Reviewerly/exHarmony huggingface dataset:
Dynamic network embedding survey

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.
SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model SwinNet for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.
Three-Qubit Randomized Benchmarking

As quantum circuits increase in size, it is critical to establish scalable multiqubit fidelity metrics. Here we investigate three-qubit randomized benchmarking (RB) with fixed-frequency transmon qubits coupled to a common bus with pairwise microwave-activated interactions (cross-resonance). We measure, for the first time, a three-qubit error per Clifford of 0.106 for all-to-all gate connectivity and 0.207 for linear gate connectivity. Furthermore, by introducing mixed dimensionality simultaneous RB --- simultaneous one- and two-qubit RB --- we show that the three-qubit errors can be predicted from the one- and two-qubit errors. However, by introducing certain coherent errors to the gates we can increase the three-qubit error to 0.302, an increase that is not predicted by a proportionate increase in the one- and two-qubit errors from simultaneous RB. This demonstrates three-qubit RB as a unique multiqubit metric.
Spatial assessment of solar energy potential at global scale. A geographical approach

Solar energy is a key renewable source for decarbonization and the future sustainable development of human society. However, the success of the worldwide governments in the large-scale implementation of solar technologies largely depends on the in-depth knowledge of global solar radiation distribution and intensity levels, which is a difficult endeavour due to the fact that up-to-date global-scale information is generally limited. This study primarily aims to analyse solar radiation distribution and intensity globally, continentally (all continents, except for Antarctica) and nationally (194 countries), based on the global horizontal irradiation (GHI) and direct normal irradiation (DNI) data, released at the best spatial resolution currently available in reliable international databases. By means of a statistical analysis of seven potential classes, delimited based on established geostatistical methods, the results showed that, globally, there are 6 major GHI hotspots (western South America, northern, eastern and southwestern Africa, the Arabian Peninsula and Australia), with annual values of >2200 kWh/m2, and 6 other well-defined DNI hotspots (southwestern North America, western South America, southwestern Africa, northwestern Arabian Peninsula, Tibetan Plateau and Australia), with values of >2500 kWh/m2. These regions, with the most intense solar radiation values, assigned to the seventh potential class (superb) of the two parameters, comprise most of the total global GHI (∼15 mil km2, 10% of the world's land area) and DNI (∼8 mil km2, 5%) superb class areas. Continentally, Africa holds the most considerable GHI solar resources (almost 10 mil km2 of the superb class, approximately one third of the continental area), while Australia holds the most abundant DNI resources (∼4 mil km2, ∼50%). Nationally, there are 12 epicentre countries for GHI, considering at least 50% superb potential threshold within national limits (9 in Africa – Namibia, 96%, Sudan, 86%, Niger, 84%, Egypt, 77%, Western Sahara, 72%, Chad, 69%, Eritrea, 58%, Libya, 56%, and Djibouti, 52%, and 3 in Asia – Oman, 92%, Yemen, 87%, and Saudi Arabia, 74%), while for DNI only 3 countries reach this percentual threshold of the maximum solar potential (Namibia, 77%, Jordan, 53%, and Australia, 51%). Our results suggest these epicentre countries (as well as others with extensive absolute GHI and DNI superb class areas, such as the US, Mexico, Chile, Peru, Bolivia, Argentina and China) are among the most favourable for the large-scale installation of photovoltaic and concentrating solar power systems, which are currently the most important technologies used in solar energy production.
On symmetric color image encryption system with permutation-diffusion simultaneous operation

In the traditional image encryption systems, the permutation-diffusion operation is often conducted in two essentially separable or independent steps, which appears not only to be inefficient but also vulnerable in being attacked since each step can be targeted separately. In this paper, we propose a new plaintext-related cryptosystem in which the permutation-diffusion operation is performed in a simultaneous manner. More specifically, the simultaneous permutation-diffusion operation (PDSO) leads to the confusion and diffusion interacted each other, and thus generates better security level as well as higher efficiency of encryption process. The proposed encryption scheme only requires a single PDSO in the corresponding encryption process, in which the first encrypted pixel’s position in cipher image is randomly chosen and its value is determined by both pixels in the original image and the elements in key-streams obtained by three one-dimensional chaotic mappings, and the current encrypting pixel’s value is set to be associated with the previous encrypted pixel. Under our framework, the proposed encryption scheme is highly sensitive to minor differences in both secret key and plain image, resulting in resisting chosen/known plaintext attacks effectively. Simulation results show that the proposed scheme demonstrates excellent security capacity with high efficiency, comparing with existing approaches in literature.
Causal Discovery with Attention-Based Convolutional Neural Networks

