Reviewerly/exHarmony huggingface dataset:
A stealthy covert storage channel for asymmetric surveillance VoLTE endpoints

The widely adaptation of shared or public cloud computing raises an amount of concerns about privacy and information security. Establishing a covert channel is a feasible solution to the problem by hiding privacy data into the overt packets, so that network information eavesdropping will not detect the existence of user's privacy data transmission. Enormous amount of VoLTE traffic makes it potentially available for stealthy message transfer. A novel covert storage channel based on RTCP (Realtime Transport Control Protocol) is proposed. Based on the analysis of the protocol and real VoLTE traffic data, the secret data is modulated by setting the jitter, or EHSNR(Extended Highest Sequence Number Received) and BLP (Bitmask of following Lost Packets) fields of the RTCP protocol, according to that the sender's network packets are captured by the ward or not. The undetectability of the proposed covert channel is verified by the K-S test (Kolmogorov–Smirnov test) under adversary transmission model.
Towards a Semiotics of Data Visualization – an Inventory of Graphic Resources

This paper outlines a framework for the analysis of data visualizations. Investigating the semiotic potentials of data visualizations means that we first have to inventorize what is within them. Using a social semiotic approach, the paper collates the graphic resources and modes deployed in data visualizations, their affordances and communicative tasks. The framework combines theoretical considerations and empirical research. It is based on both a thorough literature review of relevant publications in the field of data visualization and visual communication and an analysis of a corpus of data visualizations taken from journalism. The aim of the paper is to link the aesthetic form to its social meaning in a specific context. The framework advances understanding of how semiotic resources shape the meaning of a data visualization.
Joint Demosaicking and Blind Deblurring Using Deep Convolutional Neural Network

Despite extensive research efforts, blind image deblurring remains a challenge without general robust solutions. A long-overlooked problem of existing deblurring methods is that they are all designed to work on fully sampled RGB input images for simplicity. But, in practice, most RGB color images are reconstructed from Bayer mosaic data hence riddled with various high-frequency demosaicking artifacts, such as zippering and moiré patterns, which can easily derail a deblurring algorithm. In this paper, we propose a novel multi-scale deep convolutional neural network to solve demosaicking and deblurring jointly. By processing Bayer raw images directly, our method is free of the interference of demosaicking artifacts. Extensive experiments show that the joint approach greatly outperforms the simple cascade of state-of-art demosaicking and deblurring methods.
Computational History of Knowledge: Challenges and Opportunities

So far, the twenty-first century has been defined by an ever-increasing availability of digital data and substantial advances in computational methods. Taken together, these developments have already affected all aspects of our lives, including the ways research in the sciences and the humanities is conducted. This computational turn is often viewed with unease. But as this essay argues, it also offers exciting new perspectives for the history of knowledge. Rather than fighting these trends, the essay suggests, by embracing new possibilities and actively participating in the development of new computational methodologies the history of knowledge can act as a bridge between the world of the humanities, with its tradition of close reading and detailed understanding of individual cases, and the world of big data and computational analysis. We can gain novel perspectives on the evolution of knowledge that are both detailed and broad.
Comparing ML Algorithms on Financial Fraud Detection

The problem of Financial Fraud has reached an alarming scale nowadays. Losses due to the fraud are reaching billions of dollars every year. To reduce it, decision systems that use efficient fraud detection algorithms should be invented. With the support of modern technologies, these systems are able to manage to analyze the information and to create a prediction feature model. However, the invention of these systems is not a trivial matter but a quite challenging task due to the huge amount of different and unbalanced data. Moreover, it is not clear which machine learning algorithm should be implemented. Therefore, our research is conducted to answer the question: which is the most suitable algorithm for the dataset in this research, especially when dealing with the large amount of uncleaned data.
The lithogeochemical signatures of hydrothermal alteration in the Waihi epithermal district, New Zealand

