Reviewerly/exHarmony huggingface dataset:
How to Improve Semantics Understanding of Word Clouds

Word cloud is a text visualization technique which is widely applied in helping improve semantic understanding about target materials. One of the most important features is the font size, which represents words frequencies of a document. As the result, in this paper, we explore how to set font sizes of words, and its influence on semantic understanding through people's performance with qualitative and controlled experiments. Adopting an machine learning algorithm LDA (Latent Dirichlet Allocation) topic model, we quantify semantics of the document and judge participants' accuracy performance. The experimental results show the influence of different font size on semantic understanding performance and provide insights for ways in promoting semantic understanding of word cloud.
Poster

The website fingerprinting attack allows a low-resource attacker to compromise the privacy guarantees provided by privacy enhancing tools such as Tor. In response, researchers have proposed defenses aimed at confusing the classification tools used by attackers. As new, more powerful attacks are frequently developed, raw attack accuracy has proven inadequate as the sole metric used to evaluate these defenses. In response, two security metrics have been proposed that allow for evaluating defenses based on hand-crafted features often used in attacks. Recent state-of-the-art attacks, however, use deep learning models capable of automatically learning abstract feature representations, and thus the proposed metrics fall short once again. In this study we examine two security metrics and (1) show how these methods can be extended to evaluate deep learning-based website fingerprinting attacks, and (2) compare the security metrics and identify their shortcomings.
An Efficient Mining Algorithm of Closed Frequent Itemsets on Multi-core Processor

In this paper, we improved a sequential NOV-CFI algorithm mining closed frequent itemsets in transaction databases, called SEQ-CFI and consisting of three phases: the first phase, quickly detect a Kernel_COOC array of co-occurrences and occurrences of kernel item in at least one transaction; the second phase, we built the list of nLOOC-Tree base on the Kernel_COOC and a binary matrix of dataset (self-reduced search space); the last phase, the algorithm is a fast mining closed frequent itemsets base on nLOOC-Tree. The next step, we develop a sequential algorithm for mining closed frequent itemsets and thus parallelize the sequential algorithm to effectively demonstrate the multi-core processor, called NPA-CFI. The experimental results show that the proposed algorithms perform better than other existing algorithms, as well as to expand the parallel NPA-CFI algorithm on distributed computing systems such as Hadoop, Spark.
Is “Citizen Kane” Moment Coming? - A Research on Chinese VR Documentary Practice and Storytelling

“Citizen Kane” moment is the occasion when a work achieves mastery of the form, defining a language of storytelling for future creators. [1] As a newborn genre, VR documentary is currently somewhere in that intermediate phase. 2016 is the first year of Chinese VR documentary. Over the past three years, more than 100 artworks have emerged, besides, some of them has been exhibited in famous international film festival such as Cannas. The embryonic Chinese VR content industry also serves this fresh genre with a favorable environment and support. With the advent of 5G era, will the creation of VR documentaries in China usher in a new Kane moment, or encounter more cruel challenges? Through investigating the main distribution platforms and the storytelling characteristics of existing artworks, this research aims to analyze and discuss the practice environment, creation development and potential storytelling strategies of Chinese VR documentaries.
Social Matching Systems for Research Collaboration

Social matching systems, while popular for dating and platonic connections, can also play a role in the workplace, particularly for initiating collaborations between professionals from traditionally disparate fields of practice. In this paper we present a profile page design to support university faculty in self-presenting to nonacademic partners in their local community for collaborative research opportunities, as informed by a focus group study. We then discuss preliminary insight from a survey to faculty prompting them to create their profile page and provide feedback on its design. The created profiles indicate that faculty embrace profile pictures as tools for conveying their expertise and resources rather than their physical appearance. Furthermore, functionality to link the profiles of users who are currently working together should be considered as a way to convey combined collaboration potential.
Vessel Segmentation Based on Region-Scalable Fitting Energy

The region-scalable fitting energy (RSF) model is one of the classical active contour models, it performs well while dealing with the images with intensity inhomogeneity. However, complex vessels with more branches and different sizes make it disabled for image segmentation. In order to overcome the difficulties in complex vessels segmentation, we propose an improved region-scalable fitting energy. It consists of area term, length term, and penalty term. Due to the length term and penalty term both smoothing the zero level contour, they may prevent the level set function from evolving. Therefore, we use the arctangent function to reduce the impact of penalty term and length term to further evolve. And we modify the area term to denoise. The experiments show that the proposed method can have a better segmentation performance while dealing with complex vessels with intensity inhomogeneity. The proposed method is less sensitive to initial contour and robust to illumination.
Design of photovoltaic systems on virtual learning platform

