Reviewerly/exHarmony huggingface dataset:
Inferring Private Attributes Based on Graph Convolutional Neural Network in Social Networks

Machine Learning (ML) brings new challenges to Online Social Network (OSN) privacy preserving. Although privacy control access operation of OSN can avoid privacy disclosure in a certain extent, it still can't prevent ML-based inference attacks. Previous inference methods have shown that user hidden attributes can be reliably predicted from released OSN datasets, but there are limitations in the implementation of complex large-scale social networks. To effectively perform multi-attributes' feature learning and increase the inference accuracy, we present a private attributes inference approach based on Graph Convolutional Neural Networks for inferring attributes of social users. Our method leverages visible user profiles and social links to predict missing attributes of the target users in the published online social network datasets and it can be used to any large-scale graph-based networks. We experimentally proved that our approach can correctly infer private attributes with high probability.
Designing a Feature Selection Technique for Analyzing Mixed Data

Since large-scale data analysis often requires a vast amount of computational time, even with using high-end computational resources, researchers proposed various data analysis approaches. Although their approaches are well designed, most of them still suffer from analyzing large-scale data efficiently. To overcome the limitation, identifying the optimal numbers of features is critical. In this paper, a new technique is introduced to boost model performances by determining optimal features in noisy mixed data. It performs a continuous evaluation to determine the best possible features that suit to a chosen data analysis algorithm. The proposed technique is compared with three different feature selection techniques: Principal Component Analysis (PCA), Analysis of variance (ANOVA) test, and Mutual Information (MI). To show the effectiveness of our proposed technique, a performance evaluation was conducted with three machine learning algorithms: Decision Tree, Random Forest, and k-Nearest Neighbor (KNN). From the evaluation with three different financial datasets, we determined about 5 ~ 10% performance improvement when utilizing our proposed technique.
A method for in situ measurement of directional and spatial radiosity distributions from complex-shaped solar thermal receivers

A methodology for in-situ measurements of radiative reflection and emission losses from a solar thermal receiver under high-flux irradiation is demonstrated. It combines radiosity analysis with photogrammetry and image recognition techniques to obtain directional and spatial radiosity distributions over receiver surfaces with a simple setup, mainly consisting of a camera. A CCD camera can acquire the radiosity in the visible range, which predominantly captures reflected solar irradiation. A thermal infrared camera can acquire the radiosity in the infrared range, which predominantly captures emission losses from the hot receiver surfaces. A hyperspectral camera can be used to obtain spectrally resolved results across a range of wavelengths. Images are taken from different directions in front of the receiver, and processed in software to obtain a point cloud via three-dimensional reconstruction, allowing the image data to be mapped onto a receiver mesh model. The receiver can be any shape, including those with complex-shaped cavity-like geometries exhibiting surface occlusion and light-trapping effects. These camera-based non-contact measurements allow for the performance of a receiver to be evaluated without interrupting its normal operation. The feasibility of the method is tested by quantifying the reflection losses from a multi-cavity tubular receiver under ~850 kW/m2 concentrated solar irradiation. The proof of concept is established by comparing the measured results with those from Monte-Carlo ray-tracing simulations.
Deconstructing Google’s Web Light Service

Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers.
A Study of Deep Learning in Text Analytics

Most of the data present today on Web is in the form of text. This text data is a rich source of information, but it is difficult to process because it is unstructured in nature. Various techniques have been developed in past to process this text data for information retrieval and text mining. Many machine learning techniques have been developed in past to drive valuable information from text data. However, these methods require a lot of preprocessing and feature extraction prior to processing which increases manual task. There are a lot of human interactions which not only consumes time but also makes the whole system where rigid and hard to generalize. Deep learning architectures mitigate these limitations. They require less human intervention and create better solutions than other machine learning techniques. Deep architectures are neural networks with multiple processing layers of neuron with each layer having a specific task. Various deep learning architectures have been developed till date with each having a specific task. These architectures have contributed significantly in the domain of text processing. Word2Vec and GloVe have helped to represent text in vector forms. Vector representation helps to process text easily. Architectures like CNN & autoencoders have helped to automate the task of feature extraction. On the other hand, architectures like LSTM and RNN are used for text processing. Text classification, document summarization, question answering, machine translation, caption generation, and speech recognition are some of the areas where improvements have been found due to deep learning architectures. This paper first introduces the text preprocessing techniques and feature extraction methods. Later on, a study of contributions of deep learning architectures in text processing is presented.
Modeling and Aggregation of Complex Annotations via Annotation Distances