Whole rock lithogeochemical data from the andesite rocks which host low-sulfidation epithermal gold veins in the Waihi district, New Zealand have been used to interpret hydrothermal alteration processes that variably altered the andesite rocks. The andesite host rocks are interpreted to have similar primary chemical compositions throughout the Waihi area, as reflected in similar ratios of immobile elements (eg Zr, Ti and other high field strength elements). The geochemical data reveal that elevated K/Al ratios occur in rocks proximal to low-Au–Ag deposits, reflecting adularia alteration in the host rocks associated with the upflowing hydrothermal fluids. Silicification of host rocks is achieved both by the addition of SiO2, as well as the destruction of plagioclase (reflected in the loss of CaO and Na2O). The rocks have been variably altered, with calcite and pyrite abundance reflecting the addition of carbon and sulphur. Pathfinder element (eg As, Sb) concentrations are variable across the district, with high pathfinder element concentrations interpreted to reflect proximity to hydrothermal fluid flow. Together, the results provide a district-scale overview of hydrothermal alteration at Waihi, and emphasise the utility of lithogeochemistry for classifying and quantifying hydrothermal alteration that can then be used to interpret hydrothermal processes and assist mineral exploration.
Visual (dis)Confirmation: Validating Models and Hypotheses with Visualizations

Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study.
Seamless 3D Surround View with a Novel Burger Model

In recent years, the 3D surround view (3D-SV) system has become a hot research topic in the field of Advanced Driver Assistance Systems (ADAS). It can be used to form a stereoscopic view of the surrounding 3D environment by using 4 car-mounted surround cameras, and users can switch the viewpoints for virtual observation conveniently. However, there are still many problems in how to stitch calibrated images to the panoramic view and how to project the panorama to the surround view. In this paper, we introduce the graph cut algorithm and multi-band blending to the panorama stitching phase. In addition, we design a new hamburger-shaped 3D geometric model to be the carrier of the panorama for texture mapping. Our 3D-SV system can make drivers have an immersive visual experience. Experimental results show that the 3D-SV generated by our method is less distorted and looks more natural than the other competitors.
Learned Multimodal Convolutional Sparse Coding for Guided Image Super-Resolution

The success of deep learning in various tasks, including solving inverse problems, has triggered the need for designing deep neural networks that incorporate domain knowledge. In this paper, we design a multimodal deep learning architecture for guided image super-resolution, which refers to the problem of super-resolving a low-resolution image with the aid of a high-resolution image of another modality. The proposed architecture is based on a novel deep learning model, obtained by unfolding a proximal method that solves the problem of convolutional sparse coding with side information. We applied the proposed architecture to super-resolve near-infrared images using RGB images as side information. Experimental results report average PSNR gains of up to 2.85 dB against state-of-the-art multimodal deep learning and sparse coding models.
A Survey on Recommendation System for Bigdata using MapReduce Technology

Recently recommender system is emerging as a promising way of communication between user and massive amount of data. Recommendation system organizes the data in large amount to determine the concentration of analyst and make the information retrieval process easier for the users. For this purpose and criteria, large scale recommendation systems are available such as shopping website, movie, music etc. Many researchers have developed several methods or algorithms like collaborative filtering, keyword based filtering etc. Here we proposed University Recommendation System with keyword based sentiment analysis as an underlying algorithm. The proposed work introduces customized and personalized item recommendation and efficiently formulates the most suitable item to the user. Here, we have designed and implemented our system based on the Hadoop framework using MapReduce technology.
Give Me Two Points and I'll Tell You Who You Are

Vehicular mobility traces are datasets of vehicle location records in a region with high spatio-temporal precision. The access to this sensitive information can threaten the safety and privacy of drivers, given that the analysis of this data makes it possible to discover other contextual and latent information, such as daily home routes or workplace's address. To prevent this, many obfuscation and anonymization techniques have been proposed to mitigate the problem of user location privacy. In this work, we analyze an anonymization technique called mix-zone, which selects urban regions that promote the simultaneous anonymization of vehicles by changing their current pseudonym. We show how information about drivers' behavior in a city, such as their road preferences, can be used to reidentify their trajectories. To do this, we present a simple and efficient re-identification technique that uses only two georeferenced points as input data. We validate our technique with a real dataset of taxicabs, being able to re-identify up to 100% of the anonymized trajectories.
Capturing Long-Range Dependencies in Video Captioning

