Reviewerly/exHarmony huggingface dataset:
Used Car Traceability System Based on Blockchain

With the continuous expansion of China’s used car market., there are more and more accidents caused by the untrue information of used car quantity. And the current used car traceability system has some problems, such as incomplete information coverage and low reliability. The emergence of blockchain can improve this situation. It can be regarded as a decentralized, disintermediated and trusted public distributed ledger. Based on blockchain technology, this paper analyzes the needs of used cars from entering the market to selling, and establishes a transparent and reliable used car traceability system to realize the traceability of used cars from entering the market to selling, so as to further ensure the quality and safety of used cars.
AI Computing Comes to Memory Chips: Samsung will double performance of neural nets with processing-in-memory

John von Neumann's original computer architecture, where logic and memory are separate domains, has had a good run. But some companies are betting that it's time for a change.
Filtering Random Valued Impulse Noise from Grayscale Images through Support Vector Machine and Markov Chain

Abstract This paper presents a context-based filter to denoise grayscale images affected by random valued impulse noise. A support vector machine classifier is used for noise detection and two Markov filter variants are evaluated for their denoising capacity. The classifier needs to be trained on a set of training images. The experiments performed on another set of test images have shown that the support vector machine with the radial basis function kernel combined with the Markov+ filter is the best configuration, providing the highest noise detection accuracy. Our filter was compared with existing denoising methods, it being better on some images and comparable with them on others.
Ancient And Modern Coin Recogintion Using a Novel Rotation-Invariant Convolutional Neural Network

For decades, rotation invariance has been a difficult problem to solve in the field of coin recognition. Traditional methods, such as LBP and SIFT with bag-of-visual-words model, have some rotation invariance, but the recognition accuracy is much lower than deep learning method. However, deep learning method cannot be applied to coin recognition at arbitrary angles for its lack of rotation invariance. In this paper, we propose Rotation Transformation Network (RTN), a new coin recognition method based on convolutional neural network (CNN) with rotation invariance, which achieved both high classification accuracy and rotation invariance. We use a U.S. coin database and an ancient Roman coin database for our experiments. The reason why we choose these two kinds of coins for our work is that they significantly variant visually within a type, thus more difficult to classify than other modern coins, which also poses a greater challenge to our work and makes our network more generalizable. In the last part of this paper, we compare our method with other state-of-the-art methods, and show that our method outperforms others in recognition accuracy.
Instance selection for multi-label learning based on a scalable evolutionary algorithm

Multi-label classification has recently attracted greater research interest as a data mining task. Many current applications in data mining address problems having instances that belong to more than one class, which requires the development of new efficient methods.Instance-based classification models, such as the :-nearest neighbor rule, are among the highest performing methods on any classification task and have also been successfully applied to multi-label problems. Despite their simplicity, they achieve comparable performance compared to considerably more complex methods. One of the challenges associated with instance-based classification models is their requirement for storing all training instances in memory. To ameliorate this problem, instance selection methods have been proposed. However, their application to multi-label problems is problematic because the adaptation of most of their concepts to multi-label problems is difficult.In this paper, we propose a scalable evolutionary algorithm for instance selection for multi-label problems. As our evolutionary algorithm is solely based on the performance of a subset of selected instances, it is able to handle multi-label datasets. On a set of 12 real-world problems, our approach performs comparably to methods that use all instances while achieving a large reduction in the size of the training set.
Robust $ {H}_{\infty} $ output feedback finite-time control for interval type-2 fuzzy systems with actuator saturation

&lt;abstract&gt;&lt;p&gt;The finite-time $ {H_\infty } $ performance of the interval type-2 Takagi-Sugeno fuzzy system (IT2 T-S) in presence of immeasurable states and input saturation is investigated. At first, an observer associated with IT2 T-S states is considered to address the problem of immeasurable states. After that, the input saturation is described based on the polyhedron model, and accordingly, a robust $ {H_\infty } $ observer-based finite-time controller is proposed via non-PDC algorithm. Then, on the basis of the Lyapunov function method and LMIs theory, the sufficient conditions for the finite time stability of fuzzy systems are derived. At last, the feasibility of the designed algorithm is verified by two examples of the nonlinear mass-spring-damping system and tunnel diode circuit system, respectively.&lt;/p&gt;&lt;/abstract&gt;
Delineating the Privacy Concerns of Covid Tracing Applications: A Mixed Method Analysis

