Reviewerly/exHarmony huggingface dataset:
A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification

With the increasing prevalent of digital devices and their abuse for digital content creation, forgeries of digital images and video footage are more rampant than ever. Digital forensics is challenged into seeking advanced technologies for forgery content detection and acquisition device identification. Unfortunately, existing solutions that address image tampering problems fail to identify the device that produces the images or footage while techniques that can identify the camera is incapable of locating the tampered content of its captured images. In this paper, a new perceptual data-device hash is proposed to locate maliciously tampered image regions and identify the source camera of the received image data as a non-repudiable attestation in digital forensics. The presented image may have been either tampered or gone through benign content preserving geometric transforms or image processing operations. The proposed image hash is generated by projecting the invariant image features into a physical unclonable function (PUF)-defined Bernoulli random space. The tamper-resistant random PUF response is unique for each camera and can only be generated upon triggered by a challenge, which is provided by the image acquisition timestamp. The proposed hash is evaluated on the modified CASIA database and CMOS image sensor-based PUF simulated using 180 nm TSMC technology. It achieves a high tamper detection rate of 95.42% with the regions of tampered content successfully located, a good authentication performance of above 98.5% against standard content-preserving manipulations, and 96.25% and 90.42%, respectively, for the more challenging geometric transformations of rotation (0 ~ 360°) and scaling (scale factor in each dimension: 0.5). It is demonstrated to be able to identify the source camera with 100% accuracy and is secure against attacks on PUF.
Cross-Language Clone Detection by Learning Over Abstract Syntax Trees

Clone detection across programs written in the same programming language has been studied extensively in the literature. On the contrary, the task of detecting clones across multiple programming languages has not been studied as much, and approaches based on comparison cannot be directly applied. In this paper, we present a clone detection method based on semi-supervised machine learning designed to detect clones across programming languages with similar syntax. Our method uses an unsupervised learning approach to learn token-level vector representations and an LSTM-based neural network to predict whether two code fragments are clones. To train our network, we present a cross-language code clone dataset - which is to the best of our knowledge the first of its kind - containing around 45,000 code fragments written in Java and Python. We evaluate our approach on the dataset we created and show that our method gives promising results when detecting similarities between code fragments written in Java and Python.
Survey on frontiers of language and robotics

The understanding and acquisition of a language in a real-world environment is an important task for future robotics services. Natural language processing and cognitive robotics have both been focusing on the problem for decades using machine learning. However, many problems remain unsolved despite significant progress in machine learning (such as deep learning and probabilistic generative models) during the past decade. The remaining problems have not been systematically surveyed and organized, as most of them are highly interdisciplinary challenges for language and robotics. This study conducts a survey on the frontier of the intersection of the research fields of language and robotics, ranging from logic probabilistic programming to designing a competition to evaluate language understanding systems. We focus on cognitive developmental robots that can learn a language from interaction with their environment and unsupervised learning methods that enable robots to learn a language without hand-crafted training data.
An applied quantum Hoare logic

We derive a variant of quantum Hoare logic (QHL), called applied quantum Hoare logic (aQHL for short), by: 1. restricting QHL to a special class of preconditions and postconditions, namely projections, which can significantly simplify verification of quantum programs and are much more convenient when used in debugging and testing; and 2. adding several rules for reasoning about robustness of quantum programs, i.e. error bounds of outputs. The effectiveness of aQHL is shown by its applications to verify two sophisticated quantum algorithms: HHL (Harrow-Hassidim-Lloyd) for solving systems of linear equations and qPCA (quantum Principal Component Analysis).
Exploring Visual Guidance in 360-degree Videos

360-degree videos offer a novel viewing experience with the ability to explore virtual environments much more freely than before. Technologies and aesthetics behind this approach of film-making are not yet fully developed. The newly gained freedom creates challenges and new methods have to be established to guide users through narratives. This work provides an overview of methods to guide users visually and contributes insights from an experiment exploring visual guidance in 360-degree videos with regard to task performance and user preferences. In addition, smartphone and HMD are used as output devices to examine possible differences. The results show that using viewers preferred HMD over smartphone and visual guidance over its absence. Overall, the Object to Follow method performed best, followed by the Person to Follow method. Based on the results, we defined a set of guidelines for drawing the viewers' attention in 360-degree videos.
Which Diversity Evaluation Measures Are "Good"?

