Reviewerly/exHarmony huggingface dataset:
Accountability without Consequences: Reframing Tech Ethics Using the Pedagogy, Histories, and Licensure Ramifications of Engineering Disasters

As information and communication technologies become central to our social infrastructure, many scholars are looking for ways to embed ethics within technology, i.e., a system of “tech ethics.” One approach is to compare the tech community to older professional cultures, such as engineering. This comparison allows us to make abstract digital challenges concrete, evaluating platforms as “digital bridges,” whose vulnerabilities are very much matters of public concern; put differently, it helps us reframe “tech scandals” as “engineering disasters.” But what are the ethics and histories of engineering disasters? Research on engineering ethics pedagogy demonstrates that disaster case studies are among the most common approaches, but learning outcomes are potentially poor. We find the following four disasters presented at multiple top-ranked undergraduate programs: Ford Pinto, Space Shuttle Challenger, Space Shuttle Columbia, and Volkswagen emissions. Looking at the factual details of these cases, we find a prioritization of what we call transparency—as typified by exhaustive post-factual reports—over consequences—as might be typified by fines, firings, or loss of certification for engineers. Consequences are instead focused at the corporate level, which leaves little inspiration for implementing tech ethics into design. Other professional trends, such as certification and symbolic embodiments of ethics, also struggle to ensure broader social consideration within the design process. Socio-technical approaches, such as “comprehensive engineering,” do situate the engineer within a broader social context, but present major challenges for systemization. Overall, evaluating the history of engineering ethics, we find similar problems to—not solutions for—tech ethics.
Enhancing Data Centers To Reduce High Energy Consumption

Data centers are critical, yet energy-intensive computing infrastructures that power large-scale Internet-based services. The rise of cloud computing has created an increasing demand for more data centers, which in turn, results in significant electricity consumption by computing systems and cooling units. Recently, data centers' substantial energy usage that leads to environmental contamination has been a pressing concern. In this paper, we analyze the energy usage from data center’s components and offer state-of-the-art solutions. The paper also provides a short view on data centers usage in the future, emphasizing the need to reduce the data center’s electricity use, enhance the efficiency of data centers while reducing their environmental impact
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application

Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely unexplored. Addressing this gap, our research develops, validates, and implements an unsupervised machine learning model, utilizing a dataset of over 14,121 Airbnb properties in New York City spanning 13 months. First, we develop a machine learning model based on contrastive loss to assess visual uniqueness in over 481,747 property images, demonstrating both high accuracy and interpretability. Next, we validate our model through three distinct experiments involving various populations and methods, confirming that our model's visual uniqueness scores and heatmaps align closely with human judgment. Finally, we apply this model to longitudinal Airbnb demand data, revealing an inverse U-shaped relationship between visual uniqueness and demand and two significant moderation effects: properties with superior host reputations or higher quality gain more from visual uniqueness. This research not only addresses methodological and conceptual voids in understanding visual uniqueness but also provides valuable managerial insights for sharing economy platforms like Airbnb and their hosts, emphasizing the strategic use of visual uniqueness to enhance visual appeal and market performance.
A Civil Aviation Customer Service Ontology and Its Applications

ABSTRACT In the process of developing the C919 large aircraft customer service intelligence system, we find that heterogeneous and incomplete data cause the inefficient and inaccurate decision making. Thus, to solve this problem, we propose to introduce the idea of ontology modeling and reasoning into competitive intelligence system building in this paper. We first present the building principles and methods of the civil aviation customer service ontology. We then define the classes and properties to contribute a real-world civil aviation customer service ontology, which is published on the Web (http://www.openkg.cn/dataset/cacso). We finally design SWRL rules corresponding to different intelligence analysis targets to support reasoning in our designed competitive intelligence system.
AI Delphi: Machine-Machine collaboration for exploring the future of work

