Reviewerly/exHarmony huggingface dataset:
Defining of prospective areas for the localization of groundwater in fractured massifs (on the example of the Municipality of Brezovo, Plovdiv Region)

The northern part of the Municipality of Brezovo falls within the range of a fractured massif, which predetermines the presence of springs with a limited flow rate, insufficient for water supply for the villages there, which necessitates the localization of suitable locations for boreholes drilling. After a complex assessment of the geological-hydrogeological and tectonic conditions and field surveys, favorable sections were determined with prerequisites for the presence of probable water-saturated fractured zones. As a result of geophysical surveys carried out using the SP method, in the area of the villages of Svezhen, Chehlare and Rozovets, deep water-bearing zones were delineated, which were convincingly confirmed during the subsequent exploratory drilling.
Efficient ensembles of distance‐based label ranking trees

Abstract Ensemble of label ranking trees (LRTs) are currently the state‐of‐the‐art approaches to the label ranking problem. Recently, bagging, boosting, and random forest methods have been proposed, all based on the LRT algorithm, which adapts regression/classification trees to the label classification problem. The LRT algorithm uses theoretically grounded Mallows probability distribution to select the best split when growing the tree, and an EM‐type process to complete the rankings on the training data when they are incomplete. These two steps have proven to be accurate, but require a large computational effort. This article proposes two alternative methods that replace the use of the Mallows distribution with distance‐based criteria to select the best split at each inner node of the tree. Moreover, these distance‐based criteria allow dealing with incomplete rankings natively, so avoiding the completion process. We have carried out an extensive experimental evaluation, which shows that (1) the integration of the two proposed modifications to the LRT algorithm into ensemble methods (bagging and random forest) are an order of magnitude faster than using the original Mallows‐based LRT algorithm; (2) ensembles using the proposed LRT methods are significantly more accurate in the presence of incomplete rankings, while they are at least as accurate in the complete case; and (3) the two modified LRT algorithms are also an order of magnitude faster than the Mallows‐based LRT, while they are at least as accurate as the Mallows‐based LRT on both complete and incomplete rankings.
AI and P Versus NP problem a new method based on artificial intelligence brings us closer to the solution of most important open problem in the theoretical computer science

Abstract: A new AI-based method has opened a new path towards solving the most important problem in theoretical computer science. The purpose of this article is to explain in a simple way a method that uses artificial intelligence and the reinforcement learning method to solve the P vs Np problem.
Generative AI: An Existential Threat to Human Content Creators?

With the phenomenal rise of generative AI models (e.g., Large Language Models such as GPT or text-to-image models such as Stable Diffusion), there are increasing concerns about human content creators' future. Specifically, as generative models further advance, would they eventually take over the jobs of all human content creators? We argue that the answer is no, even if generative AI models can perform at their theoretical limit one day. Our theory has a close analogy to the familiar insight in financial economics on the impossibility of a perfectly informationally efficient market as in Grossman and Stiglitz (1980): If generative AI models can provide all the content that human needs at low variable costs, then there is no incentive for human content creators to spend resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI models can only rehash what they already know and be unable to provide up-to-date answers. This creates a paradox.
An Exploration of English Translation Methods for Long Sentences Combined with Modal Analysis from the Perspective of Translation Purpose

Abstract The complex sentence structure and complicated semantic logic of English long sentences are the difficult points that need to be overcome urgently in English translation. This paper outlines the basic direction of machine translation based on the perspective of translation purpose. Based on the grammatical categories of English words, lexical annotation is completed, and regular expressions for matching English long sentences are proposed based on the annotation results. After realizing the preprocessing of English text, the features of the text are extracted and converted into the format required by the CRF model, and the long-sentence slicing is completed through the training of CRF. The cut Encoder encoding and Decoder decoding were utilized to generate the final machine translation result. In the test of the United Nations machine translation corpus, this paper’s method improves the translation performance in the English-Chinese direction by 0.29 Bleu value and the translation performance in the Chinese-English direction by 0.13 Bleu value compared with the method of Xu and Zens. The translation performance is effectively improved by combining long sentence slice translation with modal analysis.
Off-Road Navigation of Legged Robots Using Linear Transfer Operators⋆

