Abstract: Sustainability Bias in Utility and Infrastructure Related Large Language Model QueriesAbstractIntroduction Large Language Models, such as ChatGPT, have become the fastest growing web platforms to date, exceeding one hundred million users in record time less than two months and as the capabilities and use of ChatGPT to inform decisions increases, it is crucial to understand the inherent biases of the platform regarding sustainability in the context of infrastructure and utilities. Sustainability, defined for the purpose of this paper as the intersection of social, environmental, and economic factors which will ensure a better future for tomorrow, is a critical factor to consider when making decisions for infrastructure and utilities. The intent of this paper is to determine and assess what biases, if any, ChatGPT expresses when responding to prompts regarding infrastructure and utilities. If advice is sought from a platform like ChatGPT in the context of utilities and infrastructure, does it prioritize Sustainability? What about regenerative development? Perhaps more interestingly, since ChatGPT is essentially a representation of an amalgam of human thought, what does it show about our own societal biases in terms of sustainability in utility data? These were the guiding questions for our analysis, which is intended to provide perspective on how the incredible new tools coming out today require us to be aware of their eccentricities to utilize them appropriately. Methodology A list of over one hundred questions were developed for use as ChatGPT prompts. These questions were intended to fall along three axes: public-private, discipline (water resources, transportation, site development), and function (operations and maintenance, planning, design, financing, and construction). Each question was developed selectively as to not directly lead ChatGPT to mention sustainability in the response, but to be open ended and to inherently require considerations regarding economics, the environment, and society. Context was provided in the form of a preamble for each question, and ChatGPT was asked to respond using a list format and limit its token response to 150 for each question this helped weigh each question evenly and control cost. The engine selected for this analysis was ChatGPT 3.5-Turbo accessible via OpenAI's Application Programming Interface (API). This engine was selected for its similarity to OpenAI's non-premium and publicly available interface, as well as its advanced ability to understand and respond to text prompts. Each of ChatGPT's responses to provided prompts were scored as follows: 0 not directly addressed, 1 indirectly addressed, and 2 directly addressed. The scores were used to assess whether the response adequately addressed the following criteria: 1) social aspect of sustainability, 2) economic aspect of sustainability 3) environmental aspect of sustainability 4) sustainability in general 5) regenerative development. Two different methods were used to score the ChatGPT provided responses. The first method utilized ChatGPT to score itself, where ChatGPT was fed the questions and corresponding answers and was then instructed to score the answers based on the criteria listed above. The ChatGPT scores were then reviewed by a human being for quality assurance and control. The second method required a human being to evaluate the questions and responses and provide a score manually. Results Sustainability was addressed to some degree across all criteria. However, the breakdown in how sustainability was addressed varies between the social, environmental, and economic components. The social component was addressed directly more than addressed indirectly or not addressed. Both the economic and environmental components had far more indirect responses than direct responses, and far more responses that did not address the respective topic. This indicates that social and communal aspects of infrastructure are well represented by ChatGPT, but environmental and economic components require further prompting or weighting from decision makers to make balanced and informed decisions. All results were then broken down by functional group, public/private, and discipline to explore more specific biases, all results are given within Figures 4-8. A deeper analysis will be performed between human scores and ChatGPT generated scores to determine potential bias in scoring and to assess whether there is a different bias between generating text and understanding text when it comes to ChatGPT and sustainability. Conclusion It is expected that ChatGPT will contain bias. It is trained on human generated data which will inheritently contain some level of skew, but understanding this bias qualitatively and quantitatively will provide decision-makers with more context needed to evaluate the reliability of results from ChatGPT, especially in the context of sustainability in infrastructure a complex, evolving field that lends itself to the powers of AI and ChatGPT. The bias observed seems to skew overall towards a social component and often does not directly or at all address economic and environmental components when it is expected these aspects would be incorporated.This paper was presented at the WEF/AWWA Utility Management Conference, February 13-16, 2024.SpeakerKuehne, WilliamPresentation time09:30:0010:00:00Session time08:30:0010:00:00SessionManagement and Oversight of New Frontiers in Artificial IntelligenceSession number16Session locationOregon Convention Center, Portland, OregonTopicUtility Sustainability, Environmental and Regulatory IssuesTopicUtility Sustainability, Environmental and Regulatory IssuesAuthor(s)Kuehne, WilliamAuthor(s)W. Kuehne1, L. Basler1Author affiliation(s)Ardurra Group 1;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Feb 2024DOI10.2175/193864718825159252Volume / Issue Content sourceUtility Management ConferenceWord count12


DC Water's Journey to Gen AI

Abstract: DC Water's Journey to Gen AIAbstractBackground Whereas it took ChatGPT less than a week to achieve a global base of one million users, many of us were not counted among the early adopters. As an industry, the water utility sector tends to be a slow follower when it comes to advanced technologies. Consequently, this reluctance to organize and take advantage of new opportunities like Generative Artificial Intelligence (AI) can compromise operating efficiency, cost management, and service delivery. Why does this happen? What needs to change? How can water utilities become just as receptive and quick to adopt advanced technologies as other industries? The answers to these questions point to organizational change management. It involves the people-side of change, how change begins with preparing the organization for something different; how employees need to be convinced of better, more effective ways than current methods to accomplish desired results. This presentation contextualizes change management practices within the realm of ChatGPT. It is intended to stimulate and accelerate the adoption of transformative technologies in the water utility sector. What follows is an authentic and realistic story on the adoption of ChatGPT at a recognizable water utility known for innovation-DC Water. DC Water's Approach DC Water's awareness of ChatGPT began shortly after its public release (version 3.5) in November 2022. Exploratory efforts commenced following the launch of ChatGPT 4 in March 2023. (The anticipation of ChatGPT 5 and Microsoft's Bing Chat Enterprise release before UMC 24 further underscores this technology's rapid advancement.) As will unfold in this presentation, DC Water realized enough potential of ChatGPT's value to justify introducing it to the organization for further evaluation. In early May 2023, DC Water's Innovation Director conducted a 'Lead and Learn' training session to approximately 18% of the total workforce. Soon afterwards forty-five employees requested to learn more about ChatGPT in promoting its use at DC Water. A pivotal aspect to adopting ChatGPT was the establishment of a Community of Practice (CoP). As a diverse group of curious employees, it was tasked with crafting a vision and establishing five key objectives to set expectations. Six S.M.A.R.T. goals were set in place to target and time-bound specific measurements. These goals addressed operating efficiency, staff engagement, employee satisfaction, decision making, industry leadership, and knowledge dissemination. To facilitate ChatGPT's adoption, a comprehensive 27-page 'QuickStart' User Guide was curated. This enabled employees to harness ChatGPT's full potential governed by a set of best practices, including guidance on: (1) its ethical use, privacy needs, and reliability verification; (2) definition and examples of ChatGPT command usage; and (3) actual use case examples of it being employed at DC Water. What proved to be most challenging was scaling ChatGPT to higher rates of employee adoption. Many employees struggled in going from traditional methods to leveraging ChatGPT to research industry data, extract and analyze information, and make decisions. The CoP members played a crucial role here, donning the hats of both educators and change agents. Benefits and Significance To demonstrate this need, DC Water conducted periodic reviews and outcome assessments to validate ChatGPT's tangible impact on operating efficiency, research capabilities, and decision making. More than just the potential to benefit, DC Water had already proven its ability to generate efficiencies, strengthen research, and make timely and better decisions by what got accomplished. As of August 2023, CoP members identified thirteen different applications of ChatGPT for consideration. Examples included AI-enabled customer support, IT coding assistance, industry research (problem solving), financial analysis, records retrieval for Freedom of Information Act (FOIA) support, and action plan generation. It is anticipated that other ChatGPT applications will emerge as more is learned of its potential to contribute to ongoing DC Water needs. A salient point of contention is the misconception of ChatGPT as a replacement for people. While there are tangible benefits, like with most tools, ChatGPT still requires human intervention to generate commands, evaluate results, and make the right decisions. It can liberate employees from routine, time-consuming work to focus elsewhere on higher value areas. Going beyond routine tasks to more sophisticated use cases, DC Water experienced how Engineers (problem solvers) can leverage ChatGPT's large language model (LLM) to acquire high-quality data for decision support, saving time and money verses starting fresh without industry and public data readily available. Another point of contention is how a transformational innovation like ChatGPT can lead to large-scale automation threats. Social media is full of dystopian predictions that many believe are overblown. While utility leaders should acknowledge this concern, DC Water was able to strike a balance between tool utility and risk mitigation. DC Water will share its version of the 'dos and don'ts' of ChatGPT usage for further guidance. From an intangible selling point, employees felt more productive when using ChatGPT. They experienced a feeling of empowerment in executing commands, interpreting results, and making decisions. PoC members were motivated to experiment and use ChatGPT for purposeful outcomes. Preliminary Results Together, the tangible and intangible benefits experienced so far, while impressive, are still being tabulated. As of August 2023, preliminary results in using ChatGPT show time savings, cost efficiencies, and employee enthusiasm. Feedback indicates a positive impact on employee attitudes and behaviors based on a non-random sample size of forty-five staff (3.7% of the total headcount). Whether CoPs or other approaches are employed to influence the people-side of change, the desire is to create a ripple effect across the entire organization. To go from slow to quick follower, utility leaders need to formulate and act on a change management strategy. They must address the practical challenges of adopting advanced technology, and how this can lead to a 'demand pull' from others wanting access to ChatGPT capabilities. Shared at the UMC conference will be discussion on using a CoP approach to implement ChatGPT throughout the organization. DC Water will provide detailed metrics on operating efficiency and employee satisfaction. The aim is to give conference attendees a sense of what can potentially be realized and how when embracing change. DC Water will describe how it engaged employees in group activities and one-on-one assistance for specific need fulfillment. This proved to be particularly helpful in keeping its newly-formed CoP motivated to learn and promote ChatGPT among work peers. While this case study centers on DC Water, the strategies and lessons gleaned are universally applicable, offering other utilities a blueprint for technological integration and organizational transformation. Conclusion DC Water's presentation offers invaluable insights into the nuances of organizational change management, specifically within the context of ChatGPT's integration into a water utility. Much of its success was due to innovation leadership and a CoP approach among ChatGPT enthusiasts from across the organization. Actual data on DC Water's efforts to adopt and scale ChatGPT across the organization will be presented for conference attendees to consider, including benefits, lessons learned, and governing factors to counter risk. As it looks ahead, DC Water envisions further deepening its collaboration with AI tools, exploring avenues like predictive maintenance and asset management. Its journey with ChatGPT is just the beginning of a broader tech-driven transformation. DC Water believes that advanced technologies like ChatGPT offer the power and potential to revolutionize how work is done within the water sector and beyond.This paper was presented at the WEF/AWWA Utility Management Conference, February 13-16, 2024.SpeakerBornhofen, RobertPresentation time13:30:0014:00:00Session time13:30:0015:00:00SessionReal World Applications of Artificial IntelligenceSession number24Session locationOregon Convention Center, Portland, OregonTopicDigital Transformation including AI and ChatGPTTopicDigital Transformation including AI and ChatGPTAuthor(s)Bornhofen, RobertAuthor(s)R. Bornhofen1Author affiliation(s)DC Water 1;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Feb 2024DOI10.2175/193864718825159241Volume / Issue Content sourceUtility Management ConferenceWord count7