Having insight into the causal associations in a complex system facilitates decision making, e.g., for medical treatments, urban infrastructure improvements or financial investments. The amount of observational data grows, which enables the discovery of causal relationships between variables from observation of their behaviour in time. Existing methods for causal discovery from time series data do not yet exploit the representational power of deep learning. We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data. TCDF uses attention-based convolutional neural networks combined with a causal validation step. By interpreting the internal parameters of the convolutional networks, TCDF can also discover the time delay between a cause and the occurrence of its effect. Our framework learns temporal causal graphs, which can include confounders and instantaneous effects. Experiments on financial and neuroscientific benchmarks show state-of-the-art performance of TCDF on discovering causal relationships in continuous time series data. Furthermore, we show that TCDF can circumstantially discover the presence of hidden confounders. Our broadly applicable framework can be used to gain novel insights into the causal dependencies in a complex system, which is important for reliable predictions, knowledge discovery and data-driven decision making.
Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> overing-based quality prediction method for Web services via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</b> eighborhood-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> atrix <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</b> actorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.
3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction

Abstract Cloud cover and cloud motion have a large impact on solar irradiance. One of the effective ways for direct normal irradiance (DNI) prediction is to use cloud features, which has been extensively studied. A Convolutional Neural Network (CNN) has the advantage of automatic features extraction by using strong computing capabilities. In this paper, a novel 3D-CNN method is proposed by processing multiple consecutive ground-based cloud (GBC) images in order to extract cloud features including texture and temporal information. The resulting features and the DNI data are then used to establish a DNI forecasting model. The experiments are carried out to evaluate the performance of the proposed forecasting method by using the data from January 1, 2013 to December 31, 2014. The experimental results show that the proposed method coupled with the multilayer perceptron (MLP) model achieves forecast skill of 17.06% for 10-minute ahead DNI prediction.
Analyzing the performance of a blockchain-based personal health record implementation

The Personal Health Record (PHR) and Electronic Health Record (EHR) play a key role in more efficient access to health records by health professionals and patients. It is hard, however, to obtain a unified view of health data that is distributed across different health providers. In particular, health records are commonly scattered in multiple places and are not integrated.This article presents the implementation and evaluation of a PHR model that integrates distributed health records using blockchain technology and the openEHR interoperability standard. We thus follow OmniPHR architecture model, which describes an infrastructure that supports the implementation of a distributed and interoperable PHR.Our method involves implementing a prototype and then evaluating the integration and performance of medical records from different production databases. In addition to evaluating the unified view of records, our evaluation criteria also focused on non-functional performance requirements, such as response time, CPU usage, memory occupation, disk, and network usage.We evaluated our model implementation using the data set of more than 40 thousand adult patients anonymized from two hospital databases. We tested the distribution and reintegration of the data to compose a single view of health records. Moreover, we profiled the model by evaluating a scenario with 10 superpeers and thousands of competing sessions transacting operations on health records simultaneously, resulting in an average response time below 500 ms. The blockchain implemented in our prototype achieved 98% availability.Our performance results indicated that data distributed via a blockchain could be recovered with low average response time and high availability in the scenarios we tested. Our study also demonstrated how OmniPHR model implementation can integrate distributed data into a unified view of health records.
A Survey on Conversational Agents/Chatbots Classification and Design Techniques

A chatbot can be defined as a computer program, designed to interact with users using natural language or text in a way that the user thinks he is having dialogue with a human. Most of the chatbots utilise the algorithms of artificial intelligence (AI) in order to generate required response. Earlier chatbots merely created an illusion of intelligence by employing much simpler pattern matching and string processing design techniques for their interaction with users using rule-based and generative-based models. However, with the emergence of new technologies more intelligent systems have emerged using complex knowledge-based models. This paper aims to discuss chatbots classification, their design techniques used in earlier and modern chatbots and how the two main categories of chatbots handle conversation context.
QuatNet: Quaternion-Based Head Pose Estimation With Multiregression Loss

Head pose estimation has attracted immense research interest recently, as its inherent information significantly improves the performance of face-related applications such as face alignment and face recognition. In this paper, we conduct an in-depth study of head pose estimation and present a multiregression loss function, an L2 regression loss combined with an ordinal regression loss, to train a convolutional neural network (CNN) that is dedicated to estimating head poses from RGB images without depth information. The ordinal regression loss is utilized to address the nonstationary property observed as the facial features change with respect to different head pose angles and learn robust features. The L2 regression loss leverages these features to provide precise angle predictions for input images. To avoid the ambiguity problem in the commonly used Euler angle representation, we further formulate the head pose estimation problem in quaternions. Our quaternion-based multiregression loss method achieves state-of-the-art performance on the AFLW2000, AFLW test set, and AFW datasets and is closing the gap with methods that utilize depth information on the BIWI dataset.
Novel Systolization of Subquadratic Space Complexity Multipliers Based on Toeplitz Matrix–Vector Product Approach

