Reviewerly/exHarmony huggingface dataset:
The Traffic Violation Detection System using YoloV7

In recent time surveys, the deaths and injuries due to traffic violations have increased chiefly in Indian roads. So, this needed the assistance of an automated computer vision-based object detection model, as manually identifying the vehicles violating traffic is hectic. The principle of this paper is to detect multiple violations using single video frames. The input video stream obtained from the surveillance camera is processed and annotated to carry out multiple processes. The dataset used for red-light jumping is COCO and the dataset for over boarding is created by annotating the images obtained from google. The model is trained and the output is visualized using tensorboard. The parameters used are Precision, Recall, F-measure and P-measure. The accuracy for red light skipping is 93% and the mAP value for over boarding is 0.5:0.95. This system utilizes the video stream at its maximum to detect various violations.
Artificial Intelligence Revolutionizing The Restaurant Industry - Analyzing Customer Experience Through Data Mining and Thematic Content Analysis

Artificial intelligence is rapidly changing the way restaurants operate. AI can help restaurants increase productivity, streamline processes, and boost customer satisfaction. AI can assist restaurants in creating menus that are suited to their customer's interests and preferences by evaluating data on consumer preferences and purchase history. Moreover, chatbots and virtual assistants powered by AI can be used to collect purchases, respond to consumer inquiries, and make tailored recommendations. As a result, consumers can enjoy a more smooth and delightful dining experience while also saving time and money on labour. Predictive analytics, which may forecast demand for specific items, is another method AI is utilised in restaurants. The intent of this study is to examine the application of Artificial Intelligence in restaurant industry and review the opinion of people on the introduction of Humanoid Robots in restaurants. AI is a game-changer in the industry, fully transforming the way the industry operates, hence giving a futuristic spin to dining. This research is based on reviews of people about content related to humanoid robots through YouTube videos. Data mining techniques were applied, and Thematic Content Analysis was done to analyze the attitude of people towards human-robot interaction.
Recommendation Systems for Supermarket

Numerous recommender systems offer recommendations to users depending on their interests. Several applications including those in e-commerce, healthcare and markets have adopted recommendation systems. This paper's major goal is to demonstrate various difficulties with the methods utilized for producing suggestions. The aim is to explore and develop recommendation algorithms using past sales data. It covers the ideas behind content-based filtering, hybrid model recommendation, collaborative filtering and association rules for recommendation systems. For Product recommendation, three algorithms are used: the popularity product recommendation algorithm, the frequent pattern growth algorithm and Apriori algorithm. To increase sales and market response, the proposed recommendation model yields good recommendation outcomes. Additionally, it suggests particular products to potential clients. This paper describes the research environment for recommendation systems in grocery stores. From the observed recommendation models, Popular Product Recommendation does not require customer analysis. Among the models that compare the customer behavior, FP Growth serves better than Apriori model due to its faster execution time. This paper provided a very relevant and practical business transformation scenario that helps businesses in comparable circumstances change their business models.
Rewarding Reviews with Tokens Using Smart Contracts

Research shows that 68% of online purchases are influenced by the reviews of the products that are being sold. Online retailers know the importance of online reviews and how those reviews impact their business. Online review sites are widely used in the hotel sector and by online retailers. Because these evaluations account for such many sales, there is always the risk that they may be exploited or interfered with. The issue is that these systems are highly centralized, leaving them vulnerable to manipulation and control. In this paper, we discussed the possibility of a decentralized, unbiased review system and an abstract implementation of that system. This review system will be a collection of smart contracts which can be deployed on the Ethereum blockchain. The blockchain's decentralized characteristic might be used to establish an unbiased and fair review system that increases consumer connections and confidence. On Test Networks, we build and test smart contracts in this project. The architectural flow and system components are also provided. In conventional review systems, the user usually adds a new review, and this review is stored in a centralized database of the service provider. The service provider can edit the review, it can also be permanently deleted from the platform. Since reviews drive a major part of the sales, this control over the reviews system can affect users who might have found the deleted review to be useful. A blockchain is a continuously increasing collection of linked and encrypted documents known as blocks. When a blockchain is used, a digital item's ability to duplicate indefinitely is eliminated. Consumer review systems might benefit from blockchain's decentralized and tamper resistant features.
Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries; the Opportunities and Challenges