As Covid pandemics expanded over the globe, governments implemented a series of technological measures to prevent the disease's spread. The development of Covid tracing applications was one of these measures. In this study, we employed bibliometrics and cluster-based content analysis to determine the most significant entities and research topics in 329 Scopus-indexed papers. Additionally, we identified significant privacy concerns posed by the Covid tracking apps, which gather, store, and analyse data in partnership with large technology corporations using proximity measuring technologies, artificial intelligence, and blockchain. We examined the seven key privacy threats identified in our study. These privacy risks include anti-democratic and discriminatory behaviors, politicization of care, derogation of human rights, technogovernance, citizen distrust and refusal to adopt, citizen surveillance, and mandatory legislation of the apps' installation. Finally, sixteen research gaps were identified. Then, based on the identified theoretical gaps, we recommended fourteen prospective study strands. Theoretically, this study contributes to the growing body of knowledge about the ethics and privacy of digital health.
Taylor Discrete Circadian Rhythms Neural Network for Resolving Bicriteria Optimization Problem of Redundant Robot Manipulators Perturbed by Periodic Noises

To solve the motion planning problems of redundant manipulators disturbed by the periodic noise from device hardwares or their surroundings, a Taylor-type discrete-time circadian rhythms neural network (TD-CRNN) method is proposed, developed, and studied in this article. First, a representative bicriteria optimization scheme combining torque criterion and acceleration criterion is presented for the redundant manipulator. Second, inspired by a continuous-time circadian rhythms model, the corresponding TD-CRNN model is derived based on the Taylor discrete formulation. Third, the 0-stability, convergence, and consistency of the proposed TD-CRNN model are analyzed theoretically and proved strictly. Finally, to confirm the capacity of the resisting periodic noise in the tracking problem of manipulators, two groups of comparative simulations and experiments conducted by the proposed TD-CRNN model are performed before the conclusion is given.
PREPARATION OF BACTERICADAL FILLERS FROM GEORGIAN HEULANDITE-CLINOPTILOLITE AND THEIR APPLICATION FOR PAPER PRODUCTION. II. BACTERICADAL PAPER

The purpose of present work was to obtain bactericidal materials using heulandite-clinoptilolite from the Rkoni plot of the Tedzami deposit, Eastern Georgia, and to use them as a filler for the production of bactericidal paper. Materials remaining the zeolite crystal structure, containing over 130 mg/g of silver, 70 mg/g of copper, and 55 mg/g of zinc, and showing bacteriostatic activity towards pathogenic bacteria and fungi were prepared in laboratory and the filled papers were manufactured on the production lines of the GPM company. It was found that the introduction of zeolite fillers leads to an increase in the grammage, thickness and density of the paper, as well as to a certain decrease in the tensile strength in the machine direction. The introduction of zeolite fillers containing divalent metals causes a significant change in surface properties, and samples with a certain copper content become absolutely waterproof. Testing of bacteriostatic activity by disc diffusion method revealed only activity of paper with a silver-containing filler against E. coli and staphylococcus, while the colony forming unit assay indicates the activity of all metal-containing samples against staphylococcus and all zinc-containing samples against E. coli. The greatest activity against E. coli is shown by paper with a high content of zinc, against staphylococcus, paper with a high content of copper. These results are very important from a practical point of view, since they open up the possibility of replacing silver with copper and zinc.
Restaurant Recommendation System in Dhaka City using Machine Learning Approach

Due to the huge explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In Dhaka city, there are lots of Restaurants. Sometimes many of us face a scenario where we go to a restaurant and order some food but the food is not that good or the price of the food is very high as compared to other restaurants. Apart from this, another major problem is the location of the restaurant. We cannot find the best restaurants around us based on our preferences. This research proposed a model by using a machine learning algorithm that will be able to suggest a suitable restaurant based on the user’s criteria. We have collected Dhaka city’s restaurant’s data from various websites, then we have used Weight-based score calculation and cosine similarity matrix to build our machine learning model. This recommendation system will also suggest similar restaurants based on the user’s selected restaurants.
Quality Analysis of Shopee Seller Portal by Using Category Recommendation System Approach

Consumers have shifted away from traditional transactions to online shopping as a result of the COVID-19 pandemic, digital social life, and staying at home. This has a significant impact on the volume of transactions for several ecommerce platforms, including Shopee, which must continue to improve customer satisfaction. One way to accomplish this is to improve the accuracy of the automatic category recommendation, which currently contains a significant number of errors made by the seller. The Shopee Data Scraper was used to collect data, and two qualitative analyses, namely content and thematic analysis, were used to determine how many instances of automatic category recommendation fraud were committed by the seller.According to the content analysis, there is a 29 percent error in product category selection. Meanwhile, 75.1 percent of products should be classified separately but are instead classified as ‘others.’ Following that, 72.7 percent of product titles contain the same words as existing categories.From this research, we managed to get an analysis of the percentage of errors in the automatic category recommendation mechanism from the Shopee platform which causes sellers to place their products in the wrong category so that they can be used for suggestions for improvement or further research.
A Multi-Market Trading Framework for Low-Latency Service Provision at the Edge of Networks

