Reviewerly/exHarmony huggingface dataset:
Multimodal Irony for Virtual Characters

Humor is an important communicative skill in human interactions. Intelligent virtual agents can leverage it to increase their believability and overall interaction experience. In this paper, we focus on transferring and implementing existing multimodal irony markers from the literature to a photorealistic virtual character. The verbal content is generated dynamically by an irony generator. We demonstrate how the ironic turn can be augmented with prosodic and facial markers. An expressivity parameter allows us to manipulate the encoding of the irony style.
The Influence of Avatar Interfaces on Argumentative Dialogues

Humans form opinions and justify different points of view by exchanging arguments and knowledge. Likewise to human-human interaction, the way arguments are presented influence the user's willingness to engage into a critical reflection. Especially when interacting with conversational agents the user's engagement and motivation are important factors and highly influence the success or failure of such a mixed team. To maintain the users' trust and satisfaction, the users' perception of the respective system is an important indicator. Thus, this work investigates the design of a cooperative argumentative dialogue system using a virtual avatar compared to a non-avatar interface by evaluating a crowdsourcing study conducted with 84 participants. The results indicate, that the avatar system is perceived as significantly more appealing and natural and thus, engaging which also influences the acceptance and perception of the quality of presented arguments. Furthermore, we found that the presence of the avatar often led to an increase in the anticipated level of conversational proficiency similar to that of a human interlocutor. Therefore, this work provides important insights for the design of future cooperative argumentative virtual avatar interfaces.
Research on Intelligent Digital Information Platform under Computer Artificial Intelligence Technology

This paper proposes a comprehensive smart city construction model based on infrastructure promotion, digital construction and smart application. The system uses JSON and Ajax to organize and transmit data, and uses HTML5 and WebGL technology to display 3D data, which has the advantages of plug-in free, cross-platform and openness. The B/S mode provides functions such as grid event management, grid GIS management, basic data resource management, data centralized analysis, and integration. M/S mode is adopted to realize basic data collection and other functions, and realize intelligent and scientific management such as cooperative office work, information sharing, event reporting, time push, time extension approval and so on. The system provides a 3D visualization platform for city management.
Architectural Contracts for Safe Speculation

We propose architectural contracts that specify the allowable limits of speculative execution to enable both software safety guarantees and hardware verification. Transient-execution attacks have presented a major threat in recent years, driving deployment of software mitigations and research into hardware solutions. Recent work on hardware/software contracts for secure speculation recognizes the need for cooperation between hardware guarantees and software analysis, and demonstrates that speculative execution models can enable formal analysis of programs with respect to transient-execution vulnerabilities. Therefore, we have extended these limited models into comprehensive architecture-level contracts that can be verified at a microarchitecture level. We define a set of speculation contracts for translation (TSC) and branching (BSC), and for memory ordering (MOSC). We also develop a set of directed-random test routines that reproduce all known contract violations in a prototype out-of-order processor, most of which represent known transient-execution vulnerabilities. We also extend the RiscyOO processor to enforce each contract and evaluate performance, demonstrating the practicality of the chosen contracts with an overhead between -1.2% and +1.8% for this prototype. These general-purpose contracts set the stage for specification of speculative execution for complete instruction-set architectures, and particularly for new security-focused ISA extensions.
Research on Image Database Classification Method Based on MHPA

Due to the image database size expanding, how to effectively classify and retrieve images has become an important research topic. This paper proposes an image database classification method based on a multi-step heuristic partitioning algorithm (MHPA) and convolutional neural network, aiming to improve the accuracy and efficiency of image classification, provide an overview of the principle and process of the MHPA and the methods and techniques for extracting features from images. Furthermore, this paper designs a system based on an MHPA and convolutional neural network, including image acquisition, preprocessing, and image classification modules. This paper describes the structure and parameters of the convolutional neural network model and the model training and optimization process. Finally, this paper verifies the effectiveness and superiority of this method through experiments, analyzes the experimental results, and gives conclusions and prospects.
Kolmogorov Comes to Cryptomania: On Interactive Kolmogorov Complexity and Key-Agreement