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties.
Silent Surveillance Autonomous Drone For Disaster Management And Military Security Using Artificial Intelligence

In an environment where terrorist group actions are heavily predominate, the study introduces novel modeling tools that really are adept at controlling, coordinating, manipulating, detecting, and tracing drones. Modern humans now need to simulate their surroundings in order to boost their comfort and productivity at work. The ability to imitate a person's everyday work has undergone tremendous advancement. A simulation is a representation of how a system or process would work in the actual world.
Research and enlightenment on the Graduation Rate of American Universities Based on Panel Data

The undergraduate graduation rate has an important connotation of measuring the quality of higher education and plays an important role in the evaluation and assessment of the teaching quality of American universities. Based on the panel data, this paper establishes a linear regression model (selecting the data of universities from 51 states in the United States from 2009–2020) and analyzes the relationship between graduation rate and education investment policy, faculty level, student cost and so on. The conclusion is that the ratio of students to federal financial aid, state education funding and full-time instructional staff/student amount are negative to the graduation rate. Meanwhile, the state average tuition fee has a positive effect on student graduation rate. This paper innovatively introduces the analysis of panel data to the graduation rate study, and the quantitative study of higher education data has practical significance.
A Multi-Graph Joint Learning Network with Side Information for Recommender Systems

The application of knowledge graph in recommendation algorithms effectively enhances the interpretability of recommendation results, but it still lacks the mining of deep semantic information. Aiming at the problem that the current knowledge graph based recommendation algorithm is difficult to fully mine the potentially related information between entities, this paper proposes a multi-graph joint knowledge graph recommendation algorithm. This model combines the ideas of metapath2vec and EGES to enhance the embedding representation of nodes by simultaneously learning the original graph composed of the node itself and the auxiliary graph consisting of their side information to improve the recommendation effect. Extensive experiments on public datasets show that, compared with other benchmark algorithms, the proposed approach has a certain improvement in accuracy and robustness, and has a better ability to deal with sparse data.
The Challenges of Urban Data Ethics: Big Data and Intelligent Decision Support Systems

Transparency is the main format of any initiative that uses data. When it comes to sensitive data and big data to support decision making, the combination can bring advances but also additional concerns in the field of data ethics. This article examines the Intelligent Monitoring and information System (SIMI-SP), investigating the creation of the isolation index, made available and maintained by telecommunication service providers (telecoms), through a big data platform. It offers a picture of how this data-oriented technology is being instrumented, combined with other data, generating evidence that can contribute to the management of the pandemic and increase the risks of privacy, data protection and security. Finally, it highlights the potential of technologies in relation to urban data ethics, the challenges faced and possible outputs.
A Java EE Data Persistence Layer Model Based on Design Pattern and DbUtils

Aiming at the shortcomings of low development efficiency and high code redundancy of traditional JDBC and improved JDBC in the development of data persistence layer, a new Java EE data persistence layer development model is proposed. The model uses factory design pattern to separate the creation and use of DAO, uses single instance pattern to encapsulate the database connection pool, uses dynamic proxy and reflection to create the implementation class object of DAO interface, uses annotation to mark the mapping relationship between entity class and data table, and uses DbUtils tool class to complete the interaction with database. The design idea and components of the model are described in detail. Taking the data persistence layer of the target assessment system as an application case, the implementation process of the model is described in detail. The test results show that this model can improve the development efficiency and reduce the redundancy of code, and its performance is almost the same as that of traditional JDBC.
To Interact and to Narrate: A Categorical Multidimensional Analysis of Twitter Use by US Banks and Energy Corporations

