Reviewerly/exHarmony huggingface dataset:
Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data.In some degree, single kernel methods face the challenging problem of kernel choice.To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years.However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data; (2) they disregard the highorder correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels.In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for nonlinear data clustering, namely TMKGC.Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs.Afterwards, the highorder structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently.Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function.Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.
Chartem: Reviving Chart Images with Data Embedding

In practice, charts are widely stored as bitmap images. Although easily consumed by humans, they are not convenient for other uses. For example, changing the chart style or type or a data value in a chart image practically requires creating a completely new chart, which is often a time-consuming and error-prone process. To assist these tasks, many approaches have been proposed to automatically extract information from chart images with computer vision and machine learning techniques. Although they have achieved promising preliminary results, there are still a lot of challenges to overcome in terms of robustness and accuracy. In this paper, we propose a novel alternative approach called Chartem to address this issue directly from the root. Specifically, we design a data-embedding schema to encode a significant amount of information into the background of a chart image without interfering human perception of the chart. The embedded information, when extracted from the image, can enable a variety of visualization applications to reuse or repurpose chart images. To evaluate the effectiveness of Chartem, we conduct a user study and performance experiments on Chartem embedding and extraction algorithms. We further present several prototype applications to demonstrate the utility of Chartem.
Neural software vulnerability analysis using rich intermediate graph representations of programs

Security vulnerabilities are among the major concerns of modern software engineering. Successful results of machine learning techniques in various challenging applications have led to an emerging field of research to investigate the effectiveness of machine learning, and more recently, deep learning techniques, for the problem of software vulnerability analysis and discovery. In this paper, we explore the utilization of Graph Neural Networks as the latest trend and progress in the field of artificial neural networks. To this end, we propose an original neural vulnerability analysis approach, using customized intermediate graph representations of programs to train graph neural network models. Experimental results on a public suite of vulnerable programs show that the proposed approach is effective at the task of software vulnerability analysis. Additional empirical experiments answer complementary research questions about the proposed approach. In particular, we present experimental results for the challenging task of cross-project vulnerability analysis, with interesting insights on the capabilities of our novel approach. Furthermore, a software utility that was developed in the course of this study is also published as a further contribution to the research community.
Improved vergence and accommodation via Purkinje Image tracking with multiple cameras for AR glasses

We present a personalized, comprehensive eye-tracking solution based on tracking higher-order Purkinje images, suited explicitly for eyeglasses-style AR and VR displays. Existing eye-tracking systems for near-eye applications are typically designed to work for an on-axis configuration and rely on pupil center and corneal reflections (PCCR) to estimate gaze with an accuracy of only about 0.5°to 1°. These are often expensive, bulky in form factor, and fail to estimate monocular accommodation, which is crucial for focus adjustment within the AR glasses.Our system independently measures the binocular vergence and monocular accommodation using higher-order Purkinje reflections from the eye, extending the PCCR based methods. We demonstrate that these reflections are sensitive to both gaze rotation and lens accommodation and model the Purkinje images' behavior in simulation. We also design and fabricate a user-customized eye tracker using cheap off-the-shelf cameras and LEDs. We use an end-to-end convolutional neural network (CNN) for calibrating the eye tracker for the individual user, allowing for robust and simultaneous estimation of vergence and accommodation. Experimental results show that our solution, specifically catering to individual users, outperforms state-of-the-art methods for vergence and depth estimation, achieving an accuracy of 0.3782° and 1.108cm respectively.
Type-reduced vague possibilistic fuzzy clustering for medical images

Soft computing provides the framework for dealing with the uncertainty and imprecision inherent in real-life applications. Soft computing has become a long-standing notable paradigm for medical image processing. A typical fuzzy clustering uses the fuzzy membership function. Nevertheless, there is an alternative membership representation, known as typicality or possibilistic membership. Unlike fuzzy membership that is probabilistic in nature, typicality represents an absolute membership and it is the degree of belonging of an object to a class that does not depend on its distances from the other classes. However, both fuzzy membership and typicality play important role in assigning membership to an object. This study proposes a novel clustering model that creates a vague environment enriched with the concept of fuzzy membership and typicality, while the use of type-reduction plays an essential role in capturing all the vagueness present in the data set. The proposed model is called type-reduced vague possibilistic fuzzy clustering (TVPFC), and we use MRI images to demonstrate its superior robustness over that of FCM (fuzzy c-means), PCM (possibilistic c-means), VCM (vague c-means) and IPFCM (interval-valued possibilistic fuzzy c-means).
Secure Batch Matrix Multiplication From Grouping Lagrange Encoding

In this letter, the problem of distributed Secure Batch Matrix Multiplication (SBMM) is studied, where a user wishes to compute the pairwise products of two batches of massive matrices <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {B}$ </tex-math></inline-formula> generated by two external source nodes, with the aid of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> distributed servers. The security for data matrices <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> (resp. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {B}$ </tex-math></inline-formula> ) is guaranteed against any group of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X_{\mathbf {A}}$ </tex-math></inline-formula> (resp. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X_{\mathbf {B}}$ </tex-math></inline-formula> ) colluding servers. As a result, a computation strategy is presented to characterize the trade-off between recovery threshold, system cost and system complexity, based on grouping Lagrange encoding, which unifies and improves the previous strategies for SBMM.
What (if any) behaviour change techniques do government-led cybersecurity awareness campaigns use?

