Reviewerly/exHarmony huggingface dataset:
The Impact of Artificial Intelligence and Machine Learning in Digital Marketing Strategies

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized numerous industries, including the field of digital marketing.This research paper explores the impact of AI and ML in digital marketing strategies and their implications for businesses.The paper begins by providing an overview of AI and ML, highlighting their capabilities and potential applications in digital marketing.It then delves into the various ways in which AI and ML have transformed key aspects of digital marketing, including customer segmentation, personalized targeting, content creation, and customer experience optimization.Furthermore, the paper discusses the benefits and challenges associated with incorporating AI and ML in digital marketing strategies.Additionally, it addresses the ethical considerations and potential biases that may arise from the use of AI and ML in digital marketing.Artificial intelligence is the present day era which is used to perform person tasks using computers in addition to robots.There are many every day responsibilities that can be without problems performed with the aid of computers and robots in preference to human beings.Artificial intelligence has made easy to carry out obligations which requires human brains by means of the use of green pc structures.This helps to save a whole lot of time and efforts taken by humans as it may be finished at a quicker fee using excessive tech pc.There are numerous categories of synthetic intelligence which implements various factors.The intention and goal of artificial intelligence is to carry out wondering, information gathering, arranging, gaining knowledge of, regular language handling, decision making, etc.This paper will give an explanation for the types of synthetic intelligence, its advantages in addition to challenges.AI analysts have used extensive categories of important questioning approaches which include mathematical and statistical computations, formal rationale, neural network agencies, and strategies and monetary components.Synthetic intelligence also works on improving the principles of software engineering, mind storming strategies, semantics, reasoning, and numerous clinical domain names It also discusses the evolving role of marketers in this new AI-driven landscape and the skills required to leverage these technologies effectively.Finally, the paper concludes by emphasizing the significance of AI and ML in shaping the future of digital marketing.It highlights the need for businesses to adapt and embrace these technologies to stay competitive in an increasingly data-driven and customer-centric environment.In summary, this research paper provides insights into the transformative impact of AI and ML in digital marketing strategies.It offers valuable perspectives on the opportunities and challenges that arise with the integration of these technologies, ultimately emphasizing their vital role in shaping the future of digital marketing practices.
Deep Dyna-Q for Rapid Learning and Improved Formation Achievement in Cooperative Transportation

Nowadays, academic research, disaster mitigation, industry, and transportation apply the cooperative multi-agent concept. A cooperative multi-agent system is a multi-agent system that works together to solve problems or maximise utility. The essential marks of formation control are how the multiple agents can reach the desired point while maintaining their position in the formation based on the dynamic conditions and environment. A cooperative multi-agent system closely relates to the formation change issue. It is necessary to change the arrangement of multiple agents according to the environmental conditions, such as when avoiding obstacles, applying different sizes and shapes of tracks, and moving different sizes and shapes of transport objects. Reinforcement learning is a good method to apply in a formation change environment. On the other hand, the complex formation control process requires a long learning time. This paper proposed using the Deep Dyna-Q algorithm to speed up the learning process while improving the formation achievement rate by tuning the parameters of the Deep Dyna-Q algorithm. Even though the Deep Dyna-Q algorithm has been used in many applications, it has not been applied in an actual experiment. The contribution of this paper is the application of the Deep Dyna-Q algorithm in formation control in both simulations and actual experiments. This study successfully implements the proposed method and investigates formation control in simulations and actual experiments. In the actual experiments, the Nexus robot with a robot operating system (ROS) was used. To confirm the communication between the PC and robots, camera processing, and motor controller, the velocities from the simulation were directly given to the robots. The simulations could give the same goal points as the actual experiments, so the simulation results approach the actual experimental results. The discount rate and learning rate values affected the formation change achievement rate, collision number among agents, and collisions between agents and transport objects. For learning rate comparison, DDQ (0.01) consistently outperformed DQN. DQN obtained the maximum −170 reward in about 130,000 episodes, while DDQ (0.01) could achieve this value in 58,000 episodes and achieved a maximum −160 reward. The application of an MEC (model error compensator) in the actual experiment successfully reduced the error movement of the robots so that the robots could produce the formation change appropriately.
Zero-Cerd: A Self-Blindable Anonymous Authentication System Based on Blockchain

