Reviewerly/exHarmony huggingface dataset:
Criss-Cross Attention Based Auto Encoder for Video Anomaly Event Detection

The surveillance applications generate enormous video data and present challenges to video analysis for huge human labor cost. Reconstruction-based convolutional autoencoders have achieved great success in video anomaly detection for their ability of automatically detecting abnormal event. The approaches learn normal patterns only with the normal data in an unsupervised way due to the difficulty of collecting anomaly samples and obtaining anomaly annotations. But convolutional autoencoders have limitations in global feature extraction for the local receptive field of convolutional kernels. What is more, 2-dimensional convolution lacks the capability of capturing temporal information while videos change over time. In this paper, we propose a method established on Criss-Cross attention based AutoEncoder (CCAE) for capturing global visual features of sequential video frames. The method utilizes Criss-Cross attention based encoder to extract global appearance features. Another Criss-Cross attention module is embedded into bi-directional convolutional long short-term memory in hidden layer to explore global temporal features between frames. A decoder is executed to fuse global appearance and temporal features and reconstruct the frames. We perform extensive experiments on two public datasets UCSD Ped2 and CUHK Avenue. The experimental results demonstrate that CCAE achieves superior detection accuracy compared with other video anomaly detection approaches.
Smart Contracts for Building Decentralized Applications

Smart contracts are an integral part of blockchain technology that automates business operations by removing intermediaries. Leading organizations such as real estate, finance, banking, and health care have adopted it due to its intrinsic features: transparency, integrity, privacy, efficiency, and confidentiality. There are various platforms available supported by high-level programming languages to implement and deploy smart contracts. Researchers are molding modern technologies like Artificial Intelligence (AI) into smart contracts to make them more effective and reliable. However, a few vulnerabilities or risks need to be taken care of while implementing smart contracts. This chapter gives an overview of smart contracts, including the history, advantages, and disadvantages, and the need for smart contracts. Further, the different blockchain platforms related to smart contracts are discussed. Then, the implementation details of smart contracts are explained, i.e., fundamentals of Solidity programming. Finally, this chapter shows the implementation of different smart contract use cases with Solidity source codes such as oil mining, online banking, voting, telesurgery, and remote patient monitoring systems.
Bean Optimization Algorithm Based on Differential Evolution

Inspired by the evolution of natural plant distributions, bean optimization algorithm (BOA) is proposed and become an efficient swarm intelligence algorithm. Aiming at the disadvantage of low efficiency of fine search in BOA, an algorithm (DBOA) is proposed by integrating the mutation and selection operators of differential evolution into BOA. The mutation operator enriches the population diversity and improves the local optimization speed of the algorithm. The selection operator further ensures the evolution direction and enhances the optimization accuracy of DBOA. The proposed DBOA has been tested on a set of well-known benchmark problems and compared with other typical swarm intelligence algorithms. The experimental results show that DBOA effectively improves the accuracy and speed of the BOA and has better performance in solving complex optimization problems.
Mobile Forensics Data Acquisition

Mobile technology is among the fastest developing technologies that have changed the way we live our lives. And, with the increase of the need to protect our personal information, smartphone companies have developed multiple types of security protection measures on their devices which makes the forensic data acquisition for law enforcement purposes so much harder. As we all know, one of the biggest tasks in mobile forensics investigation is the step of data acquisition, it is the step of extracting all the valuable information that will help the investigators to bring out all the evidences. In this paper, we will explain the traditional forensic data acquisition methods and the impact of encryption and security protection that been implemented in new smartphones on these methods, we will also present some new mobile forensics methods that will help to bypass the security measures in new generation smartphones, and finally, we will propose a new data extraction model using artificial intelligence.
LCAN: Light Cross-Attention Network for Collaborative Filtering Recommendation

In recent years, an emerging research work in recommendation systems aimed at exploring users’ potential interaction preferences. However, most existing methods can only capture information about the user’s purchase (or click) history. To estimate users’ potential interaction preferences more accurately, it is necessary to consider auxiliary information when modeling user-item interactions. In this paper, a Light Cross-Attention Network (LCAN) is proposed. LCAN makes full use of existing information in three parts: 1) User-Item interaction graph. The interaction history is an important signal, and the user’s interaction preferences can be obtained directly from the interaction history. 2) User-User and Item-Item relationships. The user-user and item-item graphs are additionally constructed based on the similarity between users and items to alleviate data sparseness. 3) Complementarity between graphs. Information between different graphs is interrelated, and a graph-level cross-attention network is used to capture the complementarity between graphs. Extensive experiments have been conducted by comparing state-of-the-art methods, and it shows that our LCAN model can outperform the most advanced recommendation methods.
Fast and Robust Fuzzy-based Hybrid Data-level Method to Handle Class Imbalance

