Reviewerly/exHarmony huggingface dataset:
Fed-DDM: A Federated Ledgers based Framework for Hierarchical Decentralized Data Marketplaces

Data marketplaces (DMs) promote the benefits of the Internet of Things (IoT) in smart cities. To facilitate the easy exchanges of real-time IoT data streams between device owners and third-party applications, it is required to provide scalable, interoperable, and secured services for large numbers of distributed IoT devices operated by different application vendors. Thanks to decentralization, immutability, and auditability, Blockchain is promising to enable a tamper-proof and trust-free framework to enhance performance and security issues in centralized DMs. However, directly integrating blockchains into large-scale IoT-based DMs still faces many limitations, such as high resource and energy demands, low transaction throughput, poor scalability, and challenges in privacy preservation. This paper introduces a novel Federated Ledgers-based Framework for Hierarchical Decentralized Data Marketplaces (Fed-DDM). In Fed-DDM, participants are divided into multiple permissioned domains given their registrations. Each domain leverages an efficient Byzantine Fault Tolerance (BFT) consensus protocol to commit transactions of a domain on a private intra-ledger. A public inter-ledger network adopts a scalable Proof-of-Work (PoW) consensus protocol to federate multiple private intra-ledger networks. We design a smart contract-enabled inter-ledger protocol to guarantee the security of the cross-domain operations on a public federated ledger without exposing sensitive privacy information from private ledgers. A proof-of-concept prototype is implemented, and the experimental results verify the feasibility of the proposed Fed-DDM solution with performance and security guarantees.
Multitarget Detection and Tracking Method in Remote Sensing Satellite Video

A remote sensing video satellite multiple object detection and tracking method based on road masking, Gaussian mixture model (GMM), and data association is proposed. This method first extracts the road network from the remote sensing video based on deep learning. In the detection stage, the background subtraction algorithm is used based on the GMM to obtain the detection results of the moving targets on the road. In the tracking stage, the data association of the same target detection result in adjacent frames is realized based on the neighborhood search algorithm, so as to obtain the continuous tracking trajectory of each target. The experiments about multiobject detection and tracking are conducted on data measure by real remote sensing satellites, and the results verified the feasibility of the proposed method.
Hyperparameter Tuning of ConvLSTM Network Models

Deep learning algorithms have achieved amazing performance in computer vision area. However, a biggest problem deep learning has, is the high dependency on hyper-parameters. The algorithm results may be different, depending on hyper-parameters. This paper presents an effective method for hyper-parameter tuning using deep learning. The deep neural network structure for video classification using Convolutional Long Short-Term Memory (ConvLSTM) was used. The proposed method for hyper-parameter tuning using ConvLSTM was described. This proposed method with hyper-parameter tuning methods (Grid search, Bayesian optimization and Genetic algorithm) was compared. The experiment results show that proposed approach using ConvLSTM can be compared with the results obtained from the methods analogs to the proposed approach. However, we are looking for other hyper-parameters, for example number of filters, filter size, number of epochs, batch size and training optimization algorithm. The proposed approach can be used for correct or incorrect use of face mask during COVID-19 pandemic.
BDMaaS+: Business-Driven and Simulation-Based Optimization of IT Services in the Hybrid Cloud

The maturity of heterogeneous and hybrid public Cloud environments enables service providers to deploy there their complex IT services trusting these large and complex infrastructures. At the same time, evaluating the impact of changes at service configuration before and at the runtime is still a very challenging and difficult task. Moreover, a comprehensive performance evaluation of IT service configurations should not be limited just to costs for IT resource acquisition, but also include risk related elements such as Service Level Agreement (SLA) violation penalties and other intangibles. To support IT service providers in this difficult task, we developed Business-Driven Management as a Service Plus (BDMaaS+), a novel decision support tool that can evaluate IT service configuration through simulation with realistic service and network models. By allowing service providers to define expanded operational parameters, BDMaaS+ also enables what-if scenario analysis, thereby opening interesting possibilities at the planning level. Experimental results, collected from our thorough evaluations, demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business SLA changes and data center additions.
Role of Internet of Things Technology in Promoting the Circulation Industry in the Transformation of a Resource-Based Economy