Modeling annotators and their labels is valuable for ensuring collected data quality. Though many models have been proposed for binary or categorical labels, prior methods do not generalize to complex annotations (e.g., open-ended text, multivariate, or structured responses) without devising new models for each specific task. To obviate the need for task-specific modeling, we propose to model distances between labels, rather than the labels themselves. Our models are largely agnostic to the distance function; we leave it to the requesters to specify an appropriate distance function for their given annotation task. We propose three models of annotation quality, including a Bayesian hierarchical extension of multidimensional scaling which can be trained in an unsupervised or semi-supervised manner. Results show the generality and effectiveness of our models across diverse complex annotation tasks: sequence labeling, translation, syntactic parsing, and ranking.
Go See a Specialist? Predicting Cybercrime Sales on Online Anonymous Markets from Vendor and Product Characteristics

Many cybercriminal entrepreneurs lack the skills and techniques to provision certain parts of their business model, leading them to outsource these parts to specialized criminal vendors. Online anonymous markets, from Silk Road to AlphaBay, have been used to search for these products and contract with their criminal vendors. While one listing of a product generates high sales numbers, another identical listing fails to sell. In this paper, we investigate which factors determine the performance of cybercrime products.
Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs

Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering.As KGs are often automatically constructed, they may contain incorrect facts.Detecting them is a crucial, yet extremely expensive task.Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest.Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs.In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns.Though much smaller, KG abstractions preserve inconsistency and their explanations.Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.
Semi-supervised learning method based on predefined evenly-distributed class centroids

Compared to supervised learning, semi-supervised learning reduces the dependence of deep learning on a large number of labeled samples. In this work, we use a small number of labeled samples and perform data augmentation on unlabeled samples to achieve image classification. Our method constrains all samples to the predefined evenly-distributed class centroids (PEDCC) by the corresponding loss function. Specifically, the PEDCC-Loss for labeled samples, and the maximum mean discrepancy loss for unlabeled samples are used to make the feature distribution closer to the distribution of PEDCC. Our method ensures that the inter-class distance is large and the intra-class distance is small enough to make the classification boundaries between different classes clearer. Meanwhile, for unlabeled samples, we also use KL divergence to constrain the consistency of the network predictions between unlabeled and augmented samples. Our semi-supervised learning method achieves the state-of-the-art results, with 4000 labeled samples on CIFAR10 and 1000 labeled samples on SVHN, and the accuracy is 95.10% and 97.58% respectively. Code is available in https://github.com/sweetTT/semi-supervised-method-based-on-PEDCC .
Clone Swarm: A Cloud Based Code-Clone Analysis Tool

A code clone is defined as a pair of similar code fragments within a software system. While code clones are not always harmful, they can have a detrimental effect on the overall quality of a software system due to the propagation of bugs and other maintenance implications. Because of this, software developers need to analyse the code clones that exist in a software system. However, despite the availability of several clone detection systems, the adoption of such tools outside of the clone community remains low. A possible reason for this is the difficulty and complexity involved in setting up and using these tools. In this paper, we present Clone Swarm, a code clone analytics tool that identifies clones in a project and presents the information in an easily accessible manner. Clone Swarm is publicly available and can mine any open-sourced GIT repository. Clone Swarm internally uses NiCad, a popular clone detection tool in the cloud and lets users interactively explore code clones using a web-based interface at multiple granularity levels (Function and Block level). Clone results are visualized in multiple overviews, all the way from a high-level plot down to an individual line by line comparison view of cloned fragments. Also, to facilitate future research in the area of clone detection and analysis, users can directly download the clone detection results for their projects. Clone Swarm is available online at clone-swarm.usask.ca. The source code for Clone Swarm is freely available under the MIT license on GitHub.
Digital Literacies in Virtual Reality Learning Contexts

