Reviewerly/exHarmony huggingface dataset:
Extract the Gaze Multi-dimensional Information Analysis Driver Behavior

Recent studies has been shown that most traffic accidents are related to the driver's engagement in the driving process. Driver gaze is considered as an important cue to monitor driver distraction. While there has been marked improvement in driver gaze region estimation systems, but there are many challenges exist like cross subject test, perspectives and sensor configuration. In this paper, we propose a Convolutional Neural Networks (CNNs) based multi-model fusion gaze zone estimation systems. Our method mainly consists of two blocks, which implemented the extraction of gaze features based on RGB images and estimation of gaze based on head pose features. Based on the original input image, first general face processing model were used to detect face and localize 3D landmarks, and then extract the most relevant facial information based on it. We implement three face alignment methods to normalize the face information. For the above image-based features, using a multi-input CNN classifier can get reliable classification accuracy. In addition, we design a 2D CNN based PointNet predict the head pose representation by 3D landmarks. Finally, we evaluate our best performance model on the Eighth EmotiW Driver Gaze Prediction sub-challenge test dataset. Our model has a competitive overall accuracy of 81.5144% gaze zone estimation ability on the cross-subject test dataset.
BDoTs: Blockchain-based Evaluation Scheme for Online Teaching under COVID-19 Environment

Online teaching has become mandatory across the globe during the COVID-19 pandemic situations.Hence, there is a need to uplift the online teaching technology for data privacy preservation and transparency in the system.Various online teaching schemes have been proposed by various authors, but they lack in handling decentralised governance, transparency, trust and communication issues.Blockchain (BC) Technology has emerged to provide decentralised solution in solving the real-time problems.Motivated by these facts, in this paper, we propose a BC-based decentralised online teaching scheme known as BDoTs.The security and data privacy issues in BDoTs are resolved by developing smart contracts (SCs) over BC.Moreover, the data storage cost issues are handled by the Inter Planetary File System (IPFS) protocol for Off-Chain data storage.Moreover, we present a real-time BC simulation and deployment of SC in Truffle suite.Results show that the proposed scheme performs better in comparison to the state-of-the-art schemes in terms of scalability, data storage cost, and packet loss.
'I Can't Even Buy Apples If I Don't Use Mobile Pay?'

Despite slow adoption in the US, mobile payments are thede facto solution for hundreds of millions of users in China for everything from paying bills to riding buses, from sending virtual "Red Packets'' to buying money-market funds. In this paper, we use the theoretical lens of infrastructure to study users' interactions with ubiquitous mobile payment systems in China, focusing on Alipay and WeChat Pay, the two dominant apps on the market. Based on data from a survey (n=466) and follow-up interviews (n=12) with users in China, we describe the diverse usage patterns across physical, social, and digital ubiquity, and a series of challenges people face. Reflecting on the lessons we learned from the Chinese case -- in particular, problems and pitfalls -- we discuss some implications both for design and for policy. Our findings have important implications for other countries that have been moving towards greater adoption of mobile payments.
Unsupervised Dark-Channel Attention-Guided CycleGAN for Single-Image Dehazing

In this paper, we propose a new unsupervised attention-based cycle generative adversarial network to solve the problem of single-image dehazing. The proposed method adds an attention mechanism that can dehaze different areas on the basis of the previous generative adversarial network (GAN) dehazing method. This mechanism not only avoids the need to change the haze-free area due to the overall style migration of traditional GANs, but also pays attention to the different degrees of haze concentrations that need to be changed, while retaining the details of the original image. To more accurately and quickly label the concentrations and areas of haze, we innovatively use training-enhanced dark channels as attention maps, combining the advantages of prior algorithms and deep learning. The proposed method does not require paired datasets, and it can adequately generate high-resolution images. Experiments demonstrate that our algorithm is superior to previous algorithms in various scenarios. The proposed algorithm can effectively process very hazy images, misty images, and haze-free images, which is of great significance for dehazing in complex scenes.
Guiding Multiplayer MCTS by Focusing on Yourself

In n-player sequential move games, the second root-player move appears at tree depth n + 1. Depending on n and time, tree search techniques can struggle to expand the game tree deeply enough to find multiple-move plans of the root player, which is often more important for strategic play than considering every possible opponent move in between. The minimax-based Paranoid search and BRS+ algorithms currently achieve state-of-the-art performance, especially at short time settings, by using a generally incorrect opponent model. This simplifying model enables Alpha-Beta pruning, thus allowing the search to reach follow-up root player moves at greater search depths. This paper introduces abstraction over opponent moves to MCTS in multiplayer games, and uses its synergies with progressive widening in order to outperform these state-of-the-art minimax-type baselines. Progressive widening makes the search tree selective and deep enough to reach the root player's next moves, and abstraction over opponent moves generalizes value estimates of the root player's moves online across different opponent moves. In contrast to paranoid search approaches, opponent models do not have to be simplified. Experiments show that combining progressive widening with opponent move abstraction (MCTS-OMA-PW) leads to improved performance in the multiplayer games Chinese Checkers, Rolit, and Focus. Our work thus paves the way for improved multiplayer search by online generalisation that focuses on the root player's actions, with the potential of improving real-time MCTS applications as well as training in expert iteration and other meta-algorithms where short time settings are relevant.
Shiftry: RNN inference in 2KB of RAM