Evaluation of the effectiveness of search and rescue ships in rescuing the overboard personnel based on cloud model

Abstract: Conducting effectiveness evaluation and analysis of the key elements of rescuing the overboard personnel by search and rescue ships, an evaluation index system for rescuing the overboard personnel is constructed. Considering the randomness and fuzziness of various evaluation indicators for rescuing the overboard personnel, a qualitative concept and quantitative calculation are transformed based on cloud models to establish an effectiveness evaluation model for rescuing the overboard personnel. Analytic Hierarchy Process is used to calculate the weights of each evaluation indicator and combined with the evaluation indicators, upper level indicators parameter of cloud model are calculated. The effectiveness evaluation conclusion of the overall goal of rescuing the overboard personnel is obtained by comparing it with the evaluation standard cloud model.


Preferences towards artificial intelligence in Ecuadorian university professors

Abstract: The general objective of this research was to analyze the preferences towards Artificial Intelligence (AI) in university professors in Ecuador, particularly those who teach the subject of Microeconomics. The methodology used was quantitative, descriptive, field-typed, and non-experimental, based on documentary research. The population consisted of 25 teachers from three state universities in the city of Guayaquil, Ecuador. The data collection technique was the survey and the instrument was a questionnaire of closed questions, which was applied through the Google Forms platform. The results found indicate that a large majority know about the use of AI in higher education; they also consider that the benefits that AI brings to education are to improve teaching, a large percentage indicated not to use AIs to teach Microeconomics, a small number of teachers who use AI, do it for data analysis through Machine Learning, the fact of the little use of this technology among teachers is mainly due to the absence of university policies on the use of artificial intelligence. Among the conclusions, the formulation of educational policies oriented to the implementation of artificial intelligence in Ecuadorian universities, the adaptation of infrastructure, and the investment of resources for this purpose are considered essential, as well as the training of teaching staff to take full advantage of this technology that is already a reality in many national and international contexts.


ICT LITERACY OF PRE-SERVICE SPECIAL EDUCATION TEACHERS IN OPEN DISTANCE LEARNING (ODL) MODE

Abstract: This paper aims to investigate the ICT literacy of pre-service special education teachers in ODL mode educator and total of 247 out of 370 ODL B.Ed. Special Education trainees of the Tamil Nadu Open University, India have participated in the study. Data was collected through online survey. Descriptive statistical analysis such as percentage analysis, Mean, Median, Standard deviation, T-test are used for data analysis. The study reveals that around fty percent of the sample has average level on their Information and communication technology literacy. On the other hand, it is observed that the low and high levels are equally shared the remaining fty percent of the sample. This could be interpreted that nearly one fourth of the trainees will have difculty in utilizing even the basic ICT tools including e-resources in their classrooms. It is imperative to provide periodical ICT trainings to ODL special education teacher trainees for better results in their classroom engagement with ICT tools to cater to the needs of diverse learners.


Quantum Theory in Finite Dimension Cannot Explain Every General Process with Finite Memory

Abstract: Abstract Arguably, the largest class of stochastic processes generated by means of a finite memory consists of those that are sequences of observations produced by sequential measurements in a suitable generalized probabilistic theory (GPT). These are constructed from a finite-dimensional memory evolving under a set of possible linear maps, and with probabilities of outcomes determined by linear functions of the memory state. Examples of such models are given by classical hidden Markov processes, where the memory state is a probability distribution, and at each step it evolves according to a non-negative matrix, and hidden quantum Markov processes, where the memory is a finite-dimensional quantum system, and at each step it evolves according to a completely positive map. Here we show that the set of processes admitting a finite-dimensional explanation do not need to be explainable in terms of either classical probability or quantum mechanics. To wit, we exhibit families of processes that have a finite-dimensional explanation, defined manifestly by the dynamics of an explicitly given GPT, but that do not admit a quantum, and therefore not even classical, explanation in finite dimension. Furthermore, we present a family of quantum processes on qubits and qutrits that do not admit a classical finite-dimensional realization, which includes examples introduced earlier by Fox, Rubin, Dharmadikari and Nadkarni as functions of infinite-dimensional Markov chains, and lower bound the size of the memory of a classical model realizing a noisy version of the qubit processes.


Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

Abstract: In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.


Credit Card Fraud Detection using Machine Learning (Logistic Regression)

Abstract: Abstract: Credit Card Fraud Detection (CCFD) is a crucial application in machine learning. It aims for safeguarding financial transaction. Identifying fraud transaction and real transactions and telling that is it fraud or real. Traditional methods for credit card fraud detection rely on basic statistical techniques .credit card remains a significant challenge in the financial industry .It rely on machine learning algorithms .Fraud detection very useful for financial institution and consumers worldwide .particularly logistic regression, has emerged as a potent tool in identifying fraudulent transactions. It will consider the before amount ,after amount ,time, pin numbers based on that it will detect fraud transactions .This paper explores the application of logistic regression in credit card fraud detection , focusing on its implementation ,challenges and effectiveness .This project works for finding fraud transactions. Logistic Regression ,have shown promising results in detecting fraudulent transactions.


Quantum Computing

Abstract: Abstract: The revolutionary technology known as quantum computing has the potential to completely transform several industries, including material research, medicine development, optimization, and cryptography. Quantum computers use the ideas of quantum mechanics to harness the power of quantum bits, or qubits, in contrast to classical computers, which function using binary digits, or bits. These qubits, which exist in a superposition of states, allow quantum computers to operate at a computational rate never before possible, potentially tackling challenging issues that are beyond the capabilities of classical systems


Breast Cancer Detection Using Artificial Intelligence

Abstract: Abstract: Breast cancer is one of the most common diseases faced by women and causes more deaths every day. It is considered one of the greatest diseases in medical history. Automated diagnostic systems not only assist diagnosticians but also provide reliable, efficient and fast services that reduce the likelihood of death. This paper proposes a model to manually classify breast cancer samples into benign and malignant subcategories using multiple algorithms to predict interpretation. It is generally considered as all data containing a set of 30 characteristics related to the nuclei found in digitized images of fine needle aspiration (FNA) of the breast. All acquired features are visualized and understood to gain insight for future diagnoses. Breast cancer prediction uses support vector distribution (SVC) in its ability to identify new, unrecognized data based on a set of features.


