Reviewerly/exHarmony huggingface dataset:
Embedding Shepard’s Interpolation into CNN Models for Unguided Depth Completion

When acquiring sparse data samples, an interpolation method is often needed to fill in the missing information. An example application, known as “depth completion”, consists in estimating dense depth maps from sparse observations (e.g. LiDAR acquisitions). To do this, algorithmic methods fill the depth image by performing a sequence of basic image processing operations, while recent approaches propose data-driven solutions, mostly based on Convolutional Neural Networks (CNNs), to predict the missing information. In this work, we combine learning-based and classical algorithmic approaches to ideally exploit the performance of the former with the ability to generalize of the latter. First, we define a novel architecture block called IDWBlock. This component allows to embed Shepard’s interpolation (or Inverse Distance Weighting, IDW) into a CNN model, with the advantage of requiring a small number of parameters regardless of the kernel size. Second, we propose two network architectures involving a combination of the IDWBlock and learning-based depth completion techniques. In the experimental section, we tested the models’ performances on the KITTI depth completion benchmark and NYU-depth-v2 dataset, showing how they present strong robustness to input sparsity under different densities and patterns.
Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations

Large pre-trained language models have exhibited unprecedented capabilities in producing high-quality text via prompting techniques. This fact introduces new possibilities for data collection and annotation, particularly in situations where such data are scarce, complex to gather, expensive, or even sensitive. In this paper, we explore the potential of pre-trained language models to generate and annotate goal-oriented dialogues, and conduct an in-depth analysis to evaluate their quality. Our experiments employ ChatGPT, and encompass three categories of goal-oriented dialogues (task-oriented, collaborative, and explanatory), two generation modes (interactive and one-shot), and two languages (English and Italian). Through extensive human-based evaluations, we demonstrate that the quality of generated dialogues is on par with those generated by humans. On the other side, we show that the complexity of dialogue annotation schema (e.g., for dialogue state tracking) exceeds the capacity of current language models, a task which still requires substantial human supervision.
Intrinsically Motivated High-Level Planning for Agent Exploration

This paper proposes a new open-ended learning framework which aims at implementing an autonomous agent using intrinsic motivations (IM) at two different levels. At the first level, the IM paradigm is exploited by the agent to learn new operational skills, described in terms of sub-symbolic options. After discovering the options, the agent iteratively: (1) executes them to explore the world, collecting the necessary data and (2) automatically abstracts the collected data into a high-level representation of the domain, expressed in PPDDL language. At the second level, the IM paradigm is used to exploit the abstracted representation of the domain by identifying particular symbolic states deemed promising according to a specific criterium, which in the present work is the farthest distance covered by the agent (i.e., the most promising states are those that rest at the frontier of the visited space). Once these states are identified, they can be successively reached through an internally generated high-level plan and used as promising starting points for discovering new knowledge. The presented framework is tested in the so-called Treasure Game domain described in the recent literature. The tests we have performed show that the proposed idea of implementing intrinsic motivations at two different levels of abstraction facilitates the discovery of new knowledge, compared to a previous approach proposed in the literature.
Combining Genetic Algorithms and Temporal Constraint Satisfaction for Recommending Personalized Tourist Itineraries

In this paper we propose an approach that combines genetic algorithms and temporal constraint satisfaction for generating personalized tourist itineraries. Not only does the personalization process take into account user preferences about the places to visit but also a number of temporal constraints (e.g., opening hours, time for visiting each place, time to move among places) and time-related user preferences (e.g., number of days of the visit, preferring a dense schedule vs having a lot of free time, variety of the types of attractions during a day of visit). In the paper, we discuss our approach and the results of a preliminary evaluation of a prototypical implementation that generates personalized itineraries for the city of Turin.
Can Existing 3D Monocular Object Detection Methods Work in Roadside Contexts? A Reproducibility Study

Detecting 3D objects in images from urban monocular cameras is essential to enable intelligent monitoring applications for local municipalities decision-support systems. However, existing detection methods in this domain are mainly focused on autonomous driving and limited to frontal views from sensors mounted on the vehicle. In contrast, to monitor urban areas, local municipalities rely on streams collected from fixed cameras, especially in intersections and particularly dangerous areas. Such streams represent a rich source of data for applications focused on traffic patterns, road conditions, and potential hazards. In this paper, given the lack of availability of large-scale datasets of images from roadside cameras, and the time-consuming process of generating real labelled data, we first proposed a synthetic dataset using the CARLA simulator, which makes dataset creation efficient yet acceptable. The dataset consists of 7,481 development images and 7,518 test images. Then, we reproduced state-of-the-art models for monocular 3D object detection proven to work well in autonomous driving (e.g., M3DRPN, Monodle, SMOKE, and Kinematic) and tested them on the newly generated dataset. Our results show that our dataset can serve as a reference for future experiments and that state-of-the-art models from the autonomous driving domain do not always generalize well to monocular roadside camera images. Source code and data are available at https://bit.ly/monocular-3d-odt .
Election Manipulation in Social Networks with Single-Peaked Agents

