Reviewerly/exHarmony huggingface dataset:
Object-based unawareness: Axioms

This paper provides foundations for a model of unawareness, called object-based unawareness (OBU) structures, that can be used to distinguish between what an agent is unaware of and what she simply does not know. At an informal level, this distinction plays a key role in a number of papers such as Tirole (2009) and Chung &amp; Fortnow (2016). In this paper, we give the model-theoretic description of OBU structures by showing how they assign truth conditions to every sentence of the formal language used. We then prove a model-theoretic sound and completeness theorem, which characterizes OBU structures in terms of a system of axioms. We then verify that agents in OBU structures do not violate any of the introspection axioms that are generally considered to be necessary conditions for a plausible notion of unawareness. Applications are provided in our companion paper.
MPC trajectory planner for autonomous driving solved by genetic algorithm technique

Focusing on autonomous driving algorithm development, this paper proposes a novel real-time trajectory planner formulated as a Nonlinear Model Predictive Control (NMPC) algorithm. The mathematical formulation of the problem is deeply reported and discussed. The numerical solution of the NMPC problem is the result of a novel genetic algorithm strategy that represents the innovative aspect of the work proposed. The aim of this paper is also to show how genetic algorithm can be a valid approach for motion planning strategies. Numerical results are discussed through simulations that show a reasonable behaviour of the proposed strategy in the presence of moving obstacles as well as in a wide range of road friction conditions. Moreover, a real-time implementation for research purposes is assumed as possible by considering computational time analysis reported.
Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.
LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection

The continuous development of intelligent video surveillance systems has increased the demand for enhanced vision-based methods of automated detection of anomalies within various behaviors found in video scenes. Several methods have appeared in the literature that detect different anomalies by using the details of motion features associated with different actions. To enable the efficient detection of anomalies, alongside characterizing the specificities involved in features related to each behavior, the model complexity leading to computational expense must be reduced. This paper provides a lightweight framework (LightAnomalyNet) comprising a convolutional neural network (CNN) that is trained using input frames obtained by a computationally cost-effective method. The proposed framework effectively represents and differentiates between normal and abnormal events. In particular, this work defines human falls, some kinds of suspicious behavior, and violent acts as abnormal activities, and discriminates them from other (normal) activities in surveillance videos. Experiments on public datasets show that LightAnomalyNet yields better performance comparative to the existing methods in terms of classification accuracy and input frames generation.
Design Thinking: Methodological Strategy for the Creation of a Playful Application for Children with Dyslexia

The use of a methodology to address a problem facilitates work in an efficient, effective, and highly productive way. The design thinking methodology (also known as design thinking) is user-centric and oriented towards offering solutions by breaking down a problem into small parts to analyze it, to explore it, to test the results, and to create solutions that benefit the end-user. Many children have problems related to learning disorders, such as dyslexia, which occur due to the way that their brain incorporates and processes information. This can lead to them showing difficulty in some learning areas, even when their intelligence or motivation does not appear to be affected. In this research, through a mixed approach, a playful application is developed using new information and communication technologies (ICT), following a design thinking methodology, with the aim of supporting the learning of children with dyslexia through content designed with respect to their needs in order to help improve their academic performance. Data collection was carried out through observation, an interview, and record reviews. Analysis of the didactic materials allowed for the observation that content designed for the specific needs of children can work as a reinforcement for incorporating the information in an entertaining, dynamic, and friendly way, ultimately contributing to improved academic performance.
Human Activity Recognition of Individuals with Lower Limb Amputation in Free-Living Conditions: A Pilot Study

This pilot study aimed to investigate the implementation of supervised classifiers and a neural network for the recognition of activities carried out by Individuals with Lower Limb Amputation (ILLAs), as well as individuals without gait impairment, in free living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route in the vicinity of their homes across a variety of terrains. Various machine learning classifiers were trained and tested for recognition of walking activities. Additional investigations were made regarding the detail of the activity label versus classifier accuracy and whether the classifiers were capable of being trained exclusively on non-impaired individuals' data and could recognize physical activities carried out by ILLAs. At a basic level of label detail, Support Vector Machines (SVM) and Long-Short Term Memory (LSTM) networks were able to acquire 77-78% mean classification accuracy, which fell with increased label detail. Classifiers trained on individuals without gait impairment could not recognize activities carried out by ILLAs. This investigation presents the groundwork for a HAR system capable of recognizing a variety of walking activities, both for individuals with no gait impairments and ILLAs.
Echo state networks for online, multi-step MPC relevant identification

This paper presents an online nonlinear system identification algorithm for model predictive control relevant identification, based on an echo state network (ESN) trained using the recursive least squares method with a directional forgetting factor. The proposed online ESN architecture is formed by a reservoir, with fixed recurrent connections, and a reservoir readout mechanism, which is updated online to define the network output. The ESN is used to perform a multi-step prediction task, which can be used for model-predictive control purposes. The proposed online identification algorithm is evaluated in simulation using a conical tank level process and compared with the results of other approaches from the literature. The results show that the proposed algorithm is able to identify the nonlinear characteristics of the process without considering any prior information about it. In addition, the results for long-range predictions are better than the ones obtained with a model linear in its parameters and several baseline ESN models from literature, after the adaptation phase of the proposed algorithm.
EduGram: Education Development Based on Hologram Technology

