Abstract--We propose a novel context-aware drone delivery framework for optimizing package delivery through skyway networks in smart cities. In this respect, we propose a novel line-of-sight heuristic-based context-aware composition algorithm that selects and composes near-optimal drone delivery services. We conducted an extensive experiment using a real dataset to show the robustness of our proposed approach. A skyway network is defined is a popular type of UAV that offers potential as a set of skyway segments that directly connect benefits in smart city applications [2]. Drones two nodes representing take-off and landing are increasingly becoming pervasive in their use, stations [8]. Take-off and landing stations (aka including surveillance, agriculture, and delivery network nodes) are typically from and to building of goods [3].

Elon Musk caused a stir last week when he told the (recently fired) right-wing provocateur Tucker Carlson that he plans to build "TruthGPT," a competitor to OpenAI's ChatGPT. Musk says the incredibly popular bot displays "woke" bias and that his version will be a "maximum truth-seeking AI"--suggesting only his own political views reflect reality. Musk is far from the only person worried about political bias in language models, but others are trying to use AI to bridge political divisions rather than push particular viewpoints. David Rozado, a data scientist based in New Zealand, was one of the first people to draw attention to the issue of political bias in ChatGPT. Several weeks ago, after documenting what he considered liberal-leaning answers from the bot on issues including taxation, gun ownership, and free markets, he created an AI model called RightWingGPT that expresses more conservative viewpoints.

Knowledge graphs (KG) have become an important data organization paradigm. The available textual query languages for information retrieval from KGs, as SPARQL for RDF-structured data, do not provide means for involving non-technical experts in the data access process. Visual query formalisms, alongside form-based and natural language-based ones, offer means for easing user involvement in the data querying process. ViziQuer is a visual query notation and tool offering visual diagrammatic means for describing rich data queries, involving optional and negation constructs, as well as aggregation and subqueries. In this paper we review the visual ViziQuer notation from the end-user point of view and describe the conceptual and technical solutions (including abstract syntax model, followed by a generation model for textual queries) that allow mapping of the visual diagrammatic query notation into the textual SPARQL language, thus enabling the execution of rich visual queries over the actual knowledge graphs. The described solutions demonstrate the viability of the model-based approach in translating complex visual notation into a complex textual one; they serve as semantics by implementation description of the ViziQuer language and provide building blocks for further services in the ViziQuer tool context.

Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.

Poverty is one of the fundamental issues that mankind faces. To solve poverty issues, one needs to know how severe the issue is. The Multidimensional Poverty Index (MPI) is a well-known approach that is used to measure a degree of poverty issues in a given area. To compute MPI, it requires information of MPI indicators, which are \textbf{binary variables} collecting by surveys, that represent different aspects of poverty such as lacking of education, health, living conditions, etc. Inferring impacts of MPI indicators on MPI index can be solved by using traditional regression methods. However, it is not obvious that whether solving one MPI indicator might resolve or cause more issues in other MPI indicators and there is no framework dedicating to infer empirical causal relations among MPI indicators. In this work, we propose a framework to infer causal relations on binary variables in poverty surveys. Our approach performed better than baseline methods in simulated datasets that we know ground truth as well as correctly found a causal relation in the Twin births dataset. In Thailand poverty survey dataset, the framework found a causal relation between smoking and alcohol drinking issues. We provide R CRAN package `BiCausality' that can be used in any binary variables beyond the poverty analysis context.

The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound. However, it requires the computation of expensive second-order curvature, which hinders its practical application. In this paper, we establish the connection between the recognizability of representations and the recent functional conditional mutual information (f-CMI) generalization bound, which is significantly easier to estimate. On this basis we propose a Recognizable Information Bottleneck (RIB) which regularizes the recognizability of representations through a recognizability critic optimized by density ratio matching under the Bregman divergence. Extensive experiments on several commonly used datasets demonstrate the effectiveness of the proposed method in regularizing the model and estimating the generalization gap.

Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning. Being a widely distributed system, federated machine learning requires various system design thinking. To better design a federated machine learning system, researchers have introduced multiple patterns and tactics that cover various system design aspects. However, the multitude of patterns leaves the designers confused about when and which pattern to adopt. In this paper, we present a set of decision models for the selection of patterns for federated machine learning architecture design based on a systematic literature review on federated machine learning, to assist designers and architects who have limited knowledge of federated machine learning. Each decision model maps functional and non-functional requirements of federated machine learning systems to a set of patterns. We also clarify the drawbacks of the patterns. We evaluated the decision models by mapping the decision patterns to concrete federated machine learning architectures by big tech firms to assess the models' correctness and usefulness. The evaluation results indicate that the proposed decision models are able to bring structure to the federated machine learning architecture design process and help explicitly articulate the design rationale.

We introduce string2string, an open-source library that offers a comprehensive suite of efficient algorithms for a broad range of string-to-string problems. It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarity analysis -- along with several helpful visualization tools and metrics to facilitate the interpretation and analysis of these methods. Notable algorithms featured in the library include the Smith-Waterman algorithm for pairwise local alignment, the Hirschberg algorithm for global alignment, the Wagner-Fisher algorithm for edit distance, BARTScore and BERTScore for similarity analysis, the Knuth-Morris-Pratt algorithm for lexical search, and Faiss for semantic search. Besides, it wraps existing efficient and widely-used implementations of certain frameworks and metrics, such as sacreBLEU and ROUGE, whenever it is appropriate and suitable. Overall, the library aims to provide extensive coverage and increased flexibility in comparison to existing libraries for strings. It can be used for many downstream applications, tasks, and problems in natural-language processing, bioinformatics, and computational social sciences. It is implemented in Python, easily installable via pip, and accessible through a simple API. Source code, documentation, and tutorials are all available on our GitHub page: https://github.com/stanfordnlp/string2string.

Ensuring the safety of the equipment, its environment and most importantly, the operator during robot operations is of paramount importance. Robots and complex robotic systems are appearing in more and more industrial and professional service applications. However, while mechanical components and control systems are advancing rapidly, the legislation background and standards framework for such systems and machinery are lagging behind. As part of a fundamental research work targeting industrial robots and industry 4.0 solutions for completely automated slaughtering, it was revealed that there are no particular standards addressing robotics systems applied to the agrifood domain. More specifically, within the agrifood sector, the only standards existing for the meat industry and the red meat sector are hygienic standards related to machinery. None of the identified standards or regulations consider the safety of autonomous robot operations or human robot collaborations in the abattoirs. The goal of this paper is to provide a general overview of the regulations and standards (and similar guiding documents) relevant for such applications, that could possibly be used as guidelines during the development of inherently safe robotic systems for abattoirs. Reviewing and summarizing the relevant standard and legislation landscape should also offer some instrumental help regarding the foreseen certification procedure of meat processing robots and robot cells for slaughterhouses in the near future.