This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question "Which SERP is more relevant?'' and the other based on a diversity question "Which SERP is likely to satisfy a higher number of users?'' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.
Deep reinforcement learning based parameter control in differential evolution

Adaptive Operator Selection (AOS) is an approach that controls discrete parameters of an Evolutionary Algorithm (EA) during the run. In this paper, we propose an AOS method based on Double Deep Q-Learning (DDQN), a Deep Reinforcement Learning method, to control the mutation strategies of Differential Evolution (DE). The application of DDQN to DE requires two phases. First, a neural network is trained offline by collecting data about the DE state and the benefit (reward) of applying each mutation strategy during multiple runs of DE tackling benchmark functions. We define the DE state as the combination of 99 different features and we analyze three alternative reward functions. Second, when DDQN is applied as a parameter controller within DE to a different test set of benchmark functions, DDQN uses the trained neural network to predict which mutation strategy should be applied to each parent at each generation according to the DE state. Benchmark functions for training and testing are taken from the CEC2005 benchmark with dimensions 10 and 30. We compare the results of the proposed DE-DDQN algorithm to several baseline DE algorithms using no online selection, random selection and other AOS methods, and also to the two winners of the CEC2005 competition. The results show that DE-DDQN outperforms the non-adaptive methods for all functions in the test set; while its results are comparable with the last two algorithms.
Emerging App Issue Identification from User Feedback: Experience on WeChat

It is vital for popular mobile apps with large numbers of users to release updates with rich features while keeping stable user experience. Timely and accurately locating emerging app issues can greatly help developers to maintain and update apps. User feedback (i.e., user reviews) is a crucial channel between app developers and users, delivering a stream of information about bugs and features that concern users. Methods to identify emerging issues based on user feedback have been proposed in the literature, however, their applicability in industry has not been explored. We apply the recent method IDEA to WeChat, a popular messenger app with over 1 billion monthly active users, and find that the emerging issues detected by IDEA are not stable (i.e., due to its inherent randomness, its results change when run multiple times even for the same inputs), and there are other problems such as long running time. To address these limitations, we design a novel tool, named DIVER. Different from IDEA, DIVER is more efficient (it can report real-time alerts in seconds), generates reliable results, and most importantly, achieves higher accuracy in our practice. After its deployment on WeChat, DIVER successfully detected 18 emerging issues of WeChat's Android and iOS apps in one month. Additionally, DIVER significantly outperforms IDEA by 29.4% in precision and 32.5% in recall.
Data-driven Task Allocation for Multi-task Transfer Learning on the Edge

Edge computing for machine learning has become a heated research topic. On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, one obstacle is that transfer learning imposes heavy computation burden to the resource-constrained edge devices. Motivated by the fact that only a few tasks of Multi-task Transfer Learning (MTL) have a higher potential for overall decision performance improvement, we design a novel task allocation scheme, which assigns more important tasks to more powerful edge devices to maximize the overall decision performance. In this paper, we focus on task allocation under multi-task scenarios by introducing task importance and make the following contributions. First, we reveal that it is important to measure the impact of tasks on overall decision performance improvement and quantify task importance. We also observe the long-tail property of task importance, i.e., only a few tasks are important, which facilitates more efficient task allocation. Second, we show that task allocation with task importance for MTL (TATIM) is in fact a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we innovatively propose a Data-driven Cooperative Task Allocation (DCTA) approach. Third, we evaluate the performance of our DCTA approach by applying it to a real-world industrial operation (e.g., AIOps) scenario. Experiments show that our DCTA approach can reduce 3.24 times of processing time compared with the state-of-the-art when solving TATIM. We offer our DCTA approach as an effective and practical mechanism for reducing the required resource associated with performing MTL on edge devices.
Partial reversible AMBTC-based secret image sharing with steganography

