Reviewerly/exHarmony huggingface dataset:
Effect Analysis of Image Translation by Controlling Size of Dataset and Adjusting Parameters for CycleGAN

Image translation can be achieved effectively by CycleGAN. However, it is not clear whether the dataset size and parameter adjustment would influence the performance of CycleGAN. Therefore, this paper uses the different kinds of dataset and different dataset sizes to evaluate the performance of the model. Furthermore, parameters are adjusted in CycleGAN to further evaluate the performance of the CycleGAN-based method for image translation. The experimental results show that with the increases of the size of the dataset, except for some special situations, the loss of the model will decrease when different loss shows different descending amplitude which means small size of dataset would lead to overfitting. In addition, this cycle GAN model is more suitable for the “horse to zebra” dataset, because this dataset has the least loss. Cycle consistency or its weight, hyper parameters adjusting can achieve the better unpaired image resolution than the original image, which shows this model can do a fairly good translation. Our research is of great significance to explore the best parameter settings and dataset size of image translation optimization based on CycleGAN.
Modern web application development technologies

Abstract One of the key areas of modern information technology is the area of programming. Programming is the creation of various programs. If earlier it was, for the most part, about writing programs for the interaction of a user and a computer, today the applied field of programming is no longer limited to anything. These are programs for portable gadgets, medical equipment, space nanotechnology, and so on. Within the framework of this research, the work will focus on web development and specifically - on the development of a web application. This direction is quite popular, since the Internet segment has played a key role in human life. Whether it’s a government-owned company, a private enterprise, or a personal blog, having a website or application is a kind of business card. A web application is a client-server interaction model using browser software. In terms of their structure and logic of work, web applications often resemble the most common desktop solutions, but with one condition - such applications are always launched and executed in the browser.
Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF Data

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.
SKANet: Structured Knowledge-Aware Network for Visual Dialog

Visual dialog aims to generate an answer to each question based on an image and dialog history. Despite recent progress, existing methods still undergo degradation on the condition of complex scenarios. Handling these scenarios depends on logical reasoning that requires common sense priors. In this paper, we propose a novel visual dialog pipeline, named Structured Knowledge-Aware Network (SKANet), consisting of a Multi-Modality Fusion Module, an Image Knowledge-Aware Module, and a Caption Knowledge-Aware Module. The Multi-Modality Fusion Module explores the textual context about the dialog history and visual content. To deal with the complex scenarios, the Image and Caption Knowledge-Aware Modules construct common sense knowledge graphs from ConceptNet. Experimental results on the VisDial v1.0 dataset show that our proposed method effectively outperforms comparative methods.
Simulated Adversarial Testing of Face Recognition Models

Most machine learning models are validated and tested on fixed datasets. This can give an incomplete picture of the capabilities and weaknesses of the model. Such weaknesses can be revealed at test time in the real world. The risks involved in such failures can be loss of profits, loss of time or even loss of life in certain critical applications. In order to alleviate this issue, simulators can be controlled in a finegrained manner using interpretable parameters to explore the semantic image manifold. In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios. We apply this method in a face recognition setup. We show that certain weaknesses of models trained on real data can be discovered using simulated samples. Using our proposed method, we can find adversarial synthetic faces that fool contemporary face recognition models. This demonstrates the fact that these models have weaknesses that are not measured by commonly used validation datasets. We hypothesize that this type of adversarial examples are not isolated, but usually lie in connected spaces in the latent space of the simulator. We present a method to find these adversarial regions as opposed to the typical adversarial points found in the adversarial example literature.
A Blockchain-Based Decentralized Framework for Fair Data Processing

The blockchain has been considered as a new decentralized computing paradigm that has great potential to meet various computing needs. Considering a private network (such as data center) where incentive mechanisms are not required, this paper innovatively remolds the transaction-recording blockchain for decentralized data processing. In our design, workers have different processing capacity and tasks have different resource requirements. Workers first get task information from the blockchain and then process tasks locally, and next perform the proof of useful work (PoUW) consensus to compete for a scheduler, according to the number of the consumed CPU instructions in data processing. The scheduler is responsible for dispatching task information to the blockchain. A salient feature of our decentralized data processing is that workers actively select tasks, instead of passively receiving tasks as in a centralized framework. This will lead to collisions (i.e., multiple workers select the same task). To alleviate the collisions and provide the max-min fairness of data-processing, we propose a modified fair queue (called M-FQ) algorithm for the scheduler, as well as a fair task selection with collision avoidance (called Fair-CA) scheme for workers. Extensive simulations verify that our framework can well balance the fairness and the collision, while achieving as high throughput and good fairness as centralized frameworks. This study is the first attempt toward designing a general decentralized computing framework.
Hybrid principal component analysis and K-nearest neighbour to detect the catfish disease

