AI Ethics Issues in Real World: Evidence from AI Incident Database

With the powerful performance of Artificial Intelligence (AI) also comes prevalent ethical issues. Though governments and corporations have curated multiple AI ethics guidelines to curb unethical behavior of AI, the effect has been limited, probably due to the vagueness of the guidelines. In this paper, we take a closer look at how AI ethics issues take place in real world, in order to have a more in-depth and nuanced understanding of different ethical issues as well as their social impact. With a content analysis of AI Incident Database, which is an effort to prevent repeated real world AI failures by cataloging incidents, we identified 13 application areas which often see unethical use of AI, with intelligent service robots, language/vision models and autonomous driving taking the lead. Ethical issues appear in 8 different forms, from inappropriate use and racial discrimination, to physical safety and unfair algorithm. With this taxonomy of AI ethics issues, we aim to provide a perspective for guideline makers to formulate more operable guidelines when trying to deploy AI applications ethically

Environmental life cycle assessment of emerging solid-state batteries: A review

Energy storage systems are main drivers in various fields, especially in the context of energy and mobility transition. Battery technologies are one of those options offering good technical performance in multiple stationary and mobile applications. New batteries having potentially high energy density and higher safety with lower cost are in particular ideal candidates for mobility applications. At present especially, lithium-ion batteries are used, but they are facing challenges regarding sustainability and safety issues, which can be quantitatively analyzed with Life Cycle Assessments (LCA). New developments regarding various solid-state batteries (SSBs) are very promising to tackle these challenges, but only very few studies are available on the environmental assessment of SSBs. Prospective LCA methodology is used here to analyze the environmental hotspots over the different life cycle phases for emerging SSBs. This also helps in decisions making at an early stage of development. This review critically analyzes available LCA studies on SSBs focusing on the inventory data, scope of the assessment as well as the life cycle impact assessment results. An effort has been made to compare the different LCA studies considering global warming potential indicator. As a results, the analysis highlights difficulties in comparability due to inconsistencies associated with the data sources, goal and scope, system boundaries and the method of impact assessment etc. To facilitate a consistent comparison, a unification methodology has been proposed to compare different LCAs of SSBs. Overall, the proposed methodology will help to fill the knowledge gap between different existing LCA studies on emerging solid-state battery technologies and provides recommendations for future assessments

Antitumor efficacy of combination of interferon-gamma-inducible protein 10 gene with gemcitabine, a study in murine model

<p>Abstract</p> <p>Background</p> <p>Interferon-γ-inducible protein 10 (IP-10) is a potent inhibitor of tumor angiogenesis. It has been reported that the antiangiogenic therapy combined with chemotherapy has synergistic effects.</p> <p>Methods</p> <p>To elucidate the mechanisms of IP-10 gene combined with a chemotherapy agent, we intramuscularly injected pBLAST-IP-10 expression plasmid combined with gemcitabine into tumor-bearing mice.</p> <p>Results</p> <p>The proliferation of endothelial cells was effectively inhibited by IP-10 combined with gemcitabine <it>in vitro</it>. Treatment with pBLAST-IP-10 twice a week for 4 weeks combined with gemcitabine 10 mg/kg (once a week) resulted in sustained high level of IP-10 protein in serum, inhibition of tumor growth and prolongation of the survival of tumor-bearing mice. Compared with administration of IP-10 plasmid or gemcitabine alone, the angiogenesis in tumors were apparently inhibited, and the numbers of apoptotic cells and lymphocytes in tumor increased in the combination therapy group.</p> <p>Conclusion</p> <p>Our data indicate that the gene therapy of antiangiogenesis by intramuscular delivery of plasmid DNA encoding IP-10 combined with gemcitabine has synergistic effects on tomor by inhibiting the proliferation of endothelail cells, inducing the apoptosis of tumor cells, and recruiting lymphocytes to tumor in murine models. The present findings provided evidence of antitumor effects of genetherapy combined with chemotherapy.</p

Reversible intercalation of methyl viologen as a dicationic charge carrier in aqueous batteries.