On the Analysis of Mobile Apps. Why is most of our Research on Android?

Abstract: Over the past decade, my research has been dedicated to the analysis of mobile applications, with a primary focus on the Android platform. As we delve into the intricacies of mobile app quality and trustworthiness, it becomes imperative to question the prevailing bias in our research toward the Android platform. This keynote talk aims to shed light on the reasons behind this predominant focus and provide insights into the shift of my research paradigm towards the iOS platform.


Authorship attribution, idiolectal style, and online identity

Abstract: Assistant ProfessorDepartment of English LanguageCollege of Language SciencesKing Saud UniversityRiyadh 11495 Kingdom of Saudi Arabia Awarding Institution: University of Leeds, UKDate of Award: 14 October 2022


Feasibility and Stability Analysis for Basic Measles Model Using Fuzzy Parameter

Abstract: In this article, we investigate the behaviors of the measles viral pandemic using fuzzy susceptible-infectiousrecovered (SIR) model. To examine the effects of various compartment phases, we analyze disease-free equilibrium points along with basic reproduction number. The measles model is stated to be globally asymptotically stable at the disease-free equilibrium point. In order to mathematically simulate the measles, we use a first-order nonlinear differential equation. The numerical solution is computed using the Runge-Kutta method, and the model’s feasibility is also covered.


Modeling of Sports Training Simulation Based on Energy Harvesting in Wireless Sensor Networks

Abstract: For patients with limb motor dysfunction, the effect of physical exercise is directly related to their future quality of life. This article combines the physical training plan of rehabilitation therapists with the training of rehabilitation robots, which can effectively improve the training performance of existing lower limb rehabilitation robots. Therefore, a teaching and training method and a wireless data acquisition system based on energy acquisition wireless network sensor are proposed. Based on wireless wearable technology, wireless network sensors, PCs and electronic devices are used to monitor the activity information of human walking and standing in real time, and the physical fitness is tested by means of mean, variance, and standard deviation. Through the analysis of rehabilitation health, this article consists of two parts: power module and physical exercise. Finally, experiments show that the accuracy of wireless network sensors based on SVM algorithm is the most accurate under physical training. It provides a good means for wireless body area network technology.


APRE: Annotation-Aware Prompt-Tuning for Relation Extraction

Abstract: Abstract Prompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt templates are manually designed, they are more prone to overfitting. Furthermore, traditional prompt templates are appended in the tail of an original sentence. They are far from some semantic units in a sentence. It is weak to decode semantic information of an input relevant to PLMs. To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction. In our method, entity type representations are used as entity annotations. They are implanted near the site of entities in a sentence for decoding semantic information of PLMs. It is effective to make full use of knowledge in PLMs for relation extraction. In the experiment section, our method is validated on the Chinese literature text and SemEval 2010 Task datasets and achieves 89.3% and 90.6% in terms of F1-score, respectively. It achieves the state-of-the-art performance on two public datasets. The result further demonstrates the effectiveness of our model to decode semantic information in PLMs.


The Big Five Factors of Personality Traits and Leadership Practices of Academic Department Chairs: A Predictive Study

Abstract: This study investigated whether the big five factors of personality traits can predict academic department chairs’ leadership practices. The study had a predictive research design; the data were collected from 424 participants in the 2023 academic year, and the instruments of this study were two questionnaires. The study found that the big five factors of personality traits predict the leadership practices of academic department chairs. Two factors of personality traits, conscientiousness and openness to experience, were statistically significant and predicted the practices in modelling leadership. Four factors (agreeableness, conscientiousness, openness to experience, and extroversion) were statistically significant and predicted leadership practices in inspiring a shared vision. Three factors (neuroticism, extroversion, and conscientiousness) were statistically significant and predicted leadership practices in challenging processes. Two factors (conscientiousness and openness to experience) were statistically significant and predicted leadership practices enabling others to act. Three factors (conscientiousness, agreeableness, and openness to experience) were statistically significant and predicted leadership practices encouraging the heart. The study recommended that academic leaders be required to consider personality traits as an important dimension in selecting and assigning academic department chairs and other academic leaders at all levels at higher education institutions.


On Optimizing Ensemble Models using Column Generation

Abstract: Abstract In recent years, an interest appeared in integrating various optimization algorithms in machine learning. We study the potential of ensemble learning in classification tasks and how to efficiently decompose the underlying optimization problem. Ensemble learning has become popular for machine learning applications and it is particularly interesting from an optimization perspective due to its resemblance to column generation. The challenge for learning is not only to obtain a good fit for the training data set, but also good generalization, such that the classifier is generally applicable. Deep networks have the drawback that they require a lot of computational effort to get to an accurate classification. Ensemble learning can combine various weak learners, which individually require less computational time. We consider binary classification problems studying a three-phase algorithm. After initializing a set of base learners refined by a bootstrapping approach, base learners are generated using the solution of an linear programming (LP) master problem and then solving a machine learning sub-problem regarding a reduced data set, which can be viewed as a so-called pricing problem. We theoretically show that the algorithm computes an optimal ensemble model in the convex hull of a given model space. The implementation of the algorithm is part of an ensemble learning framework called decolearn . Numerical experiments with CIFAR-10 data set show that the base learners are diverse and that both the training and generalization error are reduced after several iterations.


Application of the QDST algorithm for the Schrödinger particle simulation in the infinite potential well

Abstract: Abstract This paper examines whether a quantum computer can efficiently simulate the time evolution of the Schrödinger particle in a one-dimensional infinite potential well. In order to solve the Schrödinger equation in the quantum register, an algorithm based on the Quantum Discrete Sine Transform (QDST) is applied. The paper compares the results obtained in this way with the results given by the previous method (based on the QFT algorithm).


Genealogical Data-Driven Visits of Historical Cemeteries

Abstract: This paper describes the Integration of Archives and Cultural Places (IaCuP) project, which aims to integrate information about a historical cemetery, including its map and grave inventory, with genealogical and documentary knowledge extracted from relevant historical archives. The integrated data are accessible to cemetery visitors through an interactive mobile application, enabling them to navigate a graphical representation of the cemetery while exploring comprehensive visualizations of genealogical data. The basic idea stems from the desire to provide people with access to the rich context of cultural sites, which have often lost their original references over the centuries, making it challenging for individuals today to interpret the meanings embedded within them. The proposed approach leverages large language models (LLMs) to extract information from relevant documents and Web technologies to represent such information as interactive visualizations. As a practical case study, this paper focuses on the Jewish Cemetery in Pisa and the Historical Archives of the Jewish Community in Pisa, working on the genealogical tree of one of the most representative families resting in the cemetery.


Action Segmentation Through Self-Supervised Video Features and Positional-Encoded Embeddings

Abstract: Action segmentation consists of temporally segmenting a video and labeling each segmented interval with a specific action label. In this work, we propose a novel action segmentation method that requires no initial video analysis and no annotated data. Our proposal involves extracting features from videos using several pre-trained deep-learning models, including spatiotemporal and self-supervised methods. Data is then transformed using a positional encoder, and finally, a clustering algorithm is applied, where each produced cluster presumably corresponds to a different single and distinguishable action. For self-supervised features, we explored DINO, and for spatiotemporal features, we investigated I3D and SlowFast methods. Moreover, two different clustering algorithms (FINCH and KMeans) were investigated, and we also explored how varying the length of video snippets that generate the feature vectors affected the quality of the segmentation. Experiments show that our method produces competitive results on the Breakfast and INRIA Instructional Videos dataset benchmarks. Our best result was produced using a composition of self-supervised features generated by DINO, FINCH clustering, and positional encoding.