Only a handful candidates for computational assumptions that imply secure key-agreement protocols (KA) are known, and even fewer are believed to be quantum safe. In this paper, we present a new hardness assumption-the worst-case hardness of a promise problem related to an interactive version of Kolmogorov Complexity. Roughly speaking, the promise problem requires telling apart tuples of strings $(\pi, x, y)$ with relatively (w.r.t. $\mathrm{K}(\pi)$) low time-bounded Interactive Kolmogorov Complexity $\left(\mathrm{IK}^{t}\right)$, and those with relatively high Kolmogorov complexity, given the promise that $\mathrm{K}^{t}(x \mid y)\lt s, \mathrm{~K}^{t}(y \mid x)\lt s$ and $s=\log n$, and where $\mathrm{IK}^{t}(\pi ; x ; y)$ is defined as the length of the shortest pair of t-bounded TMs $(A, B)$ such that the interaction of $(A, B)$ lead to the transcript $\pi$ and the respective outputs $x, y$. We demonstrate that when t is some polynomial, then not only does this hardness assumption imply the existence of KA, but it is also necessary for the existence of secure KA. As such, it yields the first natural hardness assumption characterizing the existence of key-agreement protocols. We additionally show that when the threshold s is bigger (e.g., $s=55 \log n$), then the (worst-case) hardness of this problem instead characterizes the existence of one-way functions (OWFs). As such, our work also clarifies exactly what it would take to base KA on the existence of OWFs, and demonstrates that this question boils down to demonstrating a worst-case reduction between two closely related promise problems.
Thoughts and Concerns of Spanish Teachers on Returning to School After the COVID-19 Pandemic

Introduction. At the beginning of the 2020-2021 academic year, the government, health organizations, and educational institutions had to face the implementation of protocols and action plans within the classrooms to prevent COVID-19. Aim. This study aimed to analyze the thoughts and concerns of Spanish teachers during the academic year 2020-2021. Method. The study involved the participation of 965 teachers (74.3% Women; Mage = 43.5 9.9). An ad hoc questionnaire comprising 38 items was utilized, focusing on the resources offered by the administration for the return to classrooms, teachers’ concerns about this new academic year, and their thoughts on ideal teaching conditions. Results. Differences were observed based on age and gender in the management (P1) (p = 0.02) and support (P2) (p = 0.04) from the educational administration, perceived security (P3) (p = 0.01), reduction of the working day (P5) (p = 0.002), and the adequacy of material (P6) (p = 0.004) and human (P8) (p = 0.04) resources. Teachers with greater experience exhibited differences in the items P2 (p = 0.03), P3 (p = 0.03), and P8 (p = 0.03). Conclusions. Less experienced teachers expressed higher levels of concern, while those with more teaching experience highlighted the need for additional human resources and training.
Augmented Reality Enabled Telepresence Framework for Intuitive Robotic Teleoperation

In terms of telepresence for robotic teleoperation, the existing remote surgery provides the patient’s side real-time videos to remote operators. Its effectiveness is limited due to the lack of depth information, occlusion, and restricted viewing angles. To address it, this paper develops a robotic telepresence framework integrating augmented reality (AR) and 3D reconstruction technology, providing a 3D virtual patient’s side environment for remote operators. The real scene of the patient’s side is reconstructed by a monocular camera with a manipulator before the operation, and the manipulator is teleoperated by the operator at the remote’s side. The manipulator’s pose is fused into the classic SVO+REMODE algorithm to improve the reconstructed accuracy. On the remote’s side, an AR head-mounted display (HMD) is deployed to provide telepresence for operators under a similar environment, and the reconstructed scene of the patient’s side is registered to the remote room, from which the operator can see the whole scene of the patient body and surrounding objects. An ArUco cube is used to evaluate the superimposed error between virtual and real displays, and the effectiveness is validated in an experimental platform for remote surgery.
A Real-Time Person-Following Framework Based on LiDAR-Camera Fusion

Person-following technology is an essential part of the robot's human-machine interaction function and has a wide range of application scenarios. However, tracking a single person in the real world through sensors still faces many challenges, e.g., in dense crowds, the tracking target can be partially occluded or temporarily missed. In this paper, we proposed a robust person-following framework based on LiDAR-camera fusion to solve these problems. The framework consists of three main parts. Firstly, the person detection results are obtained from both sensors by using different detection methods. Secondly, a cascaded spatial-temporal data association method is used to filter out the currently tracked person. Finally, the target observations from both sensors are fed into different Kalman filters to update the target motion state in different observation coordinate systems. Experiments on KITTI and our tracking dataset show that, even under partial occlusion, our method can still track the target continuously. Moreover, the experimental results demonstrate that our method can be implemented in real-time with an average processing frame rate of 15 Hz.
DIF-LIM: A Dual Information Flow Network for Low-Light Image Enhancement

Low-light image enhancement (LIE) aims to enhance image contrast and recover fine details for images captured in low-light conditions. However, the limitations of using a single image and manually defined priors lead to inadequate information and limited adaptability, resulting in the failure to reveal image details effectively. To tackle this problem, we propose an unsupervised dual information flow LIE model that learns adaptive priors from low-light image pairs. The model is based on the assumption that priors can be learned from pairs of images. Moreover, a simple self+supervised model is designed to perform feature processing on the original input and further processing on the image to avoid the suboptimal limitations of the model. As a result, our model exhibits superior performance on the LIE task compared to other algorithms. As a result, our model exhibits improved network adaptability and superior performance in the LIE task compared to other algorithms.
Real-time solution of multi-constrained quadratic programming problem for redundant manipulator based on recurrent neural network