Existing secret image sharing (SIS) schemes with steganography and authentication are designed for uncompressed images. They cannot be applied to compressed domain. When the cover image is of significance, these schemes cannot revert the distorted stego image into its original form. To solve these problems, a (k,n) threshold partial reversible absolute moment block truncation coding (AMBTC) based SIS scheme with steganography and authentication is proposed. A secret image is split into n noise-like shares by employing the polynomial based SIS under GF(28). To efficiently manage the shares, they are concealed into the AMBTC cover image with parity bits by the proposed embedding algorithms, and n meaningful stego images are constructed. Authentication is adopted so that the integrity of stego image can be verified. Sufficient stego images can perfectly reconstruct the secret. Meanwhile, the original AMBTC cover image can be partially recovered with high probability. Theoretical analysis and experimental results are demonstrated, showing the effectiveness and advantages of the proposed scheme.
BlockCom: A Blockchain Based Commerce Model for Smart Communities using Auction Mechanism

Smart Communities seeks to thrive in a context of broadband economy, its engine and reason for being. The success of any community is a function of its economic backbone or the supply chain. A supply chain can be defined as the integration of customers, retailers, distributors and manufacturers. The changing technology has made the survival in commerce highly competitive and price sensitive. Blockchain technology can be the game-changer for decentralizing infrastructure and building a trust layer for business logic. BlockCom is a commerce model based on the emerging technology of blockchain. This paper presents a double auction scheme for energy trading between customers and suppliers. A smart contract implements a distributed algorithm to maximize individual participating profit. Parties bid to smart contract which act as auctioneer for maximizing the profit. Mathematical parameter named credibility score has been created to deal with trust issues in the decentralized network using byzantine fault tolerant mechanism. BlockCom provides a fresh perspective on the concept of supply chain and commerce.
Distortion-free wide-angle portraits on camera phones

Photographers take wide-angle shots to enjoy expanding views, group portraits that never miss anyone, or composite subjects with spectacular scenery background. In spite of the rapid proliferation of wide-angle cameras on mobile phones, a wider field-of-view (FOV) introduces a stronger perspective distortion. Most notably, faces are stretched, squished, and skewed, to look vastly different from real-life. Correcting such distortions requires professional editing skills, as trivial manipulations can introduce other kinds of distortions. This paper introduces a new algorithm to undistort faces without affecting other parts of the photo. Given a portrait as an input, we formulate an optimization problem to create a content-aware warping mesh which locally adapts to the stereographic projection on facial regions, and seamlessly evolves to the perspective projection over the background. Our new energy function performs effectively and reliably for a large group of subjects in the photo. The proposed algorithm is fully automatic and operates at an interactive rate on the mobile platform. We demonstrate promising results on a wide range of FOVs from 70° to 120°.
Adaptive Traffic Signal Control Mechanism for Intelligent Transportation Based on a Consortium Blockchain

The development of vehicular ad-hoc networks (VANETs) has facilitated adaptive traffic signal control for intelligent transportation.In this paper, we proposed the traffic signal control mechanism based on a consortium blockchain, which has saved plenty of financial and material resources.It has solved the centralization problems and minimized the high degree of human intervention in the process of traffic signal light management.As a road is congested, the vehicle forwards road condition messages.The traffic department (TD) adjusts the signal light duration to allow the synergistic optimization management, and control the traffic vehicle status through a smart contract.In addition, we propose a credibility mechanism to effectively prevent vehicles from broadcasting mendacious messages and malicious requests, thereby enhancing the credibility of vehicles and providing a secure and trustworthy communication environment for the VANETs.It is hazardous for vehicles to send plaintext messages in an open environment because their privacy and security are threatened.Thus, we utilize ElGamal encryption and group signature algorithm to guarantee the confidentiality, privacy, and non-repudiation of any information.The safety analysis and performance evaluation demonstrate that the scheme is feasible and valid, and it can facilitate the adaptive control of traffic signal lights.
Multi-Perspectives of Cloud Computing Service Adoption Quality and Risks in Higher Education

