Reviewerly/exHarmony huggingface dataset:
DynGraphGAN: Dynamic Graph Embedding via Generative Adversarial Networks

Graphs have become widely adopted as a means of representing relationships between entities in many applications. These graphs often evolve over time. Learning effective representations preserving graph topology, as well as latent patterns in temporal dynamics, has drawn increasing interests. In this paper, we investigate the problem of dynamic graph embedding that maps a time series of graphs to a low dimensional feature space. However, most existing works in the field of dynamic representation learning either consider temporal evolution of low-order proximity or treat high-order proximity and temporal dynamics separately. It is challenging to learn one single embedding that can preserve the high-order proximity with long-term temporal dependencies. We propose a Generative Adversarial Networks (GAN) based model, named DynGraphGAN, to learn robust feature representations. It consists of a generator and a discriminator trained in an adversarial process. The generator generates connections between nodes that are represented by a series of adjacency matrices. The discriminator integrates a graph convolutional network for high-order proximity and a convolutional neural network for temporal dependency to distinguish real samples from fake samples produced by the generator. With iterative boosting of the performance of the generator and discriminator, node embeddings are learned to present dynamic evolution over time. By jointly considering high-order proximity and temporal evolution, our model can preserve spatial structure with temporal dependency. DynGraphGAN is optimized on subgraphs produced by random walks to capture more complex structural and temporal patterns in the dynamic graphs. We also leverage sparsity and temporal smoothness properties to further improve the model efficiency. Our model demonstrates substantial gains over several baseline models in link prediction and reconstruction tasks on real-world datasets.
An Efficient Blockchain-Based Privacy-Preserving Collaborative Filtering Architecture

Information overload is a phenomenon of our days due to the unprecedented penetration of information and communication technologies (ICT) in our daily lives. As a result, people often end up with more options than they can process to choose from and therefore may opt for choices which do not fit best to their preferences. To address these issues, recommender systems (RSs) were proposed and have gained a lot of interest from the research community and industry. However, privacy is a big concern in these systems. While decentralized recommenders can protect privacy, they lack the needed efficiency to be widely adopted. In this article, we use blockchain as the backbone of a decentralized RS, managing to equip it with a broad set of features while simultaneously, preserving user's privacy. We introduce a new architecture, based on decentralized locality sensitive hashing classification as well as a set of recommendation methods, according to how data are managed by users. Extensive experimental results illustrate the performance and efficacy of our approach compared with state-of-the-art methods. In addition, a discussion about its benefits and opportunities provides ground for further research.
Image Aesthetic Assessment Based on Pairwise Comparison ­ A Unified Approach to Score Regression, Binary Classification, and Personalization

We propose a unified approach to three tasks of aesthetic score regression, binary aesthetic classification, and personalized aesthetics. First, we develop a comparator to estimate the ratio of aesthetic scores for two images. Then, we construct a pairwise comparison matrix for multiple reference images and an input image, and predict the aesthetic score of the input via the eigenvalue decomposition of the matrix. By varying the reference images, the proposed algorithm can be used for binary aesthetic classification and personalized aesthetics, as well as generic score regression. Experimental results demonstrate that the proposed unified algorithm provides the state-of-the-art performances in all three tasks of image aesthetics.
High-Resolution Water Segmentation for Autonomous Unmanned Surface Vehicles: a Novel Dataset and Evaluation

Even though Unmanned Surface Vehicles (USVs) are increasingly used to perform various laborious and expensive offshore tasks, they still require an extensive dedicated crew supporting and ensuring the safety of their operations. The recent developments in computer vision and robotics further fueled the interest on developing autonomous USVs that will overcome the aforementioned limitations, unleashing their full potential. One of the most vital and fundamental tasks in order to automate and ensure the safety of USV operations is to perform water segmentation. Despite the importance of developing such segmentation methods, there is a lack of high-resolution publicly available datasets, which are suitable for training and evaluating deep learning methods. The main contribution of this paper is collecting, annotating and releasing a publicly available high-resolution dataset for developing deep learning algorithms for water segmentation in a Nordic lake environment. Furthermore, we adapt a deep learning algorithm previously applied for road segmentation to the water segmentation task. While the algorithm obtains a high accuracy, the results also allow for identifying critical limitations of the approach. Finally, we propose and evaluate a novel lightweight fully convolutional neural network architecture, fully adapted to the needs of water segmentation from high-resolution images.
Discrete-Time Analysis of the Blockchain Distributed Ledger Technology

