Reviewerly/exHarmony huggingface dataset:
Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees

Graph signal processing (GSP) is an emerging field in the signal processing community. Novel GSP-based transforms, such as graph Fourier transform and graph wavelet filter banks, have been successfully utilized in image processing and pattern recognition. As a rapidly developing research area, graph signal processing aims to extend classical signal processing techniques to signals with irregular underlying structures. One of the hot topics in GSP is to develop multi-scale transforms such that novel GSP-based techniques can be applied in image processing or other related areas. For designing graph signal multi-scale frameworks, downsampling operations that ensuring multi-level downsampling should be specifically constructed. Among the existing downsampling methods in graph signal processing, the state-of-the-art method was constructed based on the maximum spanning tree (MST). However, when using this method for multi-level downsampling of graph signals defined on unweighted densely connected graphs, such as social network data, the sampling rates are not close to [Formula: see text]. This phenomenon is summarized as a new problem and called downsampling unbalance problem in this paper. Due to the unbalance, MST-based downsampling method cannot be applied to construct graph signal multi-scale transforms. In this paper, we propose a novel and efficient method to detect and reduce the downsampling unbalance generated by the MST-based method. For any given graph signal, we apply the graph density to construct a measurement of the downsampling unbalance generated by the MST-based method. If a graph signal has large unbalance possibility, the multi-level downsampling is conducted after the MST is improved. The experimental results on synthetic and real-world social network data show that downsampling unbalance can be efficiently detected and then reduced by our method.
Efficient Texture Retrieval Using Multiscale Local Extrema Descriptors and Covariance Embedding

We present an efficient method for texture retrieval using multiscale feature extraction and embedding based on the local extrema keypoints. The idea is to first represent each texture image by its local maximum and local minimum pixels. The image is then divided into regular overlapping blocks and each one is characterized by a feature vector constructed from the radiometric, geometric and structural information of its local extrema. All feature vectors are finally embedded into a covariance matrix which will be exploited for dissimilarity measurement within retrieval task. Thanks to the method’s simplicity, multiscale scheme can be easily implemented to improve its scale-space representation capacity. We argue that our handcrafted features are easy to implement, fast to run but can provide very competitive performance compared to handcrafted and CNN-based learned descriptors from the literature. In particular, the proposed framework provides highly competitive retrieval rate for several texture databases including 94.95 $$\%$$ for MIT Vistex, 79.87 $$\%$$ for Stex, 76.15 $$\%$$ for Outex TC-00013 and 89.74 $$\%$$ for USPtex.
E2SM: a security tool for adaptive cloud‐based service‐oriented applications

The issue of security in the distributed system landscape of a service-oriented architecture (SOA) is a challenging one. No longer is it limited to a local application or an application domain, security must now work across a range of applications and business processes interacting with each other. This is even more true when SOA-based applications are provisioned in the cloud. Firstly, cloud applications components, and the data they might handle, that were once silos, are now being exposed as services by distinct and distrusted tenants. Secondly, such applications are often adaptive when they are provisioned in cloud environments. This study proposes an end-to-end security model (E2SM) that aims to protect data confidentiality in adaptive cloud-based SOA applications. E2SM allows the setting of data-centric security policies that go beyond services boundaries. First, security configuration is automatically calculated starting from a few intuitive business-oriented security settings. Then, the configuration is updated with minimal overhead if security policies are dynamically modified and/or SOA architecture is reconfigured. A security tool is implemented according to the proposed model. As for validation, the tool was used to secure a healthcare business process.
Analysis and efficient solutions for 2 × 4 Chinese dark chess1

This paper considers the 2×4 variation of a popular 2-player imperfect information game Chinese dark chess (CDC). The 2×4 version is solved by computing the game-theoretical value of each position for all possible material combinations with at most four red pieces and four black pieces. The experim ental results show that where to make the first move, which must be a flip, is the most important factor to affect the outcome of a game. We find that choosing a square to flip that has the best-expected outcome may not be the best strategy. Instead, a square that has the best-expected difference before and after the first move is made should be chosen. We also discover that 2×4 CDC is unfair and it is favorable to the second player, but some openings are fair. We believe observations and techniques made here can be used to improve the performance of programs for playing the original 4×8 full version of CDC.
Geometry and symmetry in the quantum Boltzmann machine

