Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Government Data Sharing Based on Hyperledger Blockchain

With the further advancement of the government information resource sharing mechanism, the realization of data sharing between departments under the support of safe and effective technology has become focus of common concern for government departments and the public. Blockchain provides new ideas and ways for government departments to share data securely and efficiently. On the basis of analyzing the risk of disclosure of private information of citizens and enterprises in government data sharing, this paper discusses the data sharing framework of privacy preserving among government departments based on hyperledger blockchain and describes the corresponding data sharing process. Through an example of data sharing application between government departments, the feasibility and advantages of the proposed privacy preserving government data sharing framework based on blockchain technology are presented.
Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.
A High Capacity Watermarking Technique for the Printed Document

Digital watermarking technology is an effective method for copyright protection of digital information, such as images, documents, etc. In this paper, we propose a high capacity text image watermarking technique against printing and scanning processes. Firstly, this method obtains the invariant in the process of printing and scanning under the mathematical hypothesis model of print-scan transformation. Then based on the print-scan invariant, the Fourier descriptor is used to flip the trivial pixel points with high frequency information on the character boundary. Next, considering the resolution of the print-scan equipment and its influence on the print-scan invariant, a quadratic quantization function is proposed to embed watermark information of multiple bits for a single character. Finally, the QR code (Quick Response code) is researched, which has large information capacity, robust error correction ability and high decoding reliability. By using the QR code as the watermark information, we can reduce the impact of bit error rate during watermark extraction, and the robustness of the watermark information can be improved. The experimental results show that the proposed text watermarking algorithm has the advantages of anti-print scanning, anti-scaling, large capacity and good visual effects.
Cycle-Spinning GAN for Raindrop Removal from Images

Weather events such as rain, snow, and fog degrade the quality of images taken under these conditions. Enhancement of such images is critical for intelligent transport and outdoor surveillance systems. Generative Adversarial Networks (GAN) based methods have been shown to be promising for enhancing these images in recent years. In this study, we adapt the cycle-spinning technique to GAN for removal of raindrops. The experimental evaluation of the proposed method shows that the performance is improved in terms of reference-based metrics (SSIM and PSNR). In addition, the approach also results in higher object detection performance in terms of mean average precision (mAP) metric when applied before the detection process.
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role.Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature.For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications.The focus is on the usage of static camera setups, since this kind of camera is one of the most often used and belongs to the lower price segment.In order to address this task, multiple subtasks are evaluated, including the influence of existing optical flow methods for the incorporation of short-term temporal information, different forms of network setups and losses for GANs, and the use of morphological operations for further performance improvement.With these extension we achieved up to 2.4% better results.Furthermore, the final method reduced the anomaly detection error for GANbased methods by about 42.8%.
Location, Location, Location: Revisiting Modeling and Exploitation for Location-Based Side Channel Leakages

Near-field microprobes have the capability to isolate small regions of a chip surface and enable precise measurements with high spatial resolution. Being able to distinguish the activity of small regions has given rise to the location-based side-channel attacks, which exploit the spatial dependencies of cryptographic algorithms in order to recover the secret key. Given the fairly uncharted nature of such leakages, this work revisits the location side-channel to broaden our modeling and exploitation capabilities. Our contribution is threefold. First, we provide a simple spatial model that partially captures the effect of location-based leakages. We use the newly established model to simulate the leakage of different scenarios/countermeasures and follow an information-theoretic approach to evaluate the security level achieved in every case. Second, we perform the first successful location-based attack on the SRAM of a modern ARM Cortex-M4 chip, using standard techniques such as difference of means and multivariate template attacks. Third, we put forward neural networks as classifiers that exploit the location side-channel and showcase their effectiveness on ARM Cortex-M4, especially in the context of single-shot attacks and small memory regions. Template attacks and neural network classifiers are able to reach high spacial accuracy, distinguishing between 2 SRAM regions of 128 bytes each with 100% success rate and distinguishing even between 256 SRAM byte-regions with 32% success rate. Such improved exploitation capabilities revitalize the interest for location vulnerabilities on various implementations, ranging from RSA/ECC with large memory footprint, to lookup-table-based AES with smaller memory usage.
LCD codes from weighing matrices

Linear codes with complementary duals are linear codes whose intersection with their duals are trivial, shortly named LCD codes. In this paper we outline a construction for LCD codes over finite fields of order q using weighing matrices and their orbit matrices. The LCD codes constructed can be of any length dimension according to the choice of matrices used in their construction. As a special case, LCD codes of length 2n and dimension n are constructed which also have the property of being formally self-dual. Alternatively, under a condition depending on q that the codes are not LCD, this method constructs self-dual codes. To illustrate the method we construct LCD codes from weighing matrices, including the Paley conference matrices and Hadamard matrices. We also extend the construction to Hermitian LCD codes over the finite field of order 4. In addition, we propose a decoding algorithm that can be feasible for the LCD codes obtained from some of the given methods.
Final Report of the NTCIR-14 OpenLiveQ-2 Task

