Reviewerly/exHarmony huggingface dataset:
Dif-MAML: Decentralized Multi-Agent Meta-Learning

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited for this decentralized setting, where the learner benefits from information and computational power spread across the agents. Motivated by this observation, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, robustness, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregate</i> MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
Demo: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.Recently, Dirty Road Patch (DRP) attack is proposed as a state-of-the-art adversarial attack against ALC systems.We will show interactive demonstrations for the malicious road patch generation and the serious consequences.
The methodology of development of information and communication competence in teachers of the military education system applying the distance form of learning

The paper theoretically substantiates the methodology of development of information and communication competence (ICC) in teachers of the military education system applying the distance form of learning. Scientific approaches to the concepts of “methodology” have been analyzed and the author’s vision of “the methodology of development of ICC in the military education teachers” has been suggested. In particular, they determine the methodological approaches to the methodology of its development, as well as its main stages, purpose, tasks, content, methods, types of training sessions, tools and organizational forms of learning.
A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5

Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement.
G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment

The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies suggest using large language models (LLMs) as reference-free metrics for NLG evaluation, which have the benefit of being applicable to new tasks that lack human references. However, these LLM-based evaluators still have lower human correspondence than medium-size neural evaluators. In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs. We experiment with two generation tasks, text summarization and dialogue generation. We show that G-Eval with GPT-4 as the backbone model achieves a Spearman correlation of 0.514 with human on summarization task, outperforming all previous methods by a large margin. We also propose analysis on the behavior of LLM-based evaluators, and highlight the potential concern of LLM-based evaluators having a bias towards the LLM-generated texts.
En la búsqueda de nuevas formas de interacción sociodiscursiva en entornos virtuales de aprendizaje: El nuevo rol docente

In contemporary society, the technology provides great benefits to the educational processes and knowledge management. Given the avalanche of media products, education in Latin America welcomes the virtuality, as a complementing to classroom activity or as distance methodology, whose distinguishing feature is the use of Information Technology and Communication (ICT). Thus, we have new forms of social and discursive interaction that give dynamic to academic and research processes, by strengthening collective intelligence enriched by the experiences of the users. As teachers or students, we must prepare to accept with responsibility, the technological tools available.
Organizational cloud security and control: a proactive approach

Purpose The purpose of this paper is to unfold the perceptions around additional security in cloud environments by highlighting the importance of controlling mechanisms as an approach to the ethical use of the systems. The study focuses on the effects of the controlling mechanisms in maintaining an overall secure position for the cloud and the mediating role of the ethical behavior in this relationship. Design/methodology/approach A case study was conducted, examining the adoption of managed cloud security services as a means of control, as well as a large-scale survey with the views of IT decision makers about the effects of such adoption to the overall cloud security. Findings The findings indicate that there is indeed a positive relationship between the adoption of controlling mechanisms and the maintenance of overall cloud security, which increases when the users follow an ethical behavior in the use of the cloud. A framework based on the findings is built suggesting a research agenda for the future and a conceptualization of the field. Research limitations/implications One of the major limitations of the study is the fact that the data collection was based on the perceptions of IT decision makers from a cross-section of industries; however the proposed framework should also be examined in industry-specific context. Although the firm size was indicated as a high influencing factor, it was not considered for this study, as the data collection targeted a range of organizations from various sizes. Originality/value This study extends the research of IS security behavior based on the notion that individuals (clients and providers of cloud infrastructure) are protecting something separate from themselves, in a cloud-based environment, sharing responsibility and trust with their peers. The organization in this context is focusing on managed security solutions as a proactive measurement to preserve cloud security in cloud environments.
Deep Belief Network and Auto-Encoder for Face Classification

The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability ofovercoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learningmethods which are Deep Belief Network (DBN) and Stacked Auto-Encoder (SAE), to initialize deep supervisedNeural Networks (NN), besides of Back Propagation Neural Networks (BPNN) applied to face classificationtask. Moreover, our contribution is to extract hierarchical representations of face image based on the DeepLearning models which are: DBN, SAE and BPNN. Then, the extracted feature vectors of each model areused as input of NN classifier. Next, to test our approach and evaluate its performance, a simulation seriesof experiments were performed on two facial databases: BOSS and MIT. Our proposed approach which is(DBN,NN) has a significant improvement on the classification error rate compared to (SAE,NN) and BPNNwhich we get 1.14% and 1.96% in terms of error rate with BOSS and MIT respectively
Towards a QoE Model to Evaluate Holographic Augmented Reality Devices

Augmented reality (AR) technology is developing fast and provides users with new ways to interact with the real-world surrounding environment. Although the performance of holographic AR multimedia devices can be measured with traditional quality-of-service parameters, a quality-of-experience (QoE) model can better evaluate the device from the perspective of users. As there are currently no well-recognized models for measuring the QoE of a holographic AR multimedia device, we present a QoE framework and model it with a fuzzy inference system to quantitatively evaluate the device.
Modeling anti-malware use intention of university students in a developing country using the theory of planned behavior

