Reviewerly/exHarmony huggingface dataset:
CNF+CT: Context Network Fusion of Cascade-Trained Convolutional Neural Networks for Image Super-Resolution

A novel cascade learning framework to incrementally train deeper and more accurate convolutional neural networks is introduced. The proposed cascade learning facilitates the training of deep efficient networks with plain convolutional neural network (CNN) architectures, as well as with residual network (ResNet) architectures. This is demonstrated on the problem of image super-resolution (SR). We show that cascade-trained (CT) SR CNNs and CT-ResNets can achieve state-of-the-art results with a smaller number of network parameters. To further improve the network's efficiency, we propose a cascade trimming strategy that progressively reduces the network size, proceeding by trimming a group of layers at a time, while preserving the network's discriminative ability. We propose context network fusion (CNF) as a method to combine features from an ensemble of networks through context fusion layers. We show that CNF of an ensemble of CT SR networks can result in a network with better efficiency and accuracy than that of other fusion methods. CNF can also be trained by the proposed edge-aware loss function to obtain sharper edges and improve the perceptual image quality. Experiments on benchmark datasets show that our proposed deep convolutional networks achieve state-of-the-art accuracy and are much faster than existing deep super-resolution networks.
A Bayesian Hierarchical Model for Related Densities by using Pólya Trees

Summary Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution on the parameters of a mixing kernel. We propose a new hierarchical model based on the Pólya tree, which enables direct modelling of densities and enjoys some computational advantages over the Dirichlet process. The Pólya tree also enables more flexible modelling of the variation between samples, providing more informed shrinkage and permitting posterior inference on the dispersion function, which quantifies the variation between sample densities. We also show how the model can be extended to cluster samples in situations where the observed samples are believed to have been drawn from several latent populations.
Network Traffic Classification Using Machine Learning for Software Defined Networks

The recent development in industry automation and connected devices made a huge demand for network resources. Traditional networks are becoming less effective to handle this large number of traffic generated by these technologies. At the same time, Software defined networking (SDN) introduced a programmable and scalable networking solution that enables Machine Learning (ML) applications to automate networks. Issues with traditional methods to classify network traffic and allocate resources can be solved by this SDN solution. Network data gathered by the SDN controller will allow data analytics methods to analyze and apply machine learning models to customize the network management. This paper has focused on analyzing network data and implement a network traffic classification solution using machine learning and integrate the model in software-defined networking platform.
Deep learning systems as complex networks

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine-learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks (DBNs), can discover intricate statistical structure in large datasets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article, we propose to study DBNs using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.
Proposal of a Model to Apply Hyperledger in Digital Identity Solutions in a Public Organization of Ecuador

The blockchain technology and its platforms applied in different business scenarios and the reasons for adopt a solution was analyzed. The problem is the lack of digital identity model with new technology in a public organization of Ecuador. The objective is to model a blockchain network that applies Hyperledger to establish digital identity in the network and ensure the information integrity in a public organization of Ecuador. It was applied the deductive method and the exploration to examine the information of the cited articles. It turned out Blockchain network prototype in Hyperledger Fabric platform, Sequence diagram for access of users and services, List of Smart Contract functions, Logical model of data, client-server application design. It was concluded that the adoption of Hyperledger for digital identity model provides a highly confidential and scalable network, access certification, contractual procedures, immutable information and audit. It is alternative to guarantee the information security of a process from the beginning to its finalization.
Design and Development of Constructivist Augmented Reality (AR) Book Enhancing Analytical Thinking in Computer Classroom

One of the greatest advantages of Augmented Reality (AR) in education is that AR increases student knowledge construction. This study aimed to design and development of constructivist augmented reality book to enhance analytical thinking. Developmental research (Type I) was employed in this study. Research methodology is developmental research; developmental research consisted of 3 processes which were designing process, developing process, and evaluating process. The procedures were as following: (1) to examine the principles and theories, (2) to synthesize designing framework, (3) to design and develop the constructivist augmented reality book according to above mentioned designing framework, and (4) to evaluate the efficiency of the constructivist augmented reality book. The results revealed that: (1) The constructivist augmented reality book enhancing analytical thinking comprise of 6 components as follows: (1) Problem base, (2) Resources, (3) Collaboration, (4) Scaffolding, (5) Coaching and (6) Analytical thinking training centre. (2) The efficiency of the constructivist augmented reality book enhancing analytical thinking was found to be appropriate as following: learning content, media designing and augmented reality book designing.
Spacial Elliptical Model for Extended Target Tracking Using Laser Measurements