Recent virtual reality (VR) applications are on the cutting edge of learning technologies designed to trigger self-directed and holistic learning experiences. However, learning via and within VR does not start from scratch; it depends on our understanding of preceding learning technologies. This paper explores key tenets in the concept of digital literacies and discusses their applicability to VR-enhanced learning environments. More specifically, it focuses on meaning-making practices in 360-degree media that incorporate and expand upon skills and competencies associated with hypertextual reading and writing. Based on two prototype cases designed and implemented by students with no technical background knowledge, the paper identifies generic steps for the creation of 360-degree media learning environments. The student projects serve as a showcase for how VR development is moving beyond the specialized and exclusive domain of software engineers and becoming an increasingly textual practice that integrates and enhances skills and competencies associated with preceding writing technologies.
Domain-Enriched Deep Network for Micro-CT Image Segmentation

Micro-computer tomography (μCT) is increasingly used in Anthropology and Palaeontology to quantify the external and internal osteological characteristics of extant/extinct species. One of the challenging tasks on such data is the accurate segmentation of images into bone and non-bone classes. Many intensity-based segmentation approaches have been proposed to overcome this issue, moving from global-thresholding to robust (semi)automatic methods. However, researchers often resort to laborious manual segmentation when the intensity levels of bone and non-bone material are extremely similar. Recently, deep learning methods have been shown to outperform traditional approaches for image segmentation. Here we propose a novel domain enriched deep network architecture that combines the benefits of deep learning with expert knowledge of bone structure via two components - 1) a representation network capable of extracting features that are more responsive to bone structures and less responsive to non-bone structures, and 2) a segmentation network that utilizes the features obtained from the representation network to perform segmentation. Effective representation filters are obtained through a robust discriminative-features constraint that enables the discovery of novel features and enhances segmentation accuracy. Experiments performed on challenging μCT images of archaeological bones reveal practical merits of our proposal over state-of-the-art alternatives.
SEBS: A Secure Element and Blockchain Stratagem for Securing IoT

In the context of the Internet of Things (IoT), the issue of holistic data security is complex as data has to be secured at 3 critical points i.e., 1. Point of generation (IoT sensors), 2. Point of storage, 3. Point of usage (IoT actuators). Point 2 can be adequately addressed by Blockchain that offers remarkable immutability by storing data securely and transparently in a distributed setting, making it tamper-proof while in storage. However, blockchain alone cannot root out all the data security impediments in an IoT network as Points 1 and 3 still needs to be resolved. In this paper, we propose a novel Secure Element and Blockchain based stratagem called SEBS to effectively secure all 3 Points at once, thereby realizing light, holistic, efficient IoT data security in true sense. Further, we elaborate on SEBS implementation in a generic IoT network scenario and show how it improves the performance of critical security operations by as much as 31 times. Next, we address a niche problem by proposing a novel SEOVA or SE based offline verification algorithm to verify if the data received through intermediary really belongs to the blockchain.
Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Abstract It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data.
Lattice-Based Cryptoprocessor for CCA-Secure Identity-Based Encryption

In this paper, we propose a lattice-based encryption scheme for Identity-Based Encryption (IBE) which is secure against Chosen-Ciphertext Attacks (CCA), and it is deduced from a previous Chosen-Plaintext Attack (CPA) secure IBE scheme by applying Fujisaki-Okamoto transform. Also, we present the design of a lattice-based cryptoprocessor that performs the encryption or decryption of the proposed CCA IBE scheme for the security parameters n = 512 and n = 1024. In this case, the cryptoprocessor is mainly composed of one Random Number Generator (RNG), two hash function cores based on the Secure Hash Algorithm-3 (SHA-3) standard, and one Number Theoretical Transform (NTT) core for performing the polynomial ring operations. NTT core is designed using a systolic array and radix-2 multiple-path delay commutator architecture. The cryptoprocessor was synthesized on FPGA EP4SGX230KF40C2 using Quartus Prime 17.1, and verified in hardware using DE4 board and SignalTap tool. According to the obtained synthesis results, for the security parameter n = 1024, the IBE cryptoprocessor performs the encryption in 21.10 μs, and the decryption in 24.87 μs.
Knowledge Engineering and Planning for Social Human–Robot Interaction: A Case Study