Purpose This paper aims to investigate the impact of perceived price level and information security awareness on computer users’ attitude. Moreover, this study aims to investigate the effect of attitude, subjective norms and perceived behavioral control (PBC) on intention to use anti-malware software. Design/methodology/approach Data were collected using a structured questionnaire from 225 students of five public universities in Malaysia. Purposive sampling technique was used in this study. AMOS 24 was used to test the research framework using a two-step approach. Findings Findings give support to some of the hypotheses developed with R2 values of 0.521 for attitude and 0.740 for intention. Perceived price level had a negative effect on attitude while information security awareness had a positive effect on attitude and intention. Attitude, subjective norms and PBC were all positively related to intention, but perceived price level did not affect intention. This suggests that benefits of using anti-malware are more than its price value. Therefore, the price has no direct effect on intention to use. Research limitations/implications University computer networks are as open and inviting as their campuses. Therefore, this research can be helpful to the universities to safeguard their networks and encourage the students to use anti-malware. However, using anti-malware software will enable an individual to identify and prioritize security risks, quickly detect and mitigate security breaches, improve the understanding of security gaps and safeguard the sensitive data by minimizing the risks related to malware. Originality/value This study ventured to model the information security behavior of anti-malware usage by individual users by using the theory of planned behavior with the addition of two new variables, perceived price level and information security awareness to explain the behavior better.
Equational Axiomatization of Algebras with Structure

Abstract This paper proposes a new category theoretic account of equationally axiomatizable classes of algebras. Our approach is well-suited for the treatment of algebras equipped with additional computationally relevant structure, such as ordered algebras, continuous algebras, quantitative algebras, nominal algebras, or profinite algebras. Our main contributions are a generic HSP theorem and a sound and complete equational logic, which are shown to encompass numerous flavors of equational axiomizations studied in the literature.
SDBPR: Social distance-aware Bayesian personalized ranking for recommendation

Recommendation systems recommend new items to users. Because training data contain only binary forms of implicit feedback in many cases, such as in IoT and IoV, one-class collaborative filtering, which can be solved by using rating-based methods to estimate the numeric scores of items or ranking-based methods based on the preferences of each user for items, must be addressed. In addition, because of the sparsity of such data, ranking-based methods are often preferred over rating-based methods when only implicit feedback is available. Social information has recently been used to improve the accuracy of rankings. Traditional approaches simply consider the direct friends of users in a social network, but this process fails to consider the propagation of influence along connections in the social network and cannot reveal the complex graph structure of the social network. In this paper, a novel social distance-aware Bayesian personalized ranking model, called SDBPR, is proposed to generate more accurate recommendations. SDBPR uses a random walk to travel the social network and then makes pairwise assumptions about the ranking order based on the distance between users along the random walk. The experimental results on two real datasets show that the proposed approaches significantly outperform the baseline approaches in terms of ranking prediction.
Monitoring open access publishing costs at Stockholm University

Stockholm University Library (SUB) has been tracking the University’s open access (OA) publishing costs within the local accounting system since 2016. The objective is to gain an overview of the costs and to use this as a basis for decisions about how to proceed in order to support the transition to OA at Stockholm University. This article explains the reasons behind using the accounting system as the primary source of information and describes the workflow of tracking costs and how additional data are retrieved. Basic findings from the 2017 cost compilation are outlined, and the steps taken in 2018, with consequences for both the current workflow and the costs at SUB, are briefly discussed. A breakout session on this topic was presented at the UKSG Annual Conference in Glasgow in 2018.
First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

First-arrival picking is a critical step in seismic data processing. This paper proposes the first-arrival picking through sliding windows and fuzzy c-means (FPSF) algorithm with two stages. The first stage detects a range using sliding windows on vertical and horizontal directions. The second stage obtains the first-arrival travel times from the range using fuzzy c-means coupled with particle swarm optimization. Results on both noisy and preprocessed field data show that the FPSF algorithm is more accurate than classical methods.
What is 'Cyber Security'?

People experience and understand cyber security differently. Our ongoing work aims to address the fundamental challenge of how we can understand a diverse range of cyber security experiences, attitudes and behaviours in order to design better, more effective cyber security services and educational materials. In this paper, we take a lifespan approach to study the language of cyber security across three main life stages - young people, working age, and older people. By applying text feature extraction and analysis techniques to lists of cyber security features generated by each age group, we illustrate the differential language of cyber security across the lifespan and discuss the implications for design and research in HCI.
A Social Network Analysis (SNA) Study On Data Breach Concerns Over Social Media