This paper presents an approach for tracking extended targets using measurements from Light Detection and Ranging (LiDAR), where the measurement's source is distributed on the contour of the target extent. An Extended Kalman Filter (EKF) is used to estimate the target kinematic and shape simultaneously. The target shape is represented by an ellipse and the measurement's source is distributed uniformly on the contour of the ellipse. A Gaussian mixture is used to approximate the measurement likelihood. The EKF update incorporates a moment matching technique to approximate the mixture density. Data from an Ibeo LiDAR simulator is used to evaluate the performance of the proposed approach on tracking a target with a rectangular shape.
Channels of Small Log-Ratio Leakage and Characterization of Two-Party Differentially Private Computation

Consider a ppt two-party protocol $$\varPi = (\mathsf {A} ,\mathsf {B} )$$ in which the parties get no private inputs and obtain outputs $$O^{\mathsf {A} },O^{\mathsf {B} }\in \left\{ 0,1\right\} $$, and let $$V^\mathsf {A} $$ and $$V^\mathsf {B} $$ denote the parties’ individual views. Protocol $$\varPi $$ has $$\alpha $$-agreement if $$\Pr [O^{\mathsf {A} }=O^{\mathsf {B} }] = \tfrac{1}{2}+\alpha $$. The leakage of $$\varPi $$ is the amount of information a party obtains about the event $$\left\{ O^{\mathsf {A} }=O^{\mathsf {B} }\right\} $$; that is, the leakage $$\epsilon $$ is the maximum, over $$\mathsf {P} \in \left\{ \mathsf {A} ,\mathsf {B} \right\} $$, of the distance between $$V^\mathsf {P} |_{O^{\mathsf {A} }= O^{\mathsf {B} }}$$ and $$V^\mathsf {P} |_{O^{\mathsf {A} }\ne O^{\mathsf {B} }}$$. Typically, this distance is measured in statistical distance, or, in the computational setting, in computational indistinguishability. For this choice, Wullschleger [TCC ’09] showed that if $$\epsilon \ll \alpha $$ then the protocol can be transformed into an OT protocol. We consider measuring the protocol leakage by the log-ratio distance (which was popularized by its use in the differential privacy framework). The log-ratio distance between X, Y over domain $$\varOmega $$ is the minimal $$\epsilon \ge 0$$ for which, for every $$v \in \varOmega $$, $$\log \frac{\Pr [X=v]}{\Pr [Y=v]} \in [-\epsilon ,\epsilon ]$$. In the computational setting, we use computational indistinguishability from having log-ratio distance $$\epsilon $$. We show that a protocol with (noticeable) accuracy $$\alpha \in \varOmega (\epsilon ^2)$$ can be transformed into an OT protocol (note that this allows $$\epsilon \gg \alpha $$). We complete the picture, in this respect, showing that a protocol with $$\alpha \in o(\epsilon ^2)$$ does not necessarily imply OT. Our results hold for both the information theoretic and the computational settings, and can be viewed as a “fine grained” approach to “weak OT amplification”. We then use the above result to fully characterize the complexity of differentially private two-party computation for the XOR function, answering the open question put by Goyal, Khurana, Mironov, Pandey, and Sahai, [ICALP ’16] and Haitner, Nissim, Omri, Shaltiel, and Silbak [22] [FOCS ’18]. Specifically, we show that for any (noticeable) $$\alpha \in \varOmega (\epsilon ^2)$$, a two-party protocol that computes the XOR function with $$\alpha $$-accuracy and $$\epsilon $$-differential privacy can be transformed into an OT protocol. This improves upon Goyal et al. that only handle $$\alpha \in \varOmega (\epsilon )$$, and upon Haitner et al. who showed that such a protocol implies (infinitely-often) key agreement (and not OT). Our characterization is tight since OT does not follow from protocols in which $$\alpha \in o( \epsilon ^2)$$, and extends to functions (over many bits) that “contain” an “embedded copy” of the XOR function.
Exploiting Model-Level Parallelism in Recurrent Neural Network Accelerators