This article presents the development of a virtual learning platform for the design of solar photovoltaic systems. The work carried out involves the inclusion of mathematical models of solar radiation, photovoltaic panels, regulators, batteries and inverters that are integrated to offer a virtual component operation that intervenes in a system for obtaining electrical energy from the solar source. The virtual environment was implemented using the possibilities offered by a graphic programming environment, to facilitate the learning of the design parameters of solar photovoltaic systems.
On the Security of the Double-Block-Length Hash Function NCASH

In this work, we study the security analysis of a newly proposed Non-linear Cellular Automata-based Hash function, NCASH. The uncomplicated structure of this double-block-length hash function instigates us to scrutinize its construction by analyzing the security of the design. Here, we have performed a security analysis with respect to the standard model of concrete security. In addition, structural security has also been investigated by performing the correlation analysis. We have examined the security bound of this scheme by using the random oracle model. The Preimage or Second Preimage Resistance and Collision Resistance of NCASH-256 are 2 $$^{256}$$ and 2 $$^{128}$$ respectively. According to the best of our knowledge, these bounds provide better security comparing with most of the other acclaimed existing schemes.
A Case Based Deep Neural Network Interpretability Framework and Its User Study

Despite its popularity, the decision making process of a Deep Neural Network (DNN) model is opaque to users, making it difficult to understand the behaviour of the model. We present the design of a Web-based DNN interpretability framework which is based on the core notions in case-based reasoning approaches where exemplars (e.g., data points considered similar to a chosen data point) are utilised to help achieve effective interpretation. We demonstrate the framework via a Web based tool called Deep Explorer (DeX) and present the results of user acceptance studies. Our studies showed the effectiveness of the tool in gaining a better understanding of the decision making process of a DNN model as well as the efficacy of the case-based approach in improving DNN interpretability.
Development of e-Handout materials physics based android for improvement learning outcomes senior high school student

Abstract This study aims to develop a product of teaching materials e-Handout based physics android equipped audio visual for improve learning outcomes high school students of class XI semester I. Research conducted in Media Development Laboratory Department of Physics State University of Jakarta. This research uses Research and Development (R &amp; D) method with Dick &amp; Carey model modified by Atwi Suparman into Instructional Development Model (MPI). Product feasibility test is done to material expert and media expert, obtained by percentage of material expert 81,67% with excellent interpretation and media expert 93,13% with very good interpretation. The product test was conducted to physics teacher and 32 students, got percentage by physics teacher 89,48% and by 87,37% for all components with 86,88% for content fulfilment component, 88,75% For component of presentation, 87,88% for easy to see component, 86,75% for interesting component, 87,19% for useful and useful component, 87,50% for well-structured component and 86,67% for effective and efficient component The effectiveness test using the gain test obtained a result of 0.65, which indicates an increase in knowledge with moderate interpretation. Based on the results of validation and testing, the development of e-physics-based android-based e-physics teaching materials equipped with audio visual for high school students of class XI semester I can be categorized as appropriate for use as companion teaching materials and can improvement learning outcomes students.
Haze Removal Algorithm Using Improved Restoration Model Based on Dark Channel Prior

In recent years, with the rapid development of social economy and the people’s living standard, the awareness of security precaution is becoming increasingly important. However, in severe weather conditions, rain and haze have a large influence on the images obtained by video monitoring like the image contrast information, and it has a bad impact on the security work. So the clarity of images becomes very meaningful, and researchers start to pay attention to the field of image dehazing. Among many studies, the dark channel prior dehazing algorithm is a major breakthrough in the field of image dehazing technology. The advantages of this algorithm mainly focus on solving the problem of over-dependence on the physical model of sky scattering, poor adaptability to images containing sky regions and the problem that the processed images are too dark. Based on the atmospheric physical model, this research proposed a haze removal algorithm based on dark channel prior, which has good robustness to the bright sky regions, and also has a good effect on the edges. Firstly, the haze images were detected to see whether sky regions are included or not. Secondly, haze images that have sky regions were segmented into sky region and non-sky region. Then, the haze images that have sky regions and that have no sky regions were dehazed respectively. Finally, the quantitative assessment is based on NIQE (Natural Image Quality Evaluator), and the qualitative assessment is based on human subjective visual standards to compare the intrinsic images results. Quantitative and qualitative results both demonstrate that the performance of the proposed method is better than other techniques in the field of image dehazing that using dark channel prior. The proposed method can be better applied in the area of video image processing.
Increased reproducibility and comparability of data leak evaluations using ExOT

