Reviewerly/exHarmony huggingface dataset:
A Ciphertext-Policy Attribute-based Encryption Scheme with Public Verification for an IoT-Fog-Cloud Architecture

In large scale Internet of Things (IoT) systems, IoT-Cloud is a scalable and practical method to achieve high-efficiency data management accommodation through delegating the data storage and management tasks to the cloud service providers (CSPs). To cope with the low-computability and limited-resource of IoT devices and the response-latency of CSPs, recent works introduce an IoT-Fog-Cloud architecture. Yet, the existing attribute-based data sharing solutions with high computation requirements are no longer suitable for this new architecture. In this paper, we propose a ciphertext-policy attribute-based encryption scheme for the architecture to address the above challenges. The expensive offline encryption is delegated to the fog by constructing an intermediate ciphertext pool with the help of a Chameleon hash function. A public verification is performed to filter the illegitimate ciphertexts before executing the decryption operation. We provide a formal proof of the security and extensive performance analyses. These demonstrate that the scheme is suitable for resource-constrained IoT devices.
Toward the Bleaching of the Black Boxes: Minimalist Machine Learning

In the field of machine learning, there exist effective models that achieve remarkable results. However, it is criticized from the perspective of the end user that many successful models behave like “black boxes.” In the desire for achieving excellent performances, the models become more complicated and less explainable. In this context, a major challenge has emerged: it is necessary to provide the intelligent models and algorithms with the property to be explained. In this article, a new paradigm is proposed: the minimalist machine learning. The conceptual cornerstone of the new paradigm is the assumption that it is possible to make the representation of the data of any problem of classification of patterns reduced to the Cartesian plane. The proposal is to design algorithms that are capable of achieving intelligent pattern classification in two dimensions (the plane) effectively, regardless of the number of attributes that the patterns contain.
Digital Coworker: Human-AI Collaboration in Work Environment, on the Example of Virtual Assistants for Management Professions

Dominant opinion in the general public is that work automation will presumably hold negative societal implications, such as job loss, which often causes fear and misunderstanding. Contrarily to such an attitude, the approach we took in this paper is that people will experience rather positive effects of work automation, thanks to collaboration with artificial intelligence using virtual assistants. The quantitative experimental study was a business problem simulation. Participants were asked to perform tasks of a marketing manager in order to prepare a marketing campaign for a new product. Control group participants performed these tasks on their own, while experimental group participants did them in collaboration with a virtual chatbot-like assistant created specifically for this simulation. A total of 20 people participated in the study. A relevant difference in performance was observed between the groups, n = 20, t(18) = 5.25, p < 0.001. Participants collaborating with a virtual assistant achieved a 57% higher productivity (measured by tasks done) than those working on their own. Furthermore, in a post-study survery they assessed their productivity higher and were more satisfied with their performance. Results confirmed the hypothesis, proving that human-AI collaboration increased productivity within the studied sample.
Occluded offline handwritten Chinese character inpainting via generative adversarial network and self-attention mechanism

Occluded offline handwritten Chinese characters inpainting is a critical step for handwritten Chinese characters recognition. We propose to apply generative adversarial network and self-attention mechanism to inpaint occluded offline handwritten Chinese characters. First, cyclic loss is used to guarantee the cyclic consistency of the uncorrupted area between corrupted images and original real images instead of masks. Second, self-attention mechanism is combined with generative adversarial network to increase receptive field and explore more Chinese character features. Then an improved character-VGG-19 that is pre-trained with handwritten Chinese character dataset is used to calculate content loss to extract character features more effectively and assist generator to generate realistic characters. Finally, adversarial classification loss is used to make our discriminator classify input images instead of just distinguishing real images from fake images in order to learn the distribution of Chinese characters more effectively. The proposed method is evaluated on an occluded CASIA-HWDB1.1 dataset for three challenging inpainting tasks with different portions of blocks, or pixels randomly missing, or pixels randomly adding. Experimental results show that our method is more effective, compared with several state-of-the-art handwritten Chinese character inpainting methods.
Hiding Private Information in Images From AI

