Reviewerly/exHarmony huggingface dataset:
Using AR and Unity 3D to Support Geographical Phenomena Simulations

An AR and Unity-based simulation system for geographic phenomena is constructed to address the problem that students have difficulty to understand complex and abstract geographic phenomena due to the traditional didactic teaching method used in geography classroom teaching currently. A three-dimensional electronic sandbox is developed in this research, which can simulate realistic terrain and displays contour lines according to the shape of the sand in the sandbox. It also recreates geographical phenomena in nature such as rainfall, sea changes, floods receding, volcanic eruptions, etc. realistically.
Revisiting Secure Computation Using Functional Encryption: Opportunities and Research Directions

Increasing incidents of security compromises and privacy leakage have raised serious privacy concerns related to cyberspace. Such privacy concerns have been instrumental in the creation of several regulations and acts to restrict the availability and use of privacy-sensitive data. The secure computation problem, initially and formally introduced as secure two-party computation by Andrew Yao in 1986, has been the focus of intense research in academia because of its fundamental role in building many of the existing privacy-preserving approaches. Most of the existing secure computation solutions rely on garbled-circuits and homomorphic encryption techniques to tackle secure computation issues, including efficiency and security guarantees. However, it is still challenging to adopt these secure computation approaches in emerging compute-intensive and data-intensive applications such as emerging machine learning solutions. Recently proposed functional encryption scheme has shown its promise as an underlying secure computation foundation in recent privacy-preserving machine learning approaches proposed. This paper revisits the secure computation problem using emerging and promising functional encryption techniques and presents a comprehensive study. We first briefly summarize existing conventional secure computation approaches built on garbled-circuits, oblivious transfer, and homomorphic encryption techniques. Then, we elaborate on the unique characteristics and challenges of emerging functional encryption based secure computation approaches and outline several research directions.
The Abra sedimentary-hosted Pb-Ag-Cu-Au deposit, Western Australia: A geophysical case study

Abra is a high-grade sedimentary-hosted Pb deposit located in the Paleoproterozoic Edmund Basin in Western Australia. Mineralization is blind, with the top of the deposit occurring 250 m beneath the land surface. The deposit consists of a stratiform apron of Pb-Ag-Ba mineralization in laminated iron-oxide- and barite-altered dolomite and siltstone, which overlies a feeder zone of chlorite-altered, brecciated, and veined carbonatic siltstone that contains Pb-Ag mineralization in the core that transitions to Pb-Cu and Cu-Au at depth. Abra is characterized by discrete geophysical anomaly responses in magnetic, gravity, time-domain electromagnetic (TDEM), and induced polarization survey data. A +450 nT magnetic anomaly is caused by magnetite in the lower stratiform zone. Dense galena, barite, dolomite, and iron-oxide mineralization in the apron and galena in the feeder zone is surrounded by lower-density sedimentary host rocks, which results in a +1 mGal gravity anomaly. Airborne, ground, and downhole TDEM surveying resolved known mineralization as weak electromagnetic conductor responses, and petrophysical testing on core samples shows that this is caused by galena. Pole-dipole-induced polarization surveying resolved a +20 ms chargeability anomaly on the southern flank of the deposit. This chargeable anomaly response is related to disseminated galena, pyrite, chalcopyrite, and alteration. Joint audiomagnetotelluric-magnetotelluric 2D inverted data sections resolved Abra as a broad weakly conductive anomaly. Weak conductor responses associated with Abra were also resolved in 2D and 3D inversion modeling of airborne Z-axis tipper electromagnetic data. 2D seismic reflection surveying resolved Abra as strong flat-lying seismic reflectors, which are bounded and offset by faults and surrounded by a seismically bland zone. The seismic reflections are related to significant density contrasts between high-density stratiform mineralization that is in contact with low-density sedimentary host rocks, as the mineralization and host rocks have similar seismic velocities. Passive seismic horizontal to vertical spectral ratio surveying resolved the top of the deposit as a subtle layer sitting below a flat impedance contrast horizon that is interpreted as weathered siltstone on top of diagenetically cemented siltstone.
SQLIA Detection and Prevention Techniques