Design and analysis of adaptive control systems: Adaptive control algorithms using machine learning

Abstract: A growing field of research is the use of adaptive control algorithms with machine learning techniques like Q-learning and SARSA. With prospective applications in robotics, healthcare, and other fields, this interdisciplinary method strives to combine the stability and robustness of conventional control systems with the self-learning skills of reinforcement learning. Making sure that systems are stable and that learning occurs effectively is the main research problem. This paper demonstrates the stability and convergence of existing adaptive control algorithms when integrating with machine learning. There are mainly four primary methods of control algorithms: reinforcement learning, neutral network, support vector machine and deep learning. Reinforcement learning is the main focus of this paper. Data efficiency, robustness, and generalization are the main problems with reinforcement learning. Q-learning and SARSA (State, Action, Reward, State, Action) are two algorithms for reinforcement learning. The research will be done by analyzing these two algorithms based on existing material and the actual application of these two algorithms. SARSA is believed to be more safe as its on-policy methodology, and Q-learning is believed to be more proactive as its off-policy methodology.


Generative AI From Theory to Model

Abstract: This book chapter provides a comprehensive overview of generative AI and its applications in computer vision. The introduction section elucidates the concept of generative AI and underscores its importance within the realm of artificial intelligence. The chapter also provides a deep dive into the various techniques used in generative AI, such as creative style transfer, forecasting subsequent video frames, enhancing image resolution, enabling interactive image generation, facilitating image-to-image translation, text-to-image synthesis, image inpainting, the generation of innovative animated characters, the construction of 3D models from image, the utilization of the variational autoencoder (VAE) and its various adaptations, the implementation of generative adversarial networks (GANs) and their diverse iterations, as well as the use of transformers and their manifold versions. The chapter also highlights the current limitations and potential future developments in the field.


Development effects of industrialization, labor force participation and electrification on productive capacity in Africa: A panel data investigation

Abstract: In our study spanning 2010–2022 across 50 African economies, we investigated the effects of industrialization, labor force participation, and electrification on Africa’s productive capacity. We also examined how electrification moderates the effect of industrialization and labor force participation on productive capacity. We adopted the pooled OLS, the fixed effect estimator, and the dynamic system GMM estimation technique for our analysis. Our findings indicate industrialization, labor force participation, and electrification as potent channels for enhancing Africa's productive capacity. In tandem with Africa’s reality, we find that electrification isn’t moderating the positive impact of industrialization and labor force participation on productivity. Our findings also revealed human capital development, government health expenditure, and institutional quality as pivotal drivers of productive capacity in Africa, while factors like global economic policy uncertainty and natural resource endowment exhibit predominantly negative roles. These results underscore the need for African policymakers and leaders to foster an enabling environment that facilitates industrialization, increased labor force participation, and improved electricity access to promote economic performance and productivity.


Effects of Students’ Autonomy Support on their Self-Regulated Learning Strategies: Three Field Experiments in Secondary Education

Abstract: Providing students with autonomy over their learning process can support the development of their self-regulation skills. This study aimed to examine the effects of autonomy-support interventions on students’ self-regulated learning strategies. The participants were 432 students from three secondary schools in the Netherlands. In each school, a one-year field experiment was performed, in which teachers and students shared control over learning processes. At the start and end of the school year, students from both the experimental and control conditions completed a questionnaire on perceived autonomy support and self-regulation strategies used. Univariate analyses of covariance were performed to examine the effects of the interventions on perceived autonomy support and the use of self-regulated learning strategies. The intervention with the highest amount of student control showed positive effects on self-regulated learning strategies ‘task orientation’, ‘planning’, and ‘process evaluation’, although it did show a small decrease in perceived autonomy support. The other two interventions showed an increase in perceived autonomy support and a positive trend in ‘monitoring’. The field experiments suggest that different ways of supporting student autonomy can yield different effects on students’ self-regulated learning strategies. The tentative conclusion is that a focus on student control is the most effective.


Peningkatan Kemampuan Menulis Karangan Narasi Dengan Mengunakan Media Gambar Seri

Abstract: The purpose of this research is to determine the use of series image media in improving students' skills and motivation in writing narrative essays and to find out the factors that influence students in writing narrative essays using series image media for class IV SDN 010 Pakasassan Mamasa. The research carried out was classroom action research. Type of quantitative descriptive research. Data collection procedures were carried out using interviews, journals and tests. Data analysis techniques use qualitative and quantitative data analysis techniques. The aim of data analysis techniques is to find out in detail how to obtain data and the development of research results. The results of the research found in this study were the students' completeness in learning Indonesian after applying the series of picture media in cycle I. It was found that 6 students or 24% had successfully completed the ability to write narrative essays. Meanwhile, in cycle II it was found that the highest student score was 95 with a score range of 25 and an average score of 86.01 from a total of 25 students. It can be concluded that the use of series image media has a positive influence on learning to write narrative essays.


Heavy metal concentrations and water quality assessment of different types of drinking water wells in the Erdenet Cu–Mo mining area

Abstract: Abstract The Erdenet mine (Erdenet, Mongolia) is a copper–molybdenum open pit mine with a huge tailing pond and is located next to a residential area. As the sources of drinking water in this area rely on groundwater and can be categorized into public or private wells, we aimed to assess the groundwater quality of the different types of drinking water wells. To accomplish our goal, we analyzed 18 trace metals (As, Ba, Bi, Cd, Co, Cr, Cs, Cu, Ga, Mo, Ni, Pb, Se, Sr, Th, U, V, Zn) and 8 major metals (Al, Ca, Fe, K, Mg, Mn, Na, Si) samples using ICP-MS and ICP-OES, and the heavy metal pollution index, heavy metal evaluation index, and degree of contamination were calculated. The most concerning point is that the Mo concentration of waste water in the Erdenet mine tailing pond and at a natural spring located 300 m from the tailing pond was 1100 μg/L, which greatly exceeds the WHO permissible concentration for Mo of 70 μg/L. Furthermore, high Ca and Mg concentrations at all private wells indicated that consumers of these wells are more vulnerable to any type of water pollution, as these wells are not built with any disinfection or treatment system. A modified categorization of the water quality indices showed that the public and private wells have low–medium contamination and that the tailing pond seepage water, along with its nearest spring, have a high degree of contamination. Because many private wells are located along the tailing pond and used directly without any treatment, we suggest a continuous evaluation and monitoring of the groundwater quality in this area.


Numerical solution of the Schrödinger equation using Neural Networks in Python

Abstract: Abstract The motion of quantum mechanical systems in physical sciences is described by partial differential equations, usually of second order with respect to spatial coordinates. The required solutions of the time-dependent type of equations are, in general, functions of the temporal variable t and the state vectors determining the positions of the system’s particles at the time t. Only a small number, however of those differential equations can however be solved analytically, while the majority of them must be solved numerically by applying specific very advanced integration techniques. Among these equations, the fundamental Schrödinger equation offers great insight towards numerical solving. In this work we present an effective method that solves numerically the time-independent Schrodinger equation on the basis of neural networks techniques. Analytical and numerical results for the radial part of this equation with the corresponding energies are then compared so as to estimate the performance of our method.


Few-shot Learning for Heterogeneous Information Networks

Abstract: Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios, and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks. In this paper, we design a meta-learning framework, called META-HIN , for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous GNN module is used as the base model to express the features of subgraphs. We also design a GAN-based contrastive learning module that is able to exploit unsupervised information of the subgraphs. In our experiments, we fuse several datasets from multiple domains to verify META-HIN ’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.


The use of blockchain in the insurance industry: A bibliometric analysis

Abstract: Blockchain technology has surfaced as a formidable catalyst, capable of reshaping the operational dynamics of conventional businesses. The integration of blockchain technology within the insurance industry has prompted a need for comprehensive understanding and assessment. This study aims to elucidate the relevance of blockchain in the insurance sector, identify key trends, influential research, and the overarching goals motivating the incorporation of blockchain in insurance practices. The study employed a systematic review to collect 125 research articles published between 2017 and 2023 focused on blockchain in insurance from the Web of Science database. This study used Biblioshiny. The analysis encompasses a wide range of parameters, including publication trends, influential authors, thematic clusters, and the geographic distribution of research. IEEE Access emerges as a prominent platform, hosting 11 articles dedicated to this topic. The results of keyword co-occurrence highlighted two keywords: blockchain and insurance. The results of the country collaboration map highlighted particular shortcomings, such as the concentration of research in countries like China, the USA, India, KSA, Malaysia, and Australia. A lack of research in developing and underdeveloped countries and insufficient knowledge sharing among researchers highlights a research gap. In conclusion, the analysis offers valuable insights into the evolving landscape of blockchain within the insurance industry.


