Reviewerly/exHarmony huggingface dataset:
Click fraud detection for online advertising using machine learning

Advertising corporations have moved their focus to online and in-App advertisements in response to the expansion of digital technologies and social media. Online advertising represents the primary revenue source for advertising networks that serve as the middlemen between advertisers and advertisement publishers. The advertising networks pay the publisher of the advertisement based on the number of clicks through to advertisers, following the pay-per-click (PPC) payment scheme. However, there is a growing security issue with this payment approach known as Click Fraud. Click fraud is the illegal process of clicking on pay-per-click advertisements to increase publishers' revenue or deplete advertisers' budgets. Artificial intelligence techniques have been increasingly employed to solve complicated challenges in different research areas, including cybersecurity, to achieve unexpected outcomes. In this paper, several Machine Learning models were constructed to establish whether the user is a human or a bot and conducted a comparative performance analysis using a set of evaluation metrics. We used a real captured dataset detailing Internet users' behavior while navigating websites. We extracted a set of features related to users’ behaviors, including the number of webpages viewed during the browsing session, the duration of the browsing journey, and the actions performed. The empirical results revealed that all the considered models obtained good results, where the random forest algorithm surpassed all other algorithms in all evaluation metrics.
Optimizing Graph Codes for Measurement-Based Loss Tolerance

Graph codes play an important role in photonic quantum technologies as they provide significant protection against qubit loss, a dominant noise mechanism. Here, we develop methods to analyze and optimize measurement-based tolerance to qubit loss and computational errors for arbitrary graph codes. Using these tools we identify optimized codes with up to 12 qubits and asymptotically large modular constructions. The developed methods enable significant benefits for various photonic quantum technologies, as we illustrate with novel all-photonic quantum repeater states for quantum communication and high-threshold fusion-based schemes for fault-tolerant quantum computing.9 MoreReceived 15 December 2022Accepted 13 April 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020328Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingOptical quantum information processingQuantum error correctionQuantum information with atoms & lightQuantum interconnectsQuantum repeatersQuantum Information
Real time object detection using LiDAR and camera fusion for autonomous driving

Autonomous driving has been widely applied in commercial and industrial applications, along with the upgrade of environmental awareness systems. Tasks such as path planning, trajectory tracking, and obstacle avoidance are strongly dependent on the ability to perform real-time object detection and position regression. Among the most commonly used sensors, camera provides dense semantic information but lacks accurate distance information to the target, while LiDAR provides accurate depth information but with sparse resolution. In this paper, a LiDAR-camera-based fusion algorithm is proposed to improve the above-mentioned trade-off problems by constructing a Siamese network for object detection. Raw point clouds are converted to camera planes to obtain a 2D depth image. By designing a cross feature fusion block to connect the depth and RGB processing branches, the feature-layer fusion strategy is applied to integrate multi-modality data. The proposed fusion algorithm is evaluated on the KITTI dataset. Experimental results demonstrate that our algorithm has superior performance and real-time efficiency. Remarkably, it outperforms other state-of-the-art algorithms at the most important moderate level and achieves excellent performance at the easy and hard levels.
A Deep Q-Network based hand gesture recognition system for control of robotic platforms

Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years. The information obtained from the HGR systems has the potential to be helpful to control machines such as video games, vehicles, and even robots. Therefore, the key idea of the HGR system is to identify the moment in which a hand gesture was performed and it's class. Several human-machine state-of-the-art approaches use supervised machine learning (ML) techniques for the HGR system. However, the use of reinforcement learning (RL) approaches to build HGR systems for human-machine interfaces is still an open problem. This work presents a reinforcement learning (RL) approach to classify EMG-IMU signals obtained using a Myo Armband sensor. For this, we create an agent based on the Deep Q-learning algorithm (DQN) to learn a policy from online experiences to classify EMG-IMU signals. The HGR proposed system accuracy reaches up to [Formula: see text] and [Formula: see text] for classification and recognition respectively, with an average inference time per window observation of 20 ms. and we also demonstrate that our method outperforms other approaches in the literature. Then, we test the HGR system to control two different robotic platforms. The first is a three-degrees-of-freedom (DOF) tandem helicopter test bench, and the second is a virtual six-degree-of-freedom (DOF) UR5 robot. We employ the designed hand gesture recognition (HGR) system and the inertial measurement unit (IMU) integrated into the Myo sensor to command and control the motion of both platforms. The movement of the helicopter test bench and the UR5 robot is controlled under a PID controller scheme. Experimental results show the effectiveness of using the proposed HGR system based on DQN for controlling both platforms with a fast and accurate response.
A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world