Traditionally, IoT devices send collected sensor data to an intelligent cloud where machine learning (ML) inference happens. However, this course is rapidly changing and there is a recent trend to run ML on the edge IoT devices themselves. An intelligent edge is attractive because it saves network round trip (efficiency) and keeps user data at the source (privacy). However, the IoT devices are much more resource constrained than the cloud, which makes running ML on them challenging. Specifically, consider Arduino Uno, a commonly used board, that has 2KB of RAM and 32KB of read-only Flash memory. Although recent breakthroughs in ML have created novel recurrent neural network (RNN) models that provide good accuracy with KB-sized models, deploying them on tiny devices with such hard memory requirements has remained elusive. We provide, Shiftry, an automatic compiler from high-level floating-point ML models to fixed-point C-programs with 8-bit and 16-bit integers, which have significantly lower memory requirements. For this conversion, Shiftry uses a data-driven float-to-fixed procedure and a RAM management mechanism. These techniques enable us to provide first empirical evaluation of RNNs running on tiny edge devices. On simpler ML models that prior work could handle, Shiftry-generated code has lower latency and higher accuracy.
Understanding Missing Links in Bipartite Networks With MissBiN

The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this article, we propose a visual analysis system, MissBiN, to involve analysts in the loop for making sense of link prediction results. MissBiN equips a novel method for link prediction in a bipartite network by leveraging the information of bi-cliques in the network. It also provides an interactive visualization for understanding the algorithm outputs. The design of MissBiN is based on three high-level analysis questions (what, why, and how) regarding missing links, which are distilled from the literature and expert interviews. We conducted quantitative experiments to assess the performance of the proposed link prediction algorithm, and interviewed two experts from different domains to demonstrate the effectiveness of MissBiN as a whole. We also provide a comprehensive usage scenario to illustrate the usefulness of the tool in an application of intelligence analysis.
Engaging end-user driven recommender systems: personalization through web augmentation

Abstract In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills , and, above all, its introduction is beyond the scope and control of end-users . To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills , without any involvement of website providers , to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.
Attribute-based Encryption in Cloud Computing Environment

Attribute-Based Encryption (ABE) scheme as a new cryptography primitive, shows its advantages in fine-grained access control mechanism and one-to-many flexible encryption mode.By conducting an in-depth study, we demonstrate the development trace, major work and research status of ABE.This paper mainly introduces the basic concepts of ABE, analyzes the research problems, namely key abuse, revocation, multiauthorities, and its applications on resource discovery and ehealth especially in personal health record in cloud computing environment.
Triple novelty block detection and classification approach for lung tumor analysis

Abstract Recently the increased utilization of computer‐aided detection is more helpful to assist the radiologist, recognize the minute lung lesion. In this research article, we propose three novel block detection methods for lung lesion segmentation. First, an automatic algorithm for lung lesion detection and segmentation by using histogram‐based affine‐invariant detection (HAID) is proposed. HAID is a novel segmentation technique that applies enhanced maximally stable extremal region algorithm results to segment the lung lesion. The lung lesions are segmented with an accuracy of 91.5% and false positives per scan is 5.52. To reduce the false positives per scan of lung tumor segmentation, a spatial constraint‐based new fuzzy clustering (SCNFC) approach is then proposed. The experimental results proved the efficiency of the SCNFC technique in the successful segmentation of lung tumor tissues with high accuracy and reduced the false positive per scan (95.21% and 3.69 for a database of simulated computed tomography images). Finally, firefly search‐based Macqueen's K‐means clustering algorithm (FSMKC) is proposed. The performance of the FSMKC method is evaluated on series assessments using the Lung Image Database Consortium dataset (1505 images) and it achieved the accuracy of 97.18% and 96.28% of sensitivity with only 2.27 false positives per scan.
Impacts of interactions between peer assessment and learning styles on students’ mobile learning achievements and motivations in vocational design certification courses

In this study, a peer assessment-based mobile learning approach was implemented and evaluated in a vocational design certification course. A quasi-experiment was conducted to explore the impact of the proposed approach on the learning achievements and motivations of the students with active and reflective styles. It was found that the students using the peer assessment-based mobile learning approach generally learned better than those using the conventional mobile learning approach. It was also found that there were interactive effects between learning approaches and learning styles on intrinsic motivation. The students with an active learning style outperformed those with a reflective style in intrinsic motivation when conducting the peer assessment-based mobile learning. Active learning style students also had higher extrinsic motivation than those with a reflective learning style when conducting mobile learning.
Analysis for Watermark in Medical Image using Watermarking with Wavelet Transform and DCT