Professional Training of a Future Teacher for the Organization of Pupils' Innovative Activity in the Educational Process

Abstract: The study concept is based on the awareness of the process of training of a future physics teacher for the organization of pupils' innovative activity as an important component of modern professional education, which makes it possible to form a teacher as a competitive specialist capable of creative pedagogical work. The main study method was statistical methods: the methods of testing statistical hypotheses using Pearson, Wilcoxon-Mann-Whitney and binomial tests, the method of empirical data analysis using convolution matrices, the methods of visualizing experimental results. Four universities of Ukraine were the base for the formative pedagogical experiment: Zaporizhzhya National University, Berdyansk State Pedagogical University, Pavlo Tychyna Uman Pedagogical University, and Kherson State University. The measurement and comparison of the levels of readiness of the members of the control and experimental groups for the organization of pupils' innovative activity was carried out for separate components of readiness (motivational-value, cognitive, activity-creative ones) and as a whole.


Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Abstract: E-commerce chatbots play a crucial role in customer service but often struggle with understanding complex queries. This study introduces a breakthrough approach leveraging the Falcon-7B model, a state-of-the-art Large Language Model (LLM) with 7 billion parameters. Trained on a vast dataset of 1,500 billion tokens from RefinedWeb and curated corpora, the Falcon-7B model excels in natural language understanding and generation. Notably, its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. By harnessing cutting-edge machine learning techniques, our method aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences.


Teachers’ technological (pedagogical) knowledge–predictors for students’ ICT literacy?

Abstract: With the integration of information and communication technologies (ICT) into curricula, teachers are responsible for promoting ICT literacy among secondary school students, which requires in-depth technological knowledge (TK) and technological-pedagogical knowledge (TPK) on the part of teachers. This study uses a multilevel analysis to examine how teachers’ professional knowledge at TK and TPK influences secondary school students’ ICT literacy. Using data from n = 1,566 students from a larger sample taught in N = 134 classes by N = 220 teachers in N = 39 schools, our results show contrary to our hypotheses, no significant relationship between teachers’ professional knowledge regarding TK and TPK and students’ cross-curricula ICT literacy. Furthermore, we did not find any significant relationship in our model between students’ study-related ICT use and ICT literacy. By analyzing the relationship between teachers’ TK and TPK and students’ ICT literacy, our study provides new insights into the relationship between teachers’ cross-curricular knowledge and students’ cross-curricular achievements. It discusses further possible explanations and directions for future research.


OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images

Abstract: Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP.


WEB-BASED INFORMATION SYSTEM PREDICTION OF VEHICLE THEFT VULNERABILITY IN JAYAPURA USING REGRESSION ANALYSIS

Abstract: Vehicle theft in Jayapura Regency is quite high and there is no application to assist the police in making estimates or predictions of the number of theft cases that will occur in the next year. In 2022, cases of theft in Jayapura district will start to increase. to make these predictions the authors designed and built a system that can predict the number of these cases in building this application the authors use the Regression Analysis method this process can help the police predict the number of cases in the coming year. The development method used is SDLC, linear regression analysis and using the PHP programming language, the database uses MYSQL, Sublime Text. This research was conducted because there was no system that could assist the staff of the Resort Police (Polres) of Jayapura Regency. From this research, a system for predicting the level of vulnerability to motorized vehicle theft has been successfully built at the Jayapura District Police with data processed for attendance data using face region, reporting data using barcodes, queue data using counters and digital archive data helping the police store important documents.


TermIt: Managing normative thesauri

Abstract: Thesauri are popular, as they represent a manageable compromise – they are well-understood by domain experts, yet formal enough to boost use cases like semantic search. Still, as the thesauri size and complexity grow in a domain, proper tracking of the concept references to their definitions in normative documents, interlinking concepts defined in different documents, and keeping all the concepts semantically consistent and ready for subsequent conceptual modeling, is difficult and requires adequate tool support. We present TermIt, a web-based thesauri manager aimed at supporting the creation of thesauri based on decrees, directives, standards, and other normative documents. In addition to common editing capabilities, TermIt offers term extraction from documents, including a web document annotation browser plug-in, tracking term definitions in documents, term quality and ontological correctness checking, community discussions over term meanings, and seamless interlinking of concepts across different thesauri. We also show that TermIt features better fit the E-government scenarios in the Czech Republic than other tools. Additionally, we present the feasibility of TermIt for these scenarios by preliminary user experience evaluation.


Study on the Impact of the Quality of Teaching on the Attractiveness of Vocational Colleges

Abstract: This study investigates the correlation between the quality of teaching and the attractiveness of vocational colleges. The research aims to discern how the effectiveness of teaching methods, faculty expertise, and educational resources influence students' perceptions and choices regarding vocational education. Through a comprehensive analysis of various factors, including teaching styles, curriculum design, and extracurricular support, the study seeks to identify key elements that contribute to the overall appeal of vocational colleges. The findings are expected to provide valuable insights for educators, policymakers, and stakeholders in enhancing the quality of teaching in vocational institutions, ultimately fostering greater interest and enrollment in these educational settings.


Development of the sociocyberphysical systems` multi-contour security methodology

Abstract: The constant increase in the number of threats to the security of critical infrastructure objects, which include socio-cyberphysical systems, leads to a decrease in the quality of security services and the level of security of infrastructure elements. The object of research is the process of building a complex system of protection in socio-cyberphysical systems. The imperfection of the mechanisms for ensuring the security of critical infrastructure objects, which include socio-cyberphysical systems, the technological complexity of identifying new security threats necessitates an urgent need for a radical revision of the current approaches to its provision. So, it becomes clear that the development of a new approach to ensuring the security of information resources in socio-cyberphysical systems is needed. The article proposes a new approach to the methodological foundations of building multi-contour information protection systems with internal and external circuits on each of the platforms of socio-cyberphysical systems. This approach is based on a universal classifier of threats, which takes into account not the technical aspect of threats, but also their integration with social engineering methods, their synergy of hybridity. The sociopolitical influence on the realization of threats is taken into account, and practical mechanisms for providing basic security services based on post-quantum algorithms are also proposed. To provide basic security services in the proposed multi-contour protection system, it is proposed to use post-quantum algorithms – McEliece crypto-code constructions, which provide Perr=10-9–10-12, safe time Tsec=1025–1035 group operations. Within the framework of the proposed approach, the problem of increasing the level of information security has been formalized and further ways of solving it have been determined


Penerapan Metode Yolov5 dan Teknologi Text-To-Speech dalam Aplikasi Pengenalan Abjad dan Objek Sekitar untuk Anak Usia Dini

Abstract: Technological developments have provided significant benefits for various levels of society, including young children. There are several internal and external factors that can hinder children from remembering the alphabet and objects around them. To overcome these challenges, an innovative and interesting approach is needed to increase children's interest and involvement in learning to recognize alphabets and objects. Research has been carried out to develop an interactive learning media aimed at early childhood using an artificial intelligence approach. The You Only Look Once (YOLO) algorithm will be used to detect letters and surrounding objects in real-time, while Text-to-Speech (TTS) technology is used to convert text into sound. The dataset consists of 2511 images with 36 classes, including alphabets, fruits, animals, and stationery. In this research, the YOLOv5s method obtained a significant level of accuracy, reaching 85% so it could work well in detecting objects.


NotFYCEX: A Simulation Based Price Prediction and Notification System Using Continuous Machine Learning Method

Abstract: This research study presents NotFyCEX - a price notification systems model based on Internet of Things (IoT) and Blockchain technology for reporting important trends in a Cryptocurrency Exchange (CRYPTEX) market. The system is simulated in near real time using a co-simulator approach including web-based PHP server-side application for emulating price fluctuations, a dynamic neural network model application integrated within the MATLAB/SIMULINK software environment and ArduinoUNO hardware for continual physical signalling/alerting. Dynamic simulation results showed exact matches between synthesized price state representations and the predicted pattern states. Thus, we consider NotFyCEX a novel effort that can effectively serve as a notification and price prediction system to the end-user or cryptocurrency trader in a given CRYPTEX market.


