Reviewerly/exHarmony huggingface dataset:
Solving high-dimensional global optimization problems using an improved sine cosine algorithm

The sine cosine algorithm (SCA) is a relatively novel population-based optimization technique that has been proven competitive with other algorithms and it has received significant interest from researchers in different fields. However, similar to other population-based algorithms, SCA tends to be trapped in local optima and unbalanced exploitation. Additionally, to our limited knowledge, the present SCA and its variants have not been applied to the high-dimensional global optimization problems. This paper presents an improved version of the SCA (ISCA) for solving high-dimensional problems. A modified position-updating equation by introducing inertia weight is proposed to accelerate convergence and avoid falling into the local optima. In addition, to balance the exploration and exploitation of the SCA, we present a new nonlinear conversion parameter decreasing strategy based on the Gaussian function. The effectiveness of the proposed ISCA is evaluated using 24 benchmark high-dimensional (D = 30, 100, 500, 1000, and 5000) functions, large-scale global optimization problems from the IEEE CEC2010 competition, and several real-world engineering applications. The comparisons show that the proposed ISCA can better escape from local optima with faster convergence than both the traditional SCA and other population-based algorithms.
Scientific Paper Recommendation: A Survey

Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.
Review of Machine Learning Algorithms for Diagnosing Mental Illness

Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.
Near‐Eye Display and Tracking Technologies for Virtual and Augmented Reality

Abstract Virtual and augmented reality (VR/AR) are expected to revolutionise entertainment, healthcare, communication and the manufacturing industries among many others. Near‐eye displays are an enabling vessel for VR/AR applications, which have to tackle many challenges related to ergonomics, comfort, visual quality and natural interaction. These challenges are related to the core elements of these near‐eye display hardware and tracking technologies. In this state‐of‐the‐art report, we investigate the background theory of perception and vision as well as the latest advancements in display engineering and tracking technologies. We begin our discussion by describing the basics of light and image formation. Later, we recount principles of visual perception by relating to the human visual system. We provide two structured overviews on state‐of‐the‐art near‐eye display and tracking technologies involved in such near‐eye displays. We conclude by outlining unresolved research questions to inspire the next generation of researchers.
Blockchain technology and enterprise operational capabilities: An empirical test

As a new type of disruptive internet technology, blockchain technology is widely used as a technical support for enterprises to improve production processes and reduce costs. This paper reveals that existing research has only focused on the business process modelling and technology design process of a blockchain-based solution and has neglected analysis of the relationship between blockchain technology and enterprise operational capabilities based on actual data. Hence, this paper collects 50 listed blockchain technology enterprises in China and quantitatively analyses them. The results show that the expansion of the enterprise asset scale is a significant driving factor for implementing blockchain technology. In addition, this paper proves that implementation of blockchain technology has a positive impact on improving asset turnover rate and reducing sales expense rate. Based on the results of theoretical and empirical analysis, this paper provides some constructive suggestions for constructing blockchain projects in the future.
Violence Detection Using Spatiotemporal Features with 3D Convolutional Neural Network

The worldwide utilization of surveillance cameras in smart cities has enabled researchers to analyze a gigantic volume of data to ensure automatic monitoring. An enhanced security system in smart cities, schools, hospitals, and other surveillance domains is mandatory for the detection of violent or abnormal activities to avoid any casualties which could cause social, economic, and ecological damages. Automatic detection of violence for quick actions is very significant and can efficiently assist the concerned departments. In this paper, we propose a triple-staged end-to-end deep learning violence detection framework. First, persons are detected in the surveillance video stream using a light-weight convolutional neural network (CNN) model to reduce and overcome the voluminous processing of useless frames. Second, a sequence of 16 frames with detected persons is passed to 3D CNN, where the spatiotemporal features of these sequences are extracted and fed to the Softmax classifier. Furthermore, we optimized the 3D CNN model using an open visual inference and neural networks optimization toolkit developed by Intel, which converts the trained model into intermediate representation and adjusts it for optimal execution at the end platform for the final prediction of violent activity. After detection of a violent activity, an alert is transmitted to the nearest police station or security department to take prompt preventive actions. We found that our proposed method outperforms the existing state-of-the-art methods for different benchmark datasets.
Design of personnel big data management system based on blockchain

