Reviewerly/exHarmony huggingface dataset:
James ate 5 oranges = Steve bought 5 pencils: Structure-Aware Denoising for Paraphrasing Word Problems

We propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems having practical applications in online pedagogy as a means to reduce plagiarism as well as evoke reasoning capabilities on the part of the student instead of rote memorization. This task is more complex than paraphrasing general-domain corpora due to the difficulty in preserving critical information for solution consistency of the paraphrased word problem, managing the increased length of the text and ensuring diversity in the generated paraphrase. Existing approaches fail to demonstrate adequate performance on at least one, if not all, of these facets, necessitating the need for a more comprehensive solution. To this end, we model the noising search space as a composition of contextual and syntactic aspects to sample noising functions. This allows for learning a denoising function, that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection. The denoising function serves as a foundation for training a paraphrasing function, which operates solely in the input-paraphrase space without carrying any direct dependency on noise. We demonstrate that SCANING improves performance in terms of producing semantically equivalent and syntactically diverse paraphrases by 35% through extensive automated and human evaluation across 4 datasets.
Multi-Granularity Attention Model for Group Recommendation

Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored different methods for integrating individual preferences and making collective decisions that benefit the group as a whole. However, most of them heavily rely on users with rich behavior and ignore latent preferences of users with relatively sparse behavior, leading to insufficient learning of individual interests. To address this challenge, we present the Multi-Granularity Attention Model (MGAM), a novel approach that utilizes multiple levels of granularity (i.e., subsets, groups, and supersets) to uncover group members' latent preferences and mitigate recommendation noise. Specially, we propose a Subset Preference Extraction module that enhances the representation of users' latent subset-level preferences by incorporating their previous interactions with items and utilizing a hierarchical mechanism. Additionally, our method introduces a Group Preference Extraction module and a Superset Preference Extraction module, which explore users' latent preferences on two levels: the group-level, which maintains users' original preferences, and the superset-level, which includes group-group exterior information. By incorporating the subset-level embedding, group-level embedding, and superset-level embedding, our proposed method effectively reduces group recommendation noise across multiple granularities and comprehensively learns individual interests. Extensive offline and online experiments have demonstrated the superiority of our method in terms of performance.
Robust User Behavioral Sequence Representation via Multi-scale Stochastic Distribution Prediction

User behavior representation learned by self-supervised pre-training tasks is widely used in various domains and applications. Conventional methods usually follow the methodology in Natural Language Processing (NLP) to set the pre-training tasks. They either randomly mask some of the behaviors in the sequence and predict the masked ones or predict the next k behaviors. These methods fit for text sequence, in which the tokens are sequentially arranged subject to linguistic criterion. However, the user behavior sequences can be stochastic with noise and randomness. The same paradigm is intractable for learning a robust user behavioral representation.
Task-Driven Neural Natural Language Interface to Database

Natural language querying offers an intuitive and user-friendly interface. A Natural Language Interface over databases, often termed as “Text-to-SQL”, involves translating a query posed in natural language into a corresponding SQL query for structured databases. A significant number of recent methodologies, anchored in the pre-trained language model and encode-decode paradigms, have been developed to address this task. Yet, existing approaches often grapple with generating accurate SQL queries, especially in scenarios that involve multiple values and intricate column calculations. In this study, we present a task-driven Text-to-SQL model. This model breaks down the SQL prediction process into specific sub-tasks based on the unique task requirements of the query. Specifically, we amalgamate structure prediction, value extraction, and column relationship prediction into a cohesive workflow. The model is designed to construct target SQL queries incrementally, with each sub-task building upon the outcomes of its predecessors. Additionally, we introduce a novel filtering mechanism to refine and re-order candidates produced during the beam search phase. We substantiate the efficacy of our model using public datasets, showcasing its adeptness in both English and Chinese contexts.
GADESQL: Graph Attention Diffusion Enhanced Text-To-SQL with Single and Multi-hop Relations