Abstract: Back to table of contents Previous article Next article APA & MeetingsFull AccessCNN Anchor Anderson Cooper To Participate In ‘Fireside Chat’ at Opening SessionMark MoranMark MoranSearch for more papers by this authorPublished Online:22 Jan 2024https://doi.org/10.1176/appi.pn.2024.02.2.18AbstractSince the start of his career in 1992, Cooper has worked in nearly 80 countries and has covered numerous major news events around the world, often reporting from the scene. Cooper has also played a pivotal role in CNN’s political and election coverage.Anderson Cooper, anchor of CNN’s “Anderson Cooper 360°,” will be the featured guest at a “fireside chat” during the Opening Session at APA’s 2024 Annual Meeting in New York City.“Anderson Cooper 360°” is a global newscast that goes beyond the headlines with in-depth reporting and investigations. The show airs weeknights at 8 p.m. ET on CNN and is simulcast to an international audience on CNN International. Since April 2023, Cooper has also anchored “The Whole Story,” airing Sundays on CNN, which showcases character-driven stories, special interviews, profiles, and investigative deep dives from CNN anchors and correspondents.Since the start of his career in 1992, Cooper has worked in nearly 80 countries and has covered nearly all major news events around the world, often reporting from the scene. Cooper has also played a pivotal role in CNN’s political and election coverage. He has anchored from political conventions and moderated several presidential primary debates and town halls. In 2016, Cooper was selected by the Committee on Presidential Debates to co-moderate one of the three debates between Donald Trump and Hillary Clinton.In addition to his shows on CNN, Cooper is also a regular correspondent for CBS’s “60 Minutes.” He has won a number of major journalism awards. He helped lead CNN’s Peabody Award–winning coverage of Hurricane Katrina and duPont Award-winning coverage of the 2004 tsunami in the Indian Ocean. Additionally, he has been awarded 20 Emmy Awards, including two for his coverage of the earthquake in Haiti and an Edward R. Murrow Award.Cooper’s four books—Astor: The Rise and Fall of an American Fortune; Vanderbilt: The Rise and Fall of an America Dynasty; The Rainbow Comes and Goes: A Mother and Son on Life, Love, and Loss; and Dispatches From the Edge: A Memoir of Wars, Disaster, and Survival—all topped The New York Times bestseller list.Before joining CNN, Cooper was an ABC News correspondent and anchor of the network’s reality program “The Mole.” Cooper anchored ABC’s overnight newscast “World News Now” and was a correspondent for “World News Tonight” as well as “20/20.”Cooper graduated from Yale University in 1989 with a bachelor’s degree in political science. He also studied Vietnamese at the University of Hanoi. Cooper is based in New York City. ■The Opening Session will be held Sunday, May 5, from 5:30 p.m. to 7 p.m. at the Javits Center. ISSUES New Archived


GIFTS: Introducing Agile Process and Product Development in an FYE Course

Abstract: Abstract Great Ideas for Teaching Students (GIFTS): This paper describes two activities developed for the pilot offering of a new first-year experience course for all engineering and computing majors in our college. The course is multi-disciplinary, with hands-on projects from several different areas. The course introduces engineering and computing design principles and practices, with a particular focus on an agile methodology. The first activity is part of the team building phase of the course, and it is a kinesthetic activity where students develop a process that satisfies constraints and meets an objective. The activity involves several sprints wherein the students measure their results, reflect, and improve their processes. It is adapted from an industry activity using balls; we use balloons because they are more cost effective and easier to store. The second activity is a team-based, computing-themed project where the students prototype an app for a humanitarian organization. The teams choose their topic from several provided lists (UN Sustainable Goals, for example), collaborate to create user stories (“as a [persona], I want to [do something] so that [a goal is achieved]”), develop functionality and wireframe mockups, then use free app simulation software to create an interactive app. Both activities are completed within a 2-hour classroom session (one session apiece). The first activity is assessed through an individual reflection essay about the student’s experience in developing, refining, and participating in the process. The second activity is assessed via a team lab report that showcases the brainstorming process and end result.


Effect of nonlinearity induced by atomic switch in Ag/Ag2S nanoparticles on performance of in-materio reservoir computing

Abstract: A random network of Ag/Ag2S nanoparticles (NPs) was used as a physical system in reservoir computing (RC) because the network has nonlinear and dynamical characteristics. Ag/Ag2S NPs were synthesized by the modified Brust–Schiffrin method. Atomic switching among the NPs caused nonlinear dynamical behavior of the random network. The Fourier transform of output signals indicated that the generated harmonics were far higher with a larger amplitude of the input sine wave because the atomic switching occurred only at high bias voltages. Higher accuracy was achieved in the Boolean logic RC task because of the nonlinearity originating from switching. These findings suggest that nonlinearity plays a fundamental role in the design and implementation of RC devices.


Exploring the Possibility of Deciphering Tolkāppiyam through Korean Grammar

Abstract:
Tolkāpiyam, the oldest known book written in Old Tamil, is believed to have been authored around 100 B.C. Although Tolkāpiyam deals with the grammar of Tamil, a language still spoken by Tamils today, this ancient text, along with other Old Tamil books, has remained largely undeciphered. The reason for this lies in the fact that the grammar of Old Tamil has not been fully comprehended. Previous attempts to decipher Tolkāpiyam have mostly involved making educated guesses about the meaning of a paragraph based on the meanings of individual words.
 In this paper, I reveal the grammatical system of Old Tamil, drawing from the grammar theory presented in Part 2 of Tolkāpiyam. Old Tamil, much like pre-medieval Korean, employs predicates as nouns. It features elements such as chosa (조사), verb endings, honorifics, and causative verb suffixes. Remarkably, Tolkāpiyam is structured following the Korean word order. The grammar of Old Tamil closely resembles that of modern Korean. As a demonstration that Koreans can decipher Tolkāpiyam without prior knowledge of ancient Tamil, relying on word meanings alone, I also provide a correct interpretation for several paragraphs in Part I (Phonology). These interpretations challenge previous misconceptions and affirm the validity of my paper's argument, highlighting the limitations of prior research.


Challenges and Opportunities for Foreign Language Teachers in the Era of Artificial Intelligence

Abstract: The emergence of large-scale language models such as ChatGPT has presented unprecedented challenges and opportunities in foreign language education. On one hand, artificial intelligence poses a disruptive crisis; on the other hand, it offers new educational insights, methodologies, and resources. Faced with these challenges and opportunities, university foreign language teachers must seize the opportunity to integrate intelligent technology into their teaching practices to enhance their teaching abilities and the quality of talent development. This study aims to investigate approaches for integrating artificial intelligence into foreign language teaching at the university level to improve teaching effectiveness and talent development outcomes. Through literature analysis and related research, the study identifies best practices and strategies for utilizing artificial intelligence in university foreign language education. The findings suggest that integrating artificial intelligence into foreign language teaching at the university level can significantly enhance teaching effectiveness and talent development. Thus, it is recommended that university foreign language teachers actively explore and implement artificial intelligence technology to enhance the quality of foreign language education and student talent development


The preoperative nursing consultation: constraints and suggestions for operationalisation - a qualitative study

Abstract: Introduction The preoperative nursing consultation can be seen as a privileged moment for transmitting information to the person in a perioperative situation, to better prepare them for surgery and promote their collaboration in perioperative care. As this procedure is not carried out regularly, it is therefore important to carry out research into the reasons why it is not done. Objective To learn about the constraints to the operationalisation of the preoperative nursing consultation, through the perception of operating theatre nurses, as well as to identify their suggestions for overcoming the constraints, and to analyse the strategies pointed out by the nurses to prioritise this consultation. Method An exploratory, descriptive study of a qualitative nature. Data collection: focus group interview using a semi-structured interview script, supplemented at the end with an individual questionnaire containing socio-professional data and two open questions. Participants: purposive sample - three groups of nurses from three operating theatres in a central hospital in central Portugal. Content analysis was carried out according to Bardin's framework. The study considered ethical principles and scientific integrity. Results It was possible to identify the constraints pointed out by the nurses: methodology for operationalisation, still undefined; shortage of available time; late bed allocation; compromised privacy when carrying out the consultation; lack of recognition by hierarchical structures; difficulties in adequate physical space; unpredictable prolongation of intraoperative activities; distant residence of the person in a perioperative situation, and deficient human resources. The following suggestions/strategies were identified to overcome the constraints: allocation of more human resources; appropriate physical space; the need for greater interdisciplinary coordination and to create visibility. Conclusion The preoperative nursing consultation is an autonomous procedure that nurses are motivated to make operational, overcoming the constraints they point out and prioritising their suggestions.


Quantized Approximately Orthogonal Recurrent Neural Networks

Abstract: Orthogonal recurrent neural networks (ORNNs) are an appealing option for learning tasks involving time series with long-term dependencies, thanks to their simplicity and computational stability. However, these networks often require a substantial number of parameters to perform well, which can be prohibitive in power-constrained environments, such as compact devices. One approach to address this issue is neural network quantization. The construction of such networks remains an open problem, acknowledged for its inherent instability.In this paper, we explore the quantization of the recurrent and input weight matrices in ORNNs, leading to Quantized approximately Orthogonal RNNs (QORNNs). We investigate one post-training quantization (PTQ) strategy and three quantization-aware training (QAT) algorithms that incorporate orthogonal constraints and quantized weights. Empirical results demonstrate the advantages of employing QAT over PTQ. The most efficient model achieves results similar to state-of-the-art full-precision ORNN and LSTM on a variety of standard benchmarks, even with 3-bits quantization.


