Reviewerly/exHarmony huggingface dataset:
Achieving Dynamic Communication Path for Anti-Tracking Network

The increasingly rampant network monitoring and tracing bring the huge challenge on the protection of netizens' privacy. The anonymous networks mitigate the threat of network monitoring and tracing to a certain degree, but the static communication path has become the weakness. To address the problem, we propose a tracking-resistant communication mechanism with dynamic paths(TresMep). Different with the stepping stone chain like Tor, TresMep provides a chain of node groups which include at least one honest node. The message is transferred between groups. Each group uses asynchronous DC-net to hide the exit node which deliver the message to the honest node of the next group, and each group randomly chooses the exit node in each round of transmission through lagrange interpolation. Then, the transmission path would be changed dynamically and randomly to provide stronger tracking-resistance. The experimental results show that TresMep has a stronger performance of trackingresistance than the stepping stones based anti-tracking network with static communication path. The communication efficiency of TresMep is also satisfactory. But when message load is big, the communication efficiency of TresMep becomes worse. TresMep takes a tradeoff between tracking- resistance and communication efficiency.
A Proposal of Deep Learning Model for Classifying User Interests on Social Networks

In the recent years, there are huge data extracted from social networks in both static and real-time analysis, such as Facebook, Twitter, LinkedIn, and Instagram. Recently, most researchers have investigated in classifying textual contents without user interests/behaviors from huge data of social networks. This paper has presented a novel approach using a Convolution Neural Network with its new contribution of user perceptions from the social network data to classifying the user interests. Experimental results show that the proposed model performs better than the conventional algorithms in terms of classifying user interests. Additionally, the proposed model enhances a quality of classification for user interests tracking real-time in social networks.
A novel scheme for image sharpness using inflection points

Abstract It is useful to increase the sharpness in medical images. This improvement can help medical diagnoses and treatment outcomes for patients. Noise overshoot and oversharpening effects are common artifacts of conventional sharpening algorithms. In this work, we propose an adaptive sharpness algorithm that achieves better sharpening effects than standard methods. As the pixel value changes abruptly at an edge, a method that adequately emphasized sudden variations in pixel values is effective for use in the sharpening process. Measurement of the gradient norm value of each pixel is calculated and compared to a threshold value to produce a curve. The curve quickly drops at the initial stage and then decreased more slowly for higher norm values. To distinguish the edge, an inflection point is determined by taking the second derivative of the curve and identifying the turnover point (where the curvature changes sign). Norm values higher than the inflection point are identified as belonging to the edge, and a simple sharpness filter is adaptively applied to these points. The proposed approach yielded better results than global filtering and a conventional unsharp masking approach when evaluated using Pratt's figure of merit.
Towards Robust Ensemble Defense Against Adversarial Examples Attack

With recent advancements in the field of artificial intelligence, deep learning has created a niche in the technology space and is being actively used in autonomous and IoT systems globally. Unfortunately, these deep learning models have become susceptible to adversarial attacks that can severely impact its integrity. Research has shown that many state-of-the-art models are vulnerable to attacks by well- crafted adversarial examples. These adversarial examples are perturbed versions of clean data with a small amount of noise added to it. These adversarial samples are imperceptible to the human eye yet they can easily fool the targeted model. The exposed vulnerabilities of these models raise the question of their usability in safety-critical real-world applications such as autonomous driving and medical applications. In this work, we have documented the effectiveness of six different gradient-based adversarial attacks on ResNet image recognition model. Defending against these adversaries is challenging. Adversarial re-training has been one of the widely used defense technique. It aims at training a more robust model capable of handling the adversarial examples attack by itself. We showcase the limitations of traditional adversarial-retraining techniques that could be effective against some adversaries but does not protect against more sophisticated attacks. We present a new ensemble defense strategy using adversarial retraining technique that is capable of withstanding six adversarial attacks on cifar10 dataset with a minimum accuracy of 89.31%.
A Novel Automatic Transformation Method from the Business Value Model to the UML Use Case Diagram

Currently, in the field of the software engineering industry, end-users have become extremely requiring; consequently, this industry knew a critical evolution. In this vein, the model generation becomes an awakening and an essential step for many approaches among them we cite the Model Driven Architecture (MDA) approach. The Object Management Group (OMG), the founder of the MDA approach, proposes three different abstraction levels; which are the higher level, average level, and lower level. Until now, most research deal with the transformation at the average and the lowest MDA levels, while ignoring the higher level, which precisely contains the requirements’ models. Therefore, our aim in this paper is to construct a sound and more consistent higher MDA level for e-business information systems by automatically generating a higher level of functional requirements model from the Business Value model. Our source Business Value model is represented by the E3value model, whereas the UML Use Case diagram expresses the generated model. The transformation is performed automatically using ATLAS Transformation Language (ATL).
Bayesian Optimization for High-Speed Channel Equalization

