Reviewerly/exHarmony huggingface dataset:
DisenQNet

Learning informative representations for educational questions is a fundamental problem in online learning systems, which can promote many applications, e.g., difficulty estimation. Most solutions integrate all information of one question together following a supervised manner, where the representation results are unsatisfactory sometimes due to the following issues. First, they cannot ensure the presentation ability due to the scarcity of labeled data. Then, the label-dependent representation results have poor feasibility to be transferred. Moreover, aggregating all information into the unified may introduce some noises in applications since it cannot distinguish the diverse characteristics of questions. In this paper, we aim to learn the disentangled representations of questions. We propose a novel unsupervised model, namely DisenQNet, to divide one question into two parts, i.e., a concept representation that captures its explicit concept meaning and an individual representation that preserves its personal characteristics. We achieve this goal via mutual information estimation by proposing three self-supervised estimators in a large unlabeled question corpus. Then, we propose another enhanced model, DisenQNet+, that transfers the representation knowledge from unlabeled questions to labeled questions in specific applications by maximizing the mutual information between both. Extensive experiments on real-world datasets demonstrate that DisenQNet can generate effective and meaningful disentangled representations for questions, and furthermore, DisenQNet+ can improve the performance of different applications.
A New Scheme for Detecting Malicious Attacks in Wireless Sensor Networks Based on Blockchain Technology

Wireless sensor networks (WSNs) work in various domains such as smart cities, healthcare domains, smart buildings and transportation. These networks share sensitive data across multiple sensor nodes, smart devices and transceivers. These sensitive data in WSNs environment is susceptible to various cyber-attacks and threats. Therefore, an efficient security mechanism is needed to handle threats, attacks and security challenges in WSNs. This paper proposed a new scheme using Heuristic, Signature and voting detection methods to identify the optimal countermeasures to detect the malicious and security threats using Blockchain technology. In our scheme, the cluster head node (CN) use the three detection systems with Blockchain to detect the malicious sensor nodes. Also, CN uses important parameters such as sensor node-hash value, node-signature and voting degree for malicious to detect malicious nodes in WSNs. The overall results statistic showed that 94.9% of malicious messages were detected and identified successfully during our scheme’s simulation.
Sustainable Learning Environment by Mobile-Assisted Language Learning Methods on the Improvement of Productive and Receptive Foreign Language Skills: A Comparative Study for Asian Universities

In this comparative study, we investigated different influential factors of mobile-assisted language learning (MALL) for improving productive and receptive language learning skills. To do this, 100 scientific research papers were selected from the top and high-quality journals based on the implications of MALL in higher education language learning. Eight papers were selected according to the specific criteria to categorize the results based on language skills interpretation and technological concepts. Therefore, after thoroughly understanding proposed methodologies and comparing them properly, underlying assumptions about this phenomenon are elaborated, and holistic and sustainable solutions are provided to address this idea. The results of this analysis indicated that Mobile devices are being utilized extensively in developing countries, with vocabulary being the primary language learning area assisted by technology and yielding satisfactory results. According to the findings of this comparative study, LINE and WhatsApp would be of interest among university students to improve receptively (listening and reading) and productivity (writing and speaking), respectively. Moreover, the technology acceptance model (TAM) would be a prominent option for teachers to adapt their current and future educational programs instead of only in-person teaching to improve students’ learning quality.
Smart Farming: Securing Farmers Using Block Chain Technology and IOT

Agriculture is a fundamental human activity and has a lot of significance in India. The quality and quantity of production have reduced drastically due to diseases that occur and impact of bad weather. Though farmers are producing a good quantity of crops, middle men are stealing the benefits, and this leads to a curb in financial position to start the next crop. The utilization of blockchain technology in the agriculture sector can yield significant results by creation of direct markets to farmers as the blockchain tools are capable of tracing the origin of appetite as it facilitates the creation of reliable food supply chain and constructs faith among the consumer and farmer. It is the reliable process of storing data that smooth the progress of various aspects of the information motivated tools that makes smarter agriculture and this chapter examines the main areas of smart farming, insurance to farm, agricultural transactions and food supply chains. The blockchain technology is growing as an inclusive full-fledged technology which can be utilized in almost all smart applications established within future generation aspects of the Internet, and it is a flourishing technology where each and every node that is involved within the blockchain comprises a disseminated ledger that reinforces the protection and transparency aspects of data. Prohibited users will be restricted to perform any illegitimate transaction in the blockchain network as it has the potential to perform smart contract and consensus. Due to COVID-19 Indian government has taken decision to facilitate farmers to sell their crops anywhere in India and the Real time environment IoT can be combined with the block chain to get better the performance by eliminating the middleperson.
Analytic Study of a Novel Color Image Encryption Method Based on the Chaos System and Color Codes