Abstract Low- and middle-income countries (LMICs) represent a big source of data not only for endemic diseases but also for neoplasms. Data is the fuel which drives the modern era. Data when stored in digital form can be used for constructing disease models, analyzing disease trends and predicting disease outcomes in various demographic regions of the world. Most labs in developing countries don’t have resources such as whole slide scanners or digital microscopes. Owing to severe financial constraints and lack of resources, they don’t have the capability to handle large amounts of data. Due to these issues, precious data cannot be saved and utilized properly. However, digital techniques can be adopted even in low resource settings with significant financial constraints. In this review article, we suggest some of the options available to pathologists in developing countries which can enable them to start their digital journey and move forward despite resource-poor health system.
Quantum communication with RLP quantum resistant cryptography in industrial manufacturing

This paper presents the best outcome of the Quantum communication use case of industrial manufacturing, using quantum theory to achieve secure data transfer between industrial production facilities and production units, especially in the most restricted industrial manufacturing facilities (i.e., Air and Defense production units). Quantum computing has several features to support manufacturing and production units within defense industrial products. Users must validate their user credentials and other essential information to access critical data from the source to the target system. We introduce Rawal Liang and Peter's (RLP) sequence for quantum key distribution to support the security and privacy of industrial manufacturing businesses.
Digital payments system and market disruption

The traditional banking functions of lending, deposit-taking and payment intermediation are being unbundled in the new frontiers of money that extend from virtual currencies to crypto-assets and from shadow payments to quasi-money. The possibility for digital-centred change in the financial industry is illustrated by distributed ledger technology, of which ‘blockchain’ is the most prominent example of automated decision-making. Other forms of decentralised supply of money, payment services, and funding processes may allow households and businesses to obtain loans and pool risks without having recourse to financial intermediaries. This article examines the alternative provision of access to low-cost zero-friction payments from the perspective of the underbanked. Promoting innovation through alternatives to credit means integrating vulnerable and excluded customers into mainstream financial systems. Blockchain technology backed by a possible modification of the law on the recognition and transfer of property rights might prove instrumental in unlocking the value of the assets possessed by the underbanked or even the unbanked.
Sunshine and Temperature Based Models for Estimating Global Solar Radiation in Maiduguri, Nigeria

Nine existing sunshine based models and three existing temperature based models were evaluated and compared to ascertain the most suitable models for estimating global solar radiation in Maiduguri; the most suitable sunshine based and most suitable temperature based models were also compared. The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one years (1980 – 2010) was utilized and the evaluated models were tested statistically using validation indicators of coefficient of determination, Mean Bias Error, Root Mean Square Error, Mean Percentage Error, t – test, Nash – Sutcliffe Equation and Index of Agreement. The results indicated that the linear exponential sunshine based model and the logarithmic temperature based model were found more accurate for global solar radiation estimation in Maiduguri as compared to other evaluated models. Furthermore, the logarithmic temperature based model was found more accurate for estimating global solar radiation as compared to the linear exponential sunshine based model, and this was testified from the figure showing the comparison between the recommended sunshine based and temperature based models in which the recommended temperature based model depicts the best fitting with the measured global solar radiation data.
The Curse of Recursion: Training on Generated Data Makes Models Forget

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.
Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text