Abstract Catfish cultivation in Indonesia is a very promising business opportunity with a big profits. Every year, market demand continues to increase. However, this is contrary with the lack of catfish farmer’s knowledge, so that catfish yields are not optimal. This is because, for certain types of catfish such as Sangkuriang catfish, it is easy to contract certain diseases. This study aims to create an automated system that capable of detecting catfish disease based on its symptoms with image recognition techniques. Early detection of catfish disease can help to find out the causes and prevention, so that the yield remains optimally. The method that used in this study is Principal Component Analysis (PCA) for feature extraction in images combined with K-Nearest Neighbour (KNN) with Euclidean Distance to classify catfish diseases among others: white spots, edema (abdominal swelling), jaundice, and bent spinal disease (scoliosis and lordosis). Based on the results of the experiment using 30 images data for training and 20 images data for testing, 18 image data is classified correctly. This result proves that PCA and KNN able to detect catfish disease well with percentage of accuracy around 90%.
Sublinear Convergence of a Tamed Stochastic Gradient Descent Method in Hilbert Space

In this paper, we introduce the tamed stochastic gradient descent method (TSGD) for optimization problems. Inspired by the tamed Euler scheme, which is a commonly used method within the context of stochastic differential equations, TSGD is an explicit scheme that exhibits stability properties similar to those of implicit schemes. As its computational cost is essentially equivalent to that of the well-known stochastic gradient descent method (SGD), it constitutes a very competitive alternative to such methods. We rigorously prove (optimal) sublinear convergence of the scheme for strongly convex objective functions on an abstract Hilbert space. The analysis only requires very mild step size restrictions, which illustrates the good stability properties. The analysis is based on a priori estimates more frequently encountered in a time integration context than in optimization, and this alternative approach provides a different perspective also on the convergence of SGD. Finally, we demonstrate the usability of the scheme on a problem arising in a context of supervised learning.
Security Attacks on Multi-Stage Proof-of-Work

Multi-stage Proof-of-Work is a recently proposed protocol which extends the Proof-of-Work protocol used in Bitcoin. It splits Proof-of-Work into multiple stages, to achieve a more efficient block generation and a fair reward distribution. In this paper we study some of the Multi-stage Proof-of-Work security vulnerabilities. Precisely, we present two attacks: a Selfish Mining attack and a Selfish Stage-Withholding attack. We show that Multi-stage Proof-of-Work is not secure against a selfish miner owning more than 25% of the network hashing power. Moreover, we show that Selfish Stage-Withholding is a complementary strategy to boost a selfish miner's profitability.
Analysis of Lightweight Cryptography Algorithms for IoT Communication

Ubiquitous sensing enabled networks have transformed the living chores into a modern living standard in which each move is automated. The dependency of humans on IoT is increasing every day, so the IoT networks must be efficient and secure. Secure communication in the Internet of Things (IoT) networks is a hotly debated research issue. Cryptography is one of the solutions for secure data transmission. In IoT networks, designing a cryptography algorithm is a major challenge for resource constraint environment. So, when we talk about cryptography in IoT, this term transforms into "lightweight cryptography." This paper presents a review of lightweight cryptography algorithms in the form of a stream cipher, block cipher, symmetric encryption, and asymmetric encryption.
Innovative “Algebraic Methods in Digitalization of Smart Systems” Course

The emergence of new intellectual (or smart) technologies changes not only the mentality of human society, but also all spheres of its life. This change is associated with the emergence and development of a powerful and expressive language—the language of numbers and digital technology. At present, intelligent technologies and intelligent systems and smart systems are becoming an integral phenomenon in almost all spheres of human life. This, in turn, requires new courses and new mathematical methods in the field of smart systems theory. The paper contains a description of such a course developed by the authors in the field of algebraic methods in the digitalization of economic smart systems and in the field of education. The course “Algebraic methods in digitalization of smart systems” was developed by the authors on the basis of works on algebraic formalization and identification of smart systems carried out in the period from 1998 to the present time. Almost simultaneously with the development, fragments of the course were tested at a seminar on financial mathematics at the Academy of Budget and Treasury of the Ministry of Finance of the Russian Federation.
Optimized reconstruction processing of targets hidden in turbid environment from multi-perspective images assisted with sorting algorithm-based quality metrics