With the continuous development of information technology, enterprises, universities and governments are constantly stepping up the construction of electronic personnel information management system. The information of hundreds of thousands or even millions of people’s information are collected and stored into the system. So much information provides the cornerstone for the development of big data, if such data is tampered with or leaked, it will cause irreparable serious damage. However, in recent years, electronic archives have exposed a series of problems such as information leakage, information tampering, and information loss, which has made the reform of personnel information management more and more urgent. The unique characteristics of the blockchain, such as non-tampering and traceability make it have great application potential in personnel information management, and can effectively solve many problems of traditional file management. However, the blockchain is limited by its own shortcomings such as small storage space and slow synchronization time, and cannot be directly applied to the big data field. This paper proposes a personnel management system based on blockchain, we analyzed the defects of the blockchain and proposed an improved method, constructs a novel data storage model of on-chain and out-of-chain that can effectively solve the problem of data redundancy and insufficient storage space. Based on this, we developed a prototype system with query, add, modify, and track personnel information, verified the feasibility of applying blockchain to personnel information management, explore the possibility of combining blockchain with big data.
Stochastic Subgradient Method Converges on Tame Functions

This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity? We prove that the stochastic subgradient method, on any semialgebraic locally Lipschitz function, produces limit points that are all first-order stationary. More generally, our result applies to any function with a Whitney stratifiable graph. In particular, this work endows the stochastic subgradient method, and its proximal extension, with rigorous convergence guarantees for a wide class of problems arising in data science—including all popular deep learning architectures.
Night-to-Day Image Translation for Retrieval-based Localization

Visual localization is a key step in many robotics pipelines, allowing the robot to (approximately) determine its position and orientation in the world. An efficient and scalable approach to visual localization is to use image retrieval techniques. These approaches identify the image most similar to a query photo in a database of geo-tagged images and approximate the query's pose via the pose of the retrieved database image. However, image retrieval across drastically different illumination conditions, e.g. day and night, is still a problem with unsatisfactory results, even in this age of powerful neural models. This is due to a lack of a suitably diverse dataset with true correspondences to perform end-to-end learning. A recent class of neural models allows for realistic translation of images among visual domains with relatively little training data and, most importantly, without ground-truth pairings.In this paper, we explore the task of accurately localizing images captured from two traversals of the same area in both day and night. We propose ToDayGAN - a modified image-translation model to alter nighttime driving images to a more useful daytime representation. We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image. Our approach improves localization performance by over 250% compared the current state-of-the-art, in the context of standard metrics in multiple categories.
Dynamic Graph Attention for Referring Expression Comprehension

Referring expression comprehension aims to locate the object instance described by a natural language referring expression in an image. This task is compositional and inherently requires visual reasoning on top of the relationships among the objects in the image. Meanwhile, the visual reasoning process is guided by the linguistic structure of the referring expression. However, existing approaches treat the objects in isolation or only explore the first-order relationships between objects without being aligned with the potential complexity of the expression. Thus it is hard for them to adapt to the grounding of complex referring expressions. In this paper, we explore the problem of referring expression comprehension from the perspective of language-driven visual reasoning, and propose a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression. In particular, we construct a graph for the image with the nodes and edges corresponding to the objects and their relationships respectively, propose a differential analyzer to predict a language-guided visual reasoning process, and perform stepwise reasoning on top of the graph to update the compound object representation at every node. Experimental results demonstrate that the proposed method can not only significantly surpass all existing state-of-the-art algorithms across three common benchmark datasets, but also generate interpretable visual evidences for stepwise locating the objects referred to in complex language descriptions.
An Efficient Group Recommendation Model With Multiattention-Based Neural Networks

Group recommendation research has recently received much attention in a recommender system community. Currently, several deep-learning-based methods are used in group recommendation to learn preferences of groups on items and predict the next ones in which groups may be interested. However, their recommendation effectiveness is disappointing. To address this challenge, this article proposes a novel model called a multiattention-based group recommendation model (MAGRM). It well utilizes multiattention-based deep neural network structures to achieve accurate group recommendation. We train its two closely related modules: vector representation for group features and preference learning for groups on items. The former is proposed to learn to accurately represent each group's deep semantic features. It integrates four aspects of subfeatures: group co-occurrence, group description, and external and internal social features. In particular, we employ multiattention networks to learn to capture internal social features for groups. The latter employs a neural attention mechanism to depict preference interactions between each group and its members and then combines group and item features to accurately learn group preferences on items. Through extensive experiments on two real-world databases, we show that MAGRM remarkably outperforms the state-of-the-art methods in solving a group recommendation problem.
A domain-specific supercomputer for training deep neural networks