Text-To-SQL is crucial for enabling users without technical expertise to effectively extract important information from databases. The graph-based encoder has been successfully employed in this field. However, existing methods often adopt a node-centric approach, focusing on single-hop edge relations. This approach gives rise to two main issues: 1) failure to differentiate between single-hop and multi-hop relations among nodes; 2) ignoring the valuable multi-hop reasoning information between nodes. To tackle these challenges, we propose a Graph Attention Diffusion Enhanced Text-To-SQL(GADESQL) model that enables multi-hop reasoning among nodes. With GAD, information can propagate efficiently through multi-hop paths, uniquely integrating single-hop and multi-hop relations during the graph iteration process. Furthermore, we employ Semantic Dependency Parsing for natural language analysis, constructing a semantic analysis tree for questions to enhance the effective connection between question tokens and Schema structures. Experiments on the cross-domain dataset Spider demonstrate that our model possesses strong generalization capabilities, achieving certain performance improvements over existing works.
Students’ Perceptions from an Integrated Software Engineering and Databases Course

The project-based learning approach has proven its value, as students can work on complex, real-life problems that allow for meaningful learning (Breiter et al. in Comput Sci Educ 18(2):165–180, 2005; Sung and Sunny in Learning through collaborative design: a learning strategy on the internet, vol 3, pp F4F–3, 2001; Coupal and Boechler in The value of agile in experiential learning of software development, pp 113–120, 2007) [1–3]. Since 2019, the Computing Science Department of the University of Costa Rica offers a project-based course integrating the subjects of Software Engineering and Databases. We surveyed the students in each sprint to gain insight into the students’ perception of the value and difficulty of technical challenges, teamwork, planning, and negotiating with the client. We collected data from four course instances and 79 students. The descriptive statistics analysis revealed that the perceived difficulty of addressing technical challenges was higher than the perceived difficulty of other dimensions, across sprints, and course instances. Statistical tests comparing the first and third sprints were performed. Further studies are needed to validate, complement, or contrast our findings. Evaluating students’ perception of different project aspects is useful in identifying the aspects that are more difficult for students throughout the course.
The Use of WhatsApp and English Language Learning in High School Students at a Public School in Peru

Increasing numbers of secondary schools are utilizing educational platforms to better teaching methods and benefit all students through online learning. Preliminary evidence indicates that educators have not yet fully embraced WhatsApp’s educational application. This article aims to determine the degree of link between WhatsApp usage and English language acquisition among fifth-grade students in a public school in Tacna, Peru. The nature of the investigation was not experimental. Similarly, it is quantitative in character, as data were collected and analyzed using authorized instruments. As the goal of the study was to quantify or examine the relationship between two or more variables, a correlational research design was adopted. The sample consisted of 80 students in their fifth year of secondary school. The correlation coefficient between the two variables is 0.445, which indicates a positive relationship; consequently, it can be concluded that there is a significant relationship between the study variables.
The relationship of academic leadership with the organizational confidence of kindergarten teachers

The research aims to test the relationship between academic leadership as an independent variable and organizational trust as a dependent variable. Thus, it was necessary to answer a basic question: Is there a correlation and impact relationship between the dimensions of academic leadership and organizational trust? In order to verify the contents of the answer, a sample of governmental kindergarten teachers affiliated to the General Directorate of Education in Baghdad / Karkh1 was chosen and based on the importance of the research topic, and the importance of the research sample and the organization in question. The number of (240) female teachers, representing the research sample out of the total community of (376) female teachers, by adopting the questionnaire as a main tool in data collection, which included (35) paragraphs, as (245) forms were distributed and (240) forms were retrieved, of which were valid For statistical analysis, which was analyzed by adopting the statistical program (Spss V.21, Amos V.18) with the appropriate statistical methods for research, including (frequency distribution, arithmetic mean, coefficient of determination (R²), confirmatory factor analysis and others) to test its hypotheses, and the study found To a set of conclusions, the most important of which is the existence of a direct correlation and impact relationship between the dimensions of academic leadership and organizational trust.
Results of Geological and Volcanological Investigations

Results of Geological and Volcanological Investigations Rabaul Investigated and Awards AnnouncedMcNicoll remained in Rabaul as administrator during most of the four years following the 1937 eruption and was at the centre of the controversial discussion on the future of Rabaul.No further volcanic activity of any significance took place during this time, and Rabaul gradually recovered as roads and gardens were cleared, and trees and shrubs regenerated, providing welcome shade.However, the bleak forms of Vulcan and Tavurvur stood at the entrance to Simpson Harbour like sombre monuments to what had happened in May 1937, as Rabaul was investigated and recommendations were made on its suitability as a capital.Sir Raphael Cilento arrived at Rabaul on
A novel binocular vision-robot hand-eye calibration method using dual nonlinear optimization and sample screening