As computing systems are increasingly shared among different users or application domains, researchers have intensified their efforts to detect possible data leaks. In particular, many investigations highlight the vulnerability of systems w.r.t. covert and side channel attacks. However, the effort required to reproduce and compare different results has proven to be high. Therefore, we present a novel methodology for covert channel evaluation. In addition, we introduce the Experiment Orchestration Toolkit ExOT, which provides software tools to efficiently execute the methodology.Our methodology ensures that the covert channel analysis yields expressive results that can be reproduced and allow the comparison of the threat potential of different data leaks. ExOT is a software bundle that consists of easy to extend C++ libraries and Python packages. These libraries and packages provide tools for the generation and execution of experiments, as well as the analysis of the experimental data. Therefore, ExOT decreases the engineering effort needed to execute our novel methodology. We verify these claims with an extensive evaluation of four different covert channels on an Intel Haswell and an ARMv8 based platform. In our evaluation, we derive capacity bounds and show achievable throughputs to compare the threat potential of these different covert channels.
Object Tracking With Structured Metric Learning

In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning. In our method, tracking is formulated as a structured metric learning problem, which not only considers the importance of different samples, but also improves the discriminability by learning a specific distance metric for matching. Specifically, a concrete structured metric learning method is realized by making use of the constraints from the target and its neighbour training samples under the above framework. Besides, a closed-form solution is derived for the structured metric learning problem. To improve the matching robustness, the K-nearest neighbours (KNN) distance is employed to determine the final tracking result. Experimental results in the benchmark dataset demonstrate that the proposed structured metric learning based tracking method can achieve desirable performance.
HGraph: I/O-efficient Distributed and Iterative Graph Computing by Hybrid Pushing/Pulling

In the big data era, distributed computation is becoming a preferred solution for iterative graph analysis. However, graphs are rapidly growing in size and more importantly, there exist a lot of messages across iterations. For better scalability, many distributed systems keep graph data and message data on disk. Now these systems solely employ either pushing or pulling mode to manage data, but neither can always work well during the entire computation. This is mainly because I/O access patterns are dynamic and complex. This article proposes a hybrid solution. It achieves the optimal performance in different scenarios by dynamically and adaptively switching modes between pushing and pulling. Specifically, we first devise a new block-centric pulling technique. It pulls messages much more I/O-efficiently than the existing vertex-centric pulling mode. We then combine pushing and pulling. For general-purpose, we categorize graph algorithms and accordingly present two seamless switching frameworks. We also design performance prediction components specialized to the two frameworks, to decide how and when we can switch modes. Some optimization strategies are also given to further enhance performance, such as priority scheduling and lightweight fault-tolerance. Extensive experiments against state-of-the-art solutions confirm the effectiveness of our proposals.
Geology, mineralogy and stable isotope geochemistry of the Dzuunmod area in northern Mongolia: Constraints for gold ore genesis and sources

The Dzuunmod area located in the North Khentii Gold belt (NKGB) of northern Mongolia includes lode gold deposits such as Gatsuurt, Sujigtei and Boroo with several minor gold deposits and occurrences. They show similar hydrothermal alteration assemblages (sericitic, siliceous and potassic) and ore mineral assemblages (pyrite and arsenopyrite with minor amount of galena, sphalerite and chalcopyrite). Gold occurs as native form and invisible gold in pyrite and arsenopyrite. The major sulfide minerals are separated into earlier non-auriferous stage and later auriferous grains containing invisible gold. Native gold postdates the major sulfide mineralization. Gold and arsenic content of pyrite grains indicates that gold exists mainly as solid solution form (Au+1) in the Gatsuurt and Boroo deposit whereas gold nanoparticle (Au0) is present in the Sujigtei deposit. High Co/Ni and Mo/Ni ratios of pyrite grain suggest a post-sedimentary or hydrothermal origin and the ore-forming fluid was significantly affected by fluid-host rock interactions during mineralization processes. Large variation of δ34S values of pyrite and arsenopyrite from −2.6‰ to 17.2‰ indicates that sulfur seems to be mainly derived from a source with heterogeneous sulfur isotope composition, even though the role of magmatic sulfur as one of possible sulfur sources cannot be ruled out. Consistent with geological evidence, relatively positive δ34S values suggest that sulfidation plays an important role for gold and sulfide precipitation. The calculated δ18O values of hydrothermal fluid from the measured δ18O values of quartz samples (from 14.7‰ to 17.7‰) indicate a metamorphic derivation of ore-forming fluid. Gold mineralization processes in the Dzuunmod area seem to occur several times by multiple input of hydrothermal fluid and fluid-host rock interactions. The gold deposits in the Dzuunmod area are considered to be orogenic gold type influenced by fluid-host rock interactions in the deposit area.
On the Development of Object Detector Based on Capsule Neural Networks