Blockchain and distributed ledger technologies have become more and more popular and widespread during recent years. After the initial hype about the technology and many cryptocurrency related use cases, the technology slowly starts to make its way into other domains like food tracking and document management. In order to further contribute to the search of what this technology can be used for, more detailed performance evaluations are required in order to investigate key performance indicators and general limits of the technology. To this end, we develop a discrete-time queueing model that allows a detailed evaluation of the characteristics of a blockchain system, such as the transaction waiting time distribution. Furthermore, we validate the model by comparing the results to values obtained from measurements in a closed lab environment based on the Ethereum blockchain.
360° Mulsemedia: A Way to Improve Subjective QoE in 360° Videos

Previous research has shown that adding multisensory media-mulsemedia-to traditional audiovisual content has a positive effect on user Quality of Experience (QoE). However, the QoE impact of employing mulsemedia in 360° videos has remained unexplored. Accordingly, in this paper, a QoE study for watching a 360° video-with and without multisensory effects-in a full free-viewpoint VR setting is presented. The parametric space we considered to influence the QoE consists of the encoding quality and the motion level of the transmitted media. To achieve our research aim, we propose a wearable VR system that provides multisensory enhancement of 360° videos. Then, we utilise its capabilities to systematically evaluate the effects of multisensory stimulation on perceived quality degradation for videos with different motion levels and encoding qualities. Our results make a strong case for the inclusion of multisensory effects in 360° videos, as they reveal that both user-perceived quality, as well as enjoyment, are significantly higher when mulsemedia (as opposed to traditional multimedia) is employed in this context. Moreover, these observations hold true independent of the underlying 360° video encoding quality-thus QoE can be significantly enhanced with a minimal impact on networking resources.
Emergent properties, models, and laws of behavioral similarities within groups of twitter users

DNA-inspired online behavioral modeling techniques have been proposed and successfully applied to a broad range of tasks. In this paper, we investigate the fundamental laws that drive the occurrence of behavioral similarities among Twitter users, employing a DNA-inspired technique. Our findings are multifold. First, we demonstrate that, despite apparently featuring little to no similarities, the online behaviors of Twitter users are far from being uniformly random. Secondly, we benchmark different behavioral models through a number of simulations. We characterize the main properties of such models and we identify those models that better resemble human behaviors in Twitter. Then, we demonstrate that the number and the extent of behavioral similarities within a group of Twitter users obey a log-normal law, and we leverage this characterization to propose a novel bot detection system. In a nutshell, the results shed light on the fundamental properties that drive the online behaviors of groups of Twitter users, through the lenses of DNA-inspired behavioral modeling techniques. This study is based on a wealth of data gathered over several months that, for the sake of reproducibility, are publicly available for research purposes.
Cursive Handwritten Text Recognition using Bi-Directional LSTMs: A Case Study on Urdu Handwriting

Recognition of cursive handwritten text is a complex problem due challenges like context sensitive character shapes, non-uniform inter and intra word spacings, complex positioning of dots and diacritics and very low inter class variation among certain classes. This paper presents an effective technique for recognition of cursive handwritten text using Urdu as a case study (though findings can be generalized to other cursive scripts as well). We present an analytical approach based on implicit character segmentation where convolutional neural networks (CNNs) are employed as feature extractors while classification is carried out using a bi-directional Long-Short-Term Memory (LSTM) network. The proposed technique is validated on a dataset of 6000 unique handwritten text lines reporting promising character recognition rates.
Persistence of the high solar potential in Africa in a changing climate

Abstract The African continent faces several challenges and threats: high vulnerability to climate change, the fastest population increase, the lowest degree of electrification and the need for an energy transition towards renewable energies. Solar energy constitutes a viable option for addressing these issues. In a changing climate the efficient implementation of solar capacity should rely on comprehensive information about the solar resource. Here, the newest and highest resolution regional climate simulation results are used to project the future photovoltaic and concentrated solar power potentials for Africa. We show that the high potentials for solar energy will not be reduced much throughout Africa with climate change. However, the PV solar potential is projected to decrease up to about −10% in limited areas of eastern central Africa; increases are also projected to the northwest and southern Africa (up to about +5%). These changes are mostly determined by changes in solar irradiance but in certain areas the warming is a critical factor limiting PV potential.
High-speed and Large-scale Privacy Amplification Scheme for Quantum Key Distribution

