Reviewerly/exHarmony huggingface dataset:
Implementing Spot the Differences Game Using YOLO Algorithm

The requirement for object detection has been expanding with computational force. Object identification is a technique that identifies the semantic class of the objects in the image or video. In this work, we talk about implementing an application that connects with the user and distinguishes the various objects present in two generally comparable images utilizing object recognition algorithms (YOLO algorithm). The user collaborates with the application through speech and recognizes the object. This execution has a precision of 70%. Object detection is one of the most demanding task in computer vision, and it involves in finding the type of the object and knowing the location in the image. Through object detection, we can achieve both object detection and tracking, whereas object tracking generally locks the moving object. However, YOLO is one if the top algorithm for object detection it gradually increased its accuracy with version updates one by one which helped it to become the best algorithm.
How Many Cameras Do You Need? Adversarial Attacks and Countermeasures for Robust Perception in Autonomous Vehicles

Deep neural networks have been established by researchers to perform significantly better than prior algorithms in multiple domains, notably in computer vision. Naturally, this resulted in its deployment as a perception module in modern Autonomous Vehicle (AV) and in general for Advanced Driver Assistance Systems (ADAS). ADAS relies heavily on perception module, which harnesses various sensors such as camera, LiDAR, radar, ultrasonic sensor to make navigational decisions. By drawing from the adversarial attacks, which undermine a lot of machine learning applications, recent research shows that the AV perception modules are also vulnerable to adversarial attacks. Suggested countermeasures for these attacks include increasing the number of sensors, which incurs cost overhead and does not present any formal guarantee of protection. Hence, in this paper, we study the robustness and practicality of such a countermeasure. We demonstrate that it is still possible to spoof multiple cameras through adversarial object though, the attack success considerably reduces. Furthermore, the possibility of alternative countermeasures like dimensionality reduction and feature squeezing are investigated. Our study shows that these techniques, when applied together, significantly enhances the robustness of the AV perception system.
DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain

Training predictive models with decentralized medical data can boost the healthcare research and is important for healthcare applications. Although the federated learning (FL) was proposed to build the predictive models, how to improve the security and robustness of a learning system to resist the accidental or malicious modification of data records are still the open questions. In this paper, we describe DeMed, a privacy-preserving decentralized medical image analysis framework empowered by blockchain technology. While blockchain is limited in serial computing, the decentralized data interaction in blockchain is very desired to preserve the data privacy when training models. To adapt blockchain in medical image analysis, our framework consists of the self-supervised learning part running on users’ local devices and the smart contract part running on blockchain. The prior is to obtain the provable linearly separable low-dimensional representations of local medical images and the latter is to obtain the classifier by synthetically absorbing users’ self-supervised learning results. The proposed DeMed is validated on two independent medical image classification tasks on pathological data and chest X-ray. Our work provides an open platform and arena for FL, where everyone can deploy a smart contract to attract contributors for medical image classification in a secure and decentralized manner while preserving the privacy in medical images.
CmpQTS: Comparative Visual Analysis of Quantitative Timing Strategies

Quantitative timing strategies play a significate role in quantitative investment. However, as various quantitative timing strategies have been proposed, the main challenge lies in the selection of effective quantitative timing strategies to achieve excess market returns. Even for experienced investors, it is still hard to hand-pick the right one. In this paper, we propose CmpQTS, which, to our best knowledge, is the first visualization system that attempts to assist users in comprehensively exploring the performances of different quantitative timing strategies. In particular, CmpQTS compares and evaluates different quantitative timing strategies at two levels: a Multi-Strategy level, for comparing different quantitative timing strategies; and a Single-Strategy level, for evaluating a strategy in different aspects. The effectiveness of the system is demonstrated by two case studies and a user experiment using the China A-Shares of Shanghai dataset consisting of 486 stocks for 22 years (2000-2021).
The Relationship Between Knowledge Automation and Employee Creativity

Knowledge automation is the contemporary critical success factor of knowledge management. In this context, the study focuses on the relationship between knowledge automation and employee creativity. The relationship between knowledge automation and employee creativity is analyzed by gathering quantitative data and applying one-way analysis of variance method and calculating correlation between variables. The study is performed on 200 bank managers from the banking sector of Pakistan. Hypotheses are generated after the analysis of data. Results prove the relationship between employee creativity and knowledge automation is linear by applying ANOVA and trend analysis methods.
Computing the Parameterized Burrows–Wheeler Transform Online

