Reviewerly/exHarmony huggingface dataset:
A Digital Pseudo Random Number Generator Based on a Chaotic Dynamic System

Chaotic based Pseudo Random Number Generators (PRNGs) have been very popular due to their unpredictable behavior. Hardware implementations of PRNG is used in many applications including cryptography, security and communication systems. Chaotic systems demonstrate an extreme sensitivity to variations in initial conditions and control parameters which makes it ideal for such applications especially cryptography. Due to this severe sensitivity, it is essential to implement the system with a sufficient accuracy while keeping the hardware cost as low as possible. An efficient and compact digital design is proposed for a PRNG based on a low-dimensional chaotic dynamic system. In this design, CORDIC algorithm has been used to reduce the hardware cost while maintaining the accuracy high. Experimental results and error measurements indicate a maximum error of 0.011 and RMSE of 0.0015 between the proposed digital circuits and the original chaotic system. The circuits have been synthesized for an Altera FPGA board. The results indicate that the circuits are successfully able to mimic the mathematical simulation counterpart. In addition, synthesis results verify that the proposed circuits take less than 1% of the target FPGA resources. Furthermore, Static Timing Analysis demonstrate a maximum frequency of 172.09 MHz for the proposed circuits. The proposed circuits can be employed in cryptography and security applications due to their compactness and high accuracy.
3D-Mask-GAN:Unsupervised Single-View 3D Object Reconstruction

3D object reconstruction has always been a hot topic in computer vision. Especially in recent years, many methods of learning volumetric predictions achieve robust 3D reconstruction using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, the majority of methods employ strong shape priors and exist computationally waste in predicting 3D shapes. In this paper, we propose 3D-Mask-GAN, a novel framework to efficiently accomplish the task of unsupervised single-view 3D object reconstruction. We use 3D Generative Adversarial Networks (GAN) to predict the 3D shape from the single-view image and improve reconstruction accuracy by applying 2D projection masks instead of 3D priors simultaneously. The key idea is to insert a projector (a build-in camera system to approximate the true rendering pipeline) into the framework of 3D GAN to synthesize novel masks for optimization. We learn single-class and multi-class objects to evaluate our network. Experimental results show that our framework achieves impressive performance with fewer training iterations in terms of unsupervised shape predictions.
A Case Study for Presenting Bank Recommender Systems based on Bon Card Transaction Data

As with many other businesses, banking industry tends to digitalize its working processes and use state-of-the-art technique in the financial and commercial areas in its business. The main core of the bank business is managing communication with customers which eventually results in investment on customers. In this paper, the structure of a recommender system is described, whereby using the recommender technology the places for purchase in which so far, the customers have not used any special type of Bon cards but are probable to buy from them is estimated and proposed to the customers.Matrix factorization is a type of method for collaborative filtering based on models which is widely used for rating prediction concept. Generally, bank products are not rated by customers; these products are usually purchased or offered to customers by the bank. Therefore, to determine the rating, RFM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> method which is an instrument for analysis in marketing is used along with clustering algorithm to determine the customer value and place. If a place does not have any value, i.e. the data have missing values, it suggests that we do not know whether the customer prefers the place for purchase or not. In this paper, a hybrid method based on dimension reduction technique is presented. This method is able to predict the missing values in data to offer recommendation to customers. Assessment of the proposed model through Root Mean Square Error <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> indicates that the architecture in this paper has less error in comparison to common collaborative filtering methods.
Numerical Calculation of Partial Differential Equation Deduction in Adaptive Total Variation Image Denoising

Total variation model of image denoising is easy to influence the gradient and lose the details of image. Due to these weaknesses, many adaptive total variation (ATV) models of image denoising have been proposed to eliminate the Gaussian noisy additive in the image. This paper implements the model through a numerical solution, where the gradient descent method is used to derive the partial differential equation (PDE) corresponding to the ATV model. First, based on Euler-Lagrange equation, a detailed derivation process for Partial Differential Equation is used to calculate the ATV model. Then, based on gradient descent method, a numerical calculation of the model is derived from the PDE using the Direct Difference Method. Finally, several different λ parameters are compared to produce different image denoising effects and the appropriate parameter λ value is determined. Experimental results prove that our proposed numerical calculation can effectively realize the ATV denoising model.
Exploring information and communication technology among rural dwellers in sub-Saharan African communities