Equalization methods are used to recover the signal attenuated by channel loss. Different optimization algorithms are applied to find the best tap coefficients for each equalization that will improve eye opening and reduce bit error rate. The goal function for most equalization optimization algorithms is to reduce the difference between input and output signals, which is a linear optimization problem and can be solved relatively easily. This indirectly will increase eye height and improve eye diagram. Directly optimizing eye height is a non-linear problem and cannot be solved with analytical method. We are proposing FFE and DFE combined equalization optimization algorithm that optimizes directly eye height using Bayesian Optimization (BO). The proposed algorithm can be generalized for multi-level signals.
Reclaiming and Preserving Traditional Music: Aesthetics, ethics and technology

Music history is full of examples of composers drawing upon traditional repertoires for their works. Starting from the late nineteenth century in particular, many of them have looked at this specific sound material for several reasons: overcoming the limitations of tonal system, discovering different compositional strategies, finding new inspiration and aesthetics, evoking exoticism. Electronic music is no exception. Since the emergence of sound recording, sonic artists and electronic music composers have experimented with new technologies trying to integrate traditional elements in their works with different results and various purposes. In the present time, the preservation of these traditional elements could represent one of the most crucial goals. In a world characterised by a widespread globalisation, traditional music might be at risk of being neglected or even forgotten, as for local identities and cultures in general. As electronic music composers and sonic artists we should ask ourselves if it is possible to create a link between tradition and innovation, connecting these two apparently opposite realities. Can we safeguard at-risk traditions and at the same time re-present them through contemporary artistic practices and technologies? Is there a way to develop a form of expression that could reach a wide and diverse range of listeners, taking into account recent trends and studies in electronic music while preserving the main distinctive features of the traditional repertoires? The article attempts to answer the above-mentioned questions with the support of a case study: the personal research conducted into the use of traditional music from the southern Italian region of Campania in the scope of electronic music composition.
Local Binary Pattern Networks

Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial tasks related to sensor data processing and even application-level inferences from this sensor data. These devices are, however, extraordinarily resource-constrained in terms of CPU power (often Cortex M0-3 class CPUs), available memory (in few KB to MBytes), and energy. Under these constraints, we explore a novel approach to character recognition using local binary pattern networks, or LBPNet, that can learn and perform bit-wise operations in an end-to-end fashion. LBPNet has its advantage for characters whose features are composed of structured strokes and distinctive outlines. LBPNet uses local binary comparisons and random projections in place of conventional convolution (or approximation of convolution) operations, providing an important means to improve memory efficiency as well as inference speed. We evaluate LBPNet on a number of character recognition benchmark datasets as well as several object classification datasets and demonstrate its effectiveness and efficiency.
Image segmentation based on ultimate levelings: From attribute filters to machine learning strategies

Ultimate levelings are operators that extract important image contrast information from a scale-space based on levelings. Along with the residual extraction process, some residues usually come from undesirable regions, and they should be filtered out. For this, some strategies can be applied to filter these undesirable residues. In this paper, we introduce a new approach to detect desirable regions from ultimate levelings through a new hierarchical structure called residual tree. From this structure, we extract attribute vectors to build a machine learning model which gives a matching value between ground truth regions and residual tree nodes. Thus, from the selected residual tree nodes, we present a new approach to choose the best disjoint residual nodes which gives the regions of the ultimate levelings. Finally, from the ultimate levelings, we use its partition associated function to solve the segmentation problem. In order to evaluate our new approach, some experiments were carried out with a plant dataset and results report the state-of-the-art performance in plant segmentation.
ASSESSING HYDROTHERMAL ALTERATION INTENSITY IN VOLCANIC-HOSTED MASSIVE SULFIDE SYSTEMS USING PORTABLE X-RAY FLUORESCENCE ANALYSIS OF DRILL CORE: AN EXAMPLE FROM MYRA FALLS, CANADA