Parameterized strings are a generalization of strings in that their characters are drawn from two different alphabets, where one is considered to be the alphabet of static characters and the other to be the alphabet of parameter characters. Two parameterized strings are a parameterized match if there is a bijection over all characters such that the bijection transforms one string to the other while keeping the static characters (i.e., it behaves as the identity on the static alphabet). Ganguly et al. [SODA 2017] proposed the parameterized Burrows–Wheeler transform (pBWT) as a variant of the Burrows–Wheeler transform for space-efficient parameterized pattern matching. In this paper, we propose an algorithm for computing the pBWT online by reading the characters of a given input string one-by-one from right to left. Our algorithm works in $$O(|\varPi | \log n/\log \log n)$$ amortized time for each input character, where n and $$\varPi $$ denote the size of the input string and the alphabet of the parameter characters, respectively.
Deep Learning Model based Semantic Segmentation Network for Ultrasound Gallballder Images

In this paper, automated deep learning model is discussed for the segmentation of images using semantic segmentation network. In this process different types of data sets are used to train the network and the model is tested with the ultrasound gall bladder images and parameters such as accuracy, sensitivity is calculated. Comparing with the existing methods, proposed method improves the segmentation accuracy more than 5%.
Performance analysis of the QKD algorithm using RS and AES

The purpose of this paper is to present a multisensor system with quantum key exchange at the source level and non-binary data transmission at the channel level using the quantum AES algorithm. We differentiate between the classical paradigm based on numerical complexity and the quantum paradigm based on the uncertainty principle. Studies of Bit Error Rate (BER) curves compared a Reed Solomon code with compression and an unencrypted channel are carried out, obtaining a performance of 6 dB and 6.5 dB for a BER of $10^{-3}$, respectively. Detection studies are also carried out against the “man-in-the-middle” attack, reaching a probability of approximately 90%.
A Vehicle Re-ID Algorithm Based on Channel Correlation Self-attention and Lstm Local Information Loss

Recently, the rapid development of vehicle re-identification (ReID) technology has facilitated the construction of intelligent transport systems. Mainstream ReID methods rely on the fusion of global and local features. In the global feature extraction, the channel attention modules are usually exploited in the network, most of which only focus on the channels’ importance and ignore the interactions among channels. In the local feature extraction, the additional annotation-based local feature extraction methods can focus on local information and improve the model’s performance but increase the workload of the data annotation and reduce the generalizability of the model. In this article, we put forward a new ReID Algorithm called CCSAM-LL. Firstly, a plug-and-play module based on channel correlation self-attention called CCSAM is introduced, which focuses on channel relevance and improves the characterization of global features. Secondly, we propose an Lstm-based loss, named LstmLocal loss, which takes into account local features without additional annotation. LstmLocal loss is trained with Triplet Hard loss and ID loss to improve the model’s ability to capture detailed features and accuracy in the retrieval task. Experimental results demonstrate that our approach outperforms the state-of-the-art methods on the challenging dataset VeRi776. Specifically, our approach achieves 83.18% mAP, 98.79% Rank5, and 48.83% mINP. The model is available at https://gitee.com/qitiantian128/ccsam-ll .
Information Aggregation Semantic Adversarial Network for Cross-Modal Retrieval

The core of cross-modal retrieval is to measure the similarity between different modalities of data. The mainstream is to construct a common subspace using representation learning, in which different types of data can be directly compared. However, most of these methods utilize solely a portion of the information from the dataset, with varying degrees of information loss in the objective function. In this paper, we present a novel cross-modal learning framework called Information Aggregation Semantic Adversarial Network, which minimizes information loss through adversarial learning and the double constraints of two subspaces. Among them, the proposed cross-modal information aggregation constraint based on common subspace aggregates the global information and fine-grained information simultaneously to generate a common representation of cross-modal similarity and fight a discriminator used to distinguish the original modality of the common representation. Furthermore, a semantic constraint is considered to improve the semantic discrimination of common representation based on the potential association between labels and representations of a semantic subspace. Through the joint exploitation of the above, the information loss in the cross-modal process is greatly reduced. Extensive experimental results on three widely-used benchmark datasets demonstrate that the proposed method is effective in cross-modal learning and significantly outperforms the state-of-the-art cross-modal retrieval methods.
Technological Aspect of Academic and Educational Entrepreneurship