In the work described in this paper, we experimentally demonstrate an efficient method to improve reconstruction of objects embedded in turbid media. The method combines multiple polarized speckle projections and a fully automated sorting algorithm. In the experimental setup, the medium was illuminated with a polarized laser beam and multiple polarized images of the object were obtained from different viewing perspectives using a lens array and captured by a camera. In offline image processing, each sub-image was digitally cropped, extracted from the array, and qualitatively evaluated by contrast-to-noise ratio (CNR) and entropy metrics. Sub-images were sorted based on quality as determined by CNR and entropy values, with high CNR and low entropy considered as highest quality with the best resolution and contrast. The images were then shifted to a common center and then summed with other sub-images to form a single average image. Experiments to demonstrate the effectiveness of the proposed algorithm were carried out on targets with different geometrical shapes embedded within scattering media in transmission configuration. Analysis of image metrics indicated improvement in object reconstruction by using circular polarization as compared to the use of linear polarization or non-polarization states. The overall experimental results obtained in this work illustrate effective performance of the proposed method.
A Comparison Study on Shape Parameter Selection in Pattern Recognition by Radial Basis Function Neural Networks

Abstract This study investigates three choices of shape parameter selection when the so-called Radial Basis Function (RBF) is used. Under the problem of pattern recognition via RBF-Neural Network using RC-algorithm, three RBFs are focussed on; Gaussian (GA), Multiquadric (MQ), and Compactly-Supported (CS1). Two pattern recognition cases are tested and the best choice of shape parameter is validated using Model-Selection Criteria (MSC).
Integration of clinical criteria into the training of deep models: Application to glucose prediction for diabetic people

The standard way to train neural-network-based solutions in healthcare does not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the perspective of the forecasting of future glucose values of people with diabetes. We propose a new training methodology that achieves the best possible tradeoff between accuracy and medical requirements set by health authorities. Starting from a solution maximizing the prediction accuracy, we progressively relax the accuracy constraints to focus more on the medical ones. This is achieved by considering a new loss function specifically designed for glucose prediction. We evaluate the proposed approach on both people with type-1 and type-2 diabetes. We show that it improves the clinical acceptability of the predictions. Moreover, for given clinical criteria, we are able to find the optimal solution that maximizes the accuracy while at the same time meeting clinical the criteria.
A Novel Video Retrieval Method Based on Object Detection Using Deep Learning

The recent research in computer vision is focused on videos. Image and video data has been increased drastically in the last decade. This has motivated researchers to come up with different methods for image and video understanding and other applications like action recognition from videos, video retrieval, video understanding, and video summarization. This article proposes a novel and efficient method for video retrieval by using the deep learning approach. Instead of considering low-level features, videos could be best represented in terms of the high-level features for achieving efficient video retrieval. The idea of the proposed research work is novel, where objects present in the query video are used as features and are used to match against all other videos in the database. Here, object detection is based on YOLOv3, which is the current state-of-the-art method for object detection from videos. This method is tested against YouTube action dataset. It was found that the proposed method has obtained comparable results as the other state-of-the art video retrieval methods.
Book Recommendation System with TensorFlow

A collaborative filtration is the latest process done in the all over the world by using the internet tool for recommendation. Internet tool it helps in access the web and receive information from the customer which book they preferred. Online recommender filtration process can be done easily and quickly. Recommender systems need to filter, prioritize and efficiently deliver relevant information. Recommendation system provides major functionalities in websites to purchase the best book. It is the best method to sell the book quickly and it increased the profit. Rating of each user can express their view on the product. It is difficult to implement Content Based Collaborative Filtering because combining phase is not an easy task. This paper provides a solution on tensorflow techniques which is added to collaborative phases.
A Novel Approach for Developing Decentralized Storage and Sharing Systems

