Reviewerly/exHarmony huggingface dataset:
Three-party quantum secret sharing based on <i>d</i>-dimensional Bell state

A three-party quantum secret sharing scheme is proposed, in which the dealer uses the d-dimensional Bell state to distribute the secret, the participants perform the single-particle measurements to get their shares, and the dealer performs the Bell-basis measurements to check the eavesdropping. The main merit of our scheme is that the participants only need to measure the particles in one basis. Compared to the existing schemes in which the participants need to measure the particles in two bases, our scheme will be more practical.
DrawInAir: A Lightweight Gestural Interface Based on Fingertip Regression

Hand gestures form a natural way of interaction on Head-Mounted Devices (HMDs) and smartphones. HMDs such as the Microsoft HoloLens and ARCore/ARKit platform enabled smartphones are expensive and are equipped with powerful processors and sensors such as multiple cameras, depth and IR sensors to process hand gestures. To enable mass market reach via inexpensive Augmented Reality (AR) headsets without built-in depth or IR sensors, we propose a real-time, in-air gestural framework that works on monocular RGB input, termed, DrawInAir. DrawInAir uses fingertip for writing in air analogous to a pen on paper. The major challenge in training egocentric gesture recognition models is in obtaining sufficient labeled data for end-to-end learning. Thus, we design a cascade of networks, consisting of a CNN with differentiable spatial to numerical transform (DSNT) layer, for fingertip regression, followed by a Bidirectional Long Short-Term Memory (Bi-LSTM), for a real-time pointing hand gesture classification. We highlight how a model, that is separately trained to regress fingertip in conjunction with a classifier trained on limited classification data, would perform better over end-to-end models. We also propose a dataset of 10 egocentric pointing gestures designed for AR applications for testing our model. We show that the framework takes 1.73 s to run end-to-end and has a low memory footprint of 14 MB while achieving an accuracy of 88.0% on egocentric video dataset.
Business Process Privacy Analysis in Pleak

Pleak is a tool to capture and analyze privacy-enhanced business process models to characterize and quantify to what extent the outputs of a process leak information about its inputs. Pleak incorporates an extensible set of analysis plugins, which enable users to inspect potential leakages at multiple levels of detail.
Improve Computer Visualization of Architecture Based on the Bayesian Network

Computer visualization has marvelous effects when it is applied in various fields, especially in architectural design. As an emerging force in the innovation industry, architects and design agencies have already demonstrated the value of architectural visual products in actual application projects. Based on the digital image technology, virtual presentation of future scenes simulates architecture design, architectural renderings and multimedia videos. Therefore, it can help design agencies transform the theoretical design concept into a lively and realistic visual which can provide the audience with a clearer understanding of the engineering and construction projects. However, it is challenging for designers to produce satisfactory renderings due to the frequent fault data during rendering. In this paper, we use the 3Ds MAX as the operating platform and we present an algorithm based on the Bayesian network to construct a vector representation of the fault data. On this basis, a case study of 3D Max’ application has also been presented.
Adaptive tutoring system based on fuzzy logic

In recent years, education method has changed and has become very innovative and modern. In this way, online adaptive learning seems to be a revolutionary competitive method. The advancement of computer and networking technologies is the key to this whole change from the classic education to the modern online adaptive education. The majority of E-learning systems are based on Boolean logic. In fact, the system considers that the learner like or not a course characteristic but the user can prefer gradually this parameter (low, medium, high). To this end, the proposed approach exploits semantic relations between data elements and learners' preferences to determine adapted UI components appropriate to learners' characteristics based on fuzzy logic. The results of evaluation confirm the efficiency of our technique with an average of more than 77% of precision and recall.
Neural Reverse Engineering of Stripped Binaries

We address the problem of reverse engineering of stripped executables which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and due to the diverse assembly code patterns arising from compiler optimizations. We present a novel approach for predicting procedure names in stripped executables. Our approach combines static analysis with neural models. The main idea is to use static analysis to obtain augmented representations of call sites; encode the structure of these call sites using the control-flow graph (CFG) and finally, generate a target name while attending to these call sites. We use our representation to drive graph-based, LSTM-based, and Transformer-based architectures.
Creating CMDI-Profiles for Textbook Resources