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> With the development of digital technologies, Twitter allows organizations to make better use of social media for impression management, advertising, and marketing. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> As a recently developed register, Twitter has been researched as a personal-oriented communication method, but little research has been conducted on the register of corporate Twitter use. This study, exploring Twitter use by the 2020 US Fortune 500 banks and energy corporations, may be the first one to conduct register analysis of corporate Twitter. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> This study used summary language variables of Linguistic Inquiry and Word Count (LIWC) as dimensions of register variation, and also conducted categorical multidimensional analysis (CMDA) of linguistic features and features specific to Twitter. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. What are the patterns of register variation in the tweets of US banks and energy corporations based on the results of four LIWC summary variables and the CMDA method? 2. Are there any differences between tweets of the two industries within each pattern of register variation? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results and conclusions:</b> Results showed that tweets of both industries tend to display a categorical, confident self-regulating style, and a mixed tone. Tweets of banks are more formal, self-regulating, and oriented toward narrative (congratulatory, positive informational, and effortful), while tweets of energy corporations are more authentic and oriented toward interaction (advisory, routine, and affiliative). Tweets having narrative functions tend to be formal in style and positive in tone, while tweets having interactive functions tend to display corporations’ confidence and leadership. Corporate Twitter is characterized by the integration of interaction and informational narrative, or “registerial hybridity.” Overall, this study strengthens the idea that corporations use Twitter to facilitate corporate communication with a broadcasting strategy and narrative perspective, and to improve digital communication with an engaging strategy. Findings may shed light on promoting products and corporate impression management on social media.
Hardware-Implemented Lightweight Accelerator for Large Integer Polynomial Multiplication

Large integer polynomial multiplication is frequently used as a key component in post-quantum cryptography (PQC) algorithms. Following the trend that efficient hardware implementation for PQC is emphasized, in this paper, we propose a new hardware-implemented lightweight accelerator for the large integer polynomial multiplication of Saber (one of the National Institute of Standards and Technology third-round finalists). First, we provided a derivation process to obtain the algorithm for the targeted polynomial multiplication. Then, the proposed algorithm is mapped into an optimized hardware accelerator. Finally, we demonstrated the efficiency of the proposed design, e.g., this accelerator with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$v=32$</tex-math></inline-formula> has at least 48.37% less area-delay product (ADP) than the existing designs. The outcome of this work is expected to provide useful references for efficient implementation of other PQC.
PKAMNet: A Transmission Line Insulator Parallel- Gap Fault Detection Network Based on Prior Knowledge Transfer and Attention Mechanism

Insulator parallel gaps in a transmission line protect the insulator from arc burn and provide protection from lightning. Detection of insulator parallel-gap faults based on aerial inspection images of transmission lines may be hindered by various factors, such as complex background, scale distortion, and occlusion. In this study, we propose a prior knowledge transfer and attention mechanism network (PKAMNet) to detect faulty insulator parallel gaps. First, the capability of PKAMNet to learn the features of different types of parallel-gap faults is improved by constructing a prior knowledge transfer model based on visual saliency. Owing to the difficulty of effectively expressing the features of the fault target due to the deviation of the shooting angle and complex background of the inspection image, we enhance the feature expression ability of the fault area by embedding the coordinate attention block. Additionally, the parameters of the detection network are simplified, and the modules are optimized to improve detection efficiency. Comparison results on 3-year aerial inspection videos of 500 kV high-voltage transmission lines show that PKAMNet has a high detection accuracy and can ameliorate the phenomenon of missed and erroneous detection caused by insufficient expression of parallel-gap faults.
Image Comparison and Scaling via Nonlinear Elasticity

A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigate whether for linearly related images the minimization algorithm delivers the linear transformation as the unique minimizer.
Optimal Transport Between GMM for Multiscale Texture Synthesis

Using optimal transport in image processing tasks has become very popular. However, it still faces difficult computational issues when dealing with high-dimensional distributions. We propose here to use the recently introduced GMM-OT formulation, which consists in restricting the optimal transport problem to the set of Gaussian mixture models. As a proof of concept, we use it to improve the texture model Texto based on optimal transport between distributions of image patches. Using GMM-OT in this texture model allows to deal with larger patches, hence providing results with better geometric details. This new model allows for synthesis, mixing, and style transfer.
SAI: AI-Enabled Speech Assistant Interface for Science Gateways in HPC