The study used survey design and collected data using a questionnaire. Stratified random sampling was employed to identify 12 rural communities evenly spread across the three senatorial districts of Kwara State, Nigeria. From each community, 125 respondents were purposively sampled, resulting in a sample size of 1500. Data collected were analyzed using frequencies and cross-tabulations. The results show the presence of ICT in the rural communities of a sub-Saharan African country and the need for the government to educate, train and implement policies that would help rural dwellers make effective and efficient use of ICT tools and infrastructures available in their domain.
Task-in-all Domain Adaptation for Semantic Segmentation

In this work we tackle the problem of unsupervised domain adaptation for semantic segmentation. One pipeline is to sequentially train image-translation model and the final task segmentation model. In such pipeline, image translation is aimed to generate the translated source-domain images which are visually similar to the target-domain images and then the final task model is trained using the translated images and its corresponding groundtruth. However, the visually optimal translated-images are not necessarily optimal for the final task of segmenting the target-domain images. Thus we propose a Task-in-all pipeline for unsupervised domain adaptation on semantic segmentation, which incorporates image translation and final segmentation task into an end-to-end training pipeline. Our aim is to generate the translated images which better assists the final task, instead of just being visually similar to the target domain images. We show that in the task of adapting from GTA5 to Cityscapes dataset, the segmentation performance of our Task-in-all pipeline outperforms the sequentially training pipeline, with simpler model structure and less training complexity.
The Work of Art in the Age of Digital Fragility

Impermanence and fragility have become the defining conditions of the digital age. Technologies that were ubiquitous barely a decade ago, like floppy disks, now look like archaeological relics. It takes only a few years, if not months, before software environments are replaced by newer versions, often with limited backward compatibility. At the same time, digital technologies rely on hardware that has short life expectancy. The radical obsolescence of this new digital register raises a number of important questions. How are we going to prevent the fragile memories of contemporary digital cultures from receding into oblivion? This essay answers this question by looking at one of the institutions in which the problems associated with digital fragility are most especially felt, the Museum of Modern Art (MoMA), and by exploring the ontological displacements that digital objects are operating at the heart of the museum.
Improving Action Recognition with the Graph-Neural-Network-based Interaction Reasoning

Recent human action recognition methods mainly model a two-stream or 3D convolution deep learning network, with which humans spatial-temporal features can be exploited and utilized effectively. However, due to the ignoring of interaction exploiting, most of these methods cannot get good enough performance. In this paper, we propose a novel action recognition framework with Graph Convolutional Network (GCN) based Interaction Reasoning: Objects and discriminative scene patches are detected using an object detector and class active mapping (CAM), respectively; and then a GCN is introduced to model the interaction among the detected objects and scene patches. Evaluation of two widely used video action benchmarks shows that the proposed work can achieve comparable performance: the accuracy up to 43.6% at EPIC Kitchen, and 47.0% at VLOG benchmark without using optical flow, respectively.
Clustering based approach for incomplete data streams processing

Recent applications such as sensor networks generate continuous and dynamic data streams. Data streams are often gathered from multiple data sources with some incompleteness. Clustering such data is constrained by incompleteness of data, data distribution, and continuous nature of data streams. Ign oring missing values in incomplete data clustering, especially in high missing rates decreases the clustering performance. Traditional clustering is applied on the whole data without dealing with data distribution. This paper presents an efficient framework called Fuzzy c-means clustering for Incomplete Data streams (FID) that works adaptively with incomplete data streams even with high missing rates. The proposed FID estimates missing values based on the corresponding nearest-neighbors’ intervals. To overcome the previously mentioned data streams clustering problems, the continuous clustering mechanism is adopted and extended to accurately handle the incomplete data streams. Experimental results using two different data sets prove the efficiency of the proposed FID comparing to the alternative approaches.
A Blockchain-Based Trustable Framework for IoT Data Storage and Access