Most video captioning networks rely on recurrent models, including long short-term memory (LSTM). However, these recurrent models have a long-range dependency problem; thus, they are not sufficient for video encoding. To overcome this limitation, several studies investigated the relationships between objects or entities and have shown excellent performance in video classification and video captioning. In this study, we analyze a video captioning network with a non-local block in terms of temporal capacity. We introduce a video captioning method to capture long-range temporal dependencies with a non-local block. The proposed model independently uses local and non-local features. We evaluate our approach on a Microsoft Video Description Corpus (MSVD, YouTube2Text) dataset. The experimental results show that a non-local block applied along the temporal axis can solve the long-range dependency problem of the LSTM in video captioning datasets.
Difficulties and Improvements to Graph-Based Lexical Sentiment Analysis Using LISA

Lexical sentiment analysis (LSA) underlines a family of methods combining natural language processing, machine learning, or graph navigation techniques to identify the underlying sentiments or emotions carried in textual data. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LexIcal Sentiment Analysis framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-affective graph (LAG), created by connecting a typical lexical knowledgebase (KB) like WordNet, with a reliable affect KB like WordNet-Affect Hierarchy. LISA was designed in two consecutive iterations, producing two main modules: i) LISA 1.0 for affect navigation, and ii) LISA 2.0 for affect propagation and lookup. LISA 1.0 suffered from the semantic connectivity problem shared by some existing lexicon-based methods, and required polynomial execution time. This led to the development of LISA 2.0, which i) processes affective relationships separately from lexical/semantic connections (solving the semantic connectivity problem of LISA 1.0), and ii) produces a sentiment lexicon which can be searched in logarithmic time (handling LISA 1.0's efficiency problem). Experimental results on the ANEW dataset show that LISA 2.0, while completely unsupervised, is on a par with existing supervised solutions, highlighting its quality and potential.
CBR Confidence as a Basis for Confidence in Black Box Systems

Determining when to trust black box systems is a well-known challenge. An important factor affecting users' trust is confidence in system solutions. Previous case-based reasoning (CBR) research has developed criteria for assigning confidence to the solutions of a CBR system. This paper investigates whether such analysis, coupled with factors such as CBR system competence, can be used to predict confidence in the outputs of a black box system, when the black box and CBR systems are provided with the same training data. The paper presents initial strategies for using CBR confidence to predict black box system confidence. An evaluation explores the ability of the strategies to provide useful information and suggests future questions.
A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means

Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu’s threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR).
High Quality Monocular Depth Estimation Via A Multi-Scale Network And A Detail-Preserving Objective

Monocular depth estimation is an important and challenging task in computer vision. Significant progress has been made recently due to deep convolutional neural networks. However, esitmating depth maps with high quality lacks sufficient attention. This paper proposes to recover detailed depth map by training a multi-scale network architecture with a detailpreserving loss function. Firstly, we construct our architecture inspired by the design of atrous spatial pyramid pooling for semantic segmentation. Secondly, we simplify the loss on depth map gradients for preserving details. Experiments on the NYU Depth V2 dataset show that our approach is effective and it achieves state-of-the-art performance, especially in the root mean squared error.
A comprehensive overview of relevant methods of image cosegmentation