Addressing edge computing's economic issues is critical as we need to motivate edge devices as resource providers to devote their computing resources to the service. There are multiple users and resource providers in most edge computing service scenarios. The communication delays between users and providers are not the same since their physical distances are different. However, most existing works on edge computing regarding network economics adopt single market models that do not consider the influence of communication delay. Moreover, due to the high cost of deploying 5G ultra-dense small cells, barely a network operator can reach full network coverage. Users cannot rely on a single network, but existing multi-market models do not allow resource trading between different groups. Thus, in this paper, we propose a novel multi-market trading (MMT) framework to address these shortcomings. The framework combines the double auction at each group and a market selection game to enable the resource providers to participle in multiple auctions and analyze their behavior of choosing markets. Through extensive simulations using real-world datasets of vehicular networks, we show that the proposed framework can improve the social welfare by 14.45% and 36.74%, respectively, compared with classic multi-market and single market models.
A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases

Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior in a temporal database. The following two limitations have hindered the successful industrial application of this technique: (i) there exists no algorithm to find the desired patterns in columnar temporal databases, and (ii) existing algorithms are computationally expensive both in terms of runtime and memory consumption. This paper tackles these two challenging problems by proposing a novel algorithm known as Generalized Partial Periodic-Frequent Depth-First Search (GPPF-DFS). The proposed algorithm compresses a given row or columnar temporal database into a unified dictionary structure and mines this structure recursively to find all desired patterns. Experimental results demonstrate that GPPF-DFS is 2 to 156 times faster and 5 to 88 times more memory efficient than the state-of-the-art algorithm. We also describe the usefulness of our algorithm with a case study on air pollution analytics.
Inter-Pulse Feature Analysis of Radar Emitter Signals Based on Complex Network

In a dense modern electronic warfare environment, Inter-pulse advanced modulation is applied to help the radar reduce range ambiguities and improve the anti-jamming characteristics of radar. This could be hard problems to classify radar emitters one from another through the basic parameters of Pulse Descriptor Words (PDWs). In this paper, we propose a feature extraction method of intercepted radar pulse train for constructing complex networks using Limited penetrable visibility graph (LPVG). In our algorithm, we selected four pulse trains of radar emitter as sample data to analyze the topological characteristics of four networks. Furthermore, the simulation results uncovered the inter-pulse train dynamical patterns which trigger the structure of network changing among PDWs. Meanwhile, the proposed algorithm is effective and feasible in classifying radar emitter signals. In a word, this paper described explored topological characteristics in networks constructed from pulse time series data, which provides a new angle to investigate the radar pulse train and provides a new effective means for feature extraction of radar emitter signals. The experimental results show that the features extracted by the method in this paper have good separability, and the results are visualized after feature extraction and classification of the four radar emitter signals in the experiment, which proves that the method has good results.
Confluence of Cryptography and Differential Privacy: A Hybrid Approach for Privacy Preserving Collaborative Filtering

Collaborative filtering has been a popular Recommendation algorithm which received significant attention in recent years; however, privacy issues in these systems cannot be ovelooked. Specifically, K Nearest Neighbor (KNN) collaborative filtering algorithm is prone to KNN attack. The aim of this paper is to integrate cryptography and differential privacy by proposing CryptoDP algorithm. The CryptoDP algorithm helps to tackle the privacy issue in KNN based collaborative filtering. The algorithm includes two privacy preserving operations: Encryption and Perturbation. The ratings given by user are protected by encryption in individual user device. The threat on collaborative filtering algorithm is resolved with the Differential Privacy mechanism. Such mechanism perturbs the similarity score with gaussian noise which enhances the performance of recommendation system with privacy. The CryptoDP algorithm reduces the magnitude of noise introduced from the traditional differential privacy and existing private algorithms. The experimental results with two real datasets confirm that the cryptoDP algorithm provides a robust privacy guarantee with minimal accuracy loss.
5G Direct to Consumer: A blockchain-based solution 5GD2Chain