Redundant manipulators are often required to not only track target trajectories but also actively avoid obstacles in practical applications. A real-time trajectory planning method is proposed to solve the issue of redundant manipulator obstacle avoidance and tracking. Firstly, the method takes the joint angle limit formed by the physical mechanism of the manipulator itself and the obstacle limit in the workspace as constraint conditions. Additionally, the optimization objective is the error between the current trajectory and the desired trajectory, and quadratic programming technology is used to describe it. Furthermore, the method employs a recurrent neural network (RNN) for real-time optimization on the quadratic programming model. Finally, the effectiveness of the proposed method is verified by experiments on Franka manipulator. Experimental results demonstrate that the algorithm is highly efficient for manipulating trajectory planning, suitable for solving the problems of obstacle avoidance and joint angle limits while tracking trajectories.
Programa de competência em informação em contexto organizacional: processo de auditoria na controladoria geral da união regional de Santa Catarina, Brasil

The purpose of this text is to present a synthesis of studies on information users in Latin America. The starting point is the observation of the diversity that shapes this field of study: different countries, different empirical objects, different methods of collecting and analyzing data, different theoretical models. As a criterion for mapping the field, the epistemological question was used, that is, the theoretical model in which the different studies are framed. Five major study perspectives will be found: the functionalist perspective (studies on the use of information, studies on user profile, planning and evaluation of libraries and information services, usability), the critical perspective (studies on exclusion, citizenship, epistemic justice ), studies of informational behavior (centered on the cognitive perception of individuals), studies of informational practices (centered on the social constitution of the use of information) and, finally, some contemporary perspectives are presented (studies of archive users, mediation on information literacy, social network users, studies on the informational subject, decolonial, feminist, black and LGBTQIA+ thinking and studies on disinformation). The conclusion points out that Latin American research on information users oscillates between the adoption of theoretical models imported from other countries (United States and European countries) and the creation of original studio perspectives.
Integrating certifications into the cybersecurity college curriculum: The efficacy of education with certifications to increase the cybersecurity workforce

One only needs to listen to the news reports to recognize that the gap between securing the enterprise and cybersecurity threats, breaches, and vulnerabilities appears to be widening at an alarming rate. An un-tapped resource to combat these attacks lies in the students of the secondary educational system. Necessary in the cybersecurity education is a 3-tiered approach to quickly escalate the student into a workplace-ready graduate. The analogy used is a three-legged-stool, where curriculum content, hands-on skills, and certifications are equal instruments in the edification of the cybersecurity student. This paper endeavors to delve into the 3rd leg of the stool by developing the concept of vendor-specific and vendor-neutral certifications to educate the cybersecurity student and test their capability of protecting the workplace. The research data was drawn from companies in the Atlanta, Georgia area, who employ and hire cybersecurity recruits. The data from the research proves certifications are necessary as an addition to the cybersecurity curriculum in the secondary education arena. The paper reviews the need for cybersecurity graduates, the balance between cybersecurity theory and applied skillsets, the difference between a certificate and a certification, benefits to the community, classifications of certifications, relevancy of a college degree in today’s workforce, and recommendations for further study.
Improving Geological Remote Sensing Interpretation via Optimal Transport-Based Point–Surface Data Fusion

High-quality geological remote sensing interpretation (GRSI) products play a vital role in a wide range of fields, including the military, meteorology, agriculture, the environment, mapping, etc. Due to the importance of GRSI products, this research aimed to improve their accuracy. Although deep-learning (DL)-based GRSI has reduced dependence on manual interpretation, the limited accuracy of multiple geological element interpretation still poses a challenge. This issue can be attributed to small inter-class differences, the uneven distribution of geological elements, sensor limitations, and the complexity of the environment. Therefore, this paper proposes a point–surface data optimal fusion method (PSDOF) to improve the accuracy of GRSI products based on optimal transport (OT) theory. PSDOF combines geological survey data (which has spatial location and geological element information called point data) with a geological remote sensing DL interpretation product (which has limited accuracy and is called surface data) to improve the quality of the resulting output. The method performs several steps to enhance accuracy. First, it calculates the gray-scale correlation feature information for the pixels adjacent to the geological survey points. Next, it determines the distribution of the feature information for geological elements in the vicinity of the point data. Finally, it incorporates complementary information from the survey points into the geological elements’ interpretation boundary, as well as calculates the optimal energy loss for point–surface fusion, thus resulting in an optimal boundary. The experiments conducted in this study demonstrated the superiority of the proposed model in addressing the problem of the limited accuracy of GRSI products.
Traffic Sign Detection Algorithm Based on Improved Yolox