Segmenting the foreground objects from an image is an essential low-level step for many expert and intelligent systems, and the success of this key process largely depends on the amount of available training data. However, the cost for obtaining annotations is still a bottleneck, requiring enormous human effort. Thus, in order to obtain higher segmentation accuracy without the need of annotations, computer vision researchers attempted to segment simultaneously the common regions from collections of images. Indeed, when images that share common foreground objects are available, we talk about cosegmentation. The task of cosegmentation can be considered as weakly supervised segmentation, seen that the input image set is sharing common foreground objects. In fact, several methods have been proposed while displaying different frameworks, models and strategies of work, for enhancing the cosegmentation accuracy. This work presents a comprehensive review of relevant cosegmentation methods, carried out extensively and in depth and completed by performance measurements. Indeed, the existing cosegmentation methods are roughly classified in this paper into the following categories: Markov random fields-based cosegmentation, Co-saliency-based cosegmentation, Image decomposition-based cosegmentation, Random Walker-based cosegmentation, Maps-based cosegmentation, Active contours-based cosegmentation, Clustering-based cosegmentation and Deep learning-based cosegmentation. For each category, we discuss the most relevant methods while presenting the proposed problem to deal, the degree of supervision, the adopted features, the number and the complexity of input images as well as the presented models and frameworks. Then, in order to exhibit and evaluate objectively and comprehensively the most relevant state-of-the-art methods, we provide a comparative study on various challenging datasets (iCoseg, MSRC, Internet, FlickrMFC and PASCAL-VOC datasets). The objective is to show the performances and the limitations of these methods, while using different metrics and providing the computation cost for many methods. In addition, we discuss various cosegmentation challenges, issues and some applications, what can be useful and helpful to understand the cosegmentation problem. Finally, we suggest some research directions for future research on image cosegmentation.
Enabling data science for the majority

Despite great strides in the generation, collection, and processing of data at scale, data science is still extremely inconvenient for the vast majority of the population. The driving goal of our research, over the past half decade, has been to make it easy for individuals and teams---regardless of programming or analysis expertise---manage, analyze, make sense of, and draw insights from large datasets. In this article, we reflect on a comprehensive suite of tools that we've been building to empower everyone to perform data science more efficiently and effortlessly, including DataSpread, a scalable spreadsheet tool that combines the benefits of spreadsheets and databases, and ZenVisage, a visual exploration tool that accelerates the discovery of trends or patterns. Our tools have been developed in collaboration with experts in various disciplines, including neuroscience, battery science, genomics, astrophysics, and ad analytics. We will discuss some of the key technical challenges underlying the development of these tools, and how we addressed them, drawing from ideas in multiple disciplines. in the process, we will outline a research agenda for tool development to empower everyone to tap into the hidden potential in their datasets at scale.
A Path Planning Strategy for Intelligent Sweeping Robots

The path planning strategy and optimized algorithm of intelligent sweeping robot are researched in this paper. In order to achieve traversal cleaning indoor environment, a raster map is established firstly, and the inward spiral traversal method is designed for the sweeper. Then a scheme of the shortest path for out of the dead zone is proposed based on A* algorithm, due to the inward spiral traversal may fall into dead zone. Finally, the global traversal problem is considered, and the traversal cleaning task is realized by simulation. The results show that the proposed scheme and algorithm are effective.
Cryptographic Algorithms in Wearable Communications: An Empirical Analysis

In this letter, we assess the practical impact of lightweight block and stream cipher algorithms on power consumption and hardware resources for wearable devices that own low computational resources. Differently from the literature, we present an empirical and hardware-driven evaluation of the most representative encryption algorithms with regard to the requirements of wearable networks. We design and implement a cryptography library useful for wearable devices. Results confirm a strong correlation between the amount of logic/arithmetic operations, assembly instructions and power consumption for the two evaluated platforms, and they highlight the need to design encryption algorithms for wearable devices with high energy consumption efficiency, but strong security level similar to AES.
Developing a Smart Toilet System for ageing people and persons with disabilities

Elderly people, as well as people with disabilities, face many mobility problems due to either bone system degradation or various afflictions they suffer. Therefore, any solution that improves the quality of life of these people is more than welcome. This paper aims to present an approach for developing a smart toilet system for ageing people and persons of all ages with impairments/disabilities. Toilet4me is one of the most significant projects that involve smart toilet development especially for senior population and those with different disabilities who need an intelligent toilet outside their homes. Unlike the previous solution developed in iToilet project which led to a prototype for home-use (including the ability to adjust the height and position of the toilet and other auxiliary means by using several different technologies, Toilet4me is now exploring the concept and viability of adapting these technologies to semi-public spaces.