The core task of automated planning is goal-directed action selection; this task is not unique to the planning community, but is also relevant to numerous other research areas within AI. One such area is interactive systems, where a fundamental component called the interaction manager selects actions in the context of conversing with humans using natural language. Although this has obvious parallels to automated planning, using a planner to address the interaction management task relies on appropriate engineering of the underlying planning domain and planning problem to capture the necessary dynamics of the world, the agents involved, their actions, and their knowledge. In this chapter, we describe work on using domain-independent automated planning for action section in social human–robot interaction, focusing on work from the JAMES (Joint Action for Multimodal Embodied Social Systems) robot bartender project.
Color Guided Depth Map Super-Resolution based on a Deep Self-Learning Approach

RGB-D cameras have been widely used in various applications, such as gesturing or exercise games, consumer healthcare systems, and 3D measurements. However, the resolution of depth maps is lower than that of an RGB image, which significantly limits the potential applications of depth maps. Recently, deep learning-based super-resolution techniques have achieved state-of-the-art results for image resolution enhancement, in which paired high- and low-resolution images are used for training. The challenge with the depth map super resolution for RGB-D cameras is that we do not have high-resolution depth maps for training models (deep networks). We propose a deep self-learning approach for color-guided depth map super resolution. We achieve super resolution using only low-resolution depth maps to train a network, which is comparable to the ideal case (training the network using paired high- and low-resolution images. The high-resolution image is used as a guide to improve the resolution enhancement. Experimental results demonstrate that the proposed method outperforms almost as same as accuracy of trained by external datasets even not using external datasets.
: Measuring Centralization of DNS Infrastructure in the Wild

The Internet Domain Naming System (DNS) is one of the pillars for the Internet and has been the subject of various Distributed Denial-of-Service (DDoS) attacks over the years. As a countermeasure, the DNS infrastructure has been engineered with a series of replication measures, such as relying on multiple authoritative name servers and using IP anycast. Even though these measures have been in place, we have seen that, when servers rely on third-party DNS providers for reliable services, there may be certain levels of infrastructure centralization. In this case, an attack against a DNS target might affect other authoritative DNS servers sharing part of the infrastructure with the intended victim. However, measuring such levels of infrastructure sharing is a daunting task, given that researchers typically do not have access to DNS provider internals. In this paper, we introduce a methodology and associated tool dnstracker that allows measuring, to various degrees, the level of both concentration and shared infrastructure using active DNS measurements. As a case study, we analyze the authoritative name servers of all domains of the Alexa Top 1 Million most visited websites. Our results show that, in some cases, up to 12.000 authoritative name servers share the same underlying infrastructure of a third-party DNS provider. As such, in the event of an attack, those authoritative DNS servers have increased the probability of suffering from collateral damage.
Using convolutional neural network (CNN) approach for ship detection in Sentinel-1 SAR imagery

Accurate maritime ship surveillance and monitoring ensures compliance with port regulations and standards. The growing volume of waterborne traffic however, has made this goal difficult to achieve in applications like maritime traffic control, ship search and rescue, territorial regulation, and fishery management. Detection of ships is complicated, especially under unfavourable conditions, such as during night-time or on cloudy days. Synthetic aperture radar (SAR) provides high-resolution data that can overcome these limitations. Using machine-learning techniques to detect ships in a SAR based image can increase the accuracy of identification detection results as compared to traditional image-based object detection methods. Sentinel-1 SAR images from 2015 to 2018 were used in this exploratory study presenting an analysis of effective ship detection and ship count in a congested sea environment using a Convolutional Neural Networks (CNNs) method, the Faster R-CNN VGG16. Experiments using sixteen convolutional layers in the model yielded promising and significant detection, identification and ship count results for the port of Shanghai.
New optimization algorithms for neural network training using operator splitting techniques

In the following paper we present a new type of optimization algorithms adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate through numerical simulations the empirical rate of convergence of these iterative schemes toward a local minimum of the loss function, with some suitable choices of the underlying hyper-parameters. We validate the convergence of these optimizers using the results of the accuracy and of the loss function on the MNIST, MNIST-Fashion and CIFAR 10 classification datasets.