This paper analyses the establishment of a common infrastructure standard covering metadata, content, and inferred knowledge to allow collaborative work between researchers in the humanities. Interoperability between heterogeneous resources and services is the key for a properly functioning infrastructure. In this paper, we present a digital infrastructure of our textbook-related services and data, which are available and open for researchers worldwide. In this process we adhere to established standards and provide APIs for other services. In order to integrate our resources and tools into the CLARIN infrastructure and make them discoverable in the VLO (Virtual Language Observatory), we decided to use CMDI (Component MetaData Infrastructure). We focus in this paper on the creation process for a CMDI metadata profile which fulfils the needs of our projects.
Multi-task Learning for Target-Dependent Sentiment Classification

Detecting and aggregating sentiments toward people, organizations, and events expressed in unstructured social media have become critical text mining operations. Early systems detected sentiments over whole passages, whereas more recently, target-specific sentiments have been of greater interest. In this paper, we present MTTDSC, a multi-task target-dependent sentiment classification system that is informed by feature representation learnt for the related auxiliary task of passage-level sentiment classification. The auxiliary task uses a gated recurrent unit (GRU) and pools GRU states, followed by an auxiliary fully-connected layer that outputs passage-level predictions. In the main task, these GRUs contribute auxiliary per-token representations over and above word embeddings. The main task has its own, separate GRUs. The auxiliary and main GRUs send their states to a different fully connected layer, trained for the main task. Extensive experiments using two auxiliary datasets and three benchmark datasets (of which one is new, introduced by us) for the main task demonstrate that MTTDSC outperforms state-of-the-art baselines. Using word-level sensitivity analysis, we present anecdotal evidence that prior systems can make incorrect target-specific predictions because they miss sentiments expressed by words independent of target.
A Novel Fuzzy Graph Theory-Based Approach for Image Representation and Segmentation Via Graph Coloring

Graph theory is one of the most essential parts in mathematics, where many practical problems are represented as different graphs and solved by graph-based algorithms. Among various techniques in graph theory, graph coloring is a widely used technique for solving real-world problems such as scheduling, security, waste management, image segmentation, register allocation, and puzzle-related problems. In this article, a novel fuzzy graph theory-based approach is proposed on image segmentation. Our proposed approach includes an improved optimal balanced histogram thresholding (IOBHT) method for gray-scale to binary image conversion, image representation based on fuzzy graph theory (FGT), and image segmentation using Widgerson’s coloring algorithm (WCA). This efficient image segmentation methodology is implemented in MATLAB and the experimental results from simulation shows that the proposed approach can minimize the computational time and improve the accuracy of image segmentation. Comparative analysis demonstrates that the proposed algorithm outperforms other existing algorithms.
Optimal Collusion-Free Teaching

Formal models of learning from teachers need to respect certain criteria to avoid collusion. The most commonly accepted notion of collusion-freeness was proposed by Goldman and Mathias (1996), and various teaching models obeying their criterion have been studied. For each model $M$ and each concept class $\mathcal{C}$, a parameter $M$-$\mathrm{TD}(\mathcal{C})$ refers to the teaching dimension of concept class $\mathcal{C}$ in model $M$---defined to be the number of examples required for teaching a concept, in the worst case over all concepts in $\mathcal{C}$. This paper introduces a new model of teaching, called no-clash teaching, together with the corresponding parameter $\mathrm{NCTD}(\mathcal{C})$. No-clash teaching is provably optimal in the strong sense that, given any concept class $\mathcal{C}$ and any model $M$ obeying Goldman and Mathias's collusion-freeness criterion, one obtains $\mathrm{NCTD}(\mathcal{C})\le M$-$\mathrm{TD}(\mathcal{C})$. We also study a corresponding notion $\mathrm{NCTD}^+$ for the case of learning from positive data only, establish useful bounds on $\mathrm{NCTD}$ and $\mathrm{NCTD}^+$, and discuss relations of these parameters to the VC-dimension and to sample compression. In addition to formulating an optimal model of collusion-free teaching, our main results are on the computational complexity of deciding whether $\mathrm{NCTD}^+(\mathcal{C})=k$ (or $\mathrm{NCTD}(\mathcal{C})=k$) for given $\mathcal{C}$ and $k$. We show some such decision problems to be equivalent to the existence question for certain constrained matchings in bipartite graphs. Our NP-hardness results for the latter are of independent interest in the study of constrained graph matchings.
Fully encrypted high-speed microprocessor architecture: the secret computer in simulation