Structure Query Language Injection (SQLI) is one of the top most threat to web-based applications (Like e-commerce, banking, shopping, trading, blogs, etc.) which are connected to the database. The attacker has the ability to get full access and the full control of the database or the application and that drives to removing, modifying and changing significant data. This can be performed by the attacker when a sequence of malicious SQL statements are injected by the attacker into a query through an input that is not validated. Finding the proper solution to stop or mitigate the SQL injection is necessary due to the importance of security of web applications. Many researchers have studied SQLIA detection and prevention extensively and have proposed various methods. However, these techniques are not enough because usually, they have limitations cannot stop all type of attacks. This paper presents background study about classical types of SQLIA, detection and prevention techniques as well as evaluation of these approaches against those types of attacks.
Trajectory Based Human Action Recognition using Centre Symmetric Local Binary Pattern Descriptors

This paper proposes trajectory-based human action recognition using Center Symmetric-Local Binary Pattern (CSLBP) (structural descriptor). Histogram of Optical Flow (HOF) and Motion Boundary Histogram (MBH), computed along the trajectory of the action, have been taken into account as motion descriptors. Additionally, the trajectory shape descriptor is extracted. Harris corner is used to extract the key points. You Only Look Once (YOLO) has been employed to localize the human, for the KTH dataset, reducing the number of key points. Finally, the human actions are classified using K-means and Support Vector Machine (SVM) algorithms. The evaluation is performed on KTH and YouTube Action datasets, obtaining an accuracy of 91.6 percent and 90.4 percent, respectively.
Towards In-Band Non-Cryptographic Authentication

Robust, secure authentication is essential in online interactions. Current best practice is to combine factors communicated using different channels; however, in some contexts multi-factor authentication may not be feasible or appropriate. Thus there is a need for authentication strategies that do not rely on classic multiple factors. While people normally rely upon multiple factors to authenticate each other, there is anecdotal evidence that such factors are not needed to authenticate close relationships, and that in fact they can recognize each other over an extremely low-bandwidth channel: texting.
Touring System using Augmented Reality - A Case Study of Yilan Cultural Industries

As Taiwan is developing tourism industry in recent years, it has become an indispensable part of people's daily lives. However, current tourist guide system lacks an integrated solution and travelers have difficulties to obtain the information on attractions due to outdated data. Moreover, available on-line tourist guide apps with google map navigation lack technical breakthrough and innovation. In this research, augmented reality (AR) tourist guide system is developed to construct a backstage database system for automatically updating attractions data. A new framework from Android called “Actionbar Scrollable Tabs” for UI design is used with mobile web technology. The main purpose is to let users gain overall knowledge and the latest information with tour guide system, furthermore, to get rid of tourist guide problems.
Variable Universe Type-II Fuzzy Logic Control Design for the GOOGOL’s Two-Wheeled Self-Balancing Robot

In the paper, Googol's two-wheeled self-balancing robot is used in the controller design, which is introduced based on linear parameter varying technique and variable universe type-II fuzzy method. In the control process, the linear matrix inequality is used to solve the variable universe error and error derivative gain instead of the common linear quadratic regulator technique. In the case of disturbance, the position and yaw are controlled separately, the interval type-II fuzzy logic controller with linear matrix inequality will not stable, and the robot is out of balance when the disturbance increases to a certain degree. The variable universe interval type-II fuzzy logic controller are used to alleviate this problem, two interval type-II fuzzy logic controller are presented to the whole control scheme, and the self-balancing robot could reach the desired position asymptotically, and the body upright balance purpose are kept. It is found that the variable universe interval type-II fuzzy logic controller has better anti-disturbance ability and faster response speed due to the usage of type-II fuzzy sets and linear matrix inequalities.
Blockchain: Elements of Physical Architecture, Empowering Features and Applications in the Indian Power Sector

As businesses grow, trust among participating stakeholders assumes prime importance. The transparency and efficiency in the transactions that occur in these businesses are equally valuable as the profits. Digitization of the economy helps ease-out the conduct of business; however, the increase in vulnerability to cyber-attacks is also on the rise. Blockchain technology revolutionizes the way digital transactions can occur and holds much promise in securing the flow of information that primarily drives them. Such a distributed ledger framework allows the traceability of a transaction through its immutable chain of blocks. Each block registers a time-stamped information set, verified by all the stakeholders involved in the business. These sets of features and much more make the blockchain technology an immensely powerful force to bring in transparency, efficiency, and trust in various industries. The paper's primary focus is given the features that empower a blockchain to facilitate various tasks securely, efficiently, and smoothly in a power sector. It also gives impressions of pilot projects in India with blockchain applications in different sectors. Glimpses of a prototype developed by the authors for managing the trading of rooftop solar energy among a set of consumers have been provided as one of the applications in power distribution.
Tool to Retrieve Less-Filtered Information from the Internet