Several elections run in the last years have been characterized by attempts to manipulate the result of the election through the diffusion of fake or malicious news over social networks. This problem has been recognized as a critical issue for the robustness of our democracy. Analyzing and understanding how such manipulations may occur is crucial to the design of effective countermeasures to these practices. Many studies have observed that, in general, to design an optimal manipulation is usually a computationally hard task. Nevertheless, literature on bribery in voting and election manipulation has frequently observed that most hardness results melt down when one focuses on the setting of (nearly) single-peaked agents, i.e., when each voter has a preferred candidate (usually, the one closest to her own belief) and preferences of remaining candidates are inversely proportional to the distance between the candidate’s position and the voter’s belief. Unfortunately, no such analysis has been done for election manipulations run in social networks. In this work, we try to close this gap: specifically, we consider a setting for election manipulation that naturally raises (nearly) single-peaked preferences, and we evaluate the complexity of the election manipulation problem in this setting: while most of the hardness and approximation results still hold, we will show that single-peaked preferences allow to design simple, efficient and effective heuristics for election manipulation.
Mining Contrast Sequential Patterns with ASP

In this paper we address an extension of the sequential pattern mining problem which aims at detecting the significant differences between frequent sequences with respect to given classes. The resulting problem is known as contrast sequential pattern mining, since it merges the two notions of sequential pattern and contrast pattern. For this problem we present a declarative approach based on Answer Set Programming (ASP). The efficiency and the scalability of the ASP encoding are evaluated on two publicly available datasets, iPRG and UNIX User, by varying parameters, also in comparison with a hybrid ASP-based approach.
Uncovering Bias in the Face Processing Pipeline: An Analysis of Popular and State-of-the-Art Algorithms Across Demographic Groups

Numerous algorithms process face images to perform tasks such as person identification and estimation of attributes such as the race and gender. While previous work has focused on biases in face recognition systems, relatively limited work has considered the full face processing pipeline to determine if other components also exhibit any biases related to a person’s demographic attributes. An evaluation of popular and state-of-the-art methods in the face processing pipeline reveals that, although the overall performance may appear satisfactory, numerous differences are uncovered when digging deeper to consider the performance not just within a single demographic group, but also across different types of groups. Several avenues of future work are also provided.
A Structure-Sensitive Translation from Hybrid to Numeric Planning

pddl+ is an expressive planning formalism that enables the modelling of hybrid domains with both discrete and continuous dynamics. However, its expressiveness makes this language notoriously difficult to handle natively. To address this challenge, translations from time-discrete pddl+ into numeric pddl2.1 have been proposed as a way to reframe the rich expressiveness of pddl+ into a simpler and more manageable formalism. In this work, we first analyse existing translations and provide a means to compare them in terms of induced state space and the size of the reformulated tasks. Secondly, we propose a novel translation leveraging the structure of the problem to generate a compact reformulation. Our experimental results indicate that the novel translation outperforms the existing ones on a range of benchmarks.
Efficient Modal Decision Trees

Modal symbolic learning is an emerging machine learning paradigm for (non)-tabular data, and modal decision trees are its most representative schema. The underlying idea behind modal symbolic learning is that non-tabular (e.g., temporal, spatial, spatial-temporal) instances can be seen as finite Kripke structures of a suitable modal logic and propositional alphabet; from a non-tabular dataset, then, modal formulas can be extracted to solve classic tasks such as classification, regression, and association rules extraction. Although this paradigm has already been proven successful in different learning tasks, a provably correct and complete formulation of modal decision trees has only recently been found. In this paper, we prove that correct and complete modal decision trees are also efficient, learning-wise.
Mining Argument Components in Essays at Different Levels

The research of arguments in student essays has long been the subject of automatic approaches to argument mining. The task has been mostly modeled as a sequence tagging problem, where the text is either analyzed in its entirety or split into smaller homogeneous units, such as sentences or paragraphs. However, previous research has highlighted how the various essay sections may fulfill different functions, and thereby how the position of specific argument components obeys precise structural dependency criteria. Based on such underpinning we propose an approach that exploits such structural information: in this work we present a hybrid training approach that takes into account the specific structural components of the essays, in order to be able to mine different types of argument components at different levels. Our hybrid approach achieves an improvement over essay-level and paragraph-level training, in particular in the extraction of some specific argument components.
Weld line structured light stripe center extraction approach based on data dimensionality reduction