In recent years, the business scale of my country’s circulation industry has continued to expand, and the output value has continued to increase. The leading role in guiding the transformation of the industrial economy has become more and more important. Based on this, this article discusses the research on the promotion of the Internet of Things technology to the circulation industry in the transformation of the resource-based economy. The application of RFID technology and wireless sensor technology in the Internet of Things in the circulation industry can greatly improve work efficiency and information transmission accuracy. This article establishes the circulation industry based on the principles of science, system, safety, and relative independence. The evaluation index system analyzes the role of the circulation industry in the transformation of the resource-based economy in terms of circulation scale, circulation structure, circulation efficiency, circulation innovation, etc., and uses the analytic hierarchy process and entropy method to analyze the collected data. With the support of RFID technology, the output value of the circulation industry in Province Y has reached 140.508 billion yuan in 2019, accounting for about 27% of the tertiary industry, and the number of employees in the circulation industry has also increased to 57.88%, which is a strong boost to the economy of Province Y. It has a greater contribution to the total economic volume. The research of this article has realized the economic transformation of resource-based cities in the circulation industry and has a certain reference effect for the transformation and upgrading of similar cities.
Greek Handwritten Character Recognition Using Inception V3

Handwritten Greek character recognition is considered as one of the ways for genuine character recognition. In this paper, convolutional neural networks (CNNs) is applied on disconnected transcribed Greek character to identify and recognize properly. Inception V3 CNN model, with unique settings of the quantity of neurons in each layer is utilized and the interfacing route between certain layers. Yields of the CNN are set with modified adjusting codes, wherein CNN has the capacity to discard recognition along these lines. For preparing of the CNN, a mistake tests-based fortification learning methodology is created. Results show that the proposed system tend to achieve an accuracy of 99% and is far better when compared to the existing techniques.
On the 4-Adic Complexity of Quaternary Sequences of Period $2p$ with Ideal Autocorrelation

In this paper, we study the 4-adic complexity of two classes of quaternary sequences of period <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2p$</tex> with ideal autocorrelation defined by Kim et al. (ISIT, 2009). Our results show that the 4-adic complexity of these two kinds of quaternary sequences is large enough to resist the attack of the rational approximation algorithm.
Cryptanalysis of quantum secure direct communication protocol with mutual authentication based on single photons and Bell states

Recently, Yan et al. proposed a quantum secure direct communication (QSDC) protocol with authentication using single photons and Einstein-Podolsky-Rosen (EPR) pairs (Yan et al., CMC-Computers, Materials \& Continua, 63(3), 2020). In this work, we show that the QSDC protocol is not secure against intercept-and-resend attack and impersonation attack. An eavesdropper can get the full secret message by applying these attacks. We propose a modification of this protocol, which defeats the above attacks along with all the familiar attacks.
A Survey of Recommender Systems Based on Semi-supervised Learning

Khan, Aslam Hasan Siddqui, Jamshed Sohail, Shahab SaquibA recommender system is considered to provide user-specific recommendations of products or any kind of services depending upon different sectors such as business, marketing, government, education, health, etc. It is a tool or method that facilitates to manage any kind of online overloading problem between customers and organizations. Machine learning is being utilized to provide the intelligent recommender systems that are able to learn the behaviour based on historical data. A lot of work and related software based on machine learning have been proposed recently by using supervised and unsupervised learning in this area by using a collaborative, content-based and hybrid approach. But the existing work utilizes the supervised and unsupervised methods in proposing several recommender systems which are suffering from many shortcomings. The main disadvantage that we observe is that supervised and unsupervised learning techniques become incompatible and useless when data is dispersed and completely labelled data is not available. Therefore, recently a new concept of semi-supervised learning has been used to build recommender systems in various applications. In semi-supervised learning, little supervision is provided to unlabelled data by using available partial labels, which is also a cost-effective approach. Thus, it is necessary to provide a high-quality and instructive survey that reveals the importance of semi-supervised learning techniques along with collaborative content and hybrid filtering techniques, contributing to recommender systems. Hence, this paper presents a recent survey in the development of recommender systems.
Rethinking Table Structure Recognition Using Sequence Labeling Methods