The architecture of an encrypted high-performance microprocessor designed on the principle that a nonstandard arithmetic generates encrypted processor states is described here.Data in registers, in memory and on buses exists in encrypted form.Any block encryption is feasible, in principle.The processor is (initially) intended for cloud-based remote computation.An encrypted version of the standard OpenRISC instruction set is understood by the processor.It is proved here, for programs written in a minimal subset of instructions, that the platform is secure against 'Iago' attacks by the privileged operator or a subverted operating system, which cannot decrypt the program output, nor change the program's output to a particular value of their choosing.Performance measures from cycle-accurate behavioural simulation of the platform are given for 64-bit RC2 (symmetric, keyed) and 72-bit Paillier (asymmetric, additively homomorphic, no key in-processor) encryptions.Measurements are centred on a nominal 1 GHz clock with 3 ns cache and 15 ns memory latency, which is conservative with respect to available technology.
Extractive Summary: An Optimization Approach Using Bat Algorithm

A summary is the shorter version of the existing long text which elaborates the whole idea of the document. The most conventional and easy version of summarization is extractive text summarization. Extractive approach selects sentences from the original text based on some features. In automatic text summarization (ATS), it is a difficult task to select such sentences with high accuracy to meet the optimum meaning of the full text as compared to manual approaches. In this paper, author tries to represent extractive summary as an optimization problem where the objective is to cover maximum topics of the document and simultaneously minimize the redundancies between the sentences of the summary. Bat algorithm (BA) is used as an optimization technique which provides efficient result in creating an extractive summary.
Robust Semi-supervised Multi-label Learning by Triple Low-Rank Regularization

Multi-Label Learning (MLL) deals with the problem when one instance is associated with multiple labels simultaneously. Previous methods have shown promising performance by effectively exploiting the semantic correlations among different labels. However, most of the existing methods may not be robust to the situation when the training instances are labeled with noisy or incomplete labels, which are common in reality. In this paper, we propose Robust Semi-Supervised Multi-Label Learning by Triple Low-Rank Regularization approach to address this problem. Specifically, a linear self-representative model is firstly introduced to recover the possibly noisy label matrix by exploiting the label correlations. Then, our method develops a low-rank pairwise similarity matrix to capture the global relationships among labeled and unlabeled samples by taking advantage of Low-Rank Representation (LRR). In addition, by utilizing the pairwise similarity matrix defined above, we construct the graph Laplacian regularization to acquire geometric structural information from both labeled and unlabeled samples. Moreover, the proposed method concatenate the prediction models for different labels into a matrix, and introduces the matrix trace norm to capture the correlations and control the model complexity. Experimental studies across a wide range of benchmark datasets show that our method achieves highly competitive performance against other state-of-the-art approaches.
Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter.Each agent in SKF is regarded as a Kalman filter.Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum.The SKF has been shown to yield good performance in solving benchmark optimization problems.However, the exploration capability of SKF could be further improved.From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population.By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain.Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL.The COOBL is employed after the estimation process of SKF.Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly.The COOBSKF also has been compared with five other optimization algorithms and outperforms them all.
Inductive Graph Representation Learning with Recurrent Graph Neural Networks

In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN), that address the shortcomings of prior methods. By using recurrent units to capture the long-term dependency across layers, our methods can successfully identify important information during recursive neighborhood expansion. In our experiments, we show that our model class achieves state-of-the-art results on three benchmarks: the Pubmed, Reddit, and PPI network datasets. Our in-depth analyses also demonstrate that incorporating recurrent units is a simple yet effective method to prevent noisy information in graphs, which enables a deeper graph neural network.
A New Image Despeckling Method by SRAD Filter and Wavelet Transform Using Bayesian Threshold

Images containing speckle noise are difficult to de-noise since the noise is multiplicative in nature. In this paper an efficient procedure is introduced to effectively eliminate speckle noise from an image and improving the visual quality of the image. The proposed method works with the Speckle Reducing Anisotropic Diffusion (SRAD) in combination with the Discrete Wavelet Transform (DWT) using Bayesian Threshold. In the proposed technique, first the SRAD filter is applied on a log uncompressed noisy image. Then log transformation is performed on the filtered image. Finally, the de-noised image is obtained by performing the DWT using Bayesian thresholding on the log compressed image. The new technique exhibits excellent quantitative and visual performances in terms of Peak-Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index (SSIM) and Computational Time in seconds over the conventional techniques. The results are demonstrated by simulation on standard test images.
A Survey of Different Techniques for Energy-Efficient, Reliability and Fault Tolerant in Wireless Sensor Networks