Recurrent Neural Networks (RNNs) have continued to facilitate rapid progress in a variety of academic and industrial fields, though their complexity continues to make efficient deployment difficult; when the RNN model size is not properly matched to hardware resources, performance can suffer from hardware under-utilization. In this work, we propose to explore model-level parallelism for LSTM-RNN accelerators in different levels of the model using a multicore design. The multi-core design proposed in this work operates in three computing modes: multi-programming mode in which independent models are executed; multithreading mode in which parallelism among layers of an LSTM model is explored and properly scheduled; and helper-core mode in which cores collaborate on a single LSTM layer in a lower model level comparing with multithread mode. Our design can achieve up to 1.98x speedup in "multi-programming" mode, a 1.91x speedup in "multithreading" mode and a 1.88x speedup in "helper-core" mode over the single-core design.
Data allocation in distributed database systems: a novel hybrid method based on differential evolution and variable neighborhood search

Data allocation problem (DAP) in distributed database systems is a NP-hard optimization problem with significant importance in parallel processing environments. The solution of problem aims to minimize the total cost of transactions and settlement of queries in which he main cost regards to the data transmission through the distributed system. These costs are affected by the strategy how to allocate fragments to the sites. Researchers have been solving this challenging problem by applying soft computing methods especially evolutionary algorithms. This study proposes a novel hybrid method based on differential evolution (DE) algorithm and variable neighborhood search (VNS) mechanism for solving DAP problem. The suggested hybrid method (DEVNS) aims to increase the performance of DE algorithm by applying effective selection and crossover operators. Moreover, DEVNS goals to improve the solutions found so far using VNS technique. By applying VNS, more promising parts of search space can be extracted. Eventually, the introduced DEVNS explores the search space via DE and fulfills more exploitation by neighborhood search mechanism. Performance of proposed DEVNS is experimentally evaluated against nine state-of-the-art methods using well-known benchmarks reported in literature. Obtained results exhibits that proposed DEVNS takes the first position for 13 of 20 test problems. Likewise, Friedman aligned rank test is carried out to demonstrate that there is significant statistical difference between all methods.
Vehicle Detection and Tracking Using Machine Learning Techniques

More than two decades machine learning techniques have been applied in multidisciplinary fields in order to find more accurate, efficient and effective solutions. This research tries to detect vehicles in images and videos. It deploys a dataset from Udacity in order to train the developed machine learning algorithms. Support Vector Machine (SVM) and Decision Tree (DT) algorithms have been developed for the detection and tracking tasks. Python programming language have been utilized as the development language for the creation and training of both models. These two algorithms have been developed, trained, tested, and compared to each other to specify the weaknesses and strengths of each of them, although to present and suggest the best model among these two. For the evaluation purpose multiple techniques are used in order to compare and identify the more accurate model. The primary goal and target of the paper is to develop a system in which the system should be able to detect and track the vehicles automatically whether they are static or moving in images and videos.
Image Encryption and Decryption Systems Using the Jigsaw Transform and the Iterative Finite Field Cosine Transform

We propose the use of the Jigsaw transform (JT) and the iterative cosine transform over a finite field in order to encrypt and decrypt images. The JT is a nonlinear operation that allows one to increase the security over the encrypted images by adding new keys to the encryption and decryption systems. The finite field is a finite set of integer numbers where the basic mathematical operations are performed using modular arithmetic. The finite field used in the encryption and decryption systems has an order given by the Fermat prime number 257. The iterative finite field cosine transform (FFCT) was used in our work with the purpose of obtaining images that had an uniform random distribution. We used a security key given by an image randomly generated and uniformly distributed. The JT and iterative FFCT was utilized twice in the encryption and decryption systems. The encrypted images presented a uniformly distributed histogram and the decrypted images were the same original images used as inputs in the encryption system. The resulting decrypted images had a high level of image quality in comparison to the image quality of the decrypted images obtained by the actual optical decryption systems. The proposed encryption and decryption systems have three security keys represented by two random permutations used in the JTs and one random image. The key space of the proposed encryption and decryption systems is larger. The previous features of the security system allow a better protection of the encrypted image against brute force and statistical analysis attacks.
Exploring the Cost-benefit of AWS EC2 GPU Instances for Deep Learning Applications

