Reviewerly/exHarmony huggingface dataset:
Roll Call Vote Prediction with Knowledge Augmented Models

The official voting records of United States congresspeople are preserved as roll call votes.Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running for office, is important for forecasting key political decisions.Prior work has relied on past votes cast to predict future votes, and thus fails to predict voting patterns for politicians without voting records.We address this by augmenting a prior state of the art model with multiple sources of external knowledge so as to enable prediction on unseen politicians.The sources of knowledge we use are news text and Freebase, a manually curated knowledge base.We propose augmentations based on unigram features for news text, and a knowledge base embedding method followed by a neural network composition for relations from Freebase.Empirical evaluation of these approaches indicate that the proposed models outperform the prior system for politicians with complete historical voting records by 1.0% point of accuracy (8.7% error reduction) and for politicians without voting records by 33.4% points of accuracy (66.7% error reduction).We also show that the knowledge base augmented approach outperforms the news text augmented approach by 4.2% points of accuracy.
State Space Models with Dynamical and Sparse Variances, and Inference by EM Message Passing

Sparse Bayesian learning (SBL) is a probabilistic approach to estimation problems based on representing sparsity-promoting priors by Normals with Unknown Variances. This representation blends well with linear Gaussian state space models (SSMs). However, in classical SBL the unknown variances are a priori independent, which is not suited for modeling group sparse signals, or signals whose variances have structure. To model signals with, e.g., exponentially decaying or piecewise-constant (in particular block-sparse) variances, we propose SSMs with dynamical and sparse variances (SSM-DSV). These are two-layer SSMs, where the bottom layer models physical signals, and the top layer models dynamical variances that are subject to abrupt changes. Inference and learning in these hierarchical models is performed with a message passing version of the expectation maximization (EM) algorithm, which is a special instance of the more general class of variational message passing algorithms. We validated the proposed model and estimation algorithm with two applications, using both simulated and real data. First, we implemented a block-outlier insensitive Kalman smoother by modeling the disturbance process with a SSM-DSV. Second, we used SSM-DSV to model the oculomotor system and employed EM-message passing for estimating neural controller signals from eye position data.
Identifying Linguistic Areas for Geolocation

Geolocating social media posts relies on the assumption that language carries sufficient geographic information.However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions that can serve as labels.Most studies use some form of clustering to discretize the continuous coordinates (Han et al., 2016).However, the resulting regions do not always correspond to existing linguistic areas.Consequently, accuracy at 100 miles tends to be good, but degrades for finer-grained distinctions, when different linguistic regions get lumped together.We describe a new algorithm, Point-to-City (P2C), an iterative k-d tree-based method for clustering geographic coordinates and associating them with towns.We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels.Even though P2C results in substantially more labels than the baseline, model accuracy increases significantly over using traditional labels at the fine-grained level, while staying comparable at 100 miles.The results suggest that identifying meaningful linguistic areas is crucial for improving geolocation at a fine-grained level.
Digital Transformation Trends of the Energy and Mineral Resources Development Industries in the Era of the Fourth Industrial Revolution

This paper presents a stable and secure strategy for rare metals to support the sustainable growth of the fourth industrial revolution in Korea. It carefully considers digital transformation trends in the energy and mineral resources industries in terms of technical innovation. The potential for digital transformation in those Korean industries is discussed. A strategic direction is proposed to develop the techniques required by domestic industries for energy and mineral resources development based on the abovementioned situation.
Using Private and Public Assessments in Security Information Sharing Agreements

Information sharing among organizations has been gaining attention as a method for improving cybersecurity. However, the associated disclosure costs act as deterrents for firms' voluntary cooperation. In this work, we take a game-theoretic approach to understanding firms' incentives in these agreements. We propose the design of inter-temporal incentives (i.e. conditioning future cooperation on past interactions). Specifically, we show that incentives for full cooperation can be designed if firms share their private assessments of other firms' disclosure decisions through a common communication platform. We further show that similar incentives can be designed based on outcomes of a public rating/assessment system.
MHEF-TripNet: Mixed Triplet Loss with Hard Example Feedback Network for Image Retrieval

