Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Composite indicators are widely used to score or classify units evaluated on multiple criteria. Their construction involves aggregating criteria evaluations, a common practice in Multiple Criteria Decision Aiding (MCDA). In MCDA, various methods have been proposed to address key aspects of multiple criteria evaluations, such as the measurement scales of the criteria, the degree of acceptable compensation between them, and their potential interactions. However, beyond producing a final score or classification, it is essential to ensure the explainability and interpretability of results as well as the procedure's transparency. This paper proposes a method for constructing explainable and interpretable composite indicators using " if..., then... " decision rules. We consider the explainability and interpretability of composite indicators in four scenarios: (i) decision rules explain numerical scores obtained from an aggregation of numerical codes corresponding to ordinal qualifiers; (ii) an obscure numerical composite indicator classifies units into quantiles; (iii) given preference information provided by a Decision Maker in the form of classifications of some reference units, a composite indicator is constructed using decision rules; (iv) the classification of a set of units results from the application of an MCDA method and is explained by decision rules. To induce the rules from scored or classified units, we apply the Dominance-based Rough Set Approach. The resulting decision rules relate the class assignment or unit's score to threshold conditions on values of selected indicators in an intelligible way, clarifying the underlying rationale. Moreover, they serve to recommend composite indicator assessment for new units of interest.

Yurts founder and CEO Ben Van Roo breaks down concerns over DeepSeek on'The Will Cain Show.' Artificial intelligence may be the new frontier for childhood schooling, but the idea of teacherless classrooms has received mixed reviews from state education officials. Unbound Academy, a Texas-based institution billing itself as the nation's first virtual, tuition-free charter school for grades 4 through 8, reportedly employs AI to teach students in a way that can be geared toward the individual student without "frustration[s]" sometimes present in traditional schooling. While such schools have seen success in being approved to educate students in Arizona, Unbound was formally rejected by the Pennsylvania Department of Education in a letter obtained by Fox News Digital. In a letter to an Unbound Academy official with a Lancaster office address, Secretary Angela Fitterer said her office has found "deficiencies" in all five criteria needed for approval to teach Keystone State students. Pennsylvania's Charter School law denotes a school must demonstrate sustainable support for the cyber charter school plan from teachers, parents and students.

We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine. SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable functions. SCooLS incorporates two key elements: semi-supervised learning and graph neural networks (GNNs). Semi-supervised learning produces more accurate models than unsupervised learning, while not requiring the large oracle-labeled training set that supervised learning requires. GNNs enable direct analysis of smart contract bytecode without any manual feature engineering, predefined patterns, or expert rules. SCooLS is the first application of semi-supervised learning to smart contract vulnerability analysis, as well as the first deep learning-based vulnerability analyzer to identify specific vulnerable functions. SCooLS's performance is better than existing tools, with an accuracy level of 98.4%, an F1 score of 90.5%, and an exceptionally low false positive rate of only 0.8%. Furthermore, SCooLS is fast, analyzing a typical function in 0.05 seconds. We leverage SCooLS's ability to identify specific vulnerable functions to build an exploit generator, which was successful in stealing Ether from 76.9% of the true positives.

Participatory Artificial Intelligence (PAI) has recently gained interest by researchers as means to inform the design of technology through collective's lived experience. PAI has a greater promise than that of providing useful input to developers, it can contribute to the process of democratizing the design of technology, setting the focus on what should be designed. However, in the process of PAI there existing institutional power dynamics that hinder the realization of expansive dreams and aspirations of the relevant stakeholders. In this work we propose co-design principals for AI that address institutional power dynamics focusing on Participatory AI with youth.

Robotics will eventually play a key role in India's "Make in India" strategy, which aims to persuade global businesses to invest. India's position as a true hub of robotics talent is due to this. At this time, the whole population and their daily life are centred on the internet. Everything from buying to schooling to vacation planning can be done with just a few clicks on smartphones and laptops. No one could have predicted digital life a decade ago, and the same can be said for robotics.

Machine Learning getting to know is one the trending subject matter those days. Machine learning (ML) is the artwork of growing algorithms without explicitly programming. In the beyond decades, exabytes of statistics were generated, and maximum of the industries were absolutely digitized. This current statistic is utilized by Machine learning (ML) algorithms to broaden predictive fashions and automate numerous time-ingesting tasks. It defines fundamental representations that how pc can examine in future.

Technologies change how we teach and understand. Big information is among the main technological improvements that's forming the education sector in the 21st Century. New large data tools assist to reimagine and increase our plans. Technologies enable a student-centered method of schooling. They include new ways that pupils and teachers interact.

As with many of our former societal norms, the educational institution with which we are familiar is changing and evolving as a result of the COVID-19 pandemic. Students missed out on graduations, proms, and the last few months of the current academic year--what is even more disheartening is that we are faced with the possibility of schools remaining closed for the next academic year. We have seen drive-by birthday parties taking the place of large celebrations, video conferencing platforms replacing social gatherings, online classrooms, and remote work taking over the typical brick and mortar school or office setting. These adjustments have shown us that we are resilient and able to evolve and adapt in ways we would have never foreseen possible. Parents have been tasked with doing it all from upholding their careers to homeschooling while being stay-at-home parents, which despite all the difficulties, has encouraged them to consider homeschooling their children from the next academic year.