As the use of Internet of Things (IoT) devices is increasing dramatically, it is necessary to provide a trustable framework when IoT data are stored and used inside or outside IoT devices. Blockchains and smart contracts provide solutions to construct trustable environments in data storage and access. Unfortunately, current blockchain technology only suits for situations that a small or medium amount of data is stored and used, but it has a very low performance when a large amount of data gathered by IoT devices needs to be stored and accessed. This paper proposes a three-layer blockchain-based trustable framework for IoT data storage and access. In the framework, users, roles, permissions, data objects, and their relationships are formally defined. Based on these definitions, smart contracts with role-based access control (RBAC) model are developed. Additionally, a snapshot mechanism is designed to collect IoT data in order of time stamps and put it into files stored in the inter-planetary file system (IPFS). We developed a prototype of supply chain tracing system on Ethereum and IPFS for feasibility verification and performance evaluation of the proposed framework. The framework not only guarantees data integrity in storing IoT data, but also ensures data confidentiality when the IoT data is used. Moreover, simulation results illustrate that the prototype system has high performances in time, space, and gas consumption.
Prediction of ECG Signal Based on TS Fuzzy Model of Phase Space Reconstruction

ECG is an important gist for the diagnosis of heart disease, it is significant for heart disease warning in advance and ECG data repairing to predict ECG signal accurately. In this paper, the chaotic characteristics of ECG signal have been analyzed, and the ECG signal prediction based on the combination of the phase space reconstruct of ECG signal and the TS fuzzy model is proposed. The simulation experiment dealing with the typical nonlinear MG time series and the ECG data of MIT-BIH standard database shows that, and compared with other prediction algorithms, the proposed method achieves a better prediction performance, and which provides a new method for the processing of ECG data and the diagnosis of heart diseases.
Mechanism of Bitcoin and Investigation of the Studies in the Literature Related to Bitcoin

Bitcoin is a digital currency that uses cryptographic rules to regulation and generation units of currencies. In 2009, it was introduced by a person (or a group of people) using the name Satoshi Nakamoto before putting on the market as open source software. Bitcoin is a new payment system and investment tool for the purchase, storage and use of money as digitally. However, when people want to invest in bitcoin, the biggest factor they have to consider is how the price will change in the future. Recent developments in the field of machine learning have shown that some algorithms can provide appropriate solutions to predict future prices of crypto currencies. In this study, a detailed literature review is made about the studies using machine learning methods to estimate the future price of bitcoin. Basic and current information about bitcoin was included in this study. In addition, the theory of machine learning methods used is explained. The findings of the literature review show that machine learning methods can be successful in predicting bitcoin price.
Collaborative Object Transportation by Multiple Robots with Onboard Object Localization Algorithm

Collaborative object transportation has become a popular study trend with its remarkable application foreground. In previous relevant studies, localization of the transported object has always been accomplished by additional devices rather than robot onboard equipments. This paper presents a generalized multi-robot leader-follower system for collaborative object transportation and an onboard object localization algorithm for trajectory tracking of the target object. In this system, the mobile robots can directly push a cubic object without extra gripping devices, when tracking the reference trajectory. During the control process, the object is regarded as an virtual leader, whose localization information is utilized as the feedback, while the mobile robots are considered as the followers. In absence of external localization systems, the proposed onboard localization algorithm provides the real-time position information of the object using scan data from lidars equipped on the robots. A performed measurement accuracy test shows high precision of this algorithm. Finally, a lane-changing experiment of object transportation is conducted, and it verifies this multi-robot leader-follower system.
Light Field Images Compression Based on Graph Convolution Networks

The light field records all the information about the light in space. With this property, it has a very good application prospect in immersive media. Due to the large amount of light field data, light field compression technology has attracted much attention. The graph data structure of vertices and edges can well describe the relationship between the light field viewpoints, which is suitable for processing light field data. At the same time, the graph convolution network (GCN) combines graph and neural networks and has great potential for processing graph data. In this work, a light field compression scheme based on the graph convolutional network has been proposed. On the compression side, the scheme will select the anchor views and the entire light field as the input and label of the GCN, which generates a network model. This model describes the relationship between the light field viewpoints. On the decompression side, the scheme reconstructs the entire light field using the model and the extracted anchor views. Experimental results show that the proposed scheme has superior performance in reconstructed images. Compared with state-of-the-art, it can achieve a gain of up to 4 dB on PSNR. Besides, the scheme has been proved to have good generalization capability.
Pedestrian Detection Under Unmanned Aerial Vehicle an Improved Single-Stage Detector Based on RetinaNet