In this paper, we present the analysis and potential applications of computer vision system based on a novel capsule neural network. Since traditional deep convolutional neural networks are still computationally expensive and difficult to train, it is necessary to study and develop the new neural network structures in order to minimize training time and increase the ability to run computer vision services on machines without high powered GPU. Recently proposed capsule neural network shows the promising results in cases of image classification and segmentation because of rigidity to object perspective view and white-box adversarial attack, comparatively low number of learning parameters and implementation simplicity. The architecture details, dynamic routing method, parameters learning and the possibility of developing an object detection algorithm based on the considered neural network are widely discussed. Also, a custom design using available deep learning frameworks is described. Provided results of this review can be useful for building the effective and fast computer vision services as well as for understanding of modern deep networks concept.
Detecting Anomalies in the Engine Coolant Sensor Using One-Class Classifiers

In this paper we evaluate the presence of anomalies in the Engine Coolant Temperature (ECT) sensor operation by collecting telemetry data of a single car in two different operational modes. The proposed approach has evaluated ten different one-class classifiers in three different anomaly levels, defined from the sensor's malfunctioning. Based on the results from the experimental data, the evaluation has shown: the One-Class Support Vector Machine with third- degree polynomial kernel function as the best anomaly detection technique for the vehicle operation in movement trajectory and the k-nearest neighbor as the best technique for the vehicle stopped, but with the engine running.
Online Knowledge Community Governance Based on Blockchain Token Incentives

Blockchain technology as a subversive technology has been widely concerned by academia and industry. The blockchain token economy with the token incentive as its core has also brought about many innovations in incentive mechanism. Based on the characteristics of online knowledge community and the existing problems of incentive mechanism, this paper discusses the innovation of incentive mechanism brought by blockchain technology to online knowledge community. It is found that blockchain tokens can provide a variety of incentive modes, perfect incentive objects, incentive compatibility, and online knowledge community governance through incentives. Also, future research directions have prospected.
Research On Key Technology Of Quick Copy And Random Accessing Of Massive Map Tiles

This paper, inspired by mbtiles format which was provided by MapBox, proposes a method on copy of massive map tiles by combing the whole web map tiles into a series of single huge files. Firstly, the map tiles of the same zoom level are packed into a single map file and head file with the extension of .tiles and .tilehead respectively; then create a single sqlite database named sqlint.db3 by the all .tilehead files to provide high efficiently random accessing. Experiments, take the Google Map Tiles as examples, proves good results on copying the tiles into SD cards for mobiles phones. And also the random accessing of tiles from file system directly and from the huge tiles file are also proved that the method has no obvious difference.
Examinee Behavior Description Method Based on Image Captioning

Aiming at the problem of missing report of abnormal situation caused by manual observation of examination room monitoring video, this paper introduces image description in deep learning to construct a data set “Examinee Activity Captioning Dataset” suitable for examination scene, and proposes a method to describe examinee's behavior based on CNN+LSTM. This method employs a convolutional neural network to automatically extract the characteristics of examinee's behavior, and further generates sentences to describe examinee's behavior through long short-term memory network. The experimental results show that the proposed method can accurately describe five kinds of behaviors, including writing test papers, turning over test papers and looking around, which may play an important role in future smart examination room.
Combing colour detection and neural networks for gland detection

Glands are objects of interest which can be used for quantitatively analysis of histology images. Detecting glands from H&E staining histological images based on neural networks, may suffer stain variation problem. In this paper, we present a new method which combines a statistical colour detection model and a neural network to cover this problem. Colours shown at glands boundaries are pre-detected and enhanced in a pre-processing step. Then a neural network model based on Faster R-CNN is learned from these colour pixels to detect glands. This method has been tested on a Colon Histology Images Challenge Contest (GlaS) held at MICCAI 2015. The experimental results have shown the proposed method is superior to either Faster R-CNN or U-net without colour detection pre-processing. In addition, this proposed method can achieve F1-score rank 8 in detecting benign glands and rank 5 in detecting malignant glands.