Due to the growing of the use of Internet and communication media, image encryption is rapidly increased. Image sharing through unsafe open channels is vulnerable for attacking and stealing. For protecting the images from attacks, encryption techniques are required. Recently, new and efficient chaos-based techniques have been suggested to develop secure image encryption. This study presents a novel image encryption framework based on integrating the chaotic maps and color codes. Three phases are involved in the proposed image encryption technique. Piecewise chaotic linear map (PWLCM) is used in the first phase for permuting the digital image. In the second phase, substitution is done using Hill cipher which is the mixing of color codes with the permuted image. The third phase is implemented by XORing, a sequence generated by the chaotic logistic map (CLM). The proposed approach enhances the diffusion ability of the image encryption making the encrypted images resistant to the statistical differential attacks. The results of several analyses such as information entropy, histogram correlation of adjacent pixels, unified average changing intensity (UACI), number of pixel change rate (NPCR), and peak signal-to-noise ratio (PSNR) guarantee the security and robustness of the proposed algorithm. The measurements show that the proposed algorithm is a noble overall solution for image encryption. Thorough comparison with other image encryption algorithms is also carried out.
A hybrid classification method for Twitter spam detection based on differential evolution and random forest

Summary Social networking services are online platforms that are distributed across different computers over long distances. Twitter is the most popular microblogging site that allows users to share their opinions and real‐world events. Due to its popularity and ease of use, Twitter has also attracted spammers. As a result, spam detection is one of the most critical problems. In order to provide a spam‐free environment, it is necessary to identify and filter spam tweets as well as their owners. A hybrid method, which is based on Synthetic Minority Over‐sampling TEchnique (SMOTE) and Differential Evolution (DE) strategies, is presented to enhance the spam detection rate in real Twitter datasets. SMOTE is applied to tackle the imbalanced class distribution of datasets, while DE is used to tune Random Forest (RF) hyperparameters. Compared with related work and based on evaluation results, the presented method significantly enhances the classification performance in imbalanced datasets. The detection rate of optimized RF with excellent F 1 ‐score and Area Under the Receiver Operating Characteristic Curve (AUROC), which are 98.97% and 0.999, respectively, demonstrates the high efficiency of the proposed method.
GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud-Whale Optimization in Cloud Computing

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.
An investigation of cryptocurrency data: the market that never sleeps

Since the creation of Bitcoin, the adequacy of data in the cryptocurrency market has not been widely analysed by scholars. Indeed, the research conducted by Alexander and Dakos (2020) is the only one that has focused on the properties and differences of several data sources, underlining inconsistencies in the time series of prices. In our paper, we contribute to this strand of the literature by examining one of the main features of digital currencies: the cryptocurrency market never sleeps. Given that cryptocurrencies trade on a 24/7 basis, specialised crypto companies offer two kinds of prices (close and weighted prices) to proxy Bitcoin daily prices. However, scholars and practitioners have not considered this issue in their analyses. We show that these prices are statistically different, which affects the financial decisions of investors and the most relevant fields in the cryptocurrency market (efficiency, risk management and volatility forecasting). Therefore, our paper demonstrates that the data processing used by specialised crypto firms is a relevant issue that changes the underlying mechanism of Bitcoin data, affecting the results of investors and scholars.
A fast technique to detect copy-move image forgery with reflection and non-affine transformation attacks

Copy-move forgery is one of the most frequently utilized image tampering technique which uses the segment of the same image to produce manipulated image by duplicating or concealing image regions. To remove suspicious traces of forgery, various attacks are applied over the tampered image which make forgery detection process too complicated. We propose a forgery detection technique in which Center Surround Extrema (CenSurE) detector is applied for keypoint detection from images. To compute keypoint descriptors, Local Image Permutation Interval Descriptor (LIPID) is used. Keypoint matching is performed using k-Nearest Neighbor (k-NN) technique with utilization of k-d tree and Best-Bin-First (BBF) search method. Grouping over keypoints is performed using Fuzzy C-Means (FCM) clustering. We apply Random Sample Consensus (RANSAC) algorithm to remove outliers obtained during forgery detection process. Experimental results show that proposed technique can effectively detect forged images containing reflection and non-affine transformation with geometrical attacks. In addition, proposed approach also shows robustness against erosion, dilation, RGB color addition, zoom motion blur, JPEG compression, spread noise addition, and multiple copy-move attacks. Proposed scheme consumes least time in forgery detection as compared to state-of-the-art methods.
Practical Face Reconstruction via Differentiable Ray Tracing

Abstract We present a differentiable ray‐tracing based novel face reconstruction approach where scene attributes – 3D geometry, reflectance (diffuse, specular and roughness), pose, camera parameters, and scene illumination – are estimated from unconstrained monocular images. The proposed method models scene illumination via a novel, parameterized virtual light stage, which in‐conjunction with differentiable ray‐tracing, introduces a coarse‐to‐fine optimization formulation for face reconstruction. Our method can not only handle unconstrained illumination and self‐shadows conditions, but also estimates diffuse and specular albedos. To estimate the face attributes consistently and with practical semantics, a two‐stage optimization strategy systematically uses a subset of parametric attributes, where subsequent attribute estimations factor those previously estimated. For example, self‐shadows estimated during the first stage, later prevent its baking into the personalized diffuse and specular albedos in the second stage. We show the efficacy of our approach in several real‐world scenarios, where face attributes can be estimated even under extreme illumination conditions. Ablation studies, analyses and comparisons against several recent state‐of‐the‐art methods show improved accuracy and versatility of our approach. With consistent face attributes reconstruction, our method leads to several style – illumination, albedo, self‐shadow – edit and transfer applications, as discussed in the paper.
Foreign Direct Investment in GCC Countries: The Essential Influence of Governance and the Adoption of IFRS