While the Internet of things brings convenience to people's lives, it will also bring people hidden worries about data security. As an important barrier to protect data security, identity authentication is widely used in the Internet of things. However, it is necessary to protect users' identity privacy while authenticating their identity. Anonymous authentication technology is often used to solve the contradiction between legitimacy and privacy in the authentication process. The existing anonymous authentication scheme has many problems in practical application such as the inability to achieve complete anonymity, the high computational complexity of the algorithm, and the corruption of the central authority. Aiming at the privacy of authentication, we propose Zero-Cerd, a self-blindable anonymous authentication system based on blockchain and dynamic accumulator. The self-blinding properties of the credential enable the users themselves to generate a new validly pseudonymous credential. With the help of zero-knowledge proof technology, users can prove the validity of their credentials without disclosing any information. Security analysis shows that our scheme has achieved the expected security objectives. Compared with the existing schemes, our scheme has the advantages of complete anonymity and high efficiency, and is more suitable for IoT applications with privacy protection requirements.
Large Deviations Anomaly Detection (LAD) for collection of multivariate time series data: Applications to COVID-19 data

Time series anomaly detection is frequently used to identify extreme behaviors within a single time series. Identifying extreme trends in relation to a collection of other time series, on the other hand, is frequently of significant interest, such as in public health policy, social justice, and pandemic propagation. Using concepts from large deviations theory, we propose an algorithm that can scale to large collections of time series data. This paper expands on the LAD algorithm presented in Guggilam et al. (2022). The proposed algorithm is an online anomaly detection method for identifying anomalies in a collection of multivariate time series that takes advantage of the algorithm’s ability to scale to high-dimensional data. We show how the proposed Large Deviations Anomaly Detection (LAD) algorithm can be used to identify regions with anomalous trends in COVID-19 cases, deaths, biweekly growth rates, vaccinations, and fatality rates. Several of the observed anomalous trends are associated with regions that have demonstrated poor response to the COVID pandemic.
Coherent Control of Trapped-Ion Qubits with Localized Electric Fields

We present a new method for coherent control of trapped ion qubits in separate interaction regions of a multizone trap by simultaneously applying an electric field and a spin-dependent gradient. Both the phase and amplitude of the effective single-qubit rotation depend on the electric field, which can be localized to each zone. We demonstrate this interaction on a single ion using both laser-based and magnetic-field gradients in a surface-electrode ion trap, and measure the localization of the electric field.
Improved YOLOv4-tiny Target Detection Method Based on Adaptive Self-Order Piecewise Enhancement and Multiscale Feature Optimization

To improve the accuracy of material identification under low contrast conditions, this paper proposes an improved YOLOv4-tiny target detection method based on an adaptive self-order piecewise enhancement and multiscale feature optimization. The model first constructs an adaptive self-rank piecewise enhancement algorithm to enhance low-contrast images and then considers the fast detection ability of the YOLOv4-tiny network. To make the detection network have a higher accuracy, this paper adds an SE channel attention mechanism and an SPP module to this lightweight backbone network to increase the receptive field of the model and enrich the expression ability of the feature map. The network can pay more attention to salient information, suppress edge information, and effectively improve the training accuracy of the model. At the same time, to better fuse the features of different scales, the FPN multiscale feature fusion structure is redesigned to strengthen the fusion of semantic information at all levels of the network, enhance the ability of network feature extraction, and improve the overall detection accuracy of the model. The experimental results show that compared with the mainstream network framework, the improved YOLOv4-tiny network in this paper effectively improves the running speed and target detection accuracy of the model, and its mAP index reaches 98.85%, achieving better detection results.
Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal

Histological analysis with microscopy is the gold standard to diagnose and stage cancer, where slides or whole slide images are analyzed for cell morphological and spatial features by pathologists. The nuclei of cancerous cells are characterized by nonuniform chromatin distribution, irregular shapes, and varying size. As nucleus area and shape alone carry prognostic value, detection and segmentation of nuclei are among the most important steps in disease grading. However, evaluation of nuclei is a laborious, time-consuming, and subjective process with large variation among pathologists. Recent advances in digital pathology have allowed significant applications in nuclei detection, segmentation, and classification, but automated image analysis is greatly affected by staining factors, scanner variability, and imaging artifacts, requiring robust image preprocessing, normalization, and segmentation methods for clinically satisfactory results. In this paper, we aimed to evaluate and compare the digital image analysis techniques used in clinical pathology and research in the setting of gastric cancer. A literature review was conducted to evaluate potential methods of improving nuclei detection. Digitized images of 35 patients from a retrospective cohort of gastric adenocarcinoma at Oulu University Hospital in 1987-2016 were annotated for nuclei (n=9085) by expert pathologists and 14 images of different cancer types from public TCGA dataset with annotated nuclei (n=7000) were used as a comparison to evaluate applicability in other cancer types. The detection and segmentation accuracy with the selected color normalization and stain separation techniques were compared between the methods. The extracted information can be supplemented by patient’s medical data and fed to the existing statistical clinical tools or subjected to subsequent AI-assisted classification and prediction models. The performance of each method is evaluated by several metrics against the annotations done by expert pathologists. The F1-measure of 0.854 ± 0.068 is achieved with color normalization for the gastric cancer dataset, and 0.907 ± 0.044 with color deconvolution for the public dataset, showing comparable results to the earlier state-of-the-art works. The developed techniques serve as a basis for further research on application and interpretability of AI-assisted tools for gastric cancer diagnosis.
Application of Production-Oriented Approach (POA) in College English Teaching in Normal Universities

Production-oriented Approach (POA), a teaching method in English language teaching, adopts practical language usage scenarios, pays attention to students' language output, promotes students to master the English language and its culture through practice, and improves their language application abilities.This article took college English teaching in normal universities as the background and explored the application of POA in English language teaching, aiming to improve students' grades and English communication skills, and provide new ideas for language teaching.This article mainly applied experimental methods and questionnaire surveys to conduct relevant data statistics on the application of this method in college English teaching.The survey results showed that 93% of people agreed with using output oriented methods to improve English, and the majority of respondents were willing to try using output oriented methods.
VoMBaT: A Tool for Visualising Evaluation Measure Behaviour in High-Recall Search Tasks

The objective of High-Recall Information Retrieval (HRIR) is to retrieve as many relevant documents as possible for a given search topic. One approach to HRIR is Technology-Assisted Review (TAR), which uses information retrieval and machine learning techniques to aid the review of large document collections. TAR systems are commonly used in legal eDiscovery and systematic literature reviews. Successful TAR systems are able to find the majority of relevant documents using the least number of assessments. Commonly used retrospective evaluation assumes that the system achieves a specific, fixed recall level first, and then measures the precision or work saved (e.g., precision at r% recall). This approach can cause problems related to understanding the behaviour of evaluation measures in a fixed recall setting. It is also problematic when estimating time and money savings during technology-assisted reviews.
Form-NLU: Dataset for the Form Natural Language Understanding

Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases.
Dataset of Subjective Assessment for Visually Near-Lossless Image Coding based on Just Noticeable Difference

Image compression is essential in image processing, and image quality assessment (IQA) is important in determining the image compression level. This study aims to construct a dataset for evaluating image quality at low compression in this coding degradation, i.e., for high-quality images. Typical IQA databases are selected for general-purpose image degradation, not high-quality images. If one tries to evaluate high-quality images, multi-level evaluations are difficult to construct successfully. In addition, the evaluated encoder is not a de facto standard encoding algorithm. Therefore, this study constructs a dataset for subjective evaluation of visually near-lossless level image compression quality based on binary level evaluation of the just noticeable difference (JND). Experimental results showed that the new dataset was validated for correlation by various IQAs. It was also shown that more than the compression quality covered by the conventional dataset is needed for the binary evaluation of high-quality images. The dataset is available at: https://norishigefukushima.github.io/iqanearlossless/.
Smart Redbelly Blockchain: Reducing Congestion for Web3