Evaluation practices in natural language generation (NLG) have many known flaws, but improved evaluation approaches are rarely widely adopted. This issue has become more urgent, since neural generation models have improved to the point where their outputs can often no longer be distinguished based on the surface-level features that older metrics rely on. This paper surveys the issues with human and automatic model evaluations and with commonly used datasets in NLG that have been pointed out over the past 20 years. We summarize, categorize, and discuss how researchers have been addressing these issues and what their findings mean for the current state of model evaluations. Building on those insights, we lay out a long-term vision for evaluation research and propose concrete steps for researchers to improve their evaluation processes. Finally, we analyze 66 generation papers from recent NLP conferences in how well they already follow these suggestions and identify which areas require more drastic changes to the status quo.
Machine Learning in Cybersecurity: Techniques and Challenges

In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance.&#x0D;
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data

The increasing requirements for data protection and privacy have attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learning the architecture was centralised and the aggregation was done with federated averaging, meaning that a central server will orchestrate the federation using the most straightforward averaging strategy. This research is focused on testing different federated strategies in a peer-to-peer environment. The authors propose various aggregation strategies for federated learning, including weighted averaging aggregation, using different factors and strategies based on participant contribution. The strategies are tested with varying data sizes to identify the most robust ones. This research tests the strategies with several biomedical datasets and the results of the experiments show that the accuracy-based weighted average outperforms the classical federated averaging method.
The effect of financial technology on financial performance in Jordanian SMEs: The role of financial satisfaction

The research paper investigates the relationship between financial technology (FinTech) adoption, financial satisfaction, and financial performance among consumers in Jordan. The study uses Partial Least Squares-Structural Equation Modeling (PLS-SEM) to analyze data collected from a sample of 500 SMEs in Jordan. The results of the study suggest that FinTech adoption positively affects financial performance, while financial satisfaction mediates the relationship between FinTech adoption and financial performance. The study also found that financial satisfaction has a significant impact on financial performance, suggesting that customers who are satisfied with their financial situation are more likely to achieve better financial performance. In conclusion, the study provides valuable insights into the role of financial satisfaction as a mediator in the relationship between FinTech adoption and financial performance, highlighting the importance of understanding the factors that influence customer satisfaction and the adoption of FinTech services in the financial industry. These findings have implications for financial service providers and policymakers in Jordan, as well as in other countries with similar economic and social conditions, where the adoption of FinTech is rapidly increasing.
Ethical Challenges in the Development of Virtual Assistants Powered by Large Language Models

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs) such as ChatGPT has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.
Robust online active learning

Abstract In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high‐volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D‐optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.
I-SOEWM: IoT Based Solar Energized Weather Monitoring System

Objectives: To develop an IoT (Internet of Things) based Solar Energized Weather Monitoring System (I-SOEWM) that incorporates efficient mechanism to receive real-time weather reports both locally and remotely.Methods: Improved localized weather monitoring system powered by a renewable solar energy has been developed to provide continuous real-time monitoring of parameters namely temperature, humidity, pressure, air quality, etc., The developed device is GPS-enabled to provide geo-localized data.The significance of the developed work is that the parameters measured are stored simultaneously on two cloud platforms namely Thingspeak and Adafruit, thereby ensuring reliability and security of the developed system.From the literatures, it is found that several works have been carried out that are closer to the developed research.However continuous monitoring is ensured in the developed work through Solar energy based renewable energy harvesting mechanism.Further, new parameters namely UV Index, Air Quality Index (AQI), and Dew Point are also monitored when compared to existing systems.Findings: Through the inclusion of additional parameters namely UV Index, AQI and Dew Point, the air quality, intensity of sun and the temperature at which air cannot hold water vapors are measured which lacks in the existing WMS.These parameters are vital for well-being of living organism.Novelty:The proposed system is solar-powered and incorporates real-time analysis of additional parameters.It has a long battery life (2.5 years for Li-ion) because the controller uses less power and an added solar panel is built into it, and it is capable of real-time data monitoring.(It also provides interfaces for mobile and web applications for user interaction.