Reviewerly/exHarmony huggingface dataset:
Redactable Blockchain using Enhanced Chameleon Hash Function

Immutability is a core principle of blockchain platforms that help all participants to have an exact global log of transactions. Immutability is easier to achieve in permissioned blockchain platforms where a trusted group are the validators. In public platforms, this vital property of blockchain can be achieved only through decentralized economic mechanisms like Proof-of-Work(PoW). Blockchain along with the concept of asset tokenization provides liquidity to markets like stock trading and exchange other intangible objects. With changes in regulatory policies and/or government laws, there could be a requisite to alter the contents in such markets. We propose an idea of using Chameleon hash functions that will enable modification of a block without changing other block contents. In our proposal, the trapdoor key used to redact a block is split among major validators and is reconstructed using Multi-Party Computation(MPC). Redaction happens when the major validators agree the suggested changes by digitally signing the proposal. This eliminates the need to rely on a trusted party. We also propose an idea of using second trapdoor key that will be with the creator of the block. This can be used in scenarios where the block redaction should not happen without the consent of the creator.
A Local Block Coordinate Descent Algorithm for the CSC Model

The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities. By providing a global, yet tractable, model that operates on the whole image, the CSC was shown to overcome several limitations of the patch-based sparse model while achieving superior performance in various applications. Contemporary methods for pursuit and learning the CSC dictionary often rely on the Alternating Direction Method of Multipliers (ADMM) in the Fourier domain for the computational convenience of convolutions, while ignoring the local characterizations of the image. In this work we propose a new and simple approach that adopts a localized strategy, based on the Block Coordinate Descent algorithm. The proposed method, termed Local Block Coordinate Descent (LoBCoD), operates locally on image patches. Furthermore, we introduce a novel stochastic gradient descent version of LoBCoD for training the convolutional filters. This Stochastic-LoBCoD leverages the benefits of online learning, while being applicable even to a single training image. We demonstrate the advantages of the proposed algorithms for image inpainting and multi-focus image fusion, achieving state-of-the-art results.
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML's privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via extensive experiments on Amazon EC2, we demonstrate that CodedPrivateML provides significant speedup over cryptographic approaches based on multi-party computing (MPC).
Privacy, Patriarchy, and Participation on Social Media

Technology use in India is highly gendered across diverse socioeconomic backgrounds, and women have only recently come to widely adopt smartphones, mobile internet, and social media---even in urban India. We present an in-depth qualitative investigation of the appropriation of social computing technologies by women from urban, middle-income households in New Delhi and Bangalore, India. Our findings highlight the additional burden that these women must contend with, on account of gender, as they engage on social media. We discuss these findings to make three contributions. First, we extend conversations on gender in Human-Computer Interaction (HCI) by discussing how design in patriarchal contexts might be rooted in existing efforts towards change and appropriation. Second, we expand understandings of privacy in HCI as being situated in the relationship between the individual and the collective. Third, we discuss how looking at our participants' social media use across multiple platforms leads to greater insight into the link between social media engagement and privacy.
Learning multi-region features for vehicle re-identification with context-based ranking method

Vehicle re-identification is to identify a target vehicle in different cameras with non-overlapping views. It is a challenging task due to various viewpoints of vehicles, diversified illuminations and complicated environments. Most existing vehicle re-identification methods focus on learning global features, while neglecting the importance of local features. In this paper, we propose a Multi-Region Model (MRM) to learn powerful features for vehicle re-identification. In addition to extracting global region features, MRM also extracts features from a series of local regions. For each local region, instead of utilizing the rigid part to extract features directly, a Spatial Transformer Network (STN) based localization model is introduced to localize local regions which contain more distinctive visual cues. In order to further improve the performance of re-identification, we design a context-based ranking method which generates the ranking list by taking context and content into consideration to measure the similarity between neighbors. Experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset.
Microservices Anti-patterns: A Taxonomy