Decentralization promises to remedy the drawbacks of the web by executing decentralized applications (DApps) on blockchains. Unfortunately, modern blockchains cannot support realistic web application workloads mainly due to congestion.We introduce the Smart Redbelly Blockchain (SRBB), a provably correct permissionless blockchain that reduces congestion by (1) avoiding redundant propagation and validations of transactions with Transaction Validation and Propagation Reduction (TVPR) and (2) mitigating the propagation of invalid transactions within blocks by Byzantine nodes with a dedicated Reward-Penalty Mechanism (RPM). Our comparison of SRBB against Algorand, Avalanche, Diem, Ethereum, Quorum, and Solana, using the DIABLO benchmark suite, indicates that SRBB outperforms all these blockchains under real application workloads. Moreover, SRBB is the only blockchain to successfully execute real workloads of NASDAQ and Uber on a DApp without losing transactions. To demonstrate that TVPR and RPM are the causes of the improved performance, we compare SRBB with its naive baseline, which does not contain TVPR and RPM. Our results show that TVPR increases the throughput by 55× and divides the latency by 3.5, while RPM increases the throughput by 7% under flooding attacks. Finally, TVPR helps reduce transaction losses in the normal scenario while RPM goes further and mitigates transaction losses under flooding attacks.
Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems

Going beyond accuracy in the evaluation of a recommender system is an aspect that is receiving more and more attention. Among the many perspectives that can be considered, the impact of presentation bias is of central importance. Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. Page-wise within-subject studies are widely employed in the recommender systems literature to compare algorithms by displaying their results in parallel. However, no study has ever been performed to assess the impact of presentation bias in this context. In this paper, we characterize how presentation bias affects different layout options, which present the results in column- or row-wise fashion. Concretely, we present a user study where six layout variants are proposed to the users in a page-wise within-subject setting, so as to evaluate their perception of the displayed recommendations. Results show that presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a recommender system (high-level feedback). Source codes and raw results are available at https://tinyurl.com/PresBiasSIGIR2023.
Digital twins application in control systems for distributed generation of heat and electric energy

By the emergence of distributed energy resources, with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system, which can be achieved by using digital twins.The paper discusses the advantages of using digital twins in the development of control systems and operation of distributed heat and electric power generation facilities.The possibilities of using the digital doubles for increasing the efficiency of the considered objects is presented as the example of optimizing the configuration of a control system of solar collectors in the presence of heat losses in pipelines of the external circuit.Further, the total balance consumed and generated electric and heat energy are presented.Examples of algorithms for protecting equipment to improve security are given, and the possibilities of improving the reliability of distributed power systems are considered.The system use of the digital twins provides the possibility of developing and debugging control algorithms, which increase the efficiency, reliability and safety of control objects, including distributed thermal and electrical power generation complexes.
Investigating the Password Policy Practices of Website Administrators

Passwords are the de facto standard for online authentication today, and will likely remain so for the foreseeable future. As a consequence, the security community has extensively explored how users behave with passwords, producing recommendations for password policies that promote password security and usability for users. However, it is the website administrators who must adopt such recommendations to enact improvements to online authentication in practice. To date, there has been limited investigation of how web administrators manage password policies for their sites. To improve online authentication at scale, we must understand the factors behind this specific population’s behaviors and decisions, and how to help administrators deploy more secure password policies.In this paper, we explore how web administrators determine the password policies that they employ, what considerations impact a policy’s evolution, and what challenges administrators encounter when managing a site’s policy. To do so, we conduct an online survey and in-depth semi-structured interviews with 11 US-based web administrators with direct experience managing website password policies. Through our qualitative study, we identify a small set of key factors driving the majority of password policy decisions, and barriers that inhibit administrators from enacting policies that are more aligned with modern guidelines. Moving forward, we propose directions for future research and community action that may help administrators manage password policies more effectively.