Blockchain technology has become widely known and famous as a digital currency platform since the emergence of Bitcoin, which was the first and still the largest of cryptocurrencies. Furthermore, more recently, technology has started to be used for the decentralization of markets in general. Thus, the broader technology, called Distributed Ledger Technology (DLT), has been used to register, confirm, send and execute various types of intelligent contracts autonomously, ensuring decentralization, reliability, security and privacy for other parts of the network. In this article, we review in detail the next-generation use cases related to blockchain and its use in 5G networks that have emerged in the literature. Several published works have been carefully included based on their contributions to the body of knowledge of blockchain technology and 5G networks. Furthermore, as a focus of this paper, we have the proposal of 5G decentralized marketplace using the technology blockchain and the concept of Direct to Consumer (D2C) for the MVP of the 5G BR Platform Project that is explored and discussed in Section V of the article.
Depth Image Super-resolution via Two-Branch Network

With the continuous development of sensors, a number of inexpensive and effective depth cameras have shown up, which have greatly contributed to the development of autonomous driving and 3D reconstruction technologies. However, the depth images captured by low-cost depth cameras are low-resolution, which is difficult to meet the needs of practical applications. We propose a two-branch network to achieve depth map super-resolution with high-resolution guidance image, which can be viewed as a prior to guide the low-resolution depth map to restore the missing high-frequency details of structures. To emphasize the guidance role of high-resolution images, we use spatially-variant kernels based on the guidance feature map to replace the original convolution kernels. In addition, in order to extract the feature maps of the depth images more effectively, we add the channel attention mechanism between convolution layers. Our network is trained end-to-end, supporting various sizes of input images because the network backbone uses full convolution and no fully connected layers. The proposal model is trained only on a certain dataset under three super-resolution factors and utilized directly on other datasets without fine-tuning. We show the effectiveness of our model by comparing it with state-of-art methods.
Segmentation model of dorsal hand vein based on improved U-Net

Since the outbreak of COVID-19, more than 230, 000 medical staff worldwide have been infected with the new crown virus. The non-artificially contacted dorsal hand vein automatic injection method has been welcomed by many medical workers because of its good isolation. The key to realizing non-contact automatic injection of dorsal hand veins is to realize the detection and segmentation of dorsal hand veins and the decision-making of the needle point position. In this article, aiming at the problem of dorsal hand vein detection, a semantic segmentation model based on U-Net's improved guidance and attention mechanism is proposed. Based on the self-built dorsal hand vein database, the test has achieved good dorsal hand vein detection results.
Distributed Ledger Systems to Improve Data Synchronization in Enterprise Processes

Master Data Management (MDM) in enterprises deal with fixed data/information regarding different aspects of business that are considered single record of truth and form the basis for all business transactions. Enterprises manage their master data by several methods to ensure that it is a unique comprehensive representation of the entity by spending considerable resources in MDM but at the cost of time delays for updating the master data records or structure and with copies of the master data distributed across the enterprise units for operational needs that many times has modifications not reflected back into the master tables. Issues of delays in the master data updates lead to business impacts while the distributed nature of master data across an organization or organizations impacts data quality. Distributed ledger technology (DLT) is one approach that uses blockchain to obtain consensus to cut down the approval cycle time and the distributed nature makes it uniquely suited to manage synchronizing master data updates across the network of required users in the enterprise. Choice of correct business processes to benefit from using DLT for MDM is critical to obtain benefits from improvements. This paper shows a method to select from candidate business processes with coarse or fine process data and the value that can be gained from implementation of DLT for MDM.
Multimodal Analysis of TED Talks Toward Designing a Recommendation System

The efficacy of any oratory performance is primarily determined by the content of talk (text/vocabulary), the tone and modulation of an orator’s voice (audio) and nonverbal cues: facial expressions or body language (video). In particular, body language in this context may be considered to include hand gestures and pose (stance taken by a speaker) in addition to facial expressions. In this paper, we present an analysis and interpretation of audio, text and visual features of a video recording of a speech to delineate features of the speech. We use these features to design a recommendation system that profiles features from the foregoing modalities for a novice speaker and suggests improvements. The video dataset considered in the study is taken from TED talks. Through an analysis of these videos, we have evolved three coarse categories and a total of 27 fine categories to describe the ability of a speaker. The 27 categories describe the strengths and weaknesses of a speaker in each modality—audio, visual and textual content and determines the placement of a speaker based on the yardstick evolved from the reference league of speakers. These analyses and recommendations based on experimental results are envisaged to help the speaker understand their strengths and weakness better and suggest some direction for improvement. This, in turn, would help the speaker work strategically on specific areas to be a better orator in the future.