Abstract State-of-art quantum key distribution (QKD) systems are performed with several GHz pulse rates, meanwhile privacy amplification (PA) with large scale inputs has to be performed to generate the final secure keys with quantified security. In this paper, we propose a fast Fourier transform (FFT) enhanced high-speed and large-scale (HiLS) PA scheme on commercial CPU platform without increasing dedicated computational devices. The long input weak secure key is divided into many blocks and the random seed for constructing Toeplitz matrix is shuffled to multiple sub-sequences respectively, then PA procedures are parallel implemented for all sub-key blocks with correlated sub-sequences, afterwards, the outcomes are merged as the final secure key. When the input scale is 128 Mb, our proposed HiLS PA scheme reaches 71.16 Mbps, 54.08 Mbps and 39.15 Mbps with the compression ratio equals to 0.125, 0.25 and 0.375 respectively, resulting achievable secure key generation rates close to the asymptotic limit. HiLS PA scheme can be applied to 10 GHz QKD systems with even larger input scales and the evaluated throughput is around 32.49 Mbps with the compression ratio equals to 0.125 and the input scale of 1 Gb, which is ten times larger than the previous works for QKD systems. Furthermore, with the limited computational resources, the achieved throughput of HiLS PA scheme is 0.44 Mbps with the compression ratio equals to 0.125, when the input scale equals up to 128 Gb. In theory, the PA of the randomness extraction in quantum random number generation (QRNG) is same as the PA procedure in QKD, and our work can also be efficiently performed in high-speed QRNG.
Joint Prediction of Multiple Vulnerability Characteristics Through Multi-Task Learning

Software vulnerabilities seriously affect the security of computing systems and they are continuously disclosed and reported. When documenting software vulnerabilities, characterizing the severity, exploitability and impact of a vulnerability is critical for effective triaging and management of software vulnerabilities. Faced with ever-growing number of new vulnerabilities, we observe a significant lag between the disclosure of a vulnerability and the specification of its characteristics. This lag calls for automated, reliable assessment of vulnerability characteristics to assist security analysts in allocating their limited efforts to potentially most serious vulnerabilities. Existing automated techniques for vulnerability assessment require hand-crafted features and balanced data, and consider each specific characteristic independently at a time. In this paper, we propose a multi-task machine learning approach for the joint prediction of multiple vulnerability characteristics based on the vulnerability descriptions. Our approach gets rid of the requirement of balanced data, and it relies on neural networks that learn to extract features from training data. Using the large-scale vulnerability data in the Common Vulnerabilities and Exposure(CVE) database, we conduct extensive experiments to compare different configurations of neural network feature extractors, study the impact of multi-task learning versus independent-task learning, and investigate the performance of our approach for predicting the characteristics of newly disclosed vulnerabilities and the minimum requirement of historical vulnerability data for training reliable prediction model.
Scheduling bag-of-tasks applications on hybrid clouds under due date constraints

Bag-of-tasks (BoT) applications consisting of multiple tasks are widely used in a variety of application fields. Different from the traditional deadline constraint in scheduling problems, a due date constraint allows the completion time of BoT applications to exceed the pre-specified due date, but would impose the tardiness penalty. In order to reduce this tardiness penalty, the customer is willing to outsource a part of tasks to public clouds despite the new cost of using public clouds’ resources. In this scenario, the challenge is to optimally schedule tasks on hybrid clouds for the purpose of minimizing the total cost. This problem of scheduling BoT application on hybrid clouds under due date constraints can be formulated as an integer program. To solve this problem, we propose several heuristics by employing an effective task rescheduling strategy. Furthermore, we develop two effective acceleration methods, namely AM1 and AM2, which can be used by the rescheduling strategy for improving the efficiency. This conclusion is validated by proofs in theory. Extensive simulations are performed to justify the effectiveness and efficiency of the proposed scheduling heuristics. Experimental results demonstrate the performance of the task rescheduling strategy in terms of rescheduling quality and computational efficiency.
Learning Reliable Visual Saliency For Model Explanations