Image retrieval has made significant advances, fueled mainly by deep convolutional neural networks, but their training procedure is not efficient enough. Because of the large imbalance between easy examples and hard examples, networks lack direct guidance information from hard examples. In this paper, we solve the problem by developing an effective and efficient method, called mixed triplet loss with hard example feedback network (MHEF-TripNet). Since the proportion of hard examples is small, a sample selection probability matrix is introduced to select hard examples, which assists a network to focus more on enlarging the gap between the confusing categories in triplet loss. And it will be adjusted according to the feedback of test results after each training iteration. Furthermore, a mixed triplet loss function is proposed, which combines triplet loss with category loss to take advantage of association information between images and category information. The effectiveness of MHEF-TripNet is confirmed by experimentation on UC Merced Land Use and Kdelab Airplane datasets. Compared with previous image retrieval approaches, our approach obtains superior performance.
Multi-DisNet: Machine Learning-Based Object Distance Estimation from Multiple Cameras

In this paper, a novel method for distance estimation from multiple cameras to the object viewed with these cameras is presented. The core element of the method is multilayer neural network named Multi-DisNet, which is used to learn the relationship between the sizes of the object bounding boxes in the cameras images and the distance between the object and the cameras. The Multi-DisNet was trained using a supervised learning technique where the input features were manually calculated parameters of the objects bounding boxes in the cameras images and outputs were ground-truth distances between the objects and the cameras. The presented distance estimation system can be of benefit for all applications where object (obstacle) distance estimation is essential for the safety such as autonomous driving applications in automotive or railway. The presented object distance estimation system was evaluated on the images of real-world railway scenes. As a proof-of-concept, the results on the fusion of two sensors, an RGB and thermal camera mounted on a moving train, in the Multi-DisNet distance estimation system are shown. Shown results demonstrate both the good performance of Multi-DisNet system to estimate the mid (up to 200 m) and long-range (up to 1000 m) object distance and benefit of sensor fusion to overcome the problem of not reliable object detection.
Very Short-Term Solar Power Forecasting Using Genetic Algorithm Based Deep Neural Network

This paper presents a method for finding optimal parameters of a deep learning model by Genetic Algorithm (GA). The model is employed to forecast output of a solar farm 4 hours in advance. Its inputs are from both forecasted weather data and data obtained from weather monitoring instrument. Performance of four NN (Neural Network) types: DNN (Deep Neural Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and CuDNNGRU (Cuda Deep Neural Network Gated Recurrent Unit), is compared. Feature engineering by Exponential Moving Average (EMA) finds that a time-series of irradiance helps to improve the model performance. GA is exploited to find the most appropriate the number of lookback (or the window size), and the number of neurons in each and all three hidden layers. The GRU model yields the least RMSE at 7.83%. However, if the training time is to be considered, the CuDNNGRU model yields slightly higher RMSE at 7.87%, but its training time is less than half of that of the GRU.
Sequence-crafter

The hardware security domain in recent years has experienced a plethora of side-channel attacks (SCAs) with cache-based SCAs being one of the dominant threats. These SCAs function by exploiting the side-channels which invariably leak important data during an application's execution. Shutting down the side channels is not a feasible approach due to various restrictions it would pose to system performance. To overcome such concerns and protect the data integrity, we introduce Sequence-Crafter (SC) in this work. The proposed Sequence-Crafter (SC) aims to minimize the entropy in the side channel leaked information rather than attempting to close the side-channels. To achieve this, we introduce carefully crafted perturbations into the victim application which will be randomly activated to introduce perturbations, thus resulting in misleading information which looks legit that will be observed by the attacker. This methodology has been successfully tested for Flush+Reload attack and the key information observed by the attacker is seen to be completely futile, indicating the success of proposed method.
Smart Living: Ubiquitous Services Powered by Ambient Intelligence (AmI)

Optoelectronics and computing industry has made significant advances in data acquisition and communication for ubiquitous availability. As a result data-intelligence is readily available to users through Wi-Fi enabled smart devices. Internet access to information, services and all-time availability are the features making smart communities and shaping lifestyles in emerging smart world. IoT and IoE have become buzz words of the ICT industry. Those add and couple intelligence with smart interfaces and sensors with ubiquitous services. Autonomous activities range from sensing, logging to expert systems for alarm generation and decision making. These technologies coupled with knowledge management and support systems based on service oriented architectures (SOA) in the cloud domain, has led to a paradigm shift and rethinking of human machine interface. The perception of computing devices is vanishing behind the interfaces offered by smart hand-held devices. Ambient intelligence is the next generation of the smart solutions. Where the data collected by devices is used by an inference engine for taking autonomous decisions. Some of the areas where AmI has been successfully used are: eLearning, assisted-living, traffic management, eHealthcare and emergency handling etc. Paper presents examples of the smart solutions for ubiquitous computing and communications used for assisted-living, eHealthcare, Education and Surveillance systems. Various enabling technologies for ambient intelligence have been discussed, while identifying the challenges e.g. ethical issues, security and forensic analysis and so on.
Representing <i>n</i>-ary relations in the Semantic Web