In order to improve the accuracy and speed of extracting the center of the line structured light stripe when using the vision measurement technology to get the 3D information of the weld sample, this paper proposes a center extraction approach based on the idea of data dimensionality reduction. First, use the weighted gray centroid approach to get the initial center point of the light stripe. Then, build a 7×7 template centered on the pixel block where the initial point is located and performs singular value decomposition on the 49×3 matrix within the template to get the normal direction of the initial point. Finally, calculate the stripe's exact center points through second-order Taylor expansion and smooth repair by cubic spline interpolation of breakpoints. The comparison experimental results show that this approach can effectively reduce the influence of the noise outside the light stripe on the center extraction, reduce the extraction time while ensuring the measurement accuracy, which can lay a good foundation for the point cloud data processing and surface reconstruction.
PyQBench: A Python library for benchmarking gate-based quantum computers

We introduce PyQBench, an innovative open-source framework for benchmarking gate-based quantum computers. PyQBench can benchmark NISQ devices by verifying their capability of discriminating between two von Neumann measurements. PyQBench offers a simplified, ready-to-use, command line interface (CLI) for running benchmarks using a predefined family of measurements. For more advanced scenarios, PyQBench offers a way of employing user-defined measurements instead of predefined ones.
Area-efficient AdderNet hardware accelerator with merged adder tree structure

This brief introduces an area-efficient AdderNet hardware accelerator. AdderNet replaces multiply-accumulate computations of neural network processing with addition operations, thereby reducing computational cost. However, the previous accelerator uses two adders for a kernel computation to implement an absolute value computation, which still has circuit redundancy. For the efficient AdderNet acceleration, we propose a reconfigurable kernel unit and merged adder tree structure to relax such a computational circuit overhead. The proposed merged adder tree reduces the computing area by 23-28% compared to the state-of-the-art AdderNet hardware architecture.
Enhancing Collaborative Emergency Management

Emergency management involves multi-agency collaborations necessitating the trustful exchange of critical data across multiple entities. Traditional centralized systems struggle to facilitate effective inter-agency data sharing, presenting challenges related to data authenticity, traceability and privacy vulnerabilities. Consequently, there is a pressing demand for a secure and reliable data collaboration solution. This paper proposes a novel approach by leveraging Blockchain technology to create a trusted data collaboration system for emergency management scenarios. Our solution is capable for recording, sharing, verifying and tracing data among multiple entities within a distributed environment. Specifically, we implemented an application system using Hyperledger Fabric, focusing on sharing data in the practice of epidemic prevention in customs. Empirical research and analysis proved the feasibility of our solution and underscored the immense potential of Blockchain technology in Enhancing collaborative emergency management.
WYA

Spatial scene classification has long been a prominent area of research in the field of geographic information science. In the past, traditional approaches heavily relied on retrieval methods based on image features. However, given the rapid advancements in deep learning and artificial intelligence, the efficient classification of complex spatial scenes has become increasingly crucial. This paper presents a novel framework named WYA (Where You At) that combines surrounding object detection with knowledge graph to automate the process of spatial scene classification. Initially, the input images undergo processing using object detection techniques to identify key entities within the scenes. Subsequently, a knowledge graph, which encompasses various spatial scenes, entities, and their relationships, is utilized to identity spatial scene catogories. To validate the effectiveness of the framework, experiments were conducted using eight spatial scene categories as an example. The results demonstrated a high level of consistency with actual spatial types, thus affirming the efficacy of the framework and highlighting its potential application value in the domain of spatial scene classification.
A between-cluster approach for clustering skew-symmetric data

Abstract In order to investigate exchanges between objects, a clustering model for skew-symmetric data is proposed, which relies on the between-cluster effects of the skew-symmetries that represent the imbalances of the observed exchanges between pairs of objects. The aim is to detect clusters of objects that share the same behaviour of exchange so that origin and destination clusters are identified. The proposed model is based on the decomposition of the skew-symmetric matrix pertaining to the imbalances between clusters into a sum of a number of off-diagonal block matrices. Each matrix can be approximated by a skew-symmetric matrix by using a truncated Singular Value Decomposition (SVD) which exploits the properties of the skew-symmetric matrices. The model is fitted in a least-squares framework and an efficient Alternating Least Squares algorithm is provided. Finally, in order to show the potentiality of the model and the features of the resulting clusters, an extensive simulation study and an illustrative application to real data are presented.
Soft skills and academic performance of Professional School’s students of Accounting at the National University of Cañete, 2023 [Habilidades blandas y rendimiento académico de los estudiantes de la Escuela Profesional de Contabilidad de la Universidad Nacional de Cañete, 2023]