This paper proposes a novel PVF-YOLO model to extract the multi-scale traffic sign features more effectively during car driving. Firstly, the original convolution module is replaced with the Omni-Dimensional convolution (ODconv) and the feature information obtained from the shallow feature layer is incorporated into the network. Secondly, this paper proposes a parallel structure block module for capturing multi-scale features. This module uses the Large Kernel Attention (LKA) and Visual Multilayer Perceptron (Visual MLP) to capture the information generated by the network model. It enhances the representation ability of feature maps. Next, in the process of training, the gradient concentration algorithm is used to optimize the initial Stochastic Gradient Descent (SGD). Under the condition of real-time detection, it improves the detection accuracy. Finally, to improve the robustness of the model, this paper conducts extensive experiments. Tsinghua-Tencent 100K (TT100K), Changsha University of Science and Technology CCTSDB (CSUST Chinese Traffic Sign Detection Benchmark) are used as the training data set. It verifies that the PVF-YOLO method proposed in this paper enhances the detection ability of traffic signs of different scales, and the detection speed and accuracy are better than the original model.
The Language of Deception: Applying Findings on Opinion Spam to Legal and Forensic Discourses

Digital forensic investigations are becoming increasingly crucial in criminal investigations and civil litigations, especially in cases of corporate espionage and intellectual property theft as more communication occurs online via e-mail and social media. Deceptive opinion spam analysis is an emerging field of research that aims to detect and identify fraudulent reviews, comments, and other forms of deceptive online content. In this paper, we explore how the findings from this field may be relevant to forensic investigation, particularly the features that capture stylistic patterns and sentiments, which are psychologically relevant aspects of truthful and deceptive language. To assess these features’ utility, we demonstrate the potential of our proposed approach using the real-world dataset from the Enron Email Corpus. Our findings suggest that deceptive opinion spam analysis may be a valuable tool for forensic investigators and legal professionals looking to identify and analyze deceptive behavior in online communication. By incorporating these techniques into their investigative and legal strategies, professionals can improve the accuracy and reliability of their findings, leading to more effective and just outcomes.
A Block Chain Based Authentication Scheme in VANET for a Secure Data Communication Using SHAH Algorithm

Objectives: In Vehicular Ad Hoc Networks (VANETs), ensuring secure data communication is essential to uphold the integrity and reliability of vital vehicular applications. This study presents an innovative authentication scheme based on block chain technology, specifically designed to enhance data exchange security within VANETs. With a successful deployment and integration into our transportation systems, VANETs promise safer roads, reduced traffic congestion, and more efficient traffic management. These networks offer immediate communication of vital information to drivers, such as traffic conditions, accidents, and roadwork, helping to reduce accidents and save lives. VANET can enable intelligent traffic management, making real-time adjustments to traffic signals and routes based on data from the network. This can lead to reduced travel times and improved fuel efficiency, resulting in cost savings and reduced emissions. Methods: The scheme utilizes the SHAH (Secure Hash Algorithm with HMAC) Algorithm as a foundational component to safeguard data authenticity and confidentiality. Our proposed block chain-based authentication approach capitalizes on the decentralized nature of block chain technology, establishing a robust and tamper-resistant ledger for authentication purposes. This helps to secure data transmission. Findings: A comprehensive analysis of the proposed scheme showcases its efficacy in ensuring secure data communication within VANETs. These findings underscore the significant value of our proposed approach, contributing to a safer and more dependable environment for vehicular communication, thereby enhancing the overall reliability of VANET applications. Novelty: As a concluding note, this research not only introduces a unique block chain-based authentication scheme tailored specifically for VANETs but also underscores the indispensable role played by the SHAH Algorithm in achieving data security. The outcomes presented in this study inspire further exploration at the intersection of block chain and VANET technologies, ultimately advancing the state of secure vehicular communication. Keywords: VANET, Block chain, Authentication, Trusted Authority, RSU
In Search of the Essence of No-Code – Elements of Data Modeling

Low-code and no-code development has emerged as a significant trend in the software industry, empowering the masses and revolutionizing the way software applications are created. While often labeled as buzzwords, these two terms correspond in reality to a very large set of tools and growing communities. While many studies have been made for low-code, this study aims instead at better understanding the essence of no-code. To do so, we propose, a rigorous selection of 9 data centric no-code tools. Then, we study through the lens of data-modeling (e.g., UML/Class + OCL), how features proposed by those tools compare to their opposite, i.e. coding approaches. Thus, exhibiting an empirical way to define no-code through comparison with coding approaches.