This paper presents the implementation of of-road navigation on legged robots using convex optimization through linear transfer operators. Given a traversability measure that captures the of-road environment, we lift the navigation problem into the density space using the Perron-Frobenius (P-F) operator. This allows the problem formulation to be represented as a convex optimization. Due to the operator acting on an infinite-dimensional density space, we use data collected from the terrain to get a finite-dimension approximation of the convex optimization. Results of the optimal trajectory for of-road navigation are compared with a standard iterative planner, where we show how the linear operator approach generates a more traversable path for the legged robot compared to the suboptimal iterative planner.
Probabilistic RRT Connect with intermediate goal selection for online planning of autonomous vehicles

Rapidly Exploring Random Trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introduced where two trees are simultaneously formed and eventually connected. Probabilistic RRT used the concept of position probability map to introduce goal biasing for faster convergence. In this paper, we propose a modified method to combine the pRRT and RRT-Connect techniques and obtain a feasible trajectory around the obstacles quickly. Instead of forming a single tree from the start point to the destination point, intermediate goal points are selected around the obstacles. Multiple trees are formed to connect the start, destination, and intermediate goal points. These partial trees are eventually connected to form an overall safe path around the obstacles. The obtained path is tracked using an MPC + Stanley controller which results in a trajectory with control commands at each time step. The trajectories generated by the proposed methods are more optimal and in accordance with human intuition. The algorithm is compared with the standard RRT and pRRT for studying its relative performance.
How Agile Feedback Practice Use Impacts Software Quality

Although agile methodologies prescribe various practices, they universally focus on obtaining feedback. Agile practitioners argue that this feedback allows teams to better adapt to uncertainty, which can lead to increases in software quality. Using a sample of 76 agile teams across multiple organizations, we investigate the impacts of agile feedback practices of technical feedback, fast iterative delivery, and agile project management on software quality. Also, we investigate interaction effects between agile feedback practice use and six different sub-constructs of uncertainty. We find that a higher level of use of some agile feedback practices directly impacts software quality and moderates the effects of uncertainty on software quality. However, we find a more complex relationship between agile methodologies and software quality than previously identified. These findings illustrate future research opportunities into the complicated relationship between agile methodology usage and external environmental factors.
Decomposing (n+1)$(n+1)$‐Qubit Toffoli Gate with Shallow Circuit Depth and No Ancilla

Abstract Circuit depth optimization is crucial for implementing quantum algorithms in practical systems. The decomposition of ‐qubit Toffoli gate is continually optimized since this gate is a fundamental component for many quantum algorithms. Without any ancilla, ‐qubit Toffoli gate can be decomposed by standard gates with circuit depth . Here, the decomposition is further optimized and its circuit depth is reduced to . The decomposition is equivalent to ‐qubit Toffoli gate up to an overall phase and also does not need any ancilla.
Script Event Prediction Based on Causal Generalization Learning

Causal relationships between events can reflect the historical evolution of events and provide an important reference for predicting future trends. Script prediction methods based on event graphs often struggle to adequately consider the complex interdependencies among events, leading to prediction biases. The Script Event Prediction Based on Causal Generalization Learning (SEPCG) method has been proposed to enhance the accuracy of script event prediction. SEPCG uses the graph attention network to learn the direct causal relationship similarity between known events and candidate events, the direct result event similarity between known events and candidate events, and utilizes double similarity generalization to learn the predicate type between known events and candidate events. SEPCG uses a Neural Tensor Network to learn parameter-level event embeddings and improve the model’s sensitivity to parameter-level changes. Finally, based on the generalized event embeddings, the BiLSTM network is used to simultaneously learn the forward contextual information from the known event to the candidate event direction, i.e., the cause to the result information, and the reverse contextual information from the candidate event to the known event direction, i.e., the result to the cause information. The BiLSTM is used to capture the temporal information of event chains at different levels. The effectiveness of the model is verified on the NYT dataset, with a 1.84% improvement in accuracy compared to the best baseline.
WE-BTR: A Behavior Tree Recommendation Method Based on Word Embedding

The widespread application of behavior trees in modeling complex robotic tasks can attribute to their modularity and reusability. Nonetheless, the selection of required behavior trees from previously built databases can be a daunting task, especially when dealing with vast amounts of data. In this paper, we propose a word-embedding-based method for recommending behavior trees (WE-BTR), which focuses on expanding recommendations for candidate behavior trees found through keyword searches. The method segments the complete behavior tree into subtasks and then embeds them as vectors. These vectors are aggregated to generate a vector representation of the behavior tree. By this method, task-relevant information inherent in the behavior tree is precisely captured, enabling an efficient association search and recommendation. Finally, we use cosine similarity to recommend behavior trees similar to the candidate trees. We validated the effectiveness of our method using a sample dataset and demonstrated its superiority in embedding vector, clustering, and recommendation performance.
Design of an Ecological Visual Analytics Interface for Operators of Time-Constant Processes