Wireless Sensor Network (WSN) consists numerous sensor hubs which containing a preparing unit, at least one sensor, a radio for information correspondence and power unit generally outfitted with a low limit energy distributed over a geographic area for monitoring our environment and physical conditions.It has been established that vitality is the most obliging element on the functionality of such systems as they are controlled with constrained vitality and replacement of vitality resources might be difficult.While sending the data in sensor network, there might be loss of information or miscalculation could occur in receiving data during transferring.The correctness of information has incredible impact on the performance of the network.To enhance the exactness of sensor information, minimizing vitality utilization and adaptation to internal failure is vital for some WSN's applications as they operate in unpredictable conditions and ought to stay operational regardless of whether a network failure happen.This paper surveys the available energy efficient, reliability and fault tolerant in WSNs.It focuses on Residue Number System (RNS) and Agent technologies for energy-efficient and fault tolerant in WSNs respectively.However, performance evaluation was also conducted based on the energy consumption, reliability, delay in receiving the sent data and efficiency.
FPGA-based architecture of hybrid multilayered perceptron neural network

&lt;span lang="EN-MY"&gt;The HMLP is an ANN similar to the MLP, but with extra weighted connections that connect the input nodes directly to the output nodes. The architecture of the HMLP neural network for implementation on FPGA is proposed. The HMLP architecture is designed to be concurrent to demonstrate the parallel nature of the HMLP where each hidden or output node within the same hidden or output layer of the HMLP can calculate its output independently. The HMLP architecture is designed to be modular as well, such that if modification to a module is necessary, only the specific module need to be modified and all other modules can be retained. This modularity will be especially helpful when different activation function is to be swapped in to replace current activation function. All calculations in the HMLP are performed in floating-point arithmetic. The HMLP architecture is compiled, simulated and finally implemented on the Cyclone V FPGA of DE1-SoC board. The simulation outcome and FPGA outputs showed that the developed HMLP architecture is able to calculate correct output values for all test datasets.&lt;/span&gt;
Deep feature descriptor based hierarchical dense matching for X-ray angiographic images

Backgroud and Objective: X-ray angiography, a powerful technique for blood vessel visualization, is widely used for interventional diagnosis of coronary artery disease because of its fast imaging speed and perspective inspection ability. Matching feature points in angiographic images is a considerably challenging task due to repetitive weak-textured regions. Methods: In this paper, we propose an angiographic image matching method based on the hierarchical dense matching framework, where a novel deep feature descriptor is designed to compute multilevel correlation maps. In particular, the deep feature descriptor is computed by a deep learning model specifically designed and trained for angiographic images, thereby making the correlation maps more distinctive for corresponding feature points in different angiographic images. Moreover, point correspondences are further hierarchically extracted from multilevel correlation maps with the highest similarity response(s), which is relatively robust and accurate. To overcome the problem regarding the lack of training samples, the convolutional neural network (designed for deep feature descriptor) is initially trained on samples from natural images and then fine-tuned on manually annotated angiographic images. Finally, a dense matching completion method, based on the distance between deep feature descriptors, is proposed to generate dense matches between images. Results: The proposed method has been evaluated on the number and accuracy of extracted matches and the performance of subtraction images. Experiments on a variety of angiographic images show promising matching accuracy, compared with state-of-the-art methods. Conclusions: The proposed angiographic image matching method is shown to be accurate and effective for feature matching in angiographic images, and further achieves good performance in image subtraction.
A New Approach for Book Recommendation Using Opinion Leader Mining

Recommendation systems (RSs) are used by different e-commerce sites like Amazon, eBay, etc., for suggesting relevant recommendations based upon users’ preferences or items purchased by people of similar interests. Recommendation systems help the users to identify the items which may be worth to buy. This is referred to as top-N recommendations. So, the ultimate goal of RS is to find out which item or product is more relevant as per user preference. In this paper, our main focus is to elaborate the recommendation process with follow-up of book recommender systems in the existing works and challenges associated with them. In order to deal with one of the recommendation challenges, we proposed book recommendation approach based on identification of opinion leaders in detected communities of social networks by using information related to their interests, preferences, age and online available attributes on social networks. This approach can be helpful in solving user cold-start problem, i.e. generating recommendations of books to new user in the absence of availability of purchase history. The same approach can be used for solving an item cold-start problem also, if none of the users had rated it till now.