Abstract Current portable X-ray fluorescence (pXRF) technology can rapidly and inexpensively yield concentrations of geologically significant elements, typically with instrument detection limits below several tens of parts per million. Based on conventional XRF whole-rock geochemical data, both the Ishikawa alteration index and the chlorite-carbonate-pyrite index increase with proximity to sulfide mineralization at Myra Falls. However, available pXRF technology is typically unable to detect all the elements required to calculate these alteration indices. As a result, there is a need to utilize the elements that are readily detectable using pXRF and apply these to hydrothermal alteration assessment. We propose that Rb/Sr ratios provide a robust proxy for the Ishikawa alteration index and demonstrate that conventional whole-rock XRF analytical results for Rb and Sr can be reproduced using pXRF analysis from drill core surfaces. At Myra Falls, the Rb/Sr ratios vary from &amp;lt;0.1 for least altered rocks, 0.1 to 0.5 for weakly altered rocks, 0.5 to 1.0 for moderately altered rocks, 1.0 to 2.0 for strongly altered rocks, and &amp;gt;2.0 for intensely altered rocks. Downhole profiles of alteration intensity generated from systematic pXRF analysis of drill core surfaces can be used to inform drilling and targeting decisions. The application of the Rb/Sr ratio as a proxy for alteration intensity extends beyond this case study and can be applied to other hydrothermal systems that produce phyllosilicate minerals as alteration products of feldspar.
NJM-Vis

Neural joint models have been shown to outperform non-joint models on several NLP and Vision tasks and constitute a thriving area of research in AI and ML. Although several researchers have worked on enhancing the interpretability of single-task neural models, in this work we present what is, to the best of our knowledge, the first interface to support the interpretation of results produced by joint models, focusing in particular on NLP settings. Our interface is intended to enhance interpretability of these models for both NLP practitioners and domain experts (e.g., linguists).
Tadpole VR: virtual reality visualization of a simulated tadpole spinal cord

Recent advances in “developmental” approach (combining experimental study with computational modeling) of neural networks produce increasingly large data sets, in both complexity and size. This poses a significant challenge in analyzing, visualizing and understanding not only the spatial structure but also the behavior of such networks. This paper describes a virtual reality application for visualization of two biologically accurate computational models that model the anatomical structure of a neural network comprised of 1500 neurons and over 80,000 connections. The visualization enables a user to observe the complex spatiotemporal interplay between seven unique types of neurons culminating in an observable swimming pattern. We present a detailed description of the design approach for the virtual environment, based on a set of initial requirements, followed up by the implementation and optimization steps. Lastly, the results of a pilot usability study are being presented on how confident participants are in their ability to understand how the alternating firing pattern between the two sides of the tadpole’s body generates swimming motion.
Research on the application of VR technology in the digital inheritance of ancient papermaking technology

Abstract The traditional papermaking technique of ancient method is confronted with the problem of difficult inheritance. In order to enable the public to experience or learn the ancient papermaking technology with the help of virtual platform and make the ancient papermaking technology have more possibilities for future development, the VR experience project of ancient papermaking technology is designed and planned through data collection, virtual scene production, digital interactive design and other steps by means of integrating disciplines. It can retain the integrity of the information of ancient papermaking technology to the greatest extent, provide a safe, efficient and green simulation learning platform, rapidly promote the traditional ancient papermaking culture in a large range, enrich the presentation of ancient papermaking materials and predict the future development direction.
Automation of Admission Enquiry Process Through Chatbot—A Feedback-Enabled Learning System

Chatbots are existing since few years and recently it has started acquiring popularity. Earlier to chatbots, people use help desk as the enquiring medium and hence people working at help desks have to work all the days and answer all the questions. Most of the queries are repetitive in nature and answers are given from a structured database. In order to reduce the effort of humans, we can have a chatbot deployed for the same activity. This work focuses on a chatbot which has been developed to provide a faster human-like interaction for admission enquiry system. The chatbot is capable of handling negative or irrelevant scenarios and responds to the queries in faster manner. Decision making by the chatbot on choosing the right set of sentences is done using LSA algorithm and cosine similarity. In addition to answering, the chatbot also maintains data of questions which is not being answered. This data can be used for future analysis for retrieval-based system. The chatbot also takes the feedback from the customers and this data can be analyzed using the feedback category report generated by the chatbot using LDA algorithm
A Proof of Concept to Deceive Humans and Machines at Image Classification with Evolutionary Algorithms