In industrial applications where the physical parameters are highly interconnected, keeping the process flow steady is a major concern for the operators. This is caused by the sensitivity of system to the process dynamics. As a result, a slight adjustment to a control parameter can significantly affect the efficiency of the system and thus impact the financial gain. Paper pulp production is an example of such a process, where operators continuously investigate the potential of changes in the process and predict the consequences of an adjustment before making a decision. Process parameter adjustments prescribed by simulated control models cannot be fully trusted as the external disturbances and the process inherent variabilities cannot be fully incorporated into the simulations. Therefore, to assess the viability of a strategy, operators often compare the situation with the historical records and trends during which the processes in the plant ran steadily. While previous research has mostly focused on developing advanced control models to simulate complex pulp production process, this work aims to support operators analytical reasoning by provision of effective data visualization. The contributions of our design study include a domain problem characterization and a linked-view visual encoding design, which aims to enhance operator’s mental models independent of particular users or scenarios. Finally, by reflecting on the advantages of our choice of task abstraction technique, inherited from the ecological interface design framework [5], we reason for the generalizability of our approach to similar industrial applications.
Invertible Neural Networks for Trustworthy AI

This study combines research in machine learning and system engineering practices to conceptualize a paradigm enhancing trustworthiness of machine learning inference process. We explore the topic of reversibility in deep neural networks and introduce their anomaly detection capabilities to build a framework of integrity verification checkpoints across the inference pipeline of a deployed model. We leverage previous findings and principles regarding several types of autoencoders, deep generative maximum-likelihood training and invertibility of neural networks to propose an improved network architecture for anomaly detection. A remarkable ability of an Invertible Neural Network (INN) to reconstruct data from its compressed representation and to solve inverse problems is then generalized and applied in the field of Trustworthy AI. To achieve integrity verification of an inference pipeline we place the INN-based Trusted Neural Network nodes around the mission critical parts of the system, achieving an end-to-end outcome verification. This work aspires to enhance robustness and reliability of applications employing artificial intelligence, which are playing increasingly noticeable role in highly consequential decision-making processes across many industries and problem domains.
Do You Trust What You See? Toward A Multidimensional Measure of Trust in Visualization

Few concepts are as ubiquitous in computational fields as trust. However, in the case of information visualization, there are several unique and complex challenges, chief among them: defining and measuring trust. In this paper, we investigate the factors that influence trust in visualizations. We draw on the literature to identify five factors likely to affect trust: credibility, clarity, reliability, familiarity, and confidence. We then conduct two studies investigating these factors’ relationship with visualization design features. In the first study, participants’ credibility, understanding, and reliability ratings depended on the visualization design and its source. In the second study, we find these factors also align with subjective trust rankings. Our findings suggest that these five factors are important considerations for the design of trustworthy visualizations.
Let’s Chat: Integrating Large Language Models into Blended Learning of English for Specific Purposes

English for Specific Purposes (ESP) pedagogy addresses the specific needs of language learners in professional or academic contexts by enhancing their motivation, autonomy and contextual communication skills. Despite recent fruitful approaches to blended ESP pedagogy, challenges such as learner diversity, limited classroom time and teacher resources can make real-world implementation of blended ESP learning costly and challenging. To address these issues, we propose to integrate Large Language Models (LLMs) such as ChatGPT into blended ESP learning to provide personalized learning experiences through a flexible, interactive and engaging interface. We present, as an exploratory prototype, a web-based system that uses the ChatGPT API to support the administration and delivery of customizable blended ESP courses. We discuss the design, implementation and envisioned use cases of the system in a blended learning environment.
Distributed Phase Estimation at the Heisenberg Limit with Classical Light