In recent years, the proliferation of blockchain technology has opened many research directions. In this context, the combination of blockchain-based techniques and traditional methods to improve existing systems has received significant interest from researchers. In this paper, we present a decentralized storage and sharing system based on a combination of IPFS (Inter-Planetary File System), encryption technologies (including Advanced Encryption Standard (AES), Elliptic Curve Cryptosystem (ECC), ABE (Attribute-based Encryption), Multi-Authority ABE (MA-ABE)), and multichain. In particular, we facilitate the advantages of the IPFS network to store user's data in a distributed manner. Furthermore, we make use of a cryptographer to protect the privacy of data. The hash returned by the IPFS network will be stored in our multichain architecture to provide transparency for all users participating in the system. To the best of our knowledge, it is the first storage and sharing system using IPFS, cryptographer, and multichain to ensure decentralized, trustworthy, transparent characteristics for storing and sharing data.
Task-Based Continuous Authentication Using Wrist-Worn Devices

Activity-based continuous authentication methods have been shown to be effective for identifying individual users. Existing classification-based approaches typically learn models of activities of daily living (ADLs) using sensor data from an individual's mobile device. For activity-based authentication to be widely applicable, we contend that such approaches should also consider activities that are aligned with an task-related activities (e.g., picking packages in a shipping warehouse), which are typically shorter and burstier than ADLs. In this paper, we explore the feasibility of task-driven continuous authentication and implement a pipeline of machine learning approaches that capture task-based continuous authentication models. In a scenario-based evaluation using real-world ubiquitous wrist-worn sensor data, our approach can detect distinct users using task-specific models with 94% accuracy on average.
Multi-graph Multi-label Learning with Dual-granularity Labeling

Graphs are a powerful and versatile data structure that easily captures real life relationship. Multi-graph Multi-label learning (MGML) is a supervised learning task, which aims to learn a Multi-label classifier to label a set of objects of interest (e.g. image or text) with a bag-of-graphs representation. However, prior techniques on the MGML are developed based on transferring graphs into instances that does not fully utilize the structure information in the learning, and focus on learning the unseen labels only at the bag level. There is no existing work studying how to label the graphs within a bag that is of importance in many applications like image or text annotation. To bridge this gap, in this paper, we present a novel coarse and fine-grained Multi-graph Multi-label (cfMGML) learning framework which directly builds the learning model over the graphs and empowers the label prediction at both the coarse (aka. bag) level and fine-grained (aka. graph in each bag) level. In particular, given a set of labeled multi-graph bags, we design the scoring functions at both graph and bag levels to model the relevance between the label and data using specific graph kernels. Meanwhile, we propose a thresholding rank-loss objective function to rank the labels for the graphs and bags and minimize the hamming-loss simultaneously at one-step, which aims to address the error accumulation issue in traditional rank-loss algorithms. To tackle the non-convex optimization problem, we further develop an effective sub-gradient descent algorithm to handle high-dimensional space computation required in cfMGML. Experiments over various real-world datasets demonstrate cfMGML achieves superior performance than the state-of-arts algorithms.
Research on Key Distribution and Encryption Control System of Optical Network Physical Layer

With the wide application of large-capacity and high-speed optical networks, its security has become a current research focus. Due to the limitations of traditional key distribution and encryption schemes, this paper proposes an innovative key distribution and encryption control scheme based on physical layer channel feature extraction and analysis. First of all, the transceiver terminal extracts the physical characteristics of the fiber channel separately, and generates a consensus key through quantization coding. Second, use the generated key base to encrypt the transmission sequence, map the Quadrature Phase Shift Keying (QPSK) signal to the high-order 1024×1024-QAM signal, and use the noise stream to perform the high-order QAM. The signal is flooded. The system utilizes channel non-replication and randomness of channel noise, the security and randomness of the key are good, and the key formation rate is high and the key generation equipment is simple. The system uses high-order QAM modulation, which increases the security of the system. The experimental results show that the coding rate of the key generated by the system reaches 400kb/s, and the coding performance of the key is better; the operating key is generated by the key base extension, which has strong anti-interception ability; the system has a low bit error rate and secure transmission Good performance.
Edge Accelerator for Lifelong Deep Learning using Streaming Linear Discriminant Analysis

Lifelong deep learning models are expected to continuously adapt and acquire new knowledge in dynamic environments. This capability is essential for numerous vision tasks in robotics and drones, and the models must be deployed on the edge to achieve real-time performance. We propose a FPGA accelerator of a streaming classifier for lifelong deep learning, which is based on streaming linear discriminant analysis (SLDA). When combined with a frozen Convolutional Neural Network (CNN) model, the proposed system is capable of class incremental lifelong learning for object classification.