The range of applications of Neural Networks encompasses image classification. However, Neural Networks are exposed to vulnerabilities, and may misclassify adversarial images, leading to potentially disastrous consequences. We give here a proof of concept of a black-box, targeted, non-parametric attack using evolutionary algorithms to fool both neural networks and humans at the task of image classification. Our feasibility study is performed on VGG-16 trained on CIFAR-10. Given two categories $$c_i \ne c_t$$ of CIFAR-10, and an original image classified by VGG-16 as belonging to $$c_i$$, we evolve this original image to a modified image that will be classified by VGG-16 as belonging to $$c_t$$, although a human would still likely classify the modified image as belonging to $$c_i$$.
Robust Detection and Recognition of Japanese Traffic Sign in the Complex Scenes Based on Deep Learning

This study applies the object detection and recognition method using deep learning, which has been attracting considerable attention recently, for the detection and recognition of traffic signs. Furthermore, the advantages and disadvantages of the proposed method are discussed. During the detection and recognition of traffic signs, the following problems were identified. (1) The detection and recognition accuracy of a small object in an image, such as a distant road sign, was low. (2) The accuracy of the traffic sign detection and recognition was affected due to the changes in contrast at night and bad weather. Herein, we address these problems by learning the traffic signs through deep learning, which is robust against scale changes. Herein, we construct a Japanese traffic sign database and compared the methods used. Results demonstrate that the proposed approach demonstrates excellent detection and recognition accuracy.
Isarn Dharma Word Segmentation Using a Statistical Approach with Named Entity Recognition

In this study, we developed an Isarn Dharma word segmentation system. We mainly focused on solving the word ambiguity and unknown word problems in unsegmented Isarn Dharma text. Ambiguous Isarn Dharma words occur frequently in word construction due to the writing style without tone markers. Thus, words can be interpreted as having different tones and meanings in the same writing text. To overcome these problems, we developed an Isarn Dharma character cluster–(IDCC) based statistical model and affixation and integrated it with the named entity recognition method (IDCC-C-based statistical model and affixation with named entity recognition (NER)). This method integrates the IDCC-based and character-based statistical models to distinguish the word boundaries. The IDCC-based statistical model utilizes the IDCC feature to disambiguate any ambiguous words. The unknown words are handled using the character-based statistical model, based on the character features. In addition, linguistic knowledge is employed to detect the boundaries of a new word based on the construction morphology and NER. In evaluations, we compared the proposed method with various word segmentation methods. The experimental results showed that the proposed method performed slightly better than the other methods when the corpus size increased. Using the test set, the proposed method obtained the best F-measure of 92.19, an F-measure that was better than the IDCC longest matching grouping at 2.85.
Classification of Spamming Attacks to Blogging Websites and Their Security Techniques

With the explosive expansion of information on the world wide web, search engines are becoming more significant in the day-to-day lives of humans. Even though a search engine generally gives huge number of results for certain query, the majority of the search engine users simply view the first few web pages in result lists. Consequently, the ranking position has become a most important concern of internet service providers. This article addresses the vulnerabilities, spamming attacks, and countermeasures in blogging sites. In the first part, the article explores the spamming types and detailed section on vulnerabilities. In the next part, an attack scenario of form spamming is presented, and defense approach is presented. Consequently, the aim of this article is to provide review of vulnerabilities, threats of spamming associated with blogging websites, and effective measures to counter them.
Feature and spatial relationship coding capsule network

A capsule network encodes entity features into a capsule and maps a spatial relationship from the local feature to the overall feature by dynamic routing. This structure allows the capsule network to fully capture feature information but inevitably leads to a lack of spatial relationship guidance, sensitivity to noise features, and easy susceptibility to falling into local optimization. Therefore, we propose a novel capsule network based on feature and spatial relationship coding (FSc-CapsNet). Feature and spatial relationship extractors are introduced to capture features and spatial relationships, respectively. The feature extractor abstracts feature information from bottom to top, while attenuating interference from noise features, and the spatial relationship extractor provides spatial relationship guidance from top to bottom. Then, instead of dynamic routing, a feature and spatial relationship encoder is proposed to find the optimal combination of features and spatial relationships. The encoder abandons the idea of iterative optimization but adds the optimization process to the backpropagation. The experimental results show that, compared with the capsule network and its multiple derivatives, the proposed FSc-CapsNet achieves significantly better performance on both the Fashion-MNIST and CIFAR-10 datasets. In addition, compared with some mainstream deep learning frameworks, FSc-CapsNet performs quite competitively on Fashion-MNIST.