High-Performance Computing (HPC) is increasingly being used in traditional scientific domains as well as emerging areas like Deep Learning (DL). This has led to a diverse set of professionals who interact with state-of-the-art HPC systems. The deployment of Science Gateways for HPC systems like Open On-Demand has a significant positive impact on these users in migrating their workflows to HPC systems. Although computing capabilities are ubiquitously available (as on-premises or in the cloud HPC infrastructure), significant effort and expertise are required to use them effectively. This is particularly challenging for domain scientists and other users whose primary expertise lies outside of computer science. In this paper, we seek to minimize the steep learning curve and associated complexities of using state-of-the-art high-performance systems by creating SAI: an AI-Enabled Speech Assistant Interface for Science Gateways in High Performance Computing. We use state-of-the-art AI models for speech and text and fine-tune them for the HPC arena by retraining them on a new HPC dataset we create. We use ontologies and knowledge graphs to capture the complex relationships between various components of the HPC ecosystem. We finally show how one can integrate and deploy SAI in Open OnDemand and evaluate its functionality and performance on real HPC systems. To the best of our knowledge, this is the first effort aimed at designing and developing an AI-powered speech-assisted interface for science gateways in HPC.
Revisiting Residue Codes for Modern Memories

This article shows how residue codes, traditionally used for compute rather than storage error correction, can be applied to memories with surprising results. We show that adapting residue codes to modern memory systems offers a level of error correction comparable to those of traditional schemes, such as Reed–Solomon, but with fewer bits of storage. For instance, our adaptation of residue codes—multiuse error-correcting code (MUSE ECC)—can offer ChipKill protection using approximately 30% fewer bits. We use the storage gains to hold the metadata needed for emerging security functionality, such as memory tagging, or to provide better detection capabilities against Rowhammer attacks. In a system with memory tagging and MUSE, we achieve a 12% reduction in memory bandwidth utilization with the same error correction level as a traditional ECC baseline and without a noticeable performance loss. Thus, our work demonstrates a new, flexible primitive for co-designing reliability with security and performance.
Formal Models of Automatic Semantic Processing

The computer processing of the natural language also includes semantic analysis in addition to lexical and syntactic analyses.
Formal Models of Discourse Analysis

Most of the issues addressed in NLP research involve language phenomena at the word or sentence level, paying little attention to the relationship between sentences. However, natural language usually appears not as a collection of independent sentences but as a collection of interrelated sentence groups, which are called a discourse. Currently, the wide use of computers is witnessed in discourse processing. Against this backdrop, discourses encompass not only monologues and dialogues but also human-computer interactions (abbreviated as HCI).
Formal Models Based on Phrase Structure Grammar

In most rule-based natural language processing systems, phrase structure grammar (PSG), which is also called context-free grammar (CFG), is currently most widely used. This is a very important formal model in the NLP.
$$\alpha $$-Pixels for Hierarchical Analysis of Digital Objects

In this paper, we apply a scale space analysis method to digital geometry. We deal with the reconstruction of Euclidean polylines controlling the size of pixels in discrete space. Numerical examples show that the size of pixels of digital geometry processes the scale for the hierarchical reconstruction of Euclidean objects from digital objects.
Formal Models Based on Dependency and Valence

Valence is the potential combining ability of words. Nouns, verbs, and adjectives all have the potential combining ability, and they all have valence. When a word with this potential combining ability enters a sentence, it forms a dependence relation between words in the sentence, which is called “dependency” for short.
Learned Discretization Schemes for the Second-Order Total Generalized Variation

The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel discretization schemes of the total variation, there has been recent work to improve the second-order total generalized variation discretization, based on the same design idea. In this work, we propose to extend this to a general discretization scheme based on interpolation filters, for which we prove variational consistency. We then describe how to learn these interpolation filters to optimize the discretization for various imaging applications. We illustrate the performance of the method on a synthetic data set as well as for natural image denoising.