTM model.
Source-Free Domain Adaptation for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network (CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets to reduce the gap between the source and target domains during model adaptation, which are impractical in the real scenarios where the source datasets are private, and thus cannot be released along with the well-trained source models. To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation. SFDA not only enables to recover and preserve the source domain knowledge from the source model via knowledge transfer during model adaptation, but also distills valuable information from the target domain for self-supervised learning. The pixel-and patch-level optimization objectives tailored for semantic segmentation are seamlessly integrated in the framework. The extensive experimental results on numerous benchmark datasets highlight the effectiveness of our framework against the existing UDA approaches relying on source data.
PD-GAN: Probabilistic Diverse GAN for Image Inpainting

We propose PD-GAN, a probabilistic diverse GAN for image inpainting. Given an input image with arbitrary hole regions, PD-GAN produces multiple inpainting results with diverse and visually realistic content. Our PD-GAN is built upon a vanilla GAN which generates images based on random noise. During image generation, we modulate deep features of input random noise from coarse-to-fine by injecting an initially restored image and the hole regions in multiple scales. We argue that during hole filling, the pixels near the hole boundary should be more deterministic (i.e., with higher probability trusting the context and initially restored image to create natural inpainting boundary), while those pixels lie in the center of the hole should enjoy more degrees of freedom (i.e., more likely to depend on the random noise for enhancing diversity). To this end, we propose spatially probabilistic diversity normalization (SPDNorm) inside the modulation to model the probability of generating a pixel conditioned on the context information. SPDNorm dynamically balances the realism and diversity inside the hole region, making the generated content more diverse towards the hole center and resemble neighboring image content more towards the hole boundary. Meanwhile, we propose a perceptual diversity loss to further empower PD-GAN for diverse content generation. Experiments on benchmark datasets including CelebA-HQ, Places2 and Paris Street View indicate that PD-GAN is effective for diverse and visually realistic image restoration.
An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions

Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors' operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.