Entrepreneurship is the process of creating, developing, and transforming a product, service, or system through demonstrating enterprise behavior and conducting utility and value creation activities (Galloway & Brown, 2002). According to another definition, entrepreneurship is the journey of creating economic value and employment by developing a product or service with the inherent creative power of humanity (Audretsch, 2012; Drucker, 1986). Innovations throughout humanity have subsumed the factors that direct the individual; the entrepreneur who leads the enterprise in this journey (Bygrave & Hofer, 1992). For example, while the primary capital of the industrial society encompasses muscle power and natural resources, the capital of the information society is depicted by the knowledge and the knowledge production capacity of people (Castells, 2004; Drucker, 1986). Therefore, the process of change from an industrial to an information society is an important milestone for entrepreneurship. In this context, educational entrepreneurship (edupreneur) and academic entrepreneurship (academicpreneur) concepts, two terminologies widely used in the literature, attract considerable attention. While an academic entrepreneur is defined as a university lecturer who establishes a new company or institution based on technologies derived from university research or commercializes a research project with high-income expectations (Lacetera, 2009), educational entrepreneur is also defined as the change agent (Hess, 2007) who presents innovative methods with his knowledge and experience in order to create permanent change. Actually, edupreneurs are expected to be the leaders in establishing a new education mindset in line with the needs of the twenty-first century.
Implementation of High Dynamic Range Image Processing Module as a Part of Lighting Simulation Software System

This article describes approaches to implementation of software module for processing of high dynamic range images, as a part of lighting simulation and photorealistic computer graphics software. Lighting simulation software requiresits own image processing application because functionality of existing ones usually is not enough for lighting simulation analysis purposes. Processing and analysis of lighting simulation results usually requires the certain functionality that is absent in many image processing programs. The article describes an application developed by us which contains such functionality related to lighting simulation. At first, it is a dynamic range compression function for displaying images on computer monitors. Besides this, our application includes additional functionality for improving image quality, such as forced desaturation of brightest highlights on the images, and also image filtering for decreasing noise caused by stochastic methods used in simulation. For simulation result analysis it is required to transfer additional data (besides image itself) to the processing module. Such layer-based data structure of high dynamic range image files generated by lighting simulations software is described.
Urban Agricultural Eco-Economic System Based on Data Mining Technology

With the rapid development of economy, the problem of environmental pollution is becoming more and more serious. Ecological agriculture has become an important topic in China’s social development. As one of the most active, dynamic and potential areas for human survival and growth. In the process of urbanization, a large number of phenomena such as resource waste in densely populated areas and damage to the ecological environment have occurred. Due to people’s weak awareness of environmental protection, the quality of life of urban residents has decreased, and even the lack of the concept of “people and citizens” has made the problem of environmental pollution more and more serious, affecting economic development and people’s daily living standards. Therefore, based on data mining technology, this paper designs the urban agricultural eco-economic system. Firstly, this paper expounds the characteristics, development mode and development principles of ecological agriculture, and then introduces the relationship between urban agriculture and ecological economic system. After that, the application of data mining technology is studied, the urban agricultural ecological economic system is designed and developed, and the function of the system is tested. Finally, the test results show that the functional test results of the system are about 4–5 points, which shows that the system has good performance and can meet the needs of users.
Construction of Inductive Property Predicates for Mutable Data Structures

Program verification is a compelling way to ensure the dependable of software system, but the granularity of verification properties depends on the design of predicates. Definition of formal predicates is one of the prerequisites for automated software verification, the reason why predicate definitions of mutable data structures is relatively difficult, is that the trade-off between expressiveness of predicates and Satisfiability Modulo Theories (SMT) solver's proving capability. In this paper, we propose a method for constructing inductive predicates, which makes predicates more expressive and meanwhile ensures the provability of inductive properties in programs which manipulate mutable data structures through iteration. Furthermore, based on formal description of predicates about mutable data structures, a construction algorithm of complex inductive predicates is provided, which solves the dilemma of complicated programming of inductive property annotations during the verification process, and improves the automation of program verification. Experimental results show that the method can support the verification prototype to describe more complex properties about mutable data structures and alleviate the burden of automated theorem proving.
Deep-Learning-Based Automated Scoring for the Severity of Toxic Comments Using Electra