Recent technological advancements have made Large Language Models (LLMs) easily accessible through apps such as ChatGPT, Claude.ai, Google Bard, and HuggingChat, allowing text generation on diverse topics with a simple prompt. Considering this scenario, we propose three machine-machine collaboration models to streamline and accelerate Delphi execution time using LLMs' extensive knowledge. We then apply one of these models, the Iconic Minds Delphi, to run a Delphi questionnaire previously used by the Millennium Project as part of the Future Work/Technology 2050. To do so, we prompt ChatGPT to assume 18 well-known public figures from 6 different areas: Economics, Entrepreneurship, Politics, Computer Science, Sociology, and Philosophy. Our study is an essential step for leveraging AI in Futures Research, emphasizing LLMs' potential as collaborators in shaping future visions while discussing its (current) limitations. In conclusion, our research demonstrates the synergy between Delphi and LLMs, offering a glimpse into a new method of exploring crucial themes such as the future of work.
MissModal: Increasing Robustness to Missing Modality in Multimodal Sentiment Analysis

Abstract When applying multimodal machine learning in downstream inference, both joint and coordinated multimodal representations rely on the complete presence of modalities as in training. However, modal-incomplete data, where certain modalities are missing, greatly reduces performance in Multimodal Sentiment Analysis (MSA) due to varying input forms and semantic information deficiencies. This limits the applicability of the predominant MSA methods in the real world, where the completeness of multimodal data is uncertain and variable. The generation-based methods attempt to generate the missing modality, yet they require complex hierarchical architecture with huge computational costs and struggle with the representation gaps across different modalities. Diversely, we propose a novel representation learning approach named MissModal, devoting to increasing robustness to missing modality in a classification approach. Specifically, we adopt constraints with geometric contrastive loss, distribution distance loss, and sentiment semantic loss to align the representations of modal-missing and modal-complete data, without impacting the sentiment inference for the complete modalities. Furthermore, we do not demand any changes in the multimodal fusion stage, highlighting the generality of our method in other multimodal learning systems. Extensive experiments demonstrate that the proposed method achieves superior performance with minimal computational costs in various missing modalities scenarios (flexibility), including severely missing modality (efficiency) on two public MSA datasets.
AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where natural disasters occur, and extraterrestrial planets. The substantial training time required to learn an optimal DRL policy, which can be days or weeks for complex tasks, is a major hurdle to real-world implementation in AGV applications. Training entails repeated collisions with the surrounding environment over an extended time period, dependent on the complexity of the task, to reinforce positive exploratory, application specific behavior that is expensive, and time consuming in the real-world. Effectively bridging the simulation to real-world gap is a requisite for successful implementation of DRL in complex AGV applications, enabling learning of cost-effective policies. We present AutoVRL, an open-source high fidelity simulator built upon the Bullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch to train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV control, and realistic environments, with extensibility for new environments and AGV models. The simulator provides access to state-of-the-art DRL algorithms, utilizing a python interface for simple algorithm and environment customization, and simulation execution.
Experimental Validation on Aerial Vehicles of Real-Time Motion Planning with Continuous-Time Q-Learning

In this paper, we propose an algorithm and implementation for real-time optimized kinodynamic motion planning for aerial vehicles with unknown dynamics in crowded environments. A random-sampling space-filling tree is used for both planning and rapidly replanning a path through the environment. Then, continuous-time Q-learning is used to approximately solve the resulting finite-horizon optimal control problem online to optimally track the planned path. To facilitate the Q-learning, we propose an actor-critic structure with integral reinforcement learning to approximate the Hamilton-Jacobi-Bellman equation. The critic approximates the Q-function while the actor approximates the control policy. We demonstrate our approach on custom drone hardware in which all planning, learning, and control computations are conducted onboard in real-time.
Wind Field Estimation Using Multiple Quadcopters

We consider a wind field estimation problem with multiple quadcopters. The wind field is assumed to affect the motion of the quadcopters in an additive fashion. Starting with a single quadcopter case, we first design an Extended Kalman filter (EKF) for constant and spatial-varying wind estimation. We next extend the EKF wind estimator for multiple quadcopters with directed connected communication graphs. To fuse the estimates of the wind field, we develop a sequential covariance intersection (SCI) method and a sequential weighted exponential product (SWEP) method for constant and spatially-varying wind fields. The effectiveness of the designed partial state fusion methods is validated and compared in simulations for various communication topologies with constant and Rankine wind models.
Challenges in Integrating Low-level Path Following and High-level Path Planning Over Polytopic Maps