The interactions between charge carriers and electrode structures represent one of the most important considerations in the search for new energy storage devices. Currently, ionic bonding dominates the battery chemistry. Here we report the reversible insertion of a large molecular dication, methyl viologen, into the crystal structure of an aromatic solid electrode, 3,4,9,10-perylenetetracarboxylic dianhydride. This is the largest insertion charge carrier when non-solvated ever reported for batteries; surprisingly, the kinetic properties of the (de)insertion of methyl viologen are excellent with 60% of capacity retained when the current rate is increased from 100 mA g-1 to 2000 mA g-1. Characterization reveals that the insertion of methyl viologen causes phase transformation of the organic host, and embodies guest-host chemical bonding. First-principles density functional theory calculations suggest strong guest-host interaction beyond the pure ionic bonding, where a large extent of covalency may exist. This study extends the boundary of battery chemistry to large molecular ions as charge carriers and also highlights the electrochemical assembly of a supramolecular system

Radio Frequency Interference Detection Using Efficient Multi-Scale Convolutional Attention UNet

Studying the universe through radio telescope observation is crucial. However, radio telescopes capture not only signals from the universe but also various interfering signals, known as Radio Frequency Interference (RFI). The presence of RFI can significantly impact data analysis. Ensuring the accuracy, reliability, and scientific integrity of research findings by detecting and mitigating or eliminating RFI in observational data, presents a persistent challenge in radio astronomy. In this study, we proposed a novel deep learning model called EMSCA-UNet for RFI detection. The model employs multi-scale convolutional operations to extract RFI features of various scale sizes. Additionally, an attention mechanism is utilized to assign different weights to the extracted RFI feature maps, enabling the model to focus on vital features for RFI detection. We evaluated the performance of the model using real data observed from the 40-meter radio telescope at Yunnan Observatory. Furthermore, we compared our results to other models, including U-Net, RFI-Net, and R-Net, using four commonly employed evaluation metrics: precision, recall, F1 score, and IoU. The results demonstrate that our model outperforms the other models on all evaluation metrics, achieving an average improvement of approximately 5\% compared to U-Net. Our model not only enhances the accuracy and comprehensiveness of RFI detection but also provides more detailed edge detection while minimizing the loss of useful signals

"The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. To unpack the ethical concerns of LLMs for higher education, we conducted a case study consisting of stakeholder interviews (n=20) in higher education computer science. We found that students use several distinct mental models to interact with LLMs - LLMs serve as a tool for (a) writing, (b) coding, and (c) information retrieval, which differ somewhat in ethical considerations. Students and teachers brought up ethical issues that directly impact them, such as inaccurate LLM responses, hallucinations, biases, privacy leakage, and academic integrity issues. Participants emphasized the necessity of guidance and rules for the use of LLMs in higher education, including teaching digital literacy, rethinking education, and having cautious and contextual policies. We reflect on the ethical challenges and propose solutions

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

Our research is focused on the development of an at-home health care biomonitoringmobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals.Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy

Continual Learning in Predictive Autoscaling

Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. However, in practice, its prediction task often suffers from performance degradation under abnormal traffics caused by external events (such as sales promotional activities and applications re-configurations), for which a common solution is to re-train the model with data of a long historical period, but at the expense of high computational and storage costs. To better address this problem, we propose a replay-based continual learning method, i.e., Density-based Memory Selection and Hint-based Network Learning Model (DMSHM), using only a small part of the historical log to achieve accurate predictions. First, we discover the phenomenon of sample overlap when applying replay-based continual learning in prediction tasks. In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set. Then we implement hint-based network learning based on hint representation to optimize the parameters. Finally, we conduct experiments on public and industrial datasets to demonstrate that our proposed method outperforms state-of-the-art continual learning methods in terms of memory capacity and prediction accuracy. Furthermore, we demonstrate remarkable practicability of DMSHM in real industrial applications