Abstract With the surge in cyber incidents in recent years, many linked to human error, governments are quite naturally developing security campaigns to improve citizens’ security behaviour. However, it remains not only unclear how successful these campaigns are in changing behaviour, but also what established behaviour change techniques—if any—they employ in order to achieve this goal. To investigate this, we analysed 17 government-sponsored cybersecurity campaign materials. We coded the materials for their intervention functions according to the Behaviour Change Wheel and their behaviour change techniques in accordance with the Behavioural Change Technique Taxonomy (version 1). Our findings show that security campaigns are often focused on education and increasing awareness, under the assumption that as long as citizens are aware of the risk, and are provided with information on how to improve their security behaviour, behaviour will change. Additionally, there is a lack of published effectiveness studies investigating the direct effects of a governmental cybersecurity campaign. Proposed improvements to security campaigns are discussed.
Extractive Text Summarization using Dynamic Clustering and Co-Reference on BERT

The process of picking sentences directly from the story to form the summary is extractive summarization. This process is aided by scoring functions and clustering algorithms to help choose the most suitable sentences. We use the existing BERT model which stands for Bidirectional Encoder Representations from Transformers, to produce extractive summarization by clustering the embeddings of sentences by K-means clustering, but introduce a dynamic method to decide the suitable number of sentences to pick from clusters.On top of that, the study is aimed at producing summaries of higher quality by incorporating reference resolution and dynamically producing summaries of suitable sizes depending on the text. This study aims to provide students with a summarizing service to help understand the content of lecture videos of long duration which would be vital in the process of revision.
JKQ

With quantum computers on the brink of practical applicability, there is a lively community that develops toolkits for the design of corresponding quantum circuits. Many of the problems to be tackled here are similar to design problems from the classical realm for which sophisticated design automation tools have been developed in the previous decades. In this paper, we present JKQ---a set of tools for quantum computing developed at the Johannes Kepler University (JKU) Linz which utilizes this design automation expertise. By this, we offer complementary approaches for many design problems in quantum computing such as simulation, compilation, or verification. In the following, we provide an introduction of the tools for potential users who would like to work with them as well as potential developers aiming to extend them.
Enabling Privacy and Traceability in Supply Chains using Blockchain and Zero Knowledge Proofs

In recent years there have been increased efforts to make supply chains transparent and traceable to better protect the end consumer's interests against counterfeiting, contamination, false claims, and inadequate processes. The actors in the supply chain can also leverage additional visibility to proactively tune their operations in response to upstream or downstream disruptions. Blockchain technology, with its promise of immutability, transparency, and provenance is a natural fit to supply chain problems. Several works have discussed the improved design of supply chains leveraging blockchain technology. There have been attempts to address some of the privacy concerns of participants on the blockchain network by using encryption schemes to hide confidential data and group signatures to hide the participant identities. But these do not handle the complexities of the supply chain, where items are constantly packaged, repackaged, and transformed before reaching the end consumer. Our work presents a blockchain-based model for the supply chain that provides privacy and traceability with efficient contamination tracing while accounting for the aforementioned complexities of the supply chain operations. The last property enables an end consumer to be convinced about a product to be unaffected by identified faulty upstream processes (contamination) without access to the entire history of the product. Our solution makes novel use of cryptographic tools such as zero-knowledge proofs and cryptographic accumulators to provide these guarantees. The choice of the cryptographic components is grounded in concrete efficiency, and we present an experimental evaluation of the implementation of our protocol on Hyperledger Fabric, a popular platform for enterprise blockchains.
Adaptively Clustering-Driven Learning for Visual Relationship Detection

Visual relationship detection aims to describe the interactions between pairs of objects, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">person-ride-bike</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bike-next to-car</i> triplets. In reality, it is often the case that there exist some groups of strongly correlated relationships, while others are weakly related. Intuitively, the common relationships can be roughly categorized into several types such as geometric (e.g., next to), action (e.g., ride), and so on. However, previous studies ignore the relatedness discovery among multiple relationships, which only lie on a unified space to leverage visual features or statistical dependencies into categories. To tackle this problem, we propose an adaptively clustering-driven network for visual relationship detection, which can implicitly divide the unified relationship space into several subspaces with specific characteristics. Particularly, we propose two novel modules to discover the common distribution space and latent relationship association, respectively, which map pairs of object features into translation subspaces to induce the discriminative relationship clustering. Then, a fused inference is designed to integrate the group-induced representations with the language prior to facilitate the predicate inference. Especially, we design the Frobenius-norm regularization to boost the clustering. To the best of our knowledge, the proposed method is the first supervised framework to realize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subject-predicate-object</i> relationship-aware clustering for visual relationship detection. Extensive experiments show that the proposed method can achieve competing performances against the state-of-the-art methods on the Visual Genome dataset. Additional ablation studies further validate its effectiveness.
Pool-based unsupervised active learning for regression using iterative representativeness-diversity maximization (iRDM)

Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to optimally balance the representativeness and the diversity of the selected samples. Experiments on 60 datasets from various domains demonstrated its effectiveness. Our iRDM can be applied to both linear regression and kernel regression, and it even significantly outperforms supervised ALR when the number of labeled samples is small.
NLP based Skeletal Pose Estimation using mmWave Radar Point-Cloud: A Simulation Approach

Human skeletal pose estimation can find several applications ranging from remote patient monitoring, pedestrian detection to defense security and surveillance. However, traditionally used high-resolution vision based sensors suffer operationally during poor illumination or object occlusion. Radars can overcome these challenges, albeit at the cost of a lower resolution. mmWave radars, on account of a higher bandwidth, have the ability to represent a target as a sparse point-cloud, with a higher resolution than its traditional radar counterparts. A supervised learning approach is adopted for skeletal estimation from the point-cloud, as its random nature from frame-to-frame makes explicit point-to-point association non-trivial. However, the lack of available radar data-sets make it extremely difficult to develop machine-learning aided methods to improve radar based computer vision applications. In this paper, we present a study to use simulated mmWave-radar-like point-cloud data to estimate skeletal key-points, of a human target using, a natural language processing approach. The sparsity and randomness in the radar point-cloud is simulated from a Microsoft Kinect acquired data using a random sampling approach. Two consecutive frames of the simulated radar point-cloud are first voxelized and aggregated, and a seq2seq architecture is used for “summarizing” it to the desired skeletal keypoints. Simulated data obtained by randomly sampling from a combination of (i) corrupting the 3-D ground truth skeletal coordinates with Gaussian noise over a range of varying degrees of variance, and (ii) adding random point-cloud noise to the corrupted data, is used to evaluate the performance of the model. The comprehensive methodology, results and discussion is presented in this paper. The promising results from this proof-of-concept simulation study serve as a basis for future experimental study using mmWave radars which will also be made open-access for public research and development of radar based perception and computer-vision.
Streaming Data Analysis: Clustering or Classification?

This article is a position paper about models and algorithms that are generally called “stream clustering.” Semantics and methods used in this field are often co-opted from static clustering, but they do not serve well for streaming data analysis. Most “state-of-the-art” methods, such as sequential k-means, Birch, CluStream, DenStream, etc., acknowledge that the data are seen but once in real streaming analysis (e.g., intrusion detection, voter fraud, etc.). Interpretation of their outputs generally overlooks the fact that when the data cannot be saved, batch clustering ideas, such as preclustering assessment, partitioning, and cluster validity are not relevant. But in the current literature, the data, or some subset of it, are often saved for hindsight evaluation (we call this fake stream clustering). Our position? Useful analysis of real streaming data is in its infancy. We do not argue that current approaches to streaming clustering are wrong: rather, we regard them as transitional methods which will eventually lead to a new and useful paradigm for this type of computation. We think that this class of models and algorithms are actually classifiers, but with a special added component, viz., continuously updated cluster footprints of the instream processing. We need to carefully define the objectives of streaming analysis, and then choose terminology and methods that suit this evolving paradigm.
Estimating Quantum Speedups for Lattice Sieves

Quantum variants of lattice sieve algorithms are routinely used to assess the security of lattice based cryptographic constructions. In this work we provide a heuristic, non-asymptotic, analysis of the cost of several algorithms for near neighbour search on high dimensional spheres. These algorithms are key components of lattice sieves. We design quantum circuits for near neighbour search algorithms and provide software that numerically optimises algorithm parameters according to various cost metrics. Using this software we estimate the cost of classical and quantum near neighbour search on spheres. For the most performant near neighbour search algorithm that we analyse we find a small quantum speedup in dimensions of cryptanalytic interest. Achieving this speedup requires several optimistic physical and algorithmic assumptions.
Boosting Offline Handwritten Text Recognition in Historical Documents With Few Labeled Lines

In this paper, we face the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set. Three main contributions are developed. First we analyze how to perform transfer learning (TL) from a massive database to a smaller historical database, analyzing which layers of the model need a fine-tuning process. Second, we analyze methods to efficiently combine TL and data augmentation (DA). Finally, an algorithm to mitigate the effects of incorrect labelings in the training set is proposed. The methods are analyzed over the ICFHR 2018 competition database, Washington and Parzival. Combining all these techniques, we demonstrate a remarkable reduction of CER (up to 6% in some cases) in the test set with little complexity overhead.
Violence Detection in Videos using Deep Recurrent and Convolutional Neural Networks

Violence and abnormal behavior detection research have known an increase of interest in recent years, due mainly to a rise in crimes in large cities worldwide. In this work, we propose a deep learning architecture for violence detection, which combines both recurrent neural networks (RNNs) and 2-dimensional convolutional neural networks (2D CNN). In addition to video frames, we use optical flow computed using the captured sequences. CNN extracts spatial characteristics in each frame, while RNN extracts temporal characteristics. The use of optical flow allows to encode the movements in the scenes. The proposed approaches reach the same level as state-of-the-art techniques and sometimes surpass them. The techniques were validated on three databases achieving very interesting results.