This is the final report of the OpenLiveQ-2 task at NTCIR-14. This task aimed to provide an open live test environment of Yahoo Japan Corporation's community question-answering service (Yahoo! Chiebukuro) for question retrieval systems. The task was simply defined as follows: given a query and a set of questions with their answers, return a ranked list of questions. Submitted runs were evaluated both offline and online. In the online evaluation, we employed pairwise preference multileaving, a multileaving method that showed high efficiency over the other methods in a recent study. We describe the details of the task, data, and evaluation methods, and then report official results at NTCIR-14 OpenLiveQ-2. Furthermore, we demonstrate the effectiveness and efficiency of the proposed evaluation methodology.
Tracking Control of Unmanned Tracked Vehicle in Off-road Conditions with Large Curvature

In order to achieve the accurate trajectory tracking for a high-speed tracked vehicle in off-road conditions in the case where it is known that the desired trajectory is a large curvature curve, new research directions are being developed. In the framework of the Model Predictive Control (MPC) algorithm, the simplified ideal kinematics model of the tracked vehicle that ignores the sliding steering characteristics is applied to reduce the iterative solution time under high-speed driving conditions. By adjusting the weight coefficients of the objective function, the trajectory tracking accuracy is improved. This research is based on an unmanned electric drive tracked vehicle and carries out simulation experiments. Through real vehicle experiment, the control sequence of the experienced driver under the certain scene is collected, and then the vehicle control experience of human is obtained. Through simulation experiment, the vehicle tracking errors under different MPC weight coefficients of different curvature curves are obtained. In addition, the tracking control sequence is compared with the driver's control data. By the data analysis, the sensitivity of each weight coefficient to the tracking accuracy and how to create a driving mode that is closer to human by adjusting the weighting coefficients are determined.
Automated Classification of Web-Application Attacks for Intrusion Detection

In today’s information driven society and economy, web facing applications are most common way to run information dissemination, banking, e-commerce etc. Web applications are frequently targeted by attackers through intelligently crafted http requests to exploit vulnerabilities existing in the application, front-end, and the web-clients. Some of the most frequent such attacks are SQL Injection, Cross-Site Scripting, Path-traversal, Command Injection, Cross-site request forgery etc. Detecting these attacks up front and blocking them, or redirecting the request to a honey-pot could be a way to prevent web applications from being exploited. In this work, we developed a number of machine learning models for detecting and classifying http requests into normal, and various types of attacks. Currently, the models are applied as an ensemble on the http server logs, to classify and build data analytics on the http requests received by any web server in order to garner threat intelligence, and threat landscape. We also implemented an online log-analysis version that analyzes logs every 15 s to classify http requests in the recent 15 s. However, it can also be used as a web application firewall to block the http requests based on the classification results. We also have implemented an intrusion protection mechanism by redirecting http requests classified upfront as malicious towards a web honeypot. We compare various existing signature based, regular expression based, and machine learning based techniques against our models for detection and classification of http based attacks, and show that our methods achieve better performance over existing techniques.
Implementation weighted product method to determine multiple intelligence child

Abstract Multiple Intelligence can be known through words, numbers, music, pictures, physical activity or motor skills in a social-emotional way. Multiple Intelligence is basically the development of brain intelligence (IQ), emotional intelligence (EQ), and spiritual intelligence (SQ). Intelligence is the ability possessed by a person to see a problem, then solve the problem or make something that can be useful for others. Intelligence or intelligence can be viewed as the ability to understand the world, think rationally, and use resources effectively when faced with a challenge. Some argue that the notion of intelligence is the general human ability to perform actions that have goals and think in a rational way. In addition, intelligence can also be interpreted as a personal ability to understand, innovate, and provide solutions to various situations. Multi Attribute Decision Making (MADM) is used in making decision support systems using Weighted Product (WP) to define child Multiple Intelligence. 8 intelligences with 8 criteria with specified weights. The data tested based on the data of new students who have enrolled in SMP Negeri 3 Kepanjen with the amount of data 226 students who have registered and fill in the questionnaire (indicator) that has been provided through www.teskecerdasan.com. The results of these are graphs that have been ranked based on the Weighted Product (WP) method villages.
Open Space Attraction Based Navigation in Dark Tunnels for MAVs

This work establishes a novel framework for characterizing the open space of featureless dark tunnel environments for Micro Aerial Vehicles (MAVs) navigation tasks. The proposed method leverages the processing of a single camera to identify the deepest area in the scene in order to provide a collision free heading command for the MAV. In the sequel and inspired by haze removal approaches, the proposed novel idea is structured around a single image depth map estimation scheme, without metric depth measurements. The core contribution of the developed framework stems from the extraction of a 2D centroid in the image plane that characterizes the center of the tunnel’s darkest area, which is assumed to represent the open space, while the robustness of the proposed scheme is being examined under varying light/dusty conditions. Simulation and experimental results demonstrate the effectiveness of the proposed method in challenging underground tunnel environments [1].
Deep Music Analogy Via Latent Representation Disentanglement

Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces. In general, an analogy is made by partially transferring the music abstractions, i.e., high-level representations and their relationships, from one piece to another; however, this procedure requires disentangling music representations, which usually takes little effort for musicians but is non-trivial for computers. Three sub-problems arise: extracting latent representations from the observation, disentangling the representations so that each part has a unique semantic interpretation, and mapping the latent representations back to actual music. In this paper, we contribute an explicitly-constrained variational autoencoder (EC$^2$-VAE) as a unified solution to all three sub-problems. We focus on disentangling the pitch and rhythm representations of 8-beat music clips conditioned on chords. In producing music analogies, this model helps us to realize the imaginary situation of what if a piece is composed using a different pitch contour, rhythm pattern, or chord progression by borrowing the representations from other pieces. Finally, we validate the proposed disentanglement method using objective measurements and evaluate the analogy examples by a subjective study.
Hill Cipher Modification: A Simplified Approach

Most people nowadays are using the internet as a means of communication to facilitate exchanging sensitive data. Securing this data is vital considering almost all computers in the world are interconnected. Cryptography is a mechanism of sending information in the masked form so that only the intended receiver can retrieve and read this information. In connection with this, data encryption turns out to be an essential part of the secure communication of this important information. In this paper, a simplified method of encryption of data in blocks using the logical XOR and shift operations, and Radix64 for encoding and decoding the data is introduced. The results confirm that the security of the cipher improves to 53% avalanche effect percentage based on the actual experiment.
Derivation of the discrete-time constant turn rate and acceleration motion model

For vehicle tracking in automotive applications there are a number of proposed motion models. One of those models is the constant turn rate and acceleration (CTRA) model. In the original paper where the model was introduced, the state prediction function was defined, but not the process noise. In this paper, a derivation of the process noise is made. For completeness, the discrete-time prediction model is also derived, using linearized discretization.
Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging

Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Based on sMRI data, anatomical-landmark-based deep learning has been recently proposed for AD and MCI diagnosis. These methods usually first locate informative anatomical landmarks in brain sMR images, and then integrate both feature learning and classification training into a unified framework. This chapter presents the latest anatomical-landmark-based deep learning approaches for automatic diagnosis of AD and MCI. Specifically, an automatic landmark discovery method is first introduced to identify discriminative regions in brain sMR images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.
Dynamic Service Binding for Time-Aware Service Compositions

Late service binding and service re-binding react to run-time contingencies of service executions for securing promised Qualities of Service in a web service composition. In particular, for satisfying temporal requirements web service designers have to cope with possible imponderabilities of external service invocations leading to pessimistic schedules. We propose a novel solution for the dynamic binding of services in presence of temporal constraints, which guarantees the dynamic controllability of a web service composition and aims at achieving higher Qualities of Service for instances which are ahead of schedule.
RTSRGAN: Real-Time Super-Resolution Generative Adversarial Networks

The Enhanced Super-Resolution Generative Adver-sarial Networks (ESRGAN) is the state of the art deep learning based image Super-Resolution (SR) and has the best performance in perceptual quality. However, we find it is time-consuming, which makes it impractical for SR at clients' side during video delivery since SR usually uses clients' computing resources (the computational power at the clients' side should not be as powerful as GPU) and videos often require real-time playback. While Efficient Sub-Pixel Convolutional Neural Network (ESPCN) has the best real-time performance, it is still not capable of offering a smooth watching experience and has much lower perceptual quality. In order to simultaneously meet the demands on the real-time performance and the resulting pleasant artifacts of SR at the clients' side, we propose RTSRGAN to exploit the advantages of ESRGAN in image perceptual quality and ESPCN in real-time performance. Our experimental results indicate that RTSRGAN has the fastest reconstruction speed, on the average, 15 images per second on a single 2.3GHz CPU (only 6 images per second by ESPCN), and reconstructs images of a relatively acceptable perceptual quality, which validates that our proposed RTSRGAN can be used for SR at clients' side to enhance the real-time performance and ensure the image perceptual quality.
Standard Plenoptic Cameras Mapping to Camera Arrays and Calibration Based on DLT

First prototypes of standard plenoptic cameras (SPCs) were based on arrays of pinhole cameras. Despite the array nature, viewpoint pinhole arrays are not intrinsically provided by current SPC calibration tools. In this work, we start by detailing the mapping between the SPC model and a camera array of viewpoints. Then, the mapping is used to propose a calibration procedure for the SPC based on a grid of corners. Calibration involves two steps, first a linear solution and then a nonlinear optimization minimizing the ray re-projection error. The proposed calibration methodology compares favourably with state of the art calibrations and the linear solution proposed for the initial stage of the calibration outperforms the state of the art.
Unsupervised Classification of Health Content on Reddit

Online forums are easily accessible to the public and useful to acquire and disseminate health information, however, advanced methods have to be applied to correctly interpret the content. For this reason, we propose the application of an unsupervised embedding-based approach for health content classification. Specifically, we utilise word embeddings and a clustering method to create content-sensitive word clusters; we then align the health content with the clusters classifying it into illnesses/medication/disease agents. The results suggest that a cosine similarity of 0.70 is preferred for the creation of informative clusters as well as for the automatically generation of synonyms, acronyms, abbreviations and common misspellings. Our approach does not only demonstrate the potential given by discussion forums, in particular, Reddit, for unsupervised content classification but also for dictionary building from informal health content.