Systolic finite field multiplier over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ), because of its superior features such as high throughput and regularity, is highly desirable for many demanding cryptosystems. On the other side, however, obtaining high-performance systolic multiplier with relatively low hardware cost is still a challenging task due to the fact that the systolic structure usually involves large area complexity. Based on this consideration, in this paper, we propose to carry out two novel coherent interdependent efforts. First, a new digit-serial multiplication algorithm based on polynomial basis over binary field (GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> )) is proposed. Novel Toeplitz matrix-vector product (TMVP)-based decomposition strategy is employed to derive an efficient subquadratic space complexity. Second, The proposed algorithm is then innovatively mapped into a low-complexity systolic multiplier, which involves less area-time complexities than the existing ones. A series of resource optimization techniques also has been applied on the multiplier which optimizes further the proposed design (it is the first report on digit-serial systolic multiplier based on TMVP approach covering all irreducible polynomials, to the best of our knowledge). The following complexity analysis and comparison confirm the efficiency of the proposed multiplier, that is, it has lower area-delay product (ADP) than the existing ones. The extension of the proposed multiplier for bit-parallel implementation is also considered in this paper.
An optical image compression and encryption scheme based on compressive sensing and RSA algorithm

Most of the image encryption algorithms are the private-key cryptosystem, in which the encryption process and the decryption process employ the same keys. To enhance the security of image encryption system, an optical image compression and encryption scheme based on compressive sensing and RSA public-key cryptographic algorithm is proposed, where the optical compressive imaging system is utilized to sample the original image. In the encryption process, the Walsh-Hadamard transform and a measurement matrix are adopted to simulate the optical compressive imaging system to measure the original image, which can effectively reduce the redundant information in the original image. Then the pixel positions of the resulting image are scrambled with pseudorandom sequence generated by one-dimensional (1D) cascade chaotic system. Furthermore, deoxyribonucleic acid (DNA) sequence operations are exploited to change the pixel values. The keys used in the scheme are generated based on the original image and are protected by the RSA algorithm. Experimental results demonstrate the effectiveness and reliability of the proposed optical image compression and encryption scheme with considerable compression and security performance.
HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture

Credit card transaction fraud costs billions of dollars to card issuers every year. A well-developed fraud detection system with a state-of-the-art fraud detection model is regarded as essential to reducing fraud losses. The main contribution of our work is the development of a fraud detection system that employs a deep learning architecture together with an advanced feature engineering process based on homogeneity-oriented behavior analysis (HOBA). Based on a real-life dataset from one of the largest commercial banks in China, we conduct a comparative study to assess the effectiveness of the proposed framework. The experimental results illustrate that our proposed methodology is an effective and feasible mechanism for credit card fraud detection. From a practical perspective, our proposed method can identify relatively more fraudulent transactions than the benchmark methods under an acceptable false positive rate. The managerial implication of our work is that credit card issuers can apply the proposed methodology to efficiently identify fraudulent transactions to protect customers’ interests and reduce fraud losses and regulatory costs.
Application of Blockchain in Collaborative Internet-of-Things Services

Innovating business processes involves cutting-edge technologies where the Internet of Things (IoT) and Blockchain are technological breakthroughs. IoT is envisioned as a global network infrastructure consisting of numerous connected devices over the Internet. Many attempts have been made to improve and adapt business workflows for best utilizing IoT services. One possible solution is to digitize and automate internal processes using IoT services, in which Blockchain smart contract is a viable solution to establish the trust of process executions without intermediaries. Modern business processes are composed of disparate services; many of them tend to be delivered based on IoT. Interoperating with such services poses major challenges: 1) time for finality settlement of transactions is unpredictable and usually experiencing delay; 2) several implementations of permissioned Blockchain pose a major concern of trust regarding nodes that perform consensus; and 3) trust of process executions and IoT information is the major factor to the success of modern business processes, which require the composition of distributed IoT services. Traditional business processes are mostly managed by a single entity, which induces the problem of trust of process executions. In this paper, a smart contract for establishing the trust of process executions that fits into the IoT environment is presented. A consensus approach with selected validators extended from Practical Byzantine Fault Tolerance (PBFT) is introduced to address time and prejudice challenges.
APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs

Significant progress has been made with image stylization using deep learning, especially with generative adversarial networks (GANs). However, existing methods fail to produce high quality artistic portrait drawings. Such drawings have a highly abstract style, containing a sparse set of continuous graphical elements such as lines, and so small artifacts are much more exposed than for painting styles. Moreover, artists tend to use different strategies to draw different facial features and the lines drawn are only loosely related to obvious image features. To address these challenges, we propose APDrawingGAN, a novel GAN based architecture that builds upon hierarchical generators and discriminators combining both a global network (for images as a whole) and local networks (for individual facial regions). This allows dedicated drawing strategies to be learned for different facial features. Since artists' drawings may not have lines perfectly aligned with image features, we develop a novel loss to measure similarity between generated and artists' drawings based on distance transforms, leading to improved strokes in portrait drawing. To train APDrawingGAN, we construct an artistic drawing dataset containing high-resolution portrait photos and corresponding professional artistic drawings. Extensive experiments, including a user study, show that APDrawingGAN produces significantly better artistic drawings than state-of-the-art methods.