The objective of the study was to determine if there is a significant correlation between soft skills and academic performance. For this reason, the research was carried out in higher education. The type of research is applied, with the level of research being descriptive and correlational. The research was transversal; since the survey was carried out only once using the questionnaire and notes technique. The sample is simple random, 52 students from the first and second cycles enrolled in the 2023-1 semester, who constitute the study. It was shown that the use of soft skills and academic performance in students predominates at the middle level, that is, represented by 61.54% (32). Therefore, it is necessary for teachers to apply soft skills in their classes. The average of respondents regarding the use of soft skills is favorable. It was determined that more than 50% of the respondents are older than 3,06128, that is, they have a favorable possession. On average, subjects have a rank of 3.5000 (favorable). Furthermore, the average deviation was 1.06128. The minimum score was 1 and the maximum was 5. Therefore, the Use of soft skills benefits the student. It is concluded that there is an adequate significant relationship between the use of soft skills and the academic performance of university students, due to a moderate correlation of 0.819.
Research on Emotional Classification and Literary Narrative Visualization Based on Graph Convolutional Neural Network

Watching a movie for three minutes has become a popular term in contemporary life, and people also hope to spend less time understanding the emotional direction and general plot of long novels. Currently, research on emotional analysis in novels mainly focuses on foreign languages. This article introduces Graph Convolutional Neural networks into the text emotion classification of novels. Text GCN is used to conduct emotion analysis on Ba Jin’s novel “The Rapids Trilogy”, and iStoryline is used to visualize the storyline of literary works. Visual coding adjustments are made based on the results of emotion analysis in the generated storyline, where line aggregation and dispersion represent whether the characters appear in the same scene, The brighter the color of the lines, the more positive the emotions, making the visualization more vivid, full, and beautiful. The experimental results indicate that this method enables readers to easily grasp the overall direction of the novel’s plot and have a clearer understanding of the emotional changes of the characters. In addition, the accuracy of the Text GCN model is superior to traditional sentiment classification methods on Chinese datasets. This method provides a new approach for emotional analysis and narrative visualization research in literary works, and can be extended to other fields of digital entertainment.
Enhanced Frequency Information for Image Dehazing

The restoration of images affected by severe weather conditions such as heavy fog is a trending topic in the field of computer vision. Despite the fact that many image dehazing methods have achieved impressive performance, it is common that frequency information attenuation is overlooked in both feature space and frequency domain. In this paper, we propose a novel frequency guidance (FG) framework for image dehazing, which contains a recurrent frequency enhancement (RFE) module and a reconstruction module. To begin with, we develop a multi-scale decomposition (MD) block to separate the feature map into high-frequency and low-frequency components. Subsequently, both components are enhanced using the same network guided by the attention map, but with different weights. In addition to enhancing the frequency information within the feature space, we introduce a Fourier frequency loss (FFL) to provide guidance in the frequency domain, obtained via the fast Fourier transform. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple dehazing datasets.
Cyber Security Awareness and SMEs’ Profitability and Continuity: Literature Review

Small and medium-sized enterprises (SMEs) play a crucial role in the global economy, but they are also increasingly vulnerable to cyberattacks due to their limited resources and expertise in cybersecurity. Cyberattacks can have serious consequences for SMEs, including financial losses, reputational damage, and even bankruptcy. Therefore, it is essential for SMEs to be aware of the risks of cyberattacks and to implement effective cybersecurity measures to protect themselves. This research paper aims to investigate the impact of cyber security awareness on SMEs’ profitability and continuity in the Kingdom of Bahrain. The findings of this research paper are expected to contribute to the understanding of the importance of cyber security awareness for SMEs and the need for greater investment in this area. The research will highlight the challenges that SMEs face in implementing effective cybersecurity measures and the potential impact of cyberattacks on their profitability and continuity. The study will also provide recommendations for SMEs on how to improve their cyber security practices and mitigate the risks of cyberattacks. The research paper will make a significant contribution to the literature on cybersecurity awareness in SMEs and provide insights into the challenges and opportunities for SMEs in this area. The study will have important implications for policymakers, SME owners and managers, and other stakeholders interested in promoting the growth and sustainability of SMEs. By raising awareness of the importance of cybersecurity, SMEs can protect themselves from the increasing threat of cyberattacks and ensure their profitability and continuity.