With the increasing popularity of the Internet, social media plays a crucial role in people's daily communication. However, due to the anonymity of Internet, toxic comments emerge in an endless stream on the Internet, which seriously affects the health of online social environment. To effectively reduce the impact of toxic comments, automated scoring methods for the severity of toxic comments are in great demand. For that purpose, a deep-learning-based natural language processing technique is proposed using ELECTRA to automatically score the toxicity of a comment in this work. The backbone of our model is the ELECTRA discriminator, and the downstream regression task is accomplished by the following head layer. Three head layers are implemented separately: multi-layer perceptron, convolutional neural network, and attention. The dataset used for model training is from the Kaggle competition Toxic Comment Classification Challenge, and the model performance is evaluated through another Kaggle competition Jigsaw Rate Severity of Toxic Comments. By a boost from the K-Fold cross validation and an ensemble of three models with different head layers, our method can reach a competition score 0.80343. Such score ranks 71/2301 (top 3.1%) in the leaderboard and can get a silver medal in the competition. The results in this work would help filter the toxic comments and harmful text information automatically and effectively on the Internet, and could greatly reduce the cost of manual review and help build a healthier Internet environment.
Motion Segmentation Based on Pixel Distribution Learning on Unseen Videos

Motion segmentation plays an important role in many applications including autonomous driving, computer vision and robotics. Previous works mainly focus on segmenting objects from seen videos. In this paper, we present a novel approach based on pixel distribution learning for motion segmentation in unseen videos. In particular, optical flow is extracted from consecutive frames to describe motion information. We then randomly permute these modified motion features, which are used as input of a convolution neural network. The random permutation process forces the network to learn the pixels’ distributions rather than local pattern information. Consequently, the proposed approach has a favorable generalization capacity and can be applied for unseen videos. In contrast to previous approaches based on deep learning, the training videos and testing videos of our proposed approach are completely different. Experiments based on videos from the KITTI-MOD dataset demonstrates that the proposed approach achieves promising results and shows potential for better motion segmentation on unseen videos.
Extensions to the Predicate Programming Language for Efficient Graph Processing

High-performance and correct implementation of graph algorithms for parallel hardware is a challenge. A set-based approach to develop efficient and correct graph processing algorithms in the framework of predicate software engineering is proposed. The efficiency of predicate programs is achieved by applying optimizing transformations.
A Novel Framework on Biomedical Image Analysis Based on Shape and Texture Classification for Complex Disease Diagnosis

The wide acceptance of applying computer technology in medical imaging system for manipulation, display, and analysis contribute better improvement in achieving diagnostic confidence and accuracy on predicting diseases. Therefore, the need for biomedical image analysis to diagnose a particular type of disease or disorder by combining diverse images of human organs is a major challenge in most of the biomedical application systems. This chapter contributes an overview on the nature of biomedical images in electronic form facilitating computer processing and analysis of data. This describes the different types of images in the context of information gathering, screening, diagnosis, monitor, therapy, and control and evaluation. The characterization and digitization of the image content is important in the analysis and design of image transmission.
The Model of Cross-Tenant Information Access Control in SAAS Cloud

With the development of cloud computing and the application of multi-tenant technology, multi-tenant application platforms need to ensure the isolation and security of data when providing cloud services through resource sharing and on-demand customization, and also need to prohibit users from illegally accessing unauthorized resources. In addition, when users access relevant tenant information content across tenants, it is necessary to ensure the security in the cross-tenant collaboration scenario. Based on this, this paper proposes a cross-tenant user access control model, including its components, deployment mechanism and specific implementation process, and then we comprehensively analyzed the characteristics and performance of the model. This model not only ensures the security of data within each tenant, but also facilitates distributed cross-domain access among multiple tenants.
AP-HBSG: Authentication Protocol for Heterogeneous Blockchain-Based Smart Grid Environment

In the past, more research has been undertaken on the security of the smart grid (SG), with the aim of securing the SG in a manner that takes into account devices with limited resources, e.g., smart meters (SM). Consequently, several protocols for authenticating communicating participants in the SG and safeguarding the transfer of their keys have been devised over the years. However, most existing protocols work in a homogeneous context, which means that different entities operate in the same cryptographic settings. In addition, the majority of them are subject to a single point of failure, meaning that if a central device fails, all connected devices fail as well. To mitigate the previously mentioned issues and maintain the security of the SG network, an authentication protocol for heterogeneous blockchain-based smart grid environment (AP-HBSG) is designed. In the first part, an anonymous and heterogeneous certificateless authentication protocol is designed to secure and execute the session key agreement process. In addition, one participant belongs to a certificateless public cryptography (CL-PKC) whereas the other participant belongs to a public key infrastructure (PKI). In the second part, a blind signature is used to add authentication security within the blockchain nodes. Besides, a random oracle model (ROM) is used for the security analysis of the proposed protocol and we prove that it is secure under discrete logarithm (DL) problem and computational Diffie-Hellman (CDH) problem. The performance analysis shows that our proposed protocol has a lower computational and communication cost than other recently proposed protocols.