Deep Learning is a subfield of machine learning methods based on artificial neural networks. Thanks to the increased data availability and computational power, such as Graphic Process Units (GPU), training deep networks - a time-consuming process - became possible. Cloud computing is an excellent option to acquire the computational power to train these models since it provides elastic products with a pay-per-use model. Amazon Web Services (AWS), for instance, has GPU-based virtual machine instances in its catalog, which differentiates themselves by the GPU type, number of GPUs, and price per hour. The challenge consists in determining which instance is better for a specific deep learning problem. This paper presents the implications, in terms of runtime and cost, of running two different deep learning problems on AWS GPU-based instances, and it proposes a methodology, based on the previous study cases, that analyzes instances for deep learning algorithms by using the information provided by the Keras framework. Our experimental results indicate that, despite having a higher price per hour, the instances that contain the NVIDIA V100 GPUs (p3) are faster and usually less expensive to use than the instances that contain the NVIDIA K80 GPUs (p2) for the problems we analyzed. Also, the results indicate that the performance of both applications did not scale well with the number of GPUs and that increasing the batch size to improve scalability may affect the final model accuracy. Finally, the proposed methodology provides accurate cost and estimated runtime for the tested applications on different AWS instances with a small cost.
A Computational Study on Fairness of the Tendermint Blockchain Protocol

Fairness is a crucial property for blockchain systems since it affects the participation: the ones that find the system fair tend to stay or enter, the ones that find the system unfair tend to leave. While current literature mainly focuses on fairness for Bitcoin-like blockchains, little has been done to analyze Tendermint. Tendermint is a blockchain technology that uses a committee-based consensus algorithm, which finds an agreement among a set of block creators (called validators), even if some are malicious. Validators are regularly selected to the committee based on their investments. When a validator does not have enough asset to invest, it can increase it with the help of participants that delegate their assets to the validators (called delegators). In this paper, we implement the default Tendermint model and a Tendermint model for fairness in a multi-agent blockchain simulator where participants are modeled as rational agents who enter or leave the system based on their utility values. We conducted experiments for both models where agents have different investment strategies and with various numbers of delegators. In the light of our experimental evaluation, we observed that while, for both models, the fairness decreases and the system shrinks in the absence of delegators, the fairness increases, and the system expands for the second model in the presence of delegators.
Stacked Convolutional Autoencoder for Detecting Animal Images in Cluttered Scenes with a Novel Feature Extraction Framework

Detection of animals from a cluttered scene is not a trivial task. So far, convolutional neural network (CNN) architectures have served this purpose. We introduce stacked convolutional autoencoders (SCAE) for this purpose. It is an unsupervised stratified feature extractor that could be used for high-dimensional input images. We also introduce a hybrid feature extraction technique based on Fisher Vectors (FV) and stacked autoencoders (SAE). SCAE learns significant features utilizing plain stochastic gradient descent and finds a good initialization for CNNs so as to eliminate the various unique local minima of exceptionally non-convex target functions emerging in virtually all deep learning problems. We have proposed a parallel pipeline for both detecting animals in both visible and infrared images. The framework model has achieved 97% accuracy.
Accurate and efficient 3D reconstruction system for the human body with color texture based on DIC

Reconstructing 3D human body models for real people benefits a variety of applications. However, it is challenging to reconstruct high-fidelity human body models with high-accuracy. This study presents an accurate and efficient 3D human body reconstruction system based on digital image correlation (DIC), which can simultaneously capture high-quality 3D human body model and high-fidelity 2D color texture. The 3D reconstruction system is set up with twenty-seven cameras and nine speckle-pattern projectors, which is able to acquire 3D shape data and 2D color data. Specifically, the nine binocular stereo vision systems are adopted to reconstruct 3D shape, where the 3D reconstruction is carried out based on stereo-DIC with a speckle-pattern projector. The other nine color cameras are utilized to capture 2D color texture. It only needs a single-shot to record the images required for 3D reconstruction, which takes about 1.25 ms to ensure the high-accuracy and high-efficiency of the proposed 3D reconstruction system. A high-precision multi-camera calibration method is employed to calibrate the proposed multi-camera system. The mapping relationship between the reconstructed 3D object point and the 2D color image is determined by the calibrated result, which makes the color texture acquisition precisely and effectively. The proposed system can achieve accurate and high-quality 3D reconstruction of the whole human body surface with high-fidelity color texture. Experimental results demonstrate the high-performance of the proposed 3D reconstruction system.
Near-Optimal Detection for Binary Tardos Code by Estimating Collusion Strategy

A previously proposed optimal detector for bias-based fingerprinting codes such as Tardos and Nuida requires two kinds of important information: the number of colluders and the collusion strategy used to generate the pirated codeword. An estimator has now been derived for these two parameters. The bias in the pirated codeword is measured by observing the number of zeros and ones and compared with possible bias patterns calculated using information about the collusion strategy and number of colluders. Computer simulation demonstrated that the collusion strategy and number of colluders can be estimated with high probability and that the traceability of a detector using the proposed estimator is extremely close to being optimal.