Conventional classification algorithms do not provide accurate results when the data distribution (class sizes) is unequal or data is corrupted with noise because the results are biased towards the bigger class.In many real life cases, there is a requirement to uncover unusual/smaller classes.There are a bundle of examples where importance of smaller/rare class is much-much higher than the bigger class for example-brain tumor detection, credit card fraud or anomaly detection and many more.This is usually called as problem of imbalance classes.The situation becomes worst when the data is corrupted with extra impurities like noise in data or overlapping of class or any other glitch in data because in this scenario traditional methods produce more poor results.This paper proposed a fast, simple and effective data level hybrid technique based on fuzzy concept to overcome the class imbalance problem in noisy condition.To appraise the classification performance of the offered technique it is tested with 40 UCI real imbalanced data sets having imbalance ratio ranges from 1.82 to 129.44 and compared with 12 other approaches.The outcome specifies that the presented hybrid data level technique performed better and in a fast manner when compared to other approaches.
Usability Assessment and Improvement Plan for Treadmills in Campus Gym

In recent years, more and more Chinese colleges and universities have set up gyms on campus in order to enhance students’ physical fitness. As an important part of the sports fitness industry, campus gyms have huge development space, and due to the particularity of the user groups, there are certain differences from ordinary gyms. Treadmills are the most common and basic fitness equipment in campus gyms. At present, there is no treadmill specially designed for campus gyms. Therefore, there is still a lot of room for improvement in user experience of campus treadmills. This study takes the gym of Guangzhou International Campus of South China University of Technology as an example. In terms of hardware, the OWAS scale is used to evaluate the user’s running posture, and the treadmill is improved for problems such as heart rate measurement; in terms of interface, QUIS is used to evaluate the treadmill interface. And according to the evaluation results, the interface layout of the treadmill is optimized.
The value of digital technologies in inclusion

The article considers the importance of digital technologies in inclusion. The work used the analysis of literary sources and the method of heart rate variability. The results of the study showed that the introduction of digital technologies expands the problematic field of inclusive education. Students who have impaired health indicators in the context of the use of digital technologies, including in a remote format, should be attributed to inclusion. In this regard, the training of specialists in the field of cyberspace is a link between the development and implementation of new information technologies in an inclusive educational space and monitoring the health of students.
Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images

Abstract Computer vision aims to build autonomous systems that can perform some of the human visual system’s tasks (and even surpass it in many cases)among the several applications of Computer Vision, extracting the information from the natural scene images is famous and influential. The information gained from an image can vary from identification, space measurements for navigation, or augmented reality applications. These scene images contain relevant text elements as well as many non-text elements. Prior to extracting meaningful information from the text, the foremost task is to classify the text &amp; non-text elements correctly in the given images. The present paper aims to build machine learning models for accurately organizing the text and non-text elements in the benchmark dataset ICDAR 2013. The result is obtained in terms of the confusion matrix to determine the overall accuracy of the different machine learning models.
Blockchain for Supply Chain Management

Nowadays, supply chain management is a prime concern to transfer products/medicines at a time without any interference. The management of the movement of products and services is referred to as supply chain management, and it encompasses all procedures that turn raw materials into items. It includes effectively optimizing a company’s supply-side processes to increase consumer growth and achieve a competitive edge in the market. This process is shared with multiple parties; if a party is trusted, it results in a reliable supply chain. The product can be tracked using sensors in the supply chain management, but it sometimes does not provide reliable tracking. Blockchain technology addresses this issue and provides transparency to the supply chain data so that users facilitate with end-to-end visibility, which helps to locate the contaminated source quickly and generates trust in the supply chain. The use of blockchain in the supply chain has the potential to increase transparency and traceability while also lowering administrative expenses. A blockchain supply chain can assist participants in managing the supply chain by recording price, date, location, quality, certification, and other sensitive information. The accessibility of this data inside blockchain can improve material supply chain traceability, reduce counterfeit and drop market losses, enhancement of compliance and visibility, expand manufacturing of contracts, and potentially boost an organization’s performance. Blockchain uses a smart contract for the verification of stakeholders and to verify product quality. In this process, all required stakeholders are payable in their wallet address after successfully verifying the product’s quality. A blockchain-based supply chain makes a supply chain process more reliable, transparent, and easily locate and track the product.
How People Show Their Emotions towards COVID-19 on Twitter Platform