In the current era of digital devices, the concerns over data privacy and security breaches are rampant.Understanding these concerns by analyzing the messages posted on the social media from linguistic perspective has been a challenge that is increasing in complexity as the number of social media sites increase and the volume of data increases.We investigate the diffusion characteristics of the information attributed to data breach messages, first based on the literary aspects of the message and second, we build a social network of the users who are directly involved in spreading the messages.We found that the messages that involve the technicalities, threat and severity related security characteristics spread fast.Contrary to conventional news channels related posts on social media that capture wide attention, breach information diffusion follows a different pattern.The messages are widely shared across the tech-savvy groups and people involved in security-related studies.Analyzing the messages in both linguistic and visual perspective through social networks, researchers can extract grounded insights into these research questions.
Architecture of a Wheelchair Control System for Disabled People: Towards Multifunctional Robotic Solution with Neurobiological Interfaces

The aim of the study was to develop a control system for a robotic wheelchair with an extensive user interface that is able to support users with different impairments.Different concepts for a robotic wheelchair design for disabled people are discussed.The selected approach is based on a cognitive multimodal user interface to maximize autonomy of the wheelchair user and to allow him or her to communicate intentions by high-level instructions.Manual, voice, eye tracking, and BCI (brain-computer interface) signals can be used for strategic control whereas an intelligent autonomous system can perform low-level control.A semiotic model of the world processes sensory data and plans actions as a sequence of high-level tasks or behaviors for the control system.A software and hardware architecture for the robotic wheelchair and its multimodal user interface was proposed.This architecture supports several feedback types for the user including voice messages, screen output, as well as various light indications and tactile signals.The paper describes novel solutions that have been tested on real robotic devices.The prototype of the wheelchair uses a wide range of sensors such as a camera, range finders, and encoders to allow operator to move safely and provide object and scene recognition capabilities for the wheelchair.Dangerous behavior of the robot is interrupted by low-level reflexes.Additional high-level safety procedures can be implemented for the planning subsystem.The developed architecture allows utilizing user interfaces with a considerable time lag that are usually not suitable for traditional automated wheelchair control.This is achieved by increasing time allocated for processing of the interface modules, which is known to increase the accuracy of such interfaces as voice, eye tracking, and BCI.The increased latency of commands is mitigated by the increased automation of the wheelchair since high-level tasks can be given less frequently than manual control.The prospective solutions use a number of technologies based on registration of parameters of human physiological systems, including brain neural networks, in relation to the task of indirect control and interaction with mobile technical systems.
Introducing narratives in Europeana: A case study

Abstract We present a preliminary study to introduce narratives as a first-class functionality in digital libraries. The general idea is to enrich those libraries with semantic networks of events providing a meaningful contextualisation of the digital libraries’ objects. More specific motivations are presented through a set of use cases by different actors who would benefit from using narratives for different purposes. Then, we consider a specific digital library, Europeana, the largest European digital library in the cultural heritage domain. We discuss how the Europeana Data Model could be extended for representing narratives, and we introduce an ontology for narratives. We also present a semi-automatic tool, which, on the basis of the ontology, supports the creation and visualisation of narratives, and we show how the tool has been employed to create a narrative of the life of the painter Gustav Klimt as a case study. In particular, we focus our attention on the functionality of the tool that allows extracting and proposing to the user specific digital objects for each event of the narrative.
Designing Augmented Reality Applications for Personal Health Decision-Making

Augmented reality (AR) is a technology that can assist with our daily decision-making tasks by presenting information that extends the physical world.However, little work has been done to understand the effect of the layout of AR interfaces on decision-making.In this paper, we present PHARA, an AR-based personal assistant that supports decision-making for healthier food products.In a controlled user study (n=28), we explored the use of four different AR layouts on two different devices: Microsoft HoloLens and smartphone.Using subjective and objective means, we measured their effects on decision-making tasks that occur when people hold food products in their hands.We found that pie and grid layouts perform better on the smartphone, whereas a stacked layout works better on the reduced field-of-view of the Microsoft HoloLens, potentially at the cost of some affordances such as time spent and actions.
Virtual Reality: Fictional all the Way Down (and that’s OK)

Abstract Are virtual objects real? I will claim that the question sets us up for the wrong type of conclusion: Chalmers (2017) argues that a virtual calculator (like other entities) is a real calculator when it is “organizationally invariant” with its non-virtual counterpart—when it performs calculation. However, virtual reality and games are defined by the fact that they always selectively implement their source material. Even the most detailed virtual car will still have an infinite range of details which are missing (gas, engines, pistons, fuel, chemical reactions, molecules, atoms). This means that even the most detailed virtual object will still have fictional aspects. Rather than argue that virtual objects are, or aren’t, real, it is preferable to think of overlaps and continuities between the fictional and the real, where even the most painstakingly detailed virtual reality implementation of a non-virtual object is still art : a human process of selection and interpretation. Virtual reality should therefore not be philosophically understood just as a technological implementation on a trajectory to perfection, but as a cultural artifact which derives its value in part from its simplification and difference from its source material.