Autonomous vehicle navigation and control require both planning and successful following of a desired path through an environment. This is often performed by a hierarchical framework that includes both a path planning algorithm and a path following controller. However, these two levels often operate with different modeling assumptions, constraints, and cost functions, leading to several challenges when integrated. To demonstrate the challenges that arise specifically when mapped obstacles represent regions that can be traversed but with varying traversal costs, this paper builds an intuitive control hierarchy consisting of an A* path planner, a Kalman filter to estimate traversal costs, and a model predictive controller. While each of these elements have been deeply studied individually, this paper identifies challenges that can arise from their coupled behavior and categories existing literature that addresses some of these challenges. Simulation results for a ground vehicle traversing regions of varying cost illustrate both the benefits and potential pitfalls of this hierarchical integration, motivating its further study for vehicle autonomy.
Advancements in Data Ingestion and Processing using Hadoop

Hadoop, a freely available framework for managing extensive datasets, has revolutionized the approach to handling large- scale data. As the volume, diversity, and velocity of data continue to experience exponential growth, conventional techniques for data management have revealed their limitations. This research explores innovative approaches in data intake and processing utilizing Hadoop to address the obstacles presented by big data.The Apache Hadoop environment provides a robust base for the concurrent and dispersed handling of immense datasets. At the core of this structure lies the Hadoop Distributed File System (HDFS), which arranges data into partitions scattered across multiple data nodes.However, efficiently ingesting data from diverse origins, such as social media, relational databases, web server logs, and streaming data, presents unique difficulties that demand specialized tools.To address these challenges, we propose leveraging advanced components within the Hadoop ecosystem. Apache Sqoopemerges as a potent tool for seamless data transfer between Hadoop and relational databases, facilitating the smooth ingestion of structured data. By utilizing Sqoop, data can be efficiently offloaded from the Extract, Transform, Load (ETL) process into Hadoop, resulting in reduced costs and processing time.Moreover, Apache Flume proves to be indispensable in managing streaming data. Acting as an intermediary between data producers and centralized storage, Flume ensures the uninterrupted flow of data from various sources in real-time. With its dependable and fault-tolerant nature, Flume effectively captures and stores streaming data directly into Hadoop, enabling continuous data processing upon arrival.
AI Based Virtual Mouse

As we know technology is developing gradually in the overall world. So, to keep developing this project has been introduced. In the past, we saw that it was very difficult to control the screen of the computer without a mouse i.e., to move the cursor from one point to another we need to type or enter a code from a book. But when the mouse came into existence it became easier to work with computers.As the technology developed from a wired mouse, now we use a mouse that runs with the help of Bluetooth. So, to make some changes an AI Virtual mouse is coming into existence, which ignores a hardware device.
Low-illumination image enhancement with logarithmic tone mapping

Abstract For low-illumination video sequences, some existing enhancement algorithms have some problems, such as image over-enhancement, color distortion, and inadequate detail processing. Based on luminance detection, we add logarithmic tone mapping to optimize the existing algorithms. The color space of low-illumination video image is converted from the red, green, blue mode to the hue-saturation-intensity mode, and then, logarithmic tone enhancement is applied to the image. Algorithm in this study has an obvious effect on image luminance enhancement and details processing, which makes the low-illumination video show a clear image with more natural visual effect, thus improving the quality of low-illumination video. This algorithm can avoid the problems of overexposure, color distortion, and blurring of detail processing under low illumination. The infrared spectrum of the object can be captured by infrared detection equipment, and the purpose of image enhancement can be achieved by applying the infrared spectrum of the object.
Online Voting System with Face Recognition and One Time Password