Table structure recognition is an important task in document analysis and attracts the attention of many researchers. However, due to the diversity of table types and the complexity of table structure, the performances of table structure recognition methods are still not well enough in practice. Row and column separators play a significant role in the two-stage table structure recognition and a better row and column separator segmentation result can improve the final recognition results. Therefore, in this paper, we present a novel deep learning model to detect row and column separators. This model contains a convolution encoder and two parallel row and column decoders. The encoder can extract the visual features by using convolution blocks; the decoder formulates the feature map as a sequence and uses a sequence labeling model, bidirectional long short-term memory networks (BiLSTM) to detect row and column separators. Experiments have been conducted on PubTabNet and the model is benchmarked on several available datasets, including PubTabNet, UNLV ICDAR13, ICDAR19. The results show that our model has a state-of-the-art performance than other strong models. In addition, our model shows a better generalization ability. The code is available on this site (www.github.com/L597383845/row-col-table-recognition).
Gradient descent training expert system

<h2>Abstract</h2> This software is used to build, train and present data for evaluation by a gradient descent training expert system (GDES). A GDES uses a machine learning training method, gradient descent, in a manner similar to a neural network; however, instead of a multi-layer network of densely connected nodes, it uses a known-meaning rule-fact network. Thus, the logical relationships (rules) between nodes (facts) are human (or, potentially in the future, autonomously) defined and can have an identified meaning; however, their weightings are optimized using machine learning techniques. This provides the explainability of an expert system with the optimization of a neural network.
Hate in Word and Deed: The Temporal Association Between Online and Offline Islamophobia

The aim of the present study is to explore whether there is a temporal association between anti-Islamic online and offline hate, and if so in which direction. We used data on hateful Twitter content, and hate incidents and hate crimes/offences recorded by the Metropolitan Police Service in the United Kingdom to analyse this association, using time-series analysis. This study is unique in its use of newly developed technology to undertake big data analysis with recent, disaggregated online and offline hate data. Our study examined the ‘everyday’ incidents of (online and offline) hate that affect communities throughout the United Kingdom and we found that anti-Islamic hate speech followed rather than preceded Islamophobic hate offline. Our findings likely point to what we have referred to as compound retaliation, which suggests that media and social media dissemination about offline acts of hate compound already tense intergroup hostilities, providing further permission for those to express hatred online. Such a situation represents the compounding of hate and hostility through offline and online networks that are likely to be reinforcing.
A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, &lt;underline&gt;p&lt;/underline&gt;rivacy-&lt;underline&gt;a&lt;/underline&gt;ware, and secure &lt;underline&gt;c&lt;/underline&gt;ontinuous &lt;underline&gt;a&lt;/underline&gt;uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.
Research on image super-resolution algorithm based on mixed deep convolutional networks

The existing image processing methods had aimed at the problems of blurred image reconstruction, large noise, and poor visual perception. The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. Secondly, it can extract features from low-resolution images. It sends the extracted initial features into the convolutional coding and decoding structure for image features. Thirdly, the feature extraction and calculation in high-dimensions are performed using dilated convolution in reconstruction layer. The high-resolution image had been reconstructed. The proposed method had been compared with state-of-arts on Set5, Set14, BSD100, and Urban100 datasets. The experimental results can show that the Peak Signal-to-Noise Ratio is increased by some ranges, and the Structural Similarity is increased by some effective percentage points.
Latency Estimation of Blockchain-Based Distributed Access Control for Cyber Infrastructure in the IoT Environment