Quantum metrology, such as quantum phase estimation, can surpass classical sensing limits, reaching the Heisenberg‐scaling precision. So far, this kind of metrology has been thought to be only implementable with the quantum systems, which, however, are fragile to environmental noise and hardly contribute to the practical detections. Herein, it is demonstrated both theoretically and experimentally that the parameter encoded by the optical phase can also be estimated at the Heisenberg scaling in classical optics. Inspired by the quantum‐entanglement‐enhanced sensing scheme, the estimation is performed by using classically correlated beams as probes, and obtaining the probes readout after their interaction with the target system. Because the correlated beams considered are spatially separable, a distributed phase estimation scheme is given, which can sense the linear combinations of the phase shifts induced by distinct systems. The results of our experiments show an error reduction up to 3.89 dB below the classical limit when the correlated beam number for probing is 6, approaching the Heisenberg limit. Compared with quantum strategies, the proposal shows a better robustness against the environmental disturbance and keeps their performances even when the correlated beam number is relatively large. Hence, it indicates promising practical applications in the future.
Poster: Measurement on Lightning Network Performance

Off-chain networks have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. The most widely used one, the Lightning Network (LN), has developed rapidly since its introduction in 2015. While much research has been dedicated to improving LN's routing efficiency, security protocols, and network analysis, there remains a gap in understanding how end-users should establish channels to optimize transaction efficiency. In this work, we conduct a holistic measurement study that focuses on the practical aspects of Lightning Network transaction performance. By evaluating transaction latency, fees, and success rates under diverse sce-narios, we provide users with actionable insights to enhance their network efficiency. Furthermore, our research aids those interested in leveraging the LN for transaction relaying, fostering a more efficient and dynamic network ecosystem. Through empirical findings and analytical deductions, we pave the way for users to harness the full potential of the Lightning Network and contribute to its ongoing growth and development.
ECIFF: Event Causality Identification based on Feature Fusion

Event causality identification is an important task in natural language processing. However, this task is highly challenging due to the high dependency of event context, text semantic ambiguity and insignificant causality features between text events. These issues lead to the low precision of causal relationship identification between events. We propose an Event Causality Identification Based on Feature Fusion (ECIFF) to improve the causality identification precision between events by integrating the context, semantics, and syntax of natural language. Firstly, we utilize BERT to capture the contextual features of events in natural language, enhancing the contextual embedding of events in different contexts. Secondly, based on an adversarial generative graph representation method, ECIFF learns a massive amount of causal relationships in the CauseNet, which can enhance the semantic representation of causes and effects of events. Next, we exploit the shortest dependency path to shorten the length of sentences and inductively learn all possible syntactic dependency relationships. Finally, the contextual, semantic and syntactic features are fused to synthetically determine the causal relationships among events. The experimental results indicate that our proposed approach significantly outperforms the state-of-the-art method LSIN: on the CTBank dataset, the precision, recall and F1-score of our approach are improved by 1.6%, 3.2% and 2.4%; on the ESL dataset, the precision, recall and F1-score of our approach are improved by 4.0%, 4.7% and 4.3%.
Online misinformation: improving transparency in content moderation practices of social media companies

In this study, I investigate Facebook's approach to handling political misinformation as a case study to assess the effectiveness of transparency measures. Social media platforms have become powerful tools for spreading misinformation, posing a threat to democracies worldwide. While these platforms have improved communication and access to information, they have also enabled malicious actors to disseminate misinformation quickly. Companies like Facebook moderate content on their platforms based on community standards and terms of service. However, this moderation process has faced criticism for its lack of transparency and consistency.I address the current criticism surrounding social media companies' efforts to combat misinformation and their responses to regulatory demands. I argue that transparency mechanisms have the potential to enhance the understanding of how misinformation spreads and how platforms respond to it. However, I find that existing transparency measures are insufficient due to their ambiguity and inconsistency, hindering cross-platform comparisons and evaluations over time.My focus is on Facebook, given its global user base and increased oversight since 2016. I analyze Facebook's responses to political events such as the 2018 Brazilian Federal Elections, the 2019 European Parliament Elections, and the 2020 U.S. Presidential Elections. Despite ongoing improvements, I find that Facebook's content moderation and reporting remain unclear, making it challenging for independent researchers to assess their efforts.I propose standardizing disclosure practices in four key areas: distinguishing between ex ante and ex post identification of misinformation, disclosing decision-making processes in content moderation, differentiating between passive and active engagement with misinformation, and providing information on the efficacy of user awareness tools. Standardization alone is not enough, and I recommend establishing common key-performance indicators (KPIs) to evaluate platforms' actions and performance over time. These KPIs would allow platforms and governments to assess the effectiveness of current practices and identify areas for improvement, ultimately enabling better regulation of social media platforms based on reliable and measurable data.