Several companies are rearchitecting their monolithic information systems with microservices. However, many companies migrate to microservices without experience, mainly learning how to migrate from books or from practitioners’ blogs. Because of the novelty of the topic, practitioners and consultancies are learning by doing how to migrate, thus facing several issues but also several benefits. In this chapter, we introduce a catalog and a taxonomy of the most common microservices anti-patterns in order to identify common problems. Our anti-pattern catalog is based on the experience summarized by different practitioners we interviewed in the last 3 years. We identified a taxonomy of 20 anti-patterns, including organizational (team oriented and technology/tool oriented) anti-patterns and technical (internal and communication) anti-patterns. The results can be useful to practitioners to avoid experiencing the same difficult situations in the systems they develop. Moreover, researchers can benefit from this catalog and further validate the harmfulness of the anti-patterns identified.
Enhancing smart shop floor management with ubiquitous augmented reality

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users.
Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study

This paper deals with a methodical design approach of fault-tolerant controller that gives assurance for the the stabilization and acceptable control performance of the nonlinear systems which can be described by Takagi–Sugeno (T–S) fuzzy models. Takagi–Sugeno fuzzy model gives a unique edge that allows us to apply the traditional linear system theory for the investigation and blend of nonlinear systems by linear models in a different state space region. The overall fuzzy model of the nonlinear system is obtained by fuzzy combination of the all linear models. After that, based on this linear model, we employ parallel distributed compensation for designing linear controllers for each linear model. Also this paper reports of the T–S fuzzy system with less conservative stabilization condition which gives decent performance. However, the controller synthesis for nonlinear systems described by the T–S fuzzy model is a complicated task, which can be reduced to convex problems linking with linear matrix inequalities (LMIs). Further sufficient conservative stabilization conditions are represented by a set of LMIs for the Takagi–Sugeno fuzzy control systems, which can be solved by using MATLAB software. Two-rule T–S fuzzy model is used to describe the nonlinear system and this system demonstrated with proposed fault-tolerant control scheme. The proposed fault-tolerant controller implemented and validated on three interconnected conical tank system with two constraints in terms of faults, one issed to build the actuator and sond is system component (leak) respectively. The MATLAB Simulink platform with linear fuzzy models and an LMI Toolbox was used to solve the LMIs and determine the controller gains subject to the proposed design approach.
Freedom

In this paper we design and implement Freedom, a mobile VR system that deliver high quality VR content on today's mobile devices using 4G/LTE cellular networks. Compared to existing state-of-the-art, Freedom does not rely on any video frame pre- rendering or viewpoint prediction. We send a latency-adaptive VAM frame that contains pixels around the FoV. This allows the clients to render locally at a high refresh rate of 60 Hz to accommodate and compensate for the user's head movements before the next server update arrives. We demonstrate that Freedom is the first system in the world that can support dynamic and live 8K resolution VR content, while adapting to the real-world latency variations experienced in cellular networks. Compared to streaming the whole 360° panoramic VR content, we show that Freedom achieves up to 80% bandwidth savings. Finally, we provide detailed end to end latency measurements of actual VR systems by running extensive experiments in a private LTE testbed using a Mobile Edge Cloud (MEC).
QIF-Verilog: Quantitative Information-Flow based Hardware Description Languages for Pre-Silicon Security Assessment

Hardware vulnerabilities are often due to design mistakes because the designer does not sufficiently consider potential security vulnerabilities at the design stage. As a result, various security solutions have been developed to protect ICs, among which the language-based hardware security verification serves as a promising solution. The verification process will be performed while compiling the HDL of the design. However, similar to other formal verification methods, the language-based approach also suffers from scalability issue. Furthermore, existing solutions either lead to hardware overhead or are not designed for vulnerable or malicious logic detection. To alleviate these challenges, we propose a new language based framework, QIF-Verilog, to evaluate the trustworthiness of a hardware system at register transfer level (RTL). This framework introduces a quantified information flow (QIF) model and extends Verilog type systems to provide more expressiveness in presenting security rules; QIF is capable of checking the security rules given by the hardware designer. Secrets are labeled by the new type and then parsed to data flow, to which a QIF model will be applied. To demonstrate our approach, we design a compiler for QIF-Verilog and perform vulnerability analysis on benchmarks from Trust-Hub and OpenCore. We show that Trojans or design faults that leak information from circuit outputs can be detected automatically, and that our method evaluates the security of the design correctly.
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning

We propose a joint subspace recovery and enhanced locality-based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL). The RFDDL mainly improves the data representation and classification abilities by enhancing the robust property to sparse errors and encoding the locality, reconstruction error, and label consistency more accurately. First, for the robustness to noise and sparse errors in data and atoms, the RFDDL aims at recovering the underlying clean data and clean atom subspaces jointly, and then performs DL and encodes the locality in the recovered subspaces. Second, to enable the data sampled from a nonlinear manifold to be handled potentially and obtain the accurate reconstruction by avoiding the overfitting, the RFDDL minimizes the reconstruction error in a flexible manner. Third, to encode the label consistency accurately, the RFDDL involves a discriminative flexible sparse code error to encourage the coefficients to be soft. Fourth, to encode the locality well, the RFDDL defines the Laplacian matrix over recovered atoms, includes label information of atoms in terms of intra-class compactness and inter-class separation, and associates with group sparse codes and classifier to obtain the accurate discriminative locality-constrained coefficients and classifier. The extensive results on public databases show the effectiveness of our RFDDL.
Locally differentially private item-based collaborative filtering

Recently, item-based collaborative filtering has attracted a lot of attention. It recommends to users new items which may be of interests to them, based on their reported historical data (i.e., the items they have already been interested in). The reported historical data leads to significant privacy risks in case that the recommending service is not fully trusted. Many researches have focused on developing differential privacy mechanisms to protect personal data in various recommendations. However, most of these mechanisms can not ensure accuracy of the recommendations. The main reason for this problem is that these methods compute similarity directly from the perturbation data. The computed similarity is thus always inaccurate and this inaccurate similarity finally leads to inaccurate recommendation results. In this paper, we propose a locally differentially private item-based collaborative filtering framework, which protects users’ private historical data on the user-side, and on the server-side reconstructs the similarity to ensure recommendation accuracy. The similarities are reconstructed for every pair of items, by estimating the number of users who have rated neither, either one, or both of them. The final recommendation is generated by the reconstructed similarities. Experimental results show that our proposed method significantly outperforms the state-of-the-art methods in terms of the recommendation accuracy and the trade-off between privacy and accuracy.
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm

Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natural language processing (NLP) pipeline that can extract measurements and their core descriptors, such as temporality, anatomical entity, imaging observation, RadLex descriptors, series number, image number, and segment from a wide variety of radiology reports (MR, CT, and mammogram). We created a hybrid NLP pipeline that integrates rule-based feature extraction modules and conditional random field (CRF) model for extraction of the measurements from the radiology reports and links them with clinically relevant features such as anatomical entities or imaging observations. The pipeline was trained on 1117 CT/MR reports, and performance of the system was evaluated on an independent set of 100 expert-annotated CT/MR reports and also tested on 25 mammography reports. The system detected 813 out of 806 measurements in the CT/MR reports; 784 were true positives, 29 were false positives, and 0 were false negatives. Similarly, from the mammography reports, 96% of the measurements with their modifiers were extracted correctly. Our approach could enable the development of computerized applications that can utilize summarized lesion measurements from radiology report of varying modalities and improve practice by tracking the same lesions along multiple radiologic encounters.
Survey of Sentiment Analysis Using Deep Learning Techniques

This paper presents a detailed review of deep learning techniques used in Sentiment Analysis. Sentiment analysis is one of the most researched areas in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect oriented product analysis, sentiment analysis and text classification like email categorization and spam filtering. The conventional methods used for sentiment analysis is lexicon based processing. However, with the advancements in the field of artificial intelligence, the machine learning algorithms started to play a major role in sentiment analysis applications. Currently deep learning technique is the latest hotspot being used for predicting the sentiments. Several research works have been carried out in the Natural Language Processing (NLP) using the deep learning methods. The most popular deep learning methods employed includes Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) particularly the Long Short Term Memory (LSTM). These techniques are used in combination or as stand-alone based on the domain area of application. The focus of this survey is on the various flavors of the deep learning methods used in different applications of sentiment analysis at sentence level and aspect/target level. Furthermore, the advantages and drawbacks of the methods are discussed along with their performance parameters.
Learning deep neural networks for node classification