Universities worldwide are starting to turn to cloud computing. The quality characteristics, which include access to a wider network of computing resources, pay-as-you-go services, self-services, agile services, and resource centralisation provide a convincing argument for HEIs to adopt cloud services. However, the risks leading to non-adoption range from security issues to a lack of cloud vendor support. The findings suggest that security, privacy, and trust are the key determinants to non-adoption as stakeholders felt that the cloud cannot fully guarantee the safeguarding of sensitive information. Key determinants to cloud adoption include improving relationships between students and teachers via collaborative tools and proposing cloud apps for mobile devices for accessing virtual learning materials and email securely off-campus. In conclusion, university stakeholders are still unconvinced about adopting cloud services, but future advances of the cloud may help to steer their decision to adopt this innovative technology given its overwhelming potential.
A novel GSP auction mechanism for ranking Bitcoin transactions in blockchain mining

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions and mine blocks in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by the Generalized Second Price (GSP) mechanism dominantly used in online ad auctions, we propose to adopt the GSP auction model in the Bitcoin transaction confirmation game. Also, we use weighted fees as the new ranking basis, which can be calculated by user-submitted fees, transaction size, quality scores and virtual fees accumulated from the waiting time. We show that the formulated static GSP transaction fee auction with complete information has a unique Pure Strategy Nash Equilibrium. Then, we discuss the impacts of quality scores and virtual fees on users' equilibrium fee decisions and payoffs. Finally, computational experiments are designed to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism, and can help users save fees. Besides, quality scores and virtual fees are also proven to be effective on reducing users' paid fees. Moreover, the design of virtual fees allows all transactions to be processed more efficiently in a uniform pipeline, and the interests of transactions with and without associated fees are taken into consideration.
Deep Learning for Electronic Health Records Analytics

Recent technological advancements have led to a deluge of medical data from various domains. However, the recorded data from divergent sources comes poorly annotated, noisy, and unstructured. Hence, the data is not fully leveraged to establish actionable insights that can be used in clinical applications. These data recorded in hospital's Electronic Health Records (EHR) consists of patient information, clinical notes, charted events, medications, procedures, laboratory test results, diagnosis codes, and so on. Traditional machine learning and statistical methods have failed to offer insights that can be used by physicians to treat patients as they need to obtain an expert opinion assisted features before building a benchmark task model. With the rise of deep learning methods, there is a need to understand how deep learning can save lives. The purpose of this study was to offer an intuitive explanation for possible use cases of deep learning with EHR. We reflect on techniques that can be applied by health informatics professionals by giving technical intuitions and blue prints on how each clinical task can be approached by a deep learning algorithm.
Hyperspectral Mixed Gaussian and Sparse Noise Reduction

Hyperspectral images (HSIs) are often degraded by different noise types such as Gaussian and sparse noise. In this letter, a hyperspectral mixed Gaussian and sparse noise reduction technique, the HyMiNoR, is proposed. The proposed technique, hierarchically, removes the mixed noise. First, the Gaussian noise is removed using a recently developed automatic hyperspectral noise removal technique called hyperspectral restoration (HyRes). Then, we develop a novel sparse noise removal technique to remove the sparse noise, including salt and pepper noise, missing pixels, and missing lines. The performance of the proposed approach has been validated using both real and simulated data sets. Results on the simulated data set confirm considerable improvements in terms of signal-to-noise ratio and singular angle distance compared to the state-of-the-art techniques used in the experiments. In addition, visual improvements can be clearly observed in the case of real data set experiments.
Large group emergency decision-making method with linguistic risk appetites based on criteria mining

In linguistic large group emergency decision-making (LGEDM), the linguistic information of large numbers of decision makers (DMs) needs to be fused to reach a collective decision. During the decision process, individual linguistic risk appetites and the provision of incomplete information under time pressure need to be considered. In this paper, we define the generalized asymmetric linguistic D number (GALDN) and propose a corresponding fusion method to effectively express and fuse the incomplete linguistic information of a large group of DMs. In this novel method, a GALDN-extended linguistic preference relation that satisfies additive consistency is designed. Moreover, the public opinions related to emergency events on social media are taken into consideration in the LGEDM. The themes of public concern are regarded as a fundamental basis for criteria selection. Finally, a case study about a fire and explosion accident is employed to justify the validity of the proposed method.