Google's TPU supercomputers train deep neural networks 50x faster than general-purpose supercomputers running a high-performance computing benchmark.
Energy Consumption of Cryptocurrencies Beyond Bitcoin

Ulrich Gallersdörfer is a research associate in the Department of Informatics at the Technical University of Munich. His research focuses on identity management in blockchains. His interest extends to further aspects of the technology, ranging from environmental implications to data analytics applications. Lena Klaaßen is a graduate student at TUM School of Management at the Technical University of Munich. She is specialized in energy markets and accounting. Her research focuses on carbon accounting in the corporate and cryptocurrency space. She has previously analyzed blockchain-related firms for a venture capital fund. Christian Stoll conducts research at the Center for Energy and Environmental Policy Research at the Massachusetts Institute of Technology and at the Center for Energy Markets of the Technical University of Munich. His research focuses on the implications of climate change from an economic point of view.
Diving deeper into underwater image enhancement: A survey

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time; hence, a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, (1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and (2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before. We first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented, including architecture, parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.
Multiobjective Particle Swarm Optimization for Feature Selection With Fuzzy Cost

Feature selection (FS) is an important data processing technique in the field of machine learning. There have been various FS methods, but all assume that the cost associated with a feature is precise, which restricts their real applications. Focusing on the FS problem with fuzzy cost, a fuzzy multiobjective FS method with particle swarm optimization, called PSOMOFS, is studied in this article. The proposed method develops a fuzzy dominance relationship to compare the goodness of candidate particles and defines a fuzzy crowding distance measure to prune the elitist archive and determine the global leader of particles. Also, a tolerance coefficient is introduced into the proposed method to ensure that the Pareto-optimal solutions obtained satisfy decision makers' preferences. The developed method is used to tackle a series of the UCI datasets and is compared with three fuzzy multiobjective evolutionary methods and three typical multiobjective FS methods. Experimental results show that the proposed method can achieve feature sets with superior performances in approximation, diversity, and feature cost.
Learning Joint Spatial-Temporal Transformations for Video Inpainting

High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos frame by frame. However, these approaches can suffer from inconsistent attention results along spatial and temporal dimensions, which often leads to blurriness and temporal artifacts in videos. In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, we simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss. To show the superiority of the proposed model, we conduct both quantitative and qualitative evaluations by using standard stationary masks and more realistic moving object masks. Demo videos are available at https://github.com/researchmm/STTN.
Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be fooled by the adversarial example, which is crafted via introducing some perturbations into an original sample. Accordingly, some powerful defense techniques were proposed. However, existing defense techniques often require modifying the target model or depend on the prior knowledge of attacks. In this paper, we propose a straightforward method for detecting adversarial image examples, which can be directly deployed into unmodified off-the-shelf DNN models. We consider the perturbation to images as a kind of noise and introduce two classic image processing techniques, scalar quantization and smoothing spatial filter, to reduce its effect. The image entropy is employed as a metric to implement an adaptive noise reduction for different kinds of images. Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks. More than 20,000 adversarial examples against some state-of-the-art DNN models are used to evaluate the proposed method, which are crafted with different attack techniques. The experiments show that our detection method can achieve a high overall F1 score of 96.39% and certainly raises the bar for defense-aware attacks.
Lightweight IoT-based authentication scheme in cloud computing circumstance

Recently, authentication technologies integrated with the Internet of Things (IoT) and cloud computing have been promptly investigated for secure data retrieval and robust access control on large-scale IoT networks. However, it does not have a best practice for simultaneously deploying IoT and cloud computing with robust security. In this study, we present a novel authentication scheme for IoT-based architectures combined with cloud servers. To pursue the best efficiency, lightweight crypto-modules, such as one-way hash function and exclusive-or operation, are adopted in our authentication scheme. It not only removes the computation burden but also makes our proposed scheme suitable for resource-limited objects, such as sensors or IoT devices. Through the formal verification delivered by Proverif, the security robustness of the proposed authentication scheme is guaranteed. Furthermore, the performance evaluation presents the practicability of our proposed scheme in which a user-acceptable computation cost is achieved.