Deep Neural Network (DNN) has made great leaps in image classification and speech recognition in recent years. However, employing DNN for node classification such as in social network remains to be a non-trivial problem. Moreover, the current advanced method of implementing node classification tasks usually takes two steps, i.e. firstly, the embedding vector of the node is obtained through network embedding and then the classifier such as SVM is leveraged to do the task. Distinctly, this may only get the suboptimal solution of the problem. To settle the above issues, a novel Deep Neural Network method for node classification named DNNNC is proposed in the framework of Deep Learning. Specifically, we first get the positive pointwise mutual information (PPMI) matrix from the given adjacency matrix. Then, the data is fed to deep neural network composed of deep stacked sparse autoencoders and softmax layer, which could learn the node representation while encoding the rich nonlinear structural and semantic information and could be well trained for node classification under the DNN framework. Extensive experiments are conducted on real-world network datasets for node classification task and have shown that the proposed model DNNNC outperforms the state-of-the-art method in the view of superior performance.
Automatic Gender Classification through Face Segmentation

Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all six classes. We use the probability maps as gender descriptors and trained a Random Decision Forest (RDF) classifier, which classifies the face images as either male or female. The performance of the proposed framework is assessed on four publicly available datasets, namely Adience, LFW, FERET, and FEI, with results outperforming state-of-the-art (SOA).
Multistage processes linked to tectonic transition in the genesis of orogenic gold deposit: A case study from the Shanggong lode deposit, East Qinling, China

The Shanggong lode deposit represents one of the largest Mesozoic orogenic gold deposits in the East Qinling metallogenic belt of central China. Here we investigate the texture, trace element and sulfur isotopic composition of pyrite from this deposit to gain insights in the ore genesis. Three types of pyrite are recognized by its texture: Py1 is represented by coarse-grained cubic and pentagonal dodecahedron belonging to the stage I quartz-pyrite vein; Py2 comprises fine-grained disseminated pentagonal dodecahedron belonging to stage II quartz-sulfides vein; Py2 can be further divided into core and rim: porous cores (Py2c) carrying abundant, visible mineral inclusions and significantly higher arsenic concentration, and clean rims (Py2r) with low arsenic. Systematic LA-ICPMS trace elements and SIMS sulfur isotopic analyses reveal two distinct stages of mineralization with diverse source materials. 1) The early stage is represented by Py1 which contains 0.65–70 ppm Au (average 11.6 ppm), with relatively low and variable concentrations of Cu, Ag, Te, Pb, Zn, and Bi. The δ34S values of Py1 range from −2.7‰ to 7.2‰, suggesting that the sulfur source for the early stage fluid was mainly derived from the Taihua and Xiong’er Groups and that the depleted δ34S values reflect changes of pH, fO2 and fluid-rock interaction. 2) The main stage is represented by Py2 which contains Au in the range of 0.14–189 ppm (average 39.7 ppm). The Py2 carries significantly higher contents of compatible elements (Ni, Co, V, and Cr) and ore metals (e.g. Au, Ag, Pb, Zn, As, Te) than those in Py1, with the ore metals occurring as invisible lattice bound or nanosized particles and visible micron-sized inclusions. The δ34S values of Py2 show marked depletion with a range of −16.3‰ to −11.7‰, attributing to the fractionation between barite and sulfide minerals. Together with previous studies and trace element characters in Py2, we identify a contribution of Au from mantle source into the ore-forming fluid system during the main stage. We propose that the two stages of gold mineralization in the Shanggong deposit, in response to the collisional assembly of Qinling Orogenic Belt, preserve evidence for diverse metal sources with the early stage derived from partial melting of the lower crust (Taihua and Xiong’er Groups) in the compressional stage, and the main stage characterized by mantle contribution in a post-collisional extensional setting. Our findings may have important implications for the exploration of regional Triassic gold and molybdenum deposits.