The pandemic that has lasted for more than three years exerted a significant influence on the life of people, especially on their physical and mental health of people.The study intends to find out how people express their sentiments on social media platforms-Twitter.It will adopt the quantitative methodology in analyzing the data.The secondary data will be collected from the Web page via the Chrome extension with WDRA software.By means of making the analysis, the study shows that there was a great change before and after the pandemic.
An aspect sentiment analysis model based on adversarial training and multi-attention

Aiming at the disadvantages of the gradient vanishing and exploding of the Recurrent Neural Network in the traditional deep learning algorithm and the problem that the Convolutional Neural Network cannot obtain the global features of the classified text, a CNN(Convolutional Neural Network)-BiLSTM (Bidirectional Long Short-Term Memory) sentiment analysis method based on adversarial training and multi-layer attention is proposed to give full play to the ability of CNN to extract phrase-level features of text and the ability of BiLSTM to extract global structural information of text, and the multi-layer attention mechanism will assign higher weights to keywords, and the adversarial training can well solve the model instability problem of the current deep learning model. Using the public data set Laptop reviews and Restaurant Reviews from SemEval 2014 for verification, the results show that the accuracy of the model proposed in this paper is 1 and 1.9 percentage points higher than that of the original model on the two data sets. In contrast, the model is more efficient in aspect-level sentiment classification tasks.
Duplicate Detection of Crowdsourced Testing Reports

The benefit of crowdsourced testing must be carefully assessed with respect to the cost of the technique. At first place, crowdsourced testing is a scalable testing method under which large software systems can be tested with appropriate results. This is particular true when the testing is related with the feedback on GUI systems, or subjective opinions about different features.
Investigating the Effect of Using Synthetic and Semi-synthetic Images for Historical Document Font Classification

This paper studies the effect of using various data augmentation by synthetization approaches on historical image data, particularly for font classification. Historical document image datasets often lack the appropriate size to train and evaluate deep learning models, motivating data augmentation and synthetic document generation techniques for creating additional data. This work explores the effect of various semi-synthetic and synthetic historical document images, some of which appear as recent trends and others not published yet, on a font classification task. We use 10K patch samples as baseline dataset, derived from the dataset of Early Printed Books with Multiple Font Groups, and increase its size using DocCreator software and Generative Adversarial Networks (GAN). Furthermore, we fine-tune different pre-trained Convolutional Neural Network (CNN) classifiers as a baseline using the original dataset and then compare the performance with the additional semi-synthetic and synthetic images. We further evaluate the performance using additional real samples from the original dataset in the training process. DocCreator, and the additional real samples improve the performance giving the best results. Finally, for the best-performing architecture, we explore different sizes of training sets and examine how the gradual addition of data affects the performance.
A True Random Number Generator Based on ADC Random Interval Sampling

This paper presents a true random number generator (TRNG) based on traditional Microcontroller Unit (MCU) with analog-to-digital converter (ADC). Within the proposed architecture, the MCU employs an ADC to continuously sample variational input signals (rise/fall process) for generating true random numbers (TRNs). In the process, the time interval between each ADC sampling is determined by the output signal sampled by the previous ADC (called a random interval). The method improves the randomness of ADC output data by using the altering input signal (including noise) and the non-linear feature of ADC. Additionally, the simulation results (base on NIST) show the proposed TRNG can be realized using conventional MCU.
Research on the Deployment Strategy of Enterprise-Level JCOS Cloud Platform

Abstract Ruijie JCOS cloud management platform is the first cloud management platform based on OpenStack principle in China. It has the advantages of stable operation, fast deployment, wide compatibility and high performance. Taking the basic technology of cloud platform management as the core, this paper gives a general description of the deploy of the whole cloud platform, from which we can understand and analyze the shortcomings of building traditional data center, and then illustrate the general process of integrating resources and reducing costs by virtualization technology in combination with real application practice.
Design and implementation of university audit platform based on big data analysis