Abstract Knowledge representation is a central issue for Artificial Intelligence and the Semantic Web. In particular, the problem of representing n-ary relations in RDF-based languages such as RDFS or OWL by no means is an obvious one. With respect to previous attempts, we show why the solutions proposed by the well known W3C Working Group Note on n-ary relations are not satisfactory on several scores. We then present our abstract model for representing n-ary relations as directed labeled graphs, and we show how this model gives rise to a new ontological pattern (parametric pattern) for the representation of such relations in the Semantic Web. To this end, we define PROL (Parametric Relational Ontology Language). PROL is an ontological language designed to express any n-ary fact as a parametric pattern, which turns out to be a special RDF graph. The vocabulary of PROL is defined by a simple RDFS ontology. We argue that the parametric pattern may be particularly beneficial in the context of the Semantic Web, in virtue of its high expressive power, technical simplicity, and faithful meaning rendition. Examples are also provided.
Maximizing Ink in Partial Edge Drawings of k-plane Graphs

Partial edge drawing (PED) is a drawing style for non-planar graphs, in which edges are drawn only partially as pairs of opposing stubs on the respective end-vertices. In a PED, by erasing the central parts of edges, all edge crossings and the resulting visual clutter are hidden in the undrawn parts of the edges. In symmetric partial edge drawings (SPEDs), the two stubs of each edge are required to have the same length. It is known that maximizing the ink (or the total stub length) when transforming a straight-line graph drawing with crossings into a SPED is tractable for 2-plane input drawings, but $$\mathsf {NP}$$ -hard for unrestricted inputs. We show that the problem remains $$\mathsf {NP}$$ -hard even for 3-plane input drawings and establish $$\mathsf {NP}$$ -hardness of ink maximization for PEDs of 4-plane graphs. Yet, for k-plane input drawings whose edge intersection graph forms a collection of trees or, more generally, whose intersection graph has bounded treewidth, we present efficient algorithms for computing maximum-ink PEDs and SPEDs. We implemented the treewidth-based algorithms and show a brief experimental evaluation.
A Computer Vision System Supporting Blind People - The Supermarket Case

The proposed application builds on the latest advancements of computer vision with the aim to improve the autonomy of people with visual impairment at both practical and emotional level. More specifically, it is an assistive system that relies on visual information to recognise the objects and faces surrounding the user. The system is supported by a set of sensors for capturing the visual information and for transmitting the auditory messages to the users. In this paper, we present a computer vision application, e-vision, in the context of visiting the supermarket for buying groceries.
Analytical and numerical methods for efficient calculation of edge diffraction by an arbitrary incident signal

In the present study, diffraction of plane and spherically spreading signals by half-planes is considered. An existing analytical impulse response is investigated, which is exact for plane and approximate for spherical incident signals. It is shown that all its primitive functions with respect to time exist and have an explicit form involving elementary functions. The primitive functions are employed to (i) prove that the convolution of the impulse response with any bounded signal is also bounded for all times, (ii) obtain analytically the diffraction response as a combination of elementary functions for any incident signal approximated piecewise by fitting polynomials, (iii) improve the performance of the numerical convolution by orders of magnitude, and (iv) handle the convolution of very coarsely sampled incident signals. An impulse response is presented for finite-length edges, which, unlike the traditional integration formulas along the edge, is an explicit form of time. Because it is based on the impulse response for infinite edges, it inherits all aforementioned benefits associated with its primitive functions. Furthermore, it offers a substantial computational benefit compared to traditional integration formulas along the edge. Finally, the requirements for the direct application of the presented results to other impulse responses are discussed.
Improved CTC-Attention Based End-to-End Speech Recognition on Air Traffic Control