Privacy protection attracts increasing concerns these days. People tend to believe that large social platforms will comply with the agreement to protect their privacy. However, photos uploaded by people are usually not treated to achieve privacy protection. For example, Facebook, the world's largest social platform, was found leaking photos of millions of users to commercial organizations for big data analytics. A common analytical tool used by these commercial organizations is the Deep Neural Network (DNN). Today's DNN can accurately identify people's appearance, body shape, hobbies and even more sensitive personal information, such as addresses, phone numbers, emails, bank cards and so on. To enable people to enjoy sharing photos without worrying about their privacy, we propose an algorithm that allows users to selectively protect their privacy while preserving the contextual information contained in images. The results show that the proposed algorithm can select and perturb private objects to be protected among multiple optional objects so that the DNN can only identify non-private objects in images.
Benefits management and agile practices in software projects: how perceived benefits are impacted

Considerable resources are wasted on projects that deliver few or no benefits. The main objective is to better understand the characteristics of projects that are successful in delivering good client benefits. We asked 71 Norwegian software professionals to report information about projects completed between 2016 and 2018. We found that both benefits management and agile practices have a significant relationship with perceived realisation of client benefits. This includes the benefits management practices of having a plan for benefits realisation, individuals with assigned responsibility for benefits realisation, benefits management during project execution, quantification of realised benefits, evaluation of realised benefits, re-estimation of benefits during project execution, and the agile practices of a flexible scope and frequent deliveries to production. The software projects that were successful in delivering client benefits adopted benefits management and agile practices to a larger extent than the less successful ones. Future studies are required to establish more comprehensive understanding of what distinguishes projects that deliver good client benefits from the rest, including studies of the realisation of client benefits in agile software projects.
Improving the Efficiency of Blockchain Applications with Smart Contract based Cyber-insurance

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.
Performance study of affine transformation and the advanced clear-sky model to improve intra-day solar forecasts

Solar irradiance forecasting is one of the most efficient methods to handle the potential problems caused by the large and frequent photovoltaic fluctuations. For the satellite-based forecasting method, the atmospheric attenuation is paid lesser attention than other parts (notably the cloud effects). This study aims to explore the possibility of improving irradiance forecasting by using an advanced clear-sky model (i.e., the McClear model) and the running-window based affine transformation with local measurements. The McClear model notably aims at accounting for aerosol and water vapor intraday variabilities, in contrast with the European solar radiation atlas (ESRA) model based on climatological monthly means of Linke turbidity. The affine transformation with a running window of few days in the sliding past can serve as a correction procedure and has the potential to lower the impacts by inaccurate atmospheric estimation. Irradiance forecasting is carried out at lead times from 15 min to 3 h at an interval of 15 min, based on China's second-generation geostationary satellite Fengyun-4A. The measure-oriented and distribution-oriented approaches are used for a comprehensive verification. The results show that without affine transformation, the forecasting model with the McClear model outperforms that with the ESRA model, due to better estimations of atmospheric attenuation. On the other hand, affine transformation significantly improves the forecasting models. Overestimations still exist but are significantly reduced to the range of 2%–5.5%. After affine transformation, the forecasting models achieve very close performances no matter which clear-sky model is implemented, except that forecasts with the McClear model are much better calibrated at a high irradiance level (i.e., 900 W/m2).
An Encoding Mechanism for Seq2Seq based Multi-Turn Sentimental Dialogue Generation Model

For the multi-turn dialogue generation task, Seq2Seq model lacks representation of sentimental information and dialogue context in the encoder, resulting in generating sentimentally poor and contextually independent responses. To address these problems, in this paper, we propose an encoding mechanism for Seq2Seq based multi-turn sentimental dialogue generation model. To make the Seq2Seq model contain sentiment, we build sentimental word vectors and sentimental sentence vectors to strengthen the sentimental information representation. To make the generated responses more context sensitive, we build semantic vector and contextual vector. Experiments show that Seq2Seq based multi-turn sentimental dialogue generation model with our proposed methods can generate more sentimental, contextually relevant and high-quality responses.
B4SDC: A Blockchain System for Security Data Collection in MANETs

Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). Due to no fixed infrastructure of MANETs, a detection node playing as a collector should discover available routes to a collection node for data collection. Notably, route discovery suffers from many attacks (e.g., wormhole attack), thus the detection node should also collect securityrelated data during route discovery and analyze these data for determining reliable routes. However, few literatures provide incentives for security-related data collection in MANETs, and thus the detection node might not collect sufficient data, which greatly impacts the accuracy of attack detection and security measurement. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of RREQ forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization at the same time. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.
Toward Resilient Multi-Agent Actor-Critic Algorithms for Distributed Reinforcement Learning

This paper considers a distributed reinforcement learning problem in the presence of Byzantine agents. The system consists of a central coordinating authority called "master agent" and multiple computational entities called "worker agents". The master agent is assumed to be reliable, while, a small fraction of the workers can be Byzantine (malicious) adversaries. The workers are interested in cooperatively maximize a convex combination of the honest (non-malicious) worker agents' long-term returns through communication between the master agent and worker agents. A distributed actor-critic algorithm is studied which makes use of entry-wise trimmed mean. The algorithm's communication-efficiency is improved by allowing the worker agents to send only a scalar-valued variable to the master agent, instead of the entire parameter vector, at each iteration. The improved algorithm involves computing a trimmed mean over only the received scalar-valued variable. It is shown that both algorithms converge almost surely.
Deep Learning Enabled Nanophotonics

Deep learning has become a vital approach to solving a big-data-driven problem. It has found tremendous applications in computer vision and natural language processing. More recently, deep learning has been widely used in optimising the performance of nanophotonic devices, where the conventional computational approach may require much computation time and significant computation source. In this chapter, we briefly review the recent progress of deep learning in nanophotonics. We overview the applications of the deep learning approach to optimising the various nanophotonic devices. It includes multilayer structures, plasmonic/dielectric metasurfaces and plasmonic chiral metamaterials. Also, nanophotonic can directly serve as an ideal platform to mimic optical neural networks based on nonlinear optical media, which in turn help to achieve high-performance photonic chips that may not be realised based on conventional design method.
Unsupervised domain adaptation with structural attribute learning networks

Domain adaptation aims at improving the performance on an unknown target domain by transferring the knowledge learned from a related source domain. In this paper, we propose a structural attribute learning network (SAL Net) which can learn transferable domain-invariance features based on the attribute learning. Our proposed SAL Net can learn both the deep visual features which describe the appearance of objects and the semantic attribute features which are robust to the domain shift. To promote the adaptation performance, we construct a structural graph of the visual and semantic attributes by a graph convolutional network(GCN). Our structural attribute learning framework not only learns the domain-invariant attribute features but also extracts the relationship of the features. We perform a set of comparative experiments on the standard domain adaptation benchmarks. The results demonstrate that our proposed method outperforms the previous adaptation methods.
Design of Microstructure Parameters on a Small Multi-Throttle Aerostatic Guideway in Photolithography

A compact multi-throttle aerostatic guideway is the preferred structure for high precision and acceleration motion in the variable-slit system (VS) of photolithography. The presence of microstructure, such as recesses and grooves, on the guideway working surface has been found to improve the loading performance. Nevertheless, the effects on the guideway performance of changing the microstructure on the micron level are not yet clear. The mesh adaptation method, which was proposed by the authors, is employed in this paper to quantitatively study the influences of four microstructure parameters. The effect of tuning these parameters on the loading performance is revealed. The level of impact determines the proposed design process of the parameters. The characteristic feature of the proposed design process is that the working points of carrying capacity, stiffness, and rotational stiffness are unified under two-way adjusting by means of recess parameters. According to the proposed design process and tuning method, the restriction of supply pressure is lifted to a certain extent and the mutual tradeoff among the loading performances is relieved. The experimental results show that the rotational stiffness of the designed guideway, based on the tuned parameters, reached 2.14 × 104 Nm·rad−1 and increased by 69.8%. In a scanning test of the applied VS on argon fluoride laser (ArF) photolithography, the average scanning acceleration reached 67.5 m·s−2, meeting the design specification.
The pragmatics of text-emoji co-occurrences on Chinese social media