While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume.
Accurate and real-time human-joint-position estimation for a patient-transfer robot using a two-level convolutional neutral network

Human-joint-position estimation is crucial for patient-transfer robots. However, high accuracy and real-time property are difficult to achieve simultaneously. To tackle the problem, we develop a new convolutional neural network (CNN), containing two levels of subnetworks, to fuse the information in color and depth images. The first-level subnetwork generates two-dimensional (2D) human joint positions from a color image by the part-affinity-fields method. The second-level subnetwork estimates 3D human-joint positions from 2D ones and corresponding depth images. Here, strong feature-extraction function of the CNN may suppress the negative effect caused by invalid information in depth images. Meanwhile, all the estimations are implemented with the 2D CNNs, which may cause higher time-efficiency than 3D ones (mostly used in previous studies). To assess the validity, first we employed the CNN to estimate human joint positions, and obtained the accuracy and speed of respectively 90.3% and 210 ms (implemented with an affordable processing unit). Then we applied the CNN to a dual-arm nursing-care robot and found that the accuracy and processing speed satisfied the requirements in practical usage; these validated the effectiveness of our proposal and provided a new approach to generate 3D-human-joint positions through information fusion of color and depth images.
Huntalent: A candidates recommendation system for automatic recruitment via LinkedIn

In the era of Big Data, users do not express or can't clearly express their wishes and demands. Our work focuses on heterogeneous data extraction and analysis in order to make it easily accessible and exploitable by users or decision-makers. Recently recommendation systems are proved to be efficient in many sectors and newly for human resources to facilitate the recruitment process. For this reason, we present Huntalent, a candidate recommendation system that can represent an interesting solution to optimize the recruitment. This project makes use of Apache Spark, a distributed big data processing framework. Spark gives the advantage of handling iterative and interactive algorithms with efficiency and minimal processing time as compared to traditional map-reduce paradigm. We use content based recommendation techniques to recommend and identify potential candidates from the professional social network LinkedIn. The output of this work can be used by employers to find the right candidate for the right position.
Attention-based Deep Learning for Visual Servoing

The traditional image based visual servo(IBVS) system relies on manually extracted features, the process of estimating the feature Jacobian is very complicated and difficult. This paper proposes an approach of visual servo control which has an end-to-end structure. We find the excellent performance of convolutional neural network in classification and regression tasks and make use of it, attention mechanism is introduced and region of interest(ROI) is extracted through the feature extraction to strengthen the expression of feature information. The corresponding relationship between image space and pose space is successfully obtained. The dataset is collected by performing perspective transformation on the image. The proposed dual-stream network processes the input images which representing the current task pose and the desired task pose at the same time, and The command obtained from the network output can control the robot to complete the corresponding visual servo task. The proposed approach has achieved good results in the corresponding experimental scenes.
Blockchain Technology as Information Infrastructure in the Public Sector

This chapter will explore how and to what extent blockchain technologies can grow into an information infrastructure for various types of applications in the public sector, e.g. secure document/digital asset management. The chapter is partly conceptual and analytical, but the analysis will be illustrated by some existing use cases. Firstly, we will explore different types of blockchains, their characteristics, strengths, and weaknesses. Secondly, we compare possible architectural development trajectories of blockchain technologies with those of the Internet. Thirdly, we will address some essential and critical challenges related to interoperability between different types of blockchain implementations. To illustrate our analysis, we will describe some use cases to discuss possible changes and future work that needs to be done for blockchain technology to evolve from platforms into an information infrastructure.
Local Binary Pattern and Census, Which One is Better in Stereo Matching