An analysis for increasing the transparency of digital watermarking of medical images, using discrete Wavelet transform and DCT is presented. The analysis ensures watermarking with high resistant of the watermark, to commonly used types of malicious image processing. The results for the analysis of the transparency on the watermark image is based on the signal to noise ration and mean-square error after watermarking the image.
Engineering two-qubit mixed states with weak measurements

It is known that protocols based on weak measurements can be used to steer quantum systems into pre-designated pure states. Here we show that weak-measurement-based steering protocols can be harnessed for on-demand engineering of $\textit{mixed}$ states. In particular, through a continuous variation of the protocol parameters, one can guide a classical target state to a discorded one, and further on, towards an entangled target state.
Dynamic Representation Learning for Large-Scale Attributed Networks

Network embedding, which aims at learning low-dimensional representations of nodes in a network, has drawn much attention for various network mining tasks, ranging from link prediction to node classification. In addition to network topological information, there also exist rich attributes associated with network structure, which exerts large effects on the network formation. Hence, many efforts have been devoted to tackling attributed network embedding tasks. However, they are also limited in their assumption of static network data as they do not account for evolving network structure as well as changes in the associated attributes. Furthermore, scalability is a key factor when performing representation learning on large-scale networks with huge number of nodes and edges. In this work, we address these challenges by developing the DRLAN-Dynamic Representation Learning framework for large-scale Attributed Networks. The DRLAN model generalizes the dynamic attributed network embedding from two perspectives: First, we develop an integrative learning framework with an offline batch embedding module to preserve both the node and attribute proximities, and online network embedding model that recursively updates learned representation vectors. Second, we design a recursive pre-projection mechanism to efficiently model the attribute correlations based on the associative property of matrices. Finally, we perform extensive experiments on three real-world network datasets to show the superiority of DRLAN against state-of-the-art network embedding techniques in terms of both effectiveness and efficiency. The source code is available at: https://github.com/ZhijunLiu95/DRLAN.
Explainable Recommender Systems via Resolving Learning Representations

Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.
SI(FS)<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si8.svg"><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup></mml:math>: Fast simultaneous instance and feature selection for datasets with many features

Data reduction is becoming increasingly relevant due to the enormous amounts of data that are constantly being produced in many fields of research. Instance selection is one of the most widely used methods for this task. At the same time, most recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of variables significantly hinders classification and recognition tasks. There are efficiency issues, too, because the speed of many classification algorithms is greatly improved when the complexity of the data is reduced. Thus, feature selection is also a widely used method for data reduction and for gaining an understanding of feature information. Although most methods address instance and feature selection separately, the two problems are interwoven, and benefits are expected from performing these two tasks jointly. However, few algorithms have been proposed for simultaneously addressing the tasks of instance and feature selection. Furthermore, most of those methods are based on complex heuristics that are very difficult to scale up even to moderately large datasets. This paper proposes a new algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection using a simple heuristic search and several scaling-up mechanisms that can be successfully applied to datasets with millions of features and instances. In the proposed method, a forward selection search is performed in the feature space jointly with the application of standard instance selection in a constructive subspace built stepwise. Several simplifications are adopted in the search to obtain a scalable method. An extensive comparison using 95 large datasets shows the usefulness of our method and its ability to deal with millions of instances and features simultaneously. The method is able to obtain better classification performance results than state-of-the-art approaches while achieving considerable data reduction.
The Enslaved Dataset

Ontology alignment has taken a critical place for helping heterogeneous resources to interoperate. It has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers to find simple 1:1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. Even if the complex alignment systems are developed, the performance of finding complex relations still has a lot of room for improvement. One reason for this limitation may be that there are still few applicable alignment benchmarks that contain such complex relationships that can raise researchers' interests. In this paper, we propose a real-world dataset from the Enslaved project as a potential complex alignment benchmark. The benchmark consists of two resources, the Enslaved Ontology along with a Wikibase repository holding a large number of instance data from the Enslaved project, as well as a manually created reference alignment between them. The alignment was developed in consultation with domain experts in the digital humanities. The alignment not only includes simple 1:1 equivalence correspondences, but also more complex m:n equivalence and subsumption correspondences and are provided in both Expressive and Declarative Ontology Alignment Language (EDOAL) format and rule syntax. The Enslaved benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) 2020 and is completely free for public use to assist the researchers in developing and evaluating their complex alignment algorithms.
Fast Variational Learning in State-Space Gaussian Process Models

Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation. However, for non-Gaussian likelihoods, this requires application of approximate inference methods which can make the implementation difficult, e.g., expectation propagation can be numerically unstable and variational inference can be computationally inefficient. In this paper, we propose a new method that removes such difficulties. Building upon an existing method called conjugate-computation variational inference, our approach enables linear-time inference via Kalman recursions while avoiding numerical instabilities and convergence issues. We provide an efficient JAX implementation which exploits just-in-time compilation and allows for fast automatic differentiation through large for-loops. Overall, our approach leads to fast and stable variational inference in state-space GP models that can be scaled to time series with millions of data points.