To reduce the effects of random errors in vision robotic systems, a double-layer LM (Levenberg- Marquardt) optimization (DLMO) method was designed to improve the calibration accuracy of the robot hand-eye matrix based on the principle of nonlinear optimization. Firstly, a hand-eye matrix equation was established, and the initial value of the hand-eye matrix was solved by using a linear least square method. Secondly, Euler angle transformation was performed on the rotation matrix part in the hand-eye matrix, to ensure the orthogonality of the rotation matrix. Then, an optimization model of the hand-eye matrix was constructed, and the initial value of the hand-eye matrix was optimized for the first time by using the traditional LM nonlinear optimization method. Finally, the optimization model of the hand-eye matrix was modified, and then the LM nonlinear optimization method with the function of sample point screening was used to optimize the hand-eye matrix for the second time. Using the proposed DLMO, the hand-eye calibration tests were carried out on two industrial robots equipped with binocular vision systems. The average position errors of the calibration results were 0.33 mm and 0.51 mm, respectively. The results showed that the accuracy of the proposed calibration method met the working requirements of the vision-robot in the industrial field.
Strategies for realizing the creative potential of student youth (gender aspect)

The article reveals strategies for realizing the creative potential of school youth in the gender dimension. Indicators characterizing the ability of schoolchildren to be creative and constituting their creative potential have been singled out. Such indicators of the creative potential of students include novelty, originality of the solution and independence. It is emphasized that in relation to this issue, the creative potential of schoolchildren is the ability to define and formulate a problem, generate new ideas and carry out their selection, propose and justify original solutions to problems, performing research with a high degree of independence. Two main trends are observed in the strategy of applying criteria for evaluating the ability of schoolchildren to creative activity: to build and use a single-criterion evaluation system and to the complex use of various criteria, taking into account the individual weight of each criterion in a specific situation. We propose to use the last approach in the comprehensive assessment of the creative potential of student youth engaged in creative activities. Key words: student youth, creative potential, strategies for realizing creative potential, giftedness, gender.
Hydrodynamical formulation of Wigner–Dunkl quantum mechanics

The hydrodynamical form of Wigner–Dunkl quantum mechanics has been formulated. The main step to get such formulation was to derive a pair of Madelung-like equations (called Madelung–Wigner–Dunkl equations) from the Schrödinger equation appearing in Wigner–Dunkl quantum mechanics. Some aspects of both equations contained in the pair, i.e. the inhomogeneous continuity equation and the quantum Hamilton–Jacobi equation, have been investigated and discussed. With certain assumption, the relation between the expectation value of the quantum potential appearing in the quantum Hamilton–Jacobi equation and the so-called Fisher information has been derived. Then an uncertainty relation between momentum and position which may be tighter than the standard uncertainty relation has been derived by using Cramér–Rao inequality and Fisher information.
[Prospects for the Application of Healthcare Big Data Combined With Large Language Models].

The application of big data technology combined with large language models is expected to make an enormous impact in the field of medicine. Herein, the prospects for the application of healthcare big data combined with large language models were discussed in several aspects, including first in assisting doctors in making diagnosis and differential diagnosis and, then, in the field of evidence-based medicine. In addition, healthcare big data combined with large language models could also be applied in assisting doctors to conduct clinical and medical research. Through combining healthcare big data with large language models, medical diagnosis and treatment with improved precision, efficiency, and intelligence will be realized and greater contributions will be made to the field of human health.
International Clinical Research Data Ecosystem: From Data Standardization to Federated Analysis

Within the HORIZON 2020 project ORCHESTRA, patient data from numerous clinical studies in Europe related to COVID-19 were harmonized to create new knowledge on the disease. In this article, we describe the ecosystem that was established for the management of data collected and contributed by project partners. Study protocols elements were mapped to interoperability standards to establish a common terminology. That served as the basis of identifying common concepts used across several studies. Harmonized data were used to perform analysis directly on a central database and also through federated analysis when data was not permitted to leave the local server(s). This ecosystem facilitates the answering of research questions and generation of new knowledge available for the scientific community.
AI polarization compensation algorithm using a asymmetry entangled photon source