Abstract This paper explores the pragmatics of emojis co-occurring with or embedded in text on Chinese social media with this central research question: what are the patterns and the communicative functions manifested by emojis in co-occurrence with Chinese text? Building on the metafunctional approach of multimodal analysis, popular online posts from Sina Weibo which contain both emoji(s) and text have been collected and analyzed to discover the representational, interactive, and compositional features manifested by emojis co-occurring with text. We have found that these emojis on Weibo appear most frequently at the end of the posts and reflect some unique Chinese cultural and linguistic features. Based on recurring pragmatic and functional patterns of text-emoji co-occurrences, it is proposed that emojis are used to perform speech acts, highlight subjective interpretations, and enhance informality, while substituting, reinforcing, and complementing the meanings conveyed by verbal language.
Continuous Finger Gesture Spotting and Recognition Based on Similarities Between Start and End Frames

Touchless in-car devices controlled by single and continuous finger gestures can provide comfort and safety on driving while manipulating secondary devices. Recognition of finger gestures is a challenging task due to (i) similarities between gesture and non-gesture frames, and (ii) the difficulty in identifying the temporal boundaries of continuous gestures. In addition, (iii) the intraclass variability of gestures’ duration is a critical issue for recognizing finger gestures intended to control in-car devices. To address difficulties (i) and (ii), we propose a gesture spotting method where continuous gestures are segmented by detecting boundary frames and evaluating hand similarities between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">start</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end</i> boundaries of each gesture. Subsequently, we introduce a gesture recognition based on a temporal normalization of features extracted from the set of spotted frames, which overcomes difficulty (iii). This normalization enables the representation of any gesture with the same limited number of features. We ensure real-time performance by proposing an approach based on compact deep neural networks. Moreover, we demonstrate the effectiveness of our proposal with a second approach based on hand-crafted features performing in real-time, even without GPU requirements. Furthermore, we present a realistic driving setup to capture a dataset of continuous finger gestures, which includes more than 2,800 instances on untrimmed videos covering safety driving requirements. With this dataset, our both approaches can run at 53 fps and 28 fps on GPU and CPU, respectively, around 13 fps faster than previous works, while achieving better performance (at least 5% higher mean tIoU).
Multi-Task Supervised Pretraining for Neural Domain Adaptation

Two main transfer learning approaches are used in recent work in NLP to improve neural networks performance for under-resourced domains in terms of annotated data. 1) Multitask learning consists in training the task of interest with related tasks to exploit their underlying similarities.2) Mono-task pretraining, where target model's parameters are pretrained on large-scale labelled source domain and then fine-tuned on labelled data from the target domain (the domain of interest).In this paper, we propose a new approach that takes advantage of both approaches by learning a hierarchical model trained across multiple tasks from the source domain, then fine-tuned on multiple tasks from the target domain.Our experiments on four NLP tasks applied to social media texts show that our proposed method leads to significant improvements compared to both approaches.
An Edge Computing Based Fall Detection System for Elderly Persons

Image processing based fall detection has been widely studied. However, due to the different customers and rooms, the system may not work well with the prepared training data. Therefore, a fall detection system which can automatically adapt the environment is important. It is a good solution that system selects the most suitable model to detect the fall and uses the daily life images of the users to re-train the model while mis-detection occurs. This work studies how to select the most suitable model to detect the fall. Moreover, if a mis-detection occurs, the mis-detected frames are uploaded to cloud server and the model is retrained and sent to the edge node to detect the fall. Deep learning methods are employed to test the training data, and GoogLeNet gives the best performance.