Digital university audit platform has become an important part of digital campus construction. How to combine big data analysis technology with university audit research, so as to promote the in-depth development of university audit work, is an urgent problem to be solved. Audit work in Colleges and universities is an important work at present. This paper aims to study the construction of university audit platform under the background of big data, explain the current situation of the construction of audit platform, the framework structure of audit platform and the problems and reasons of audit platform, accelerate the construction of information audit technology system, improve governance ability, and assist universities and education authorities in making major decisions.
Analyzing the Structural Complexity of Software Systems Using Complex Network Theory

Abstract Software systems have nearly been used in all walks of life, playing an increasingly important role. Thus, how to understand and measure complex software systems has become an ever-important step to ensure a high-quality software system. The traditional analysis of software system structure focuses on a single module. However, the traditional software structural metrics mainly focus on analyzing the local structure of software systems and fail to characterize the properties of software as a whole. Complex network theory provides us with a new way to understand the internal structure of software systems, and many researchers have introduced the theory of complex networks into the examination of software systems by building software networks from the source code of software systems. In this paper, we combine software structure analysis and complex network theory together and propose a SCANT (Software Complexity Analysis using complex Network Theory) approach to probe the internal complexity of software systems.
Building Trust in Blockchain Network Using Collective Signing

Collective signing is used to protect the authorities and clients from malicious users. It uses the consigning protocol (CoSi) that gives affirmation of the authentication. CoSi protocol ensures the validity of the authoritative disclosure. Nowadays, bitcoin can be a game-changer in the digital world. However, completing any bitcoin transaction still requires around 10 min for the transaction to be committed. A protocol based on byzantine consensus, a ByzCoin, is introduced to solve this problem. The byzcoin maximizes the scalable collective signing and executing the transactions in a few seconds compared to the bitcoin transactions of committing. The byzcoin maintains the public memberships of bitcoin by dynamically creating a new group in such a way that it establishes group based who has high power for calculating hash and which displays the successful block mines. Byzcoin removes problems like double spending and self-mining by generating collectively signed transaction blocks in a few seconds of executing the transaction. This chapter discusses the fundamentals regarding collective signing. However, it is utilized in byzcoin, the problems faced in deploying byzcoin, and how BLS multisignature plays a crucial role.
Bayes Linear Bayes Networks with an Application to Prognostic Indices

Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment. The use of Bayes linear kinematics eliminates the problem of non-commutativity which was observed in earlier work involving moment-based belief updates. In this paper we describe this approach and investigate its application to the rapid computation of prognostic index values in survival when a patient’s values may only be available for a subset of covariates. We consider the use of covariates of various kinds and introduce the use of non-conjugate marginal updates. We apply the technique to an example concerning patients with non-Hodgkin’s lymphoma, in which we treat the linear predictor of the lifetime distribution as a latent variable and use its expectation, given whatever covariates are available, as a prognostic index.
Authoritative and Unbiased Responses to Geographic Queries

A protocol for processing geographic data is proposed to guarantee authoritative and unbiased responses to geographic queries, without the need to rely on trusted third parties.The integrity of the proposed authoritative and unbiased geographic services (AUGS) protocol is guaranteed by employing novel hash tree based authenticated data structures (ADS) in conjunction with a blockchain ledger.Hash tree based ADSes are used to incrementally compute a succinct dynamic commitments to AUGS data.A blockchain ledger is used to record 1) transactions that trigger updates to AUGS data, and 2) the updated cryptographic commitments to AUGS data.Untrusted service providers are required to provide verification objects (VOs) as proof-of-correctness of their responses to AUGS queries.Anyone with access to commitments in ledger entries can verify the proof.
Classification of Crowdsourced Testing Reports

In order to attract workers, testing tasks are often financially compensated, especially for these failed reports. Under this context, workers may submit thousands of test reports. However, these test reports often have many false positives, i.e., a test report marked as failed that actually involves correct behavior or behavior that was considered outside of the studied software system. Project managers or testers need to manually verify whether a submitted test report reveals fault—-true fault. However, such process is tedious and cumbersome, given the high volume workload. Our observation on industrial crowdsourced testing platforms shows that, approximately 100 projects are delivered per month from a typical platform, and more than 1000 test reports are submitted per day on average.