The electoral process is the foundation of democracy and governance. Within the last few decades, the election system has effectively undergone several modifications. Despite having the largest majority rule government in the world, India still uses either secret ballot voting (SBV) or electronic voting machines (EVM), both of which are expensive, labour-intensive, and wasteful. The current method merely checked identifying documents, increasing the likelihood of fraudulent votes. The main goal of this system is to provide an online voting system that, by using a camera for face recognition and OTP generation, will help to reduce voter fraud in manual voting systems and earlier iterations of online voting. Additionally, we are building a method of remote voting. to voters who are unable to travel to their hometown's polling place. To guarantee the dependability of the device, we are supplying software that has multiple layers of verification, including face recognition or verification, OTP verification, and validation data. Only after being authenticated and matched with the provided voter database may a single voter access the system. The voter will be able to continue choosing their chosen candidate from the panel once the corresponding face has been matched with the data provided.
Measuring user costs of enterprise multifactor authentication policies

Multifactor authentication (MFA) is one of the most important security controls, toppingmost lists of cyber hygiene activities advocated by experts. While the security benefits may besubstantial, less attention has been paid to the impact on users by the added friction introducedby the more strigent precautions. In this paper, we construct and analyze a dataset of authentication logs from a University population spanning two years. We focus on two types of costs experienced by users: (1) the elapsed time resulting from errors and failed authentications and (2) the time spent away from IT applications following a failed authentication before attemptingto reauthenticate. The first measure tracks the excess time dedicated to the authenticationwhen users encounter problems, while the second captures how user frustration can manifestby avoiding or delaying future engagement after experiencing failures. Following an exogenouschange in MFA policy from a deny/approve mobile notification to a more cumbersometwo-digit code mobile notification confirmation, we observe significant increases to the timespent away following failures.
A Study on the Impact and Effectiveness of Legislations and Machine Learning Through Detecting Cryptocurrencies in Money Laundering Over the Years, the Effects of Experiments Used in European Cryptocurrency Exchanges and Recommendations

The first cryptocurrency, Bitcoin, was introduced in 2009 by Satoshi Nakamoto. Although many people think the term "cryptocurrency" is a pseudonym and the creator(s)' real identities are still unknown, it is clear that cryptocurrencies have profoundly changed how money is used around the world. As a result, there are a tonne of brand-new methods for money laundering thanks to cryptocurrency. This study investigates how to identify anonymous bitcoin money launderers using machine learning (ML). Cryptocurrencies are used for money laundering by the transfer of payments to various accounts and subsequent exchanges of the cryptocurrency. This currency exchange process is carried out through cryptocurrency exchanges. Because current preventative measures are ineffective, machine learning may present fresh ways to identify unauthorised cash transfers. This study investigates the application of Machine Learning to prevent cryptocurrency-based money laundering consequently. The following procedures utilised in this study are explained in the methodology chapter 3. The Bitcoin Elliptic Dataset was used to compare four supervised learning methods. The approach included a quantitative evaluation of the algorithmic performance that captured variations in the three important evaluation metrics of F1-scores, precision, and recall. GP for Bitcoin real-world trade data is used to track the development of bitcoin over time, and quantitative data on the performance of the algorithms in European Union cryptocurrency exchanges is used.
Implementation of Eye-Tracking Recording Tool into Narupa iMD VR Environment for Application in Chemistry Education

Application of modern technologies, including virtual reality (VR) and eye-tracking (ET), to educational practice has been vigorously researched over the past decade with a focus on enhancing and improving school and university educational process and students’ results. The main aim of this technological report is to describe an implementation of a novel toolkit for tracking eye movements in a VR headset. The toolkit is implemented in Narupa iMD─an environment that allows visualizing molecular dynamics and interactions in VR for chemistry education. The toolkit is flexible and can be adapted to other VR environments and for subject areas other than chemistry. The HTC Vive Pro Eye was chosen as a head-mounted display (HMD) which supports eye-tracking. The toolkit is designed to primarily work with that device, but can be adapted to other HMDs with ET module. The current report describes a step-by-step process of developing and implementing the software to track eye movements during the learning process. We discuss future prospects for usage of this technology in research and educational practice.
MEDIA AS A MEANS OF EDUCATING AND DEVELOPING POTENTIAL OPPORTUNITIES OF STUDENTS

The article deals with the problems of competent approach in education for competence shaping required for. competent personality
Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation

Abstract Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.