Recently, many end-to-end speech recognition systems have been proposed aim to directly transcribes speech to text without any predefined alignments. In this paper, we improved the architecture of joint CTC-attention based encoder-decoder model for Mandarin speech recognition on Air Traffic Control speech recognition task. Our improved system include a Vggblstm based encoder, an attention LSTM based decoder decoded with CTC mechanism and a LSTM based ATC language model. In addition, several tricks are used for effective model training, including L2 regularization, attention smoothing and frame skipping. In this paper, we compare our improved model with other three popular end-to-end systems on ATC corpus. Result shows that our improved CTC-attention model outperforms CTC, attention and original CTC-attention model without any tricks and language model. Taken these tricks together we finally achieve a character error rate (CER) of 13.15% and a sentence error rate (SER) of 33.43% on the ATC dataset. While together with a LSTM language model, CER and SER reach 11.01% and 22.75%, respectively.
Modelling the Quality of Visual Creations in Iconoscope

This paper presents the current state of the online game Iconoscope and analyzes the data collected from almost 45 months of continuous operation. Iconoscope is a freeform creation game which aims to foster the creativity of its users through diagrammatic lateral thinking, as users are required to depict abstract concepts as icons which may be misinterpreted by other users as different abstract concepts. From users’ responses collected from an online gallery of all icons drawn with Iconoscope, we collect a corpus of over 500 icons which contain annotations of visual appeal. Several machine learning algorithms are tested for their ability to predict the appeal of an icon from its visual appearance and other properties. Findings show the impact of the representation on the model’s accuracy and highlight how such a predictive model of quality can be applied to evaluate new icons (human-authored or generated).
Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack.
Trustworthy Graph Algorithms (Invited Talk)

The goal of the LEDA project was to build an easy-to-use and extendable library of correct and efficient data structures, graph algorithms and geometric algorithms. We report on the use of formal program verification to achieve an even higher level of trustworthiness. Specifically, we report on an ongoing and largely finished verification of the blossom-shrinking algorithm for maximum cardinality matching.
MR Image Denoising Using Adaptive Wavelet Soft Thresholding

Medical imaging has played an important role in medical disease detection, diagnosis, and research related findings and therapy. Removal of noise in a medical image is mandatory step for quality assessment. This paper presents a feature preserved Magnetic Resonance (MR) denoising algorithm which is based on well-accepted multiresolution and statistical modeling. An adaptive wavelet soft thresholding method is designed to remove Gaussian noise from MR image. Orthogonality property of the wavelet transform is used to find the noise variance, using a Median Absolute Deviation (MAD) estimator. Distribution parameters of the wavelet coefficients are estimated by modeling the coefficients using a Normal Inverse Gaussian (NIG) probability density function (PDF). The threshold value is calculated considering the noise and signal information and the wavelet coefficients are updated accordingly. Estimation and analysis of the performance of the proposed method is performed using peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance evaluation parameters; these parameters demonstrate the noise suppression ability of the proposed method. Superiority of the proposed denoising method is further established through visual inspection of the denoised images obtained from proposed as well as other contemporary reported methods.
Stochasticity and Skip Connection Improve Knowledge Transfer

Deep neural networks have achieved state-of-the-art performance in various fields. However, DNNs need to be scaled down to fit real-word applications where memory and computation resources are limited. As a means to compress the network yet still maintain the performance of the network, knowledge distillation has brought a lot of attention. This technique is based on the idea to train a student network using the provided output of a teacher network. Deploying multiple teacher networks facilitates learning of the student network, however, it causes to some extent waste of resources. In the proposed approach, we generate multiple teacher networks from a teacher network by exploiting stochastic block and skip connection. Thus, they can play the role of multiple teacher networks and provide sufficient knowledge to the student network without additional resources. We observe the improved performance of student network with the proposed approach using several dataset.
Three-Party Quantum Key Agreement Protocol Based on Continuous Variable Single-Mode Squeezed States*

Abstract The difficulty of quantum key agreement is to realize its security and fairness at the same time. This paper presents a new three-party quantum key agreement protocol based on continuous variable single-mode squeezed state. The three parties participating in the agreement are peer entities, making same contributions to the final key. Any one or two participants of the agreement cannot determine the shared key separately. The security analysis shows that the proposed protocol can resist both external and internal attacks.