As a kind of single-stage detection network, RetinaNet has excellent performance in object detection, but it still has some difficulties in small object detection in unmanned aerial vehicle (UAV) images. Based on RetinaNet, this paper uses improved ResNet50 as backbone to extract features. We increase the number of anchor ratios, but reduce the number of anchor scales so that the number of anchors won't increase too much. We add the shallower features to predict smaller targets. We also add Receptive Field Block after each predicted feature to increase the receptive field. We have changed the values of parameters in the loss function for better detection performance. The improved model effectively increases the detection rate of small targets so that it can be applied to pedestrian detection under UAV. In our UAV pedestrian dataset, the improved model increases average precision (AP) from 51.29% to 84.05%.
Long-Term Autonomous Ocean Monitoring with Streaming Samples

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method — standard Gaussian process (GP) regression — becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand.
Deep Learning Based In-Loop Filter for Video Coding

In lossy video coding, in-loop filter can improve the image reconstruction by reducing compression artifacts and distortions. Recently, several CNN-based in-loop filtering algorithms are proposed to improve HEVC. However, something deficient is still limiting their capability to further boost the coding efficiency. Specifically, things like prediction residuals and coding-unit boundaries, which is quite relevant to the compression artifacts, were usually neglected; moreover, the loss functions adopted in these methods usually haven't been regularized properly. To improve these inadequacies, we propose a deep learning based in-loop filter for HEVC to improve its rate-distortion performance. Firstly, prediction residuals, the compression of which is actually where the encoding noise directly results from, are skillfully considered to help filtering. Then, we unify a block-based measurement-reconstruction process and a neighborhood-based filtering process into one end-to-end all-convolutional architecture to catch the relevance between compression noise and the pixel's related position to the coding block boundaries. Finally, for network training, total-variation model is adopted to derive the loss function by MAP (maximum a posteriori) estimation for better regularization. Experimentally, our proposed in-loop filtering method brings average 6.5% and 4.3% BD-rate reduction under AI and RA configuration respectively.
k Is the Magic Number—Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

Most convex and nonconvex clustering algorithms come with one crucial parameter: the k in k-means. To this day, there is not one generally accepted way to accurately determine this parameter. Popular methods are simple yet theoretically unfounded, such as searching for an elbow in the curve of a given cost measure. In contrast, statistically founded methods often make strict assumptions over the data distribution or come with their own optimization scheme for the clustering objective. This limits either the set of applicable datasets or clustering algorithms. In this paper, we strive to determine the number of clusters by answering a simple question: given two clusters, is it likely that they jointly stem from a single distribution? To this end, we propose a bound on the probability that two clusters originate from the distribution of the unified cluster, specified only by the sample mean and variance. Our method is applicable as a simple wrapper to the result of any clustering method minimizing the objective of k-means, which includes Gaussian mixtures and Spectral Clustering. We focus in our experimental evaluation on an application for nonconvex clustering and demonstrate the suitability of our theoretical results. Our SpecialK clustering algorithm automatically determines the appropriate value for k, without requiring any data transformation or projection, and without assumptions on the data distribution. Additionally, it is capable to decide that the data consists of only a single cluster, which many existing algorithms cannot.
Formalising oblivious transfer in the semi-honest and malicious model in CryptHOL

Multi-Party Computation (MPC) allows multiple parties to compute a function together while keeping their inputs private. Large scale implementations of MPC protocols are becoming practical thus it is important to have strong guarantees for the whole development process, from the underlying cryptography to the implementation. Computer aided proofs are a way to provide such guarantees.
Text Summarization in Social Networks by using Deep Learning

Social networks are user-based platforms where users share their thoughts and feelings visually or in writing. Social networks are also networks where users can comment on the topic (s). Emotion analysis and text summarization on shared data and big data in social networks have become popular today. In this study, Word2Vec model was created with data collected from many social networks. With the Word2vec model, a semantic context is created on the text that users share. With all the data collected, the GRU (Gated Recurrent Unit) neural network was trained and a 2-output negative-positive model was formed. The aim is to generate summary text for the user by gathering comments under a label on Twitter. At the same time, the generated abstract text is given a sense with the created model. In the study, 94% success rate was found and the text was found successfully with LSA text summarization algorithm.