Local binary patterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have been successfully applied in various applications, such as texture classification, face recognition, object detection, and segmentation, while Census has only been used to investigate stereo correspondence problem. Therefore, this paper investigates the LBP and Census using a non-local-based stereo matching method in order to analyze and discuss the main differences between LBP and Census. Moreover, as many as one hundred variants of LBP have been published to solve various problems, while only a few modifications of the Census exist for stereo matching. Comprehensive experiments with the indoor, Middlebury dataset stated that some novel LBPs that perform well in texture classification and face recognition also work well in a stereo matching application. In most cases, LBP and its variants compare favorably to Census in terms of the accuracy of the stereo method. These results proved that LBP and its variants are suitable for using in solving the stereo correspondence problem or improving the performance of existing stereo methods.
Face Video Generation from a Single Image and Landmarks

In this paper, we are concerned with the challenging problem of producing a full image sequence of a deformable face given only an image and generic facial motions encoded by a set of sparse landmarks. To this end, we build upon recent breakthroughs in image-to-image translation such as pix2pix, CycleGAN and StarGAN which learn Deep Convolutional Neural Networks (DCNNs) that learn to map aligned pairs of images between different domains (i.e., having different labels) and propose a new architecture which is not driven any more by labels but by spatial maps, facial landmarks. In particular, we propose the MotionGAN which transforms an input face image into a new one according to a heatmap of target landmarks. We show that it is possible to create very realistic face videos using a single image and a set of target landmarks. Furthermore, our method can be used to edit a facial image with arbitrary motions according to landmarks (e.g., expression, speech, etc.). This provides much more flexibility to face editing, expression transfer, facial video creation, etc. than models based on discrete expressions, audio or action units.
ARIMA-Based Aging Prediction Method for Cloud Server System

Abstract Long-running software system tends to show performance degradation and sudden failures, due to error accumulation or resource exhaustion over time. This phenomenon is usually called software aging. Software aging is an important factor that influences software reliability. This paper presents a prediction method to investigate software aging in an OpenStack cloud system. At first, the performance data in an OpenStack cloud system is monitored and collected. Then, an autoregressive integrated moving averages (ARIMA) approach is used to predict the performance data. Finally, the experimental results and statistical analysis of collected data validate the presence of software aging in the OpenStack cloud system.
Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction

Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions. Even from a single image of a clothed person, a realistic looking body shape can be inferred that captures a users’ weight group and body shape type well. This opens up a whole spectrum of applications – in particular in fashion – where virtual try-on and recommendation systems can make use of these new and automatized cues. However, a realistic depiction of the undressed body is regarded highly private and therefore might not be consented by most people. Hence, we ask if the automatic extraction of such information can be effectively evaded. While adversarial perturbations have been shown to be effective for manipulating the output of machine learning models – in particular, end-to-end deep learning approaches – state of the art shape estimation methods are composed of multiple stages. We perform the first investigation of different strategies that can be used to effectively manipulate the automatic shape estimation while preserving the overall appearance of the original image.
ALMI—A Generic Active Learning System for Computational Object Classification in Marine Observation Images

In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from these data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. As a priori knowledge about the objects of interest in the images is almost never available, computational methods are required that are not dependent on the posterior availability of a large training data set of annotated images. In this paper, we propose a new strategy for collecting and using training data for machine learning-based observatory image interpretation much more efficiently. The method combines the training efficiency of a special active learning procedure with the advantages of deep learning feature representations. The method is tested on two highly disparate data sets. In our experiments, we can show that the proposed method ALMI achieves on one data set a classification accuracy A &gt; 90% with less than N = 258 data samples and A &gt; 80% after N = 150 iterations, i.e., training samples, on the other data set outperforming the reference method regarding accuracy and training data required.
Particle Swarm Optimization with Multiple Adaptive Subswarms

Abstract Benefiting from its simplicity and efficiency, particle swarm optimization (PSO) algorithm has shown great performance on various problems. However, for different optimization problems or different search areas, it is still difficult to achieve a satisfying trade-off between exploration and exploitation. On the basis of canonical PSO algorithm, a variety of improved algorithms have been proposed, which have different capabilities of exploitation and exploration, and each of them performs effective in some problems. This paper proposes a particle swarm optimization with multiple adaptive sub-swarms (PSOMAS). It uses multiple subswarms strategy, in which each sub-swarm is evolved by different algorithms, and an adaptive strategy is also used to reduce the consumption of computing resources. A comprehensive experimental study is conducted on 30 benchmark functions, to compare with several well-known variants of PSO algorithms. The results show that PSOMAS with RT=100 could obtain a better overall performance than all others. Moreover, PSOMAS could find high-quality solution in different problems by varying the value of RT.