Quantum Boltzmann machine extends the classical Boltzmann machine learning to the quantum regime, which makes its power to simulate the quantum states beyond the classical probability distributions. We develop the BFGS algorithm to study the corresponding optimization problem in quantum Boltzmann machine, especially focus on the target states being a family of states with parameters. As an typical example, we study the target states being the real symmetric two-qubit pure states, and we find two obvious features shown in the numerical results on the minimal quantum relative entropy: First, the minimal quantum relative entropy in the first and the third quadrants is zero; Second, the minimal quantum relative entropy is symmetric with the axes $y=x$ and $y=-x$ even with one qubit hidden layer. Then we theoretically prove these two features from the geometric viewpoint and the symmetry analysis. Our studies show that the traditional physical tools can be used to help us to understand some interesting results from quantum Boltzmann machine learning.
Area Segmentation of Images Using Watershed and Anisotropic Gaussian Kernels

Image segmentation as one of the primary steps in image analysis for object identification has received much attention lately, the main aim being to recognize homogeneous regions of an image which belong to different objects. A noise-robust edge detector SCHEME based on anisotropic Gaussian Kernels (ANGKs) is proposed in this paper by combining the region-merging algorithm to obtain higher quality segmentation results, which consist of three basic blocks. The first two blocks give rise to an initial partition of an image, while the last block attains the final segmentation by iteratively merging similar fragmented regions. A series of experiments is used to evaluate the performance of the method. The experimental results show that the proposed method has good noise robustness and localization accuracy, and can solve the problem of over-segmentation effectively, the simulation result supports our proposed main ideas.
Privacy preserving weighted similarity search scheme for encrypted data

Cloud computing has become increasingly popular among individuals and enterprises because of the benefits it provides by outsourcing their data to cloud servers. However, the security of the outsourced data has become a major concern. For privacy concerns, searchable encryption, which supports searching over encrypted data, has been proposed and developed rapidly in secure Boolean search and similarity search. However, different users may have different requirements on their queries, which mean different weighted searches. This problem can be solved perfectly in the plaintext domain, but hard to be addressed over encrypted data. In this study, the authors use locality-sensitive hashing (LSH) and searchable symmetric encryption (SSE) to deal with a privacy preserving weighted similarity search. In the authors’ scheme, data users can generate a search request and set the weight for each attribute according to their requirements. They treat the LSH values as keywords and mix them into the framework of SSE. They use homomorphic encryption to securely address the weight problem and return the top-k data without revealing any weight information of data users. They formally analysed the security strength of their scheme. Extensive experiments on actual datasets showed that their scheme is extremely effective and efficient.
Parallel evolutionary approaches for game playing and verification using Intel Xeon Phi

Automatic generation of artificial players is an important subject for the videogames industry. Different strategies have been proposed to implement realistic and intelligent agents for gameplaying and verification. This article presents a parallel evolutionary approach for the automation of computer player generation for video games. A learning pipeline model is defined to study the generation problem for Nintendo Entertainment System games composed of three stages: objective inference, objective refinement and artificial intelligence generation. Two case studies based on the defined pipeline are presented: an evolutionary algorithm to learn how to play the game Pinball, offloading the evaluation of the fitness function to a Xeon Phi coprocessor, and a full pipeline implementation that uses neuroevolution to generate RNNs that can play different games successfully. Results show that the proposed pipeline can be applied for the automatic generation of artificial players for the studied games.
Hello CRIS, can a Library Software solution help you?

The aim of this paper is to present how Brocade, a Library Information Management System, is used to set up an institutional repository and how this repository fits in and contributes to the development of a virtual CRIS system for the University of Antwerp. We focus on both organizational and technical challenges. Although not initially our intention, during the process of continuously optimizing the repository and fulfilling different needs, we ended up with a solution that is able to support research information management in various ways. An example of such an optimization was adding the possibility to describe projects in Brocade, an addition that was needed to make the repository OpenAire compliant. Storing more detailed information about affiliations in order to better allocate publications to research groups is another example. The repository of the University of Antwerp captures related metadata about scientific output (publications, non-written output and datasets), researchers, projects and organisations. The repository is fed automatically with metadata from bibliographic, human resources and research databases. The database today gives an overview and/or contains metadata of about 130,000 repository items, 6,500 researchers, 17,000 projects and 400 organisations. Full text documents are archived in the repository for the majority of peer-reviewed bibliographic items and made open access available where possible. By demonstrating how Brocade in relation with other applications is used for research information management at the university of Antwerp we conclude that it is possible to serve CERIF and CRIS needs by adapting existing systems and make them interoperable
Desired Speed of Reply During Text-Based Communication via Smartphones

Text-based forms of communication have long been communicated via PCs and more recently via mobile phones and smartphones. This chapter has tried to explore how the speed of text message exchanges has become a nonverbal cue in the asynchronous communications that occurs via mobile phones and smartphones. This chapter presents the investigation of the speed of exchange relevant to the LINE text-messenger application. Specifically, the authors surveyed Japanese university students who provided free-response descriptions of situations when (a) a quick reply is preferred and (b) a late reply is acceptable. The main finding was that judging when a quick reply is preferred and when a late reply is acceptable is overwhelmingly influenced by the convenience of the sender (who is waiting for a reply) over the convenience of the recipient who will reply.
Approximation of Ensemble Boundary Using Spectral Coefficients