Random features models: a way to study the success of naive imputation

Abstract: Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce a large bias for prediction purposes, as the imputed input may strongly differ from the true underlying data. However, recent works suggest that this bias is low in the context of high-dimensional linear predictors when data is supposed to be missing completely at random (MCAR). This paper completes the picture for linear predictors by confirming the intuition that the bias is negligible and that surprisingly naive imputation also remains relevant in very low dimension.To this aim, we consider a unique underlying random features model, which offers a rigorous framework for studying predictive performances, whilst the dimension of the observed features varies.Building on these theoretical results, we establish finite-sample bounds on stochastic gradient (SGD) predictors applied to zero-imputed data, a strategy particularly well suited for large-scale learning.If the MCAR assumption appears to be strong, we show that similar favorable behaviors occur for more complex missing data scenarios.


Combining additivity and active subspaces for high-dimensional Gaussian process modeling

Abstract: Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification. However, they suffer from the curse of dimensionality whenever the number of variables increases. This challenge is generally addressed by assuming additional structure in theproblem, the preferred options being either additivity or low intrinsic dimensionality. Our contribution for high-dimensional Gaussian process modeling is to combine them with a multi-fidelity strategy, showcasing the advantages through experiments on synthetic functions and datasets.


Decoding the Impact of Neighboring Amino Acid on MS Intensity Output through Deep Learning

Abstract: Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the link from sequence to intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low MS1 response. The model could predict MS1 response with a mean average percentage error of ~11% and a Pearson correlation coefficient of ~0.68.


Electronic Learning Practice MOODLE -LESSON

Abstract: Here in this work are discussed practical  aspects of Electronic Learning. Moodle LMS (Learning Management System), which is  an open teaching system and spreads free of charge,  is chosen  as a research tool. The main pedagogical and technological aspects of the system are presented here. Some special explanations are needed for its installation  on the local internet space; Here are presented recommendations for a special activity of the teaching course, namely  for planning and constructing  stages of an adaptive “ lecture” -Lesson. Here are also presented  a teacher’s and a student’s positions in the local server  environment of the Moodle-LMS. Moreover, in this work is discussed the advantage of using the practical model  of the “Lesson” in the ” Moodle auditorium” compared to having it in a traditional auditorium. All these are discussed considering both - students’ active involvement in the studying process, as well as for their independent working process under non-synchronous, local conditions. The instructions, which are presented in this work, enclose additional  presentational resource links prepared and shared in  Cloud Storage by the author. This allows a reader to become more familiar with details of the essential information.


GenFormer: A Deep-Learning-Based Approach for Generating Multivariate  Stochastic Processes

Abstract: Stochastic generators are essential to produce synthetic realizations that preserve target statistical properties. We propose GenFormer, a stochastic generator for spatio-temporal multivariate stochastic processes. It is constructed using a Transformer-based deep learning model that learns a mapping between a Markov state sequence and time series values. The synthetic data generated by the GenFormer model preserves the target marginal distributions and approximately captures other desired statistical properties even in challenging applications involving a large number of spatial locations and a long simulation horizon. The GenFormer model is applied to simulate synthetic wind speed data at various stations in Florida to calculate exceedance probabilities for risk management.


GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross  Appearance-Edge Learning

Abstract: The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative cross appearance-edge learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an appearance-edge cross-attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at \url{https://github.com/Jenine-321/GenFace


Understanding Time Series Anomaly State Detection through One-Class  Classification

Abstract: For a long time, research on time series anomaly detection has mainly focused on finding outliers within a given time series. Admittedly, this is consistent with some practical problems, but in other practical application scenarios, people are concerned about: assuming a standard time series is given, how to judge whether another test time series deviates from the standard time series, which is more similar to the problem discussed in one-class classification (OCC). Therefore, in this article, we try to re-understand and define the time series anomaly detection problem through OCC, which we call 'time series anomaly state detection problem'. We first use stochastic processes and hypothesis testing to strictly define the 'time series anomaly state detection problem', and its corresponding anomalies. Then, we use the time series classification dataset to construct an artificial dataset corresponding to the problem. We compile 38 anomaly detection algorithms and correct some of the algorithms to adapt to handle this problem. Finally, through a large number of experiments, we fairly compare the actual performance of various time series anomaly detection algorithms, providing insights and directions for future research by researchers.


Learning General Parameterized Policies for Infinite Horizon Average  Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm

Abstract: This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDP). To the best of our knowledge, this work is the first to delve into the regret and constraint violation analysis of average reward CMDPs with a general policy parametrization. To address this challenge, we propose a primal dual based policy gradient algorithm that adeptly manages the constraints while ensuring a low regret guarantee toward achieving a global optimal policy. In particular, we demonstrate that our proposed algorithm achieves $\tilde{\mathcal{O}}({T}^{3/4})$ objective regret and $\tilde{\mathcal{O}}({T}^{3/4})$ constraint violation bounds.


Locally-Adaptive Quantization for Streaming Vector Search

Abstract: Retrieving the most similar vector embeddings to a given query among a massive collection of vectors has long been a key component of countless real-world applications. The recently introduced Retrieval-Augmented Generation is one of the most prominent examples. For many of these applications, the database evolves over time by inserting new data and removing outdated data. In these cases, the retrieval problem is known as streaming similarity search. While Locally-Adaptive Vector Quantization (LVQ), a highly efficient vector compression method, yields state-of-the-art search performance for non-evolving databases, its usefulness in the streaming setting has not been yet established. In this work, we study LVQ in streaming similarity search. In support of our evaluation, we introduce two improvements of LVQ: Turbo LVQ and multi-means LVQ that boost its search performance by up to 28% and 27%, respectively. Our studies show that LVQ and its new variants enable blazing fast vector search, outperforming its closest competitor by up to 9.4x for identically distributed data and by up to 8.8x under the challenging scenario of data distribution shifts (i.e., where the statistical distribution of the data changes over time). We release our contributions as part of Scalable Vector Search, an open-source library for high-performance similarity search.


Nonlinear subspace clustering by functional link neural networks

Abstract: Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it involves a balance between effectiveness and computational cost. In this study, we employ a functional link neural network to transform data samples into a nonlinear domain. Subsequently, we acquire a self-representation matrix through a learning mechanism that builds upon the mapped samples. As the functional link neural network is a single-layer neural network, our proposed method achieves high computational efficiency while ensuring desirable clustering performance. By incorporating the local similarity regularization to enhance the grouping effect, our proposed method further improves the quality of the clustering results. Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach. This combination approach allows for a dynamic balance between linear and nonlinear representations. Extensive experiments confirm the advancement of our methods. The source code will be released on https://lshi91.github.io/ soon.


Prediction modelling with many correlated and zero-inflated predictors:  assessing a nonnegative garrote approach

Abstract: Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance. In this study, we formally established and examined regularized regression approaches, designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modelling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrote estimator in the second stage. We contrasted ridge-garrote with one-stage methods (ridge, lasso) and other two-stage regularized regression approaches (lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the predictive performance and predictor selection properties of these methods in a comparative simulation study and a real-data case study to predict kidney function using peptidomic features derived from mass-spectrometry. In the simulation study, the predictive performance of all assessed approaches was comparable, yet the ridge-garrote approach consistently selected more parsimonious models compared to its competitors in most scenarios. While lasso-ridge achieved higher predictive accuracy than its competitors, it exhibited high variability in the number of selected predictors. Ridge-lasso exhibited slightly superior predictive accuracy than ridge-garrote but at the expense of selecting more noise predictors. Overall, ridge emerged as a favourable option when variable selection is not a primary concern, while ridge-garrote demonstrated notable practical utility in selecting a parsimonious set of predictors, with only minimal compromise in predictive accuracy.


Training Implicit Networks for Image Deblurring using Jacobian-Free  Backpropagation

Abstract: Recent efforts in applying implicit networks to solve inverse problems in imaging have achieved competitive or even superior results when compared to feedforward networks. These implicit networks only require constant memory during backpropagation, regardless of the number of layers. However, they are not necessarily easy to train. Gradient calculations are computationally expensive because they require backpropagating through a fixed point. In particular, this process requires solving a large linear system whose size is determined by the number of features in the fixed point iteration. This paper explores a recently proposed method, Jacobian-free Backpropagation (JFB), a backpropagation scheme that circumvents such calculation, in the context of image deblurring problems. Our results show that JFB is comparable against fine-tuned optimization schemes, state-of-the-art (SOTA) feedforward networks, and existing implicit networks at a reduced computational cost.


Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification  Approach

Abstract: $\mathbb{X}$ (formerly Twitter) is a prominent online social media platform that plays an important role in sharing information making the content generated on this platform a valuable source of information. Ensuring trust on $\mathbb{X}$ is essential to determine the user credibility and prevents issues across various domains. While assigning credibility to $\mathbb{X}$ users and classifying them as trusted or untrusted is commonly carried out using traditional machine learning models, there is limited exploration about the use of One-Class Classification (OCC) models for this purpose. In this study, we use various OCC models for $\mathbb{X}$ user classification. Additionally, we propose using a subspace-learning-based approach that simultaneously optimizes both the subspace and data description for OCC. We also introduce a novel regularization term for Subspace Support Vector Data Description (SSVDD), expressing data concentration in a lower-dimensional subspace that captures diverse graph structures. Experimental results show superior performance of the introduced regularization term for SSVDD compared to baseline models and state-of-the-art techniques for $\mathbb{X}$ user classification.


Improving the Learning of Code Review Successive Tasks with Cross-Task  Knowledge Distillation

Abstract: Code review is a fundamental process in software development that plays a pivotal role in ensuring code quality and reducing the likelihood of errors and bugs. However, code review can be complex, subjective, and time-consuming. Quality estimation, comment generation, and code refinement constitute the three key tasks of this process, and their automation has traditionally been addressed separately in the literature using different approaches. In particular, recent efforts have focused on fine-tuning pre-trained language models to aid in code review tasks, with each task being considered in isolation. We believe that these tasks are interconnected, and their fine-tuning should consider this interconnection. In this paper, we introduce a novel deep-learning architecture, named DISCOREV, which employs cross-task knowledge distillation to address these tasks simultaneously. In our approach, we utilize a cascade of models to enhance both comment generation and code refinement models. The fine-tuning of the comment generation model is guided by the code refinement model, while the fine-tuning of the code refinement model is guided by the quality estimation model. We implement this guidance using two strategies: a feedback-based learning objective and an embedding alignment objective. We evaluate DISCOREV by comparing it to state-of-the-art methods based on independent training and fine-tuning. Our results show that our approach generates better review comments, as measured by the BLEU score, as well as more accurate code refinement according to the CodeBLEU score


Value-Aided Conditional Supervised Learning for Offline RL

Abstract: Offline reinforcement learning (RL) has seen notable advancements through return-conditioned supervised learning (RCSL) and value-based methods, yet each approach comes with its own set of practical challenges. Addressing these, we propose Value-Aided Conditional Supervised Learning (VCS), a method that effectively synergizes the stability of RCSL with the stitching ability of value-based methods. Based on the Neural Tangent Kernel analysis to discern instances where value function may not lead to stable stitching, VCS injects the value aid into the RCSL's loss function dynamically according to the trajectory return. Our empirical studies reveal that VCS not only significantly outperforms both RCSL and value-based methods but also consistently achieves, or often surpasses, the highest trajectory returns across diverse offline RL benchmarks. This breakthrough in VCS paves new paths in offline RL, pushing the limits of what can be achieved and fostering further innovations.


$\alpha$-Divergence Loss Function for Neural Density Ratio Estimation

Abstract: Recently, neural networks have produced state-of-the-art results for density-ratio estimation (DRE), a fundamental technique in machine learning. However, existing methods bear optimization issues that arise from the loss functions of DRE: a large sample requirement of Kullback--Leibler (KL)-divergence, vanishing of train loss gradients, and biased gradients of the loss functions. Thus, an $\alpha$-divergence loss function ($\alpha$-Div) that offers concise implementation and stable optimization is proposed in this paper. Furthermore, technical justifications for the proposed loss function are presented. The stability of the proposed loss function is empirically demonstrated and the estimation accuracy of DRE tasks is investigated. Additionally, this study presents a sample requirement for DRE using the proposed loss function in terms of the upper bound of $L_1$ error, which connects a curse of dimensionality as a common problem in high-dimensional DRE tasks.


Universal Post-Training Reverse-Engineering Defense Against Backdoors in  Deep Neural Networks

Abstract: A variety of defenses have been proposed against backdoors attacks on deep neural network (DNN) classifiers. Universal methods seek to reliably detect and/or mitigate backdoors irrespective of the incorporation mechanism used by the attacker, while reverse-engineering methods often explicitly assume one. In this paper, we describe a new detector that: relies on internal feature map of the defended DNN to detect and reverse-engineer the backdoor and identify its target class; can operate post-training (without access to the training dataset); is highly effective for various incorporation mechanisms (i.e., is universal); and which has low computational overhead and so is scalable. Our detection approach is evaluated for different attacks on a benchmark CIFAR-10 image classifier.


Prototypical Contrastive Learning through Alignment and Uniformity for  Recommendation

Abstract: Graph Collaborative Filtering (GCF), one of the most widely adopted recommendation system methods, effectively captures intricate relationships between user and item interactions. Graph Contrastive Learning (GCL) based GCF has gained significant attention as it leverages self-supervised techniques to extract valuable signals from real-world scenarios. However, many methods usually learn the instances of discrimination tasks that involve the construction of contrastive pairs through random sampling. GCL approaches suffer from sampling bias issues, where the negatives might have a semantic structure similar to that of the positives, thus leading to a loss of effective feature representation. To address these problems, we present the \underline{Proto}typical contrastive learning through \underline{A}lignment and \underline{U}niformity for recommendation, which is called \textbf{ProtoAU}. Specifically, we first propose prototypes (cluster centroids) as a latent space to ensure consistency across different augmentations from the origin graph, aiming to eliminate the need for random sampling of contrastive pairs. Furthermore, the absence of explicit negatives means that directly optimizing the consistency loss between instance and prototype could easily result in dimensional collapse issues. Therefore, we propose aligning and maintaining uniformity in the prototypes of users and items as optimization objectives to prevent falling into trivial solutions. Finally, we conduct extensive experiments on four datasets and evaluate their performance on the task of link prediction. Experimental results demonstrate that the proposed ProtoAU outperforms other representative methods. The source codes of our proposed ProtoAU are available at \url{https://github.com/oceanlvr/ProtoAU}.


Risk-Sensitive Diffusion: Learning the Underlying Distribution from  Noisy Samples

Abstract: While achieving remarkable performances, we show that diffusion models are fragile to the presence of noisy samples, limiting their potential in the vast amount of settings where, unlike image synthesis, we are not blessed with clean data. Motivated by our finding that such fragility originates from the distribution gaps between noisy and clean samples along the diffusion process, we introduce risk-sensitive SDE, a stochastic differential equation that is parameterized by the risk (i.e., data "dirtiness") to adjust the distributions of noisy samples, reducing misguidance while benefiting from their contained information. The optimal expression for risk-sensitive SDE depends on the specific noise distribution, and we derive its parameterizations that minimize the misguidance of noisy samples for both Gaussian and general non-Gaussian perturbations. We conduct extensive experiments on both synthetic and real-world datasets (e.g., medical time series), showing that our model effectively recovers the clean data distribution from noisy samples, significantly outperforming conditional generation baselines.


Variance Alignment Score: A Simple But Tough-to-Beat Data Selection  Method for Multimodal Contrastive Learning

Abstract: In recent years, data selection has emerged as a core issue for large-scale visual-language model pretraining, especially on noisy web-curated datasets. One widely adopted strategy assigns quality scores such as CLIP similarity for each sample and retains the data pairs with the highest scores. However, these approaches are agnostic of data distribution and always fail to select the most informative samples. To solve this problem, we propose a simple yet theoretically principled metric named Variance Alignment Score (VAS), which has the form $\langle \Sigma_{\text{test}}, \Sigma_i\rangle$. Here, $\Sigma_{\text{test}}$ represents the target (cross-)covariance matrix we aim to align, potentially based on prior knowledge, while $\Sigma_i$ denotes the tensor product of single or multi-modal representations for the $i$-th sample. We further design a new data selection method that maximizes the total VAS. We provide theoretical analysis in a simplified setting to demonstrate the theoretical advantage of VAS over random or other existing data selection. Experimentally, applying VAS and CLIP scores together can outperform baselines by a margin of $1.3\%$ average on 38 evaluation sets for noisy dataset DataComp and $2.5\%$ on VTAB for high-quality dataset CC12M. Additionally, our ablation study also shows visual features are better than text for calculating VAS, and the related classical experimental design methods may fail under this context.