This paper presents an analysis of the factors affecting foreign direct investments, focusing on governance quality and adoption of International Financial Reporting Standards on countries of the Gulf Cooperation Council, which are a special case of study due to their idiosyncratic characteristics, rich natural resources and geographical position. Panel data analysis was conducted, implementing three different models (Fixed Effect, Random Effect, and Arellano Bond Dynamic Model). The results show that the adoption of International Financial Reporting Standards is a strong determinant that promotes foreign direct investments. As regards the governance quality, the block of Gulf Cooperation Council countries has fulfilled the minimum level of governance pre-conditions relative to foreign direct investments. In addition, governance indicators associated with law, rules, and corruption are more influential determinants for foreign direct investments.
Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population

The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data.All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013.For the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline.Using advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.
DeepIPR: Deep Neural Network Ownership Verification With Passports

With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious threats to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN inference performance of an original task will be significantly deteriorated due to forged passports. In other words, genuine passports are not only verified by looking for the predefined signatures, but also reasserted by the unyielding DNN model inference performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code is available at https://github.com/kamwoh/DeepIPR.
Long-term variations of surface solar radiation in China from routine meteorological observations

Accurate observations of long-term surface solar radiation (SSR) are crucial for hydrological processes, agricultural production, and photovoltaic power generation. The main purpose of this study is to investigate the spatial-temporal variations and driving factors of SSR over mainland China. Firstly, ten selected models for estimating SSR were evaluated at 61 China Meteorological Administration (CMA) stations with SSR measurements. Then, the Yang's hybrid model (YHM) with the best model accuracy was used to construct a daily mean SSR dataset at 2474 CMA meteorological stations in China during 1961–2014. The results indicated that the annual mean SSR showed a fluctuating downward trend (−0.16 × 10−1 MJ m−2 year−1) during 1961–2014 under all sky condition. The annual mean SSR showed dimming trend over mainland China during 1961–1989 and 1990–2005, while the annual mean SSR experienced an increasing trend (0.02 × 10−1 MJ m−2 year−1) during 2005–2014 under all sky condition. Meanwhile, the annual mean SSR showed downward trend (−0.02 × 10−1 MJ m−2 year−1) during 1980–2014 under clear sky condition, while an increasing trend (0.06 × 10−1 MJ m−2 year−1) appeared under clear sky conditions from 2005 to 2014. The Qinghai-Tibet Plateau was always the area with the relatively higher SSR values, while the Sichuan Basin was generally the area with the relatively lower SSR values. Finally, the potential relationships between SSR and climate factors were analyzed. The result showed that the variation of annual mean SSR negatively correlates with the optical thickness of all cloud (TC) and the aerosol optical depth (AOD). The SSR had a significant positive correlation with sunshine duration (SD) under all sky conditions. The aerosol loading and SD were the primary factor of variation in clear sky SSR. The research will contribute to the studies related to climate change and land surface process.
A Survey on Smart Contract Platforms and Features

Smart contracts are set of instructions which are stored on a blockchain. These instructions will be executed automatically without middle-man interaction. Smart contracts in the blockchain allows the transactions and contracts to be executed across undesignated nodes without an interaction of central system or external enforcement. The transactions are transparent, traceable and irreversible. In blockchain all the data stored are secure and immutable. So blockchain would be the suitable environment for smart contracts. Smart contract data is encrypted and placed in a distributed ledger, which means the information stored in the blocks cannot be deleted or modified. There are many blockchain platforms supporting smart contracts. This paper explores the information about various smart contract platforms and its features.
ReTRACe: Revocable and Traceable Blockchain Rewrites using Attribute-based Cryptosystems

In this paper, we study efficient and authorized rewriting of transactions already written to a blockchain. Mutable transactions will make a fraction of all blockchain transactions, but will be a necessity to meet the needs of privacy regulations, such as the General Data Protection Regulation (GDPR). The state-of-the-art rewriting approaches have several shortcomings, such as being coarse-grained, inability to expunge data, absence of revocation mechanisms, lack of user anonymity, and inefficiency. We present ReTRACe, an efficient framework for transaction-level blockchain rewrites, that is fine-grained and supports revocation. ReTRACe is designed by composing a novel revocable chameleon hash with ephemeral trapdoor scheme, a novel revocable fast attribute based encryption scheme, and a dynamic group signature scheme. We discuss ReTRACe, and its constituent primitives in detail, along with their security analyses, and present experimental results to demonstrate scalability.