A spectral analysis of a Boolean function is proposed for approximating the decision boundary of an ensemble of classifiers, and an intuitive explanation of computing Walsh coefficients for the functional approximation is provided. It is shown that the difference between the first- and third-order coefficient approximations is a good indicator of optimal base classifier complexity. When combining neural networks, the experimental results on a variety of artificial and real two-class problems demonstrate under what circumstances ensemble performance can be improved. For tuned base classifiers, the first-order coefficients provide performance similar to the majority vote. However, for weak/fast base classifiers, higher order coefficient approximation may give better performance. It is also shown that higher order coefficient approximation is superior to the Adaboost logarithmic weighting rule when boosting weak decision tree base classifiers.
A Low Cost Solution of Hand Gesture Recognition Using a Three-Dimensional Radar Array

In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.
Dynamic reencryption of return addresses

The authors present dynamic reencryption of return addresses to mitigate their leakage. The authors’ method enforces programs to save return addresses as encrypted and renew the encryption states with fresh keys before or after vulnerable operations. When a function returns, it should restore the return address from its encryption using the most recent key not to cause a crash. Under the protection of their method, return addresses and keys may leak, but the disclosed bits become garbage because keys govern all return addresses through encryption, while changing before control-flow proceeds into a vulnerable region. As a result, it becomes probabilistically infeasible to build exploits for intercepting control-flow by using leaked return addresses or keys. They implemented the proposed method as an extension of the LLVM compiler that inserts reencryption code where necessary. They also have confirmed its effectiveness against information leak attacks carried out in the early stage of blind return-oriented programming (BROP). The performance overhead ranges below 11.6% for processor-intensive programs and 4.12% or less for web servers.
Firefly: Virtual Illumination Drones for Interactive Visualization

Light specification in three dimensional scenes is a complex problem and several approaches have been presented that aim to automate this process. However, there are many scenarios where a static light setup is insufficient, as the scene content and camera position may change. Simultaneous manual control over the camera and light position imposes a high cognitive load on the user. To address this challenge, we introduce a novel approach for automatic scene illumination with Fireflies. Fireflies are intelligent virtual light drones that illuminate the scene by traveling on a closed path. The Firefly path automatically adapts to changes in the scene based on an outcome-oriented energy function. To achieve interactive performance, we employ a parallel rendering pipeline for the light path evaluations. We provide a catalog of energy functions for various application scenarios and discuss the applicability of our method on several examples.
Graph node rank based important keyword detection from Twitter

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.
Image-Based Aspect Ratio Selection

Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.
A Hybrid Security System Based on Bit Rotation and Chaotic Maps

Background: This paper presents an image security system by combining bit rotation with block based chaotic maps cryptography. Methods: The system uses permutation technique that divides the image into blocks before applying right/left rotation of bits to the pixel values based on a randomly generated key. Then, the image blocks are fused together. A scrambling operation followed by chaotic map is applied on the rotated image to diffuse the image pixels using another randomly generated key. The chaotic map scatters all the pixel positions in the image. The decryption is the complete reversal operation of the encryption process. Results &amp; Conclusion: The performance of the proposed technique is evaluated using several metrics: Histograms of both original and cipher images, correlation of adjacent pixels and correlation between the original and cipher images, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The results indicate a very secure technique to protect all types of images.
Optimum Backup Policies with Update Failure at Random Times

In order to ensure the data security, plans of incremental and differential backups are usually set up to save the costs which suffer from full backups. In this paper, we suppose that failures occur at data updating times and backups are implemented only at the end of data updates, and full backups are done at time [Formula: see text], over time [Formula: see text] and update [Formula: see text] to balance the costs of data backup and failure recovery. Using the theory of renewal process, we obtain the expected costs of backup and recovery and the expected cost rates for full backups. Optimum solutions of [Formula: see text] and [Formula: see text] to minimize the expected cost rates are obtained, and their comparisons are discussed from the optimizations of the integrated models in analytical ways.
Connected Components Labeling on DRAGs: Implementation and Reproducibility Notes

In this paper we describe the algorithmic implementation details of “Connected Components Labeling on DRAGs” (Directed Rooted Acyclic Graphs), studying the influence of parameters on the results. Moreover, a detailed description of how to install, setup and use YACCLAB (Yet Another Connected Components LAbeling Benchmark) to test DRAG is provided.