We adopt a permissioned blockchain framework to enable the Internet of Things (IoT) device access control, primarily to protect cyber infrastructure (cloud, fog, web servers, application servers, routers, switches, gateways, remote sensors, computers, controllers, actuators, radio transceivers, storage, including computing grid and data grid) of the IoT networks. The connected smart devices or critical cyber infrastructures continuously contribute to collect and transfer a vast amount of data using the internet. Unauthorized access and unauthenticated control of these devices would generate severe catastrophes. Despite the risk of having single-point-of-failure that occurs due to the centralized nature, Certificate Authorities (CAs) contribute to the security when devices hold CA-signed certificates. This study develops a comprehensive latency model on blockchain-based distributed critical infrastructure access control in the IoT networks. Decentralized Security Access Administrators (SAAs) are used instead of centralized CA to provide blockchain-based distributed access control to protect critical infrastructure. For our estimation, we consider (i) communication latency, (ii) digest computation latency, (iii) message authentication codes (MACs) computation latency, (iv) latency for device access query via SAA, (v) latency for authorization and device access, and (vi) latency for adding a transaction to the blockchain. Additionally, we compare the estimated time consumption of our model with a prior study. Our results indicate that the existing model underestimates latency by 75.5%. We estimate accurate time to access a target object using blockchain securely. This model is practical (i) to observe the scalability performance of a network and (ii) to detect malicious traffic by observing time. Adding a new block to blockchain incurs more delays than it claims, especially in the blockchain-based IoT environment. Understanding the accurate delay, theoretically, is essential to such systems. Our study moves a step forward towards this direction.
Cumulus: A Secure BFT-based Sidechain for Off-chain Scaling

Sidechains enable off-chain scaling by sending transactions in a private network rather than broadcasting them in the public blockchain (i.e., the mainchain) network. To this end, classic Byzantine fault-tolerant (BFT) consensus protocols such as PBFT seem an excellent fit to fuel sidechains for their permissioned settings and inherent robustness. However, designing a secure and efficient BFT-based sidechain protocol remains an open challenge.This paper presents Cumulus, a novel BFT-based sidechain framework for blockchains to achieve off-chain scaling without compromising any security and efficiency properties of both sides’ consensus protocols. Cumulus encompasses a novel cryptographic sortition algorithm called Proof-of-Wait to fairly select sidechain nodes to communicate with the mainchain in an efficient and decentralized manner. To further reduce the operational cost, Cumulus provides an optimistic checkpointing approach in which the mainchain will not verify checkpoints unless disputes happen. Meanwhile, end-users enjoy a two-step withdrawal protocol, ensuring that they can safely collect assets back to the mainchain without relying on the BFT committee. Our experiments show that Cumulus sidechains outperform ZK-Rollup, another promising sidechain construction, achieving one and two orders of magnitude improvement in throughput and latency while retaining comparable operational cost.
Designing a Computer-Vision Application: A Case Study for Hand-Hygiene Assessment in an Open-Room Environment

Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food-handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance. This leads to the development of our hierarchical system that incorporates a variety of modalities (RGB, optical flow, hand masks, and human skeleton joints) for recognizing subsets of hand-hygiene actions. Using hand-washing video recorded from several locations in a commercial kitchen, we demonstrate the effectiveness of our system for detecting hand hygiene actions in untrimmed videos. In addition, we discuss recommendations for designing a computer vision system for a real application.
RDFFrames: knowledge graph access for machine learning tools

Abstract Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.
Spam Detection System Using Supervised ML

Spam is one of the major problems around the world today. To resolve this problem, different spam filtering techniques and approaches were used. in our proposed system, we are using a Naïve Bayesian classifier, a content-based technique for classifying the spam from the consistent messages or emails using a Supervised Machine learning algorithm. Naive Bayesian is one of the familiar methods of spam classification because of its efficiency and simplicity. This application will detect the message as Spam or not spam and convey the result to the user.