Abstract Satellite-based entanglement distribution is essential for global-scale practical quantum network and foundational tests of quantum physics. Owing to relative motion of the satellite and the ground station, however, using high intensity reference laser to compensate polarization rotation is necessary. Here we propose an artificial intelligence (AI) algorithm to compensate polarization using entangled photon pairs instead of high intensity reference laser. Our AI polarization compensation algorithm opens a new avenue to quantum communications and fundamental quantum optics experiments.
Mutualistic Compute Continuum: A Network Economics Analysis

In the transformative data-driven era, Compute Continuum – consisting of the edge, fog and cloud - has been introduced as a viable long-term economic model. In this setting the problem of utilizing the appropriate compute resources at optimal processing points (edge, fog, cloud) in the system, becomes of paramount importance. In this paper, we address this issue by introducing the novel concept of mutualistic computing. Specifically, we do not simply account for the interactions among the involved actors, but we consider that the edge, fog, and the cloud computing providers establish an obligate symbiosis with the users in order to serve their computing demands. A network economics-based analysis is introduced in order to jointly enable the computing providers and the users to maximize their profit and utility, respectively. Two pricing models are studied to determine the computing providers' optimal prices in terms of maximizing their profit. The first one - free market pricing model - enables the full competition among the computing providers and is formulated as a non-cooperative game among the computing providers. The second one - oligopoly pricing - is introduced among the cloud computing provider (leader) and the edge, fog computing providers (followers), and is treated via a Stackelberg game. The performance evaluation of the proposed approach is achieved via modeling and simulation, and the tradeoffs of the two different pricing models within the mutualistic computing paradigm are highlighted.
Video Surveillance on Mobile Edge Networks: Exploiting Multi-Exit Network

Video surveillance systems are playing increasingly important roles in our everyday lives. To get meaningful surveillance information in a timely and accurate manner, it is vital to optimally allocate computation and communication resources for image classification tasks. In this paper, taking face recognition as an example, we propose a novel end-to-edge collaborative computing system based on a multi-exit network to dynamically allocate computation at the front end (the camera sensor) and back end (the mobile edge computing server). With the ∊-greedy algorithm for reinforcement learning, the decision module decides whether to obtain recognition results from earlier exits at the front end or transmit the feature maps to the back end to obtain more accurate results. The module balances recognition accuracy and time overhead under different channel conditions. Experimental results show that the proposed system can significantly save inference time and maintain competitive accuracy in various communication channel conditions.
Towards Reliable App Marketplaces: Machine Learning-Based Detection of Fraudulent Reviews

Online reviews significantly influence consumer decisions, making the increasing prevalence of fake reviews in app marketplaces concerning. These deceptive reviews distort the competitive landscape, providing unfair advantages or disadvantages to certain apps. Despite ongoing efforts to detect fake reviews, the sophistication of fake review generation continues to evolve, necessitating continuous improvements in detection models. Current models often focus on precision, potentially overlooking many fake reviews. This research addresses these challenges by developing a machine learning model since experiments on app reviews were published on a popular App Marketplace. The developed model detects fake reviews based on the textual content and the reviewer’s behavior, offering a relevant approach to enhancing the integrity of app marketplaces.
Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via Parallel Edge Offloading

To identify dense and small-size pedestrians in surveillance systems, high-resolution cameras are widely deployed, where high-resolution images are captured and delivered to off-the-shelf pedestrian detection models. However, given the highly computation-intensive workload brought by the high resolution, the resource-constrained cameras fail to afford accurate inference in real time. To address that, we propose Hode, an offloaded video analytic framework that utilizes multiple edge nodes in proximity to expedite pedestrian detection with high-resolution inputs. Specifically, Hode can intelligently split high-resolution images into respective regions and then offload them to distributed edge nodes to perform pedestrian detection in parallel. A spatio-temporal flow filtering method is designed to enable context-aware region partitioning, as well as a DRL-based scheduling algorithm to allow accuracy-aware load balance among heterogeneous edge nodes. Extensive evaluation results using realistic prototypes show that Hode can achieve up to 2.01× speedup with very mild accuracy loss.