By highlighting important features that contribute to model prediction, visual saliency is used as a natural form to interpret the working mechanism of deep neural networks. Numerous methods have been proposed to achieve better saliency results. However, we find that previous visual saliency methods are not reliable enough to provide meaningful interpretation through a simple sanity check: saliency methods are required to explain the output of non-maximum prediction classes, which are usually not ground-truth classes. For example, let the methods interpret an image of “dog” given a wrong class label “fish” as the query. This procedure can test whether these methods reliably interpret model's predictions based on existing features that appear in the data. Our experiments show that previous methods failed to pass the test by generating similar saliency maps or scattered patterns. This false saliency response can be dangerous in certain scenarios, such as medical diagnosis. We find that these failure cases are mainly due to the attribution vanishing and adversarial noise within these methods. In order to learn reliable visual saliency, we propose a simple method that requires the output of the model to be close to the original output while learning an explanatory saliency mask. To enhance the smoothness of the optimized saliency masks, we then propose a simple Hierarchical Attribution Fusion (HAF) technique. In order to fully evaluate the reliability of visual saliency methods, we propose a new task Disturbed Weakly Supervised Object Localization (D-WSOL) to measure whether these methods can correctly attribute the model's output to existing features. Experiments show that previous methods fail to meet this standard, and our approach helps to improve the reliability by suppressing false saliency responses. After observing a significant layout difference in saliency masks between real and adversarial samples. we propose to train a simple CNN on these learned hierarchical attribution masks to distinguish adversarial samples. Experiments show that our method can improve detection performance over other approaches significantly.
Image Interpolation Using a Rational Bi-Cubic Ball

: This study deals with the application of new rational bi-cubic Ball function with six parameters in image interpolation, especially for the grayscale image. These six free parameters can be modified to get better and quality image resolution, and refine the shape of the interpolating surface. This bivariate rational Ball function has been extended from univariate cases by using a tensor product approach. The proposed scheme is tested for image upscaling with factors of two and four through an efficient algorithm. The effectiveness of the proposed scheme is measured by using an image quality assessment (IQA), such as peak-signal-to-noise-ratio (PSNR), root mean square error (RMSE) or feature similarity (FSIM) index. Numerical and graphical results with comparisons against some existing scheme are presented by using MATLAB. The proposed scheme resulted in higher PSNR and FSIM, and smaller RMSE. Thus, the new rational bi-cubic Ball with six parameters is better than the existing scheme via an efficient algorithm.
eScriptorium: An Open Source Platform for Historical Document Analysis

We describe the new open source document analysis and annotation platform eScriptorium. It allows to upload document collections, transcribe and segment them manually or automatically with the help of the kraken OCR engine.
Efficiently Stealing your Machine Learning Models

Machine Learning as a Service (MLaaS) is a growing paradigm in the Machine Learning (ML) landscape. More and more ML models are being uploaded to the cloud and made accessible from all over the world. Creating good ML models, however, can be expensive and the used data is often sensitive. Recently, Secure Multi-Party Computation (SMPC) protocols for MLaaS have been proposed, which protect sensitive user data and ML models at the expense of substantially higher computation and communication than plaintext evaluation. In this paper, we show that for a subset of ML models used in MLaaS, namely Support Vector Machines (SVMs) and Support Vector Regression Machines (SVRs) which have found many applications to classifying multimedia data such as texts and images, it is possible for adversaries to passively extract the private models even if they are protected by SMPC, using known and newly devised model extraction attacks. We show that our attacks are not only theoretically possible but also practically feasible and cheap, which makes them lucrative to financially motivated attackers such as competitors or customers. We perform model extraction attacks on the homomorphic encryption-based protocol for privacy-preserving SVR-based indoor localization by Zhang et al. (International Workshop on Security 2016). We show that it is possible to extract a highly accurate model using only 854 queries with the estimated cost of $0.09 on the Amazon ML platform, and our attack would take only 7 minutes over the Internet. Also, we perform our model extraction attacks on SVM and SVR models trained on publicly available state-of-the-art ML datasets.
Factors affecting usability of 3D model learning in a virtual reality environment

The aim of the present study was to explore the learning usability factors of 3D modeling in virtual reality environment (VRE). Users' 3D modeling usability factors in VR were investigated through principal component analysis (PCA). Fifty industrial design students participated in the 3D modeling learning in VR experience experiment, and their average age was 23.9 years (SD = 1.6). System Usability Scale (SUS) scores and user experience of the 3D modeling interface of the participants in the VR environment were also assessed. The results show that there are three major usability factors in 3D modeling learning in VR work: interactive quality, dynamic compatibility, and flow effects. In addition, the users' learning experiences of the 3D modeling in VR are described. The results can be provided to 3D modeling software developers as a key reference in VR learning interface design.
Entanglement improvement via a quantum scissor in a realistic environment

An efficient scheme for enhancing quantum state amplification and entanglement is proposed. In comparison to previous methods, the usage of quantum scissors leads to a higher probability of success, better robustness against decoherence, and an overall reduction of resources.