Education is one of the most important areas of life that affect the development and progress of societies, and the usage of images and visual representation methods is of real value in the educating process. Over time, different simple methods were used to display the information visually, which mostly are considered weak methods that may not perform its full purpose and the information may be transferred slowly and in an incomplete manner. Technology has contributed from the beginning of its emergence in the development of education and improve its output, and one of the most prominent contributions made by technology is the developments in the field of displaying information visually using different technologies, as the three-dimensional displaying technologies that are considered as an advanced solution which provides people with a more comprehensive view and facilitates the task of transferring information to learners and so improving the educating process. Many technologies are used to create and display the 3D visual content, and two of the most important 3D display technologies are Augmented Reality technology and Hologram technology, they both insert a three-dimensional image to the real world, but there are many differences between the two technologies in many aspects. In the first part of this research, and after defining and comparing the two technologies and the effect of each one on education, Hologram technology showed features that enabled it to be a suitable option to be used in education for displaying 3D educational content. This research then introduces the method for implementing the usage of Hologram technology in education as a 3D educational content displaying tool, introducing an implementation model by first transferring a sample of a 2D educational image to Holograms and using the Hologram fan projector to display it to the students. The results of a simple questionnaire on a number of people showed the effectiveness of using Holograms instead of the traditional 2D content found in school curricula, and a good level of people's acceptance to use this technology.
Blind and Secured Adaptive Digital Image Watermarking Approach for High Imperceptibility and Robustness

In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide high imperceptibility and security to digitally transmitted images. In this paper, a novel blind digital image watermarking scheme is introduced tackling secured transmission of digital images, which provides a higher quality regarding both imperceptibility and robustness parameters. A block based hybrid IWT- SVD transform is implemented for robust transmission of digital images. To ensure high watermark security, the watermark is encrypted using a Pseudo random key which is generated adaptively from cover and watermark images. An encrypted watermark is embedded in randomly selected low entropy blocks to increase the security as well as imperceptibility. Embedding positions within the block are identified adaptively using a Blum-Blum-Shub Pseudo random generator. To ensure higher visual quality, Initial Scaling Factor (ISF) is chosen adaptively from a cover image using image range characteristics. ISF can be optimized using Nature Inspired Optimization (NIO) techniques for higher imperceptibility and robustness. Specifically, the ISF parameter is optimized by using three well-known and novel NIO-based algorithms such as Genetic Algorithms (GA), Artificial Bee Colony (ABC), and Firefly Optimization algorithm. Experiments were conducted for the proposed scheme in terms of imperceptibility, robustness, security, embedding rate, and computational time. Experimental results support higher effectiveness of the proposed scheme. Furthermore, performance comparison has been done with some of the existing state-of-the-art schemes which substantiates the improved performance of the proposed scheme.
Digital Competence of University Teachers of Social and Legal Sciences from a Gender Perspective

Higher Education institutions must respond to the major challenges posed by the technological transformations of recent years. For this, one of the key aspects is that the teachers working in them are trained to incorporate these technologies in teaching–learning processes, which requires them to be digitally competent. To analyse how gender relates to the level of competence of teachers, the types of ICT tools that they use, and their attitudes towards their implementation in teaching–learning processes, this quantitative research was carried out with the participation of 121 university teachers from the Autonomous Community of Aragon, all of them from the area of Social and Legal Sciences. The results show a differentiated profile between men and women in terms of their attitudes, beliefs, and self-perception regarding their management skills and effectiveness.
Harris Hawks Optimization with Multi-Strategy Search and Application

The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.
Developing Integrated Smartphones Notification of Server Resource Monitoring System Using Zabbix, Webhook, and Telegram

Server monitoring activities are important to ensure the availability of resources used for the execution of its internal processes. Generally, Administrators monitor the use of server resources through a computer using a browser. The problem with monitoring through a computer is less flexible and slow for checking notifications about the state of problem server resources in real-time anytime and anywhere using smartphones. This study aims to develop notification integration on smartphones from a server resource monitoring system using Zabbix Webhook and Telegram. The research method used is the Cisco PPDIOO Lifecycle Approach to Network Design and Implementation. As a case study, it is located at the Center for Empowerment of Informatics and Nuclear Strategic Area, the National Nuclear Energy Agency. The results of the study are notifications on smartphones via the Telegram bot account indicating a problem with resource usage. The results of this study have been used by server Administrators. The server Administrators have provided information about the benefits of this system, for flexibility, speed of getting information in resource usage, identifying, and resolving problems before there are complaints so that the quality of service is getting better.
Anti-Counterfeit Product System Using Blockchain Technology

Abstract: Fake Products is a serious issue, as in today’s world we can’t really distinguish between real and fake product. And some people make these fake products just to make some profit without thinking about its impact on user and also affects company’s name, profit and sales. Blockchain technology can be used to detect whether the product is real or fake and assure user about the authenticity of the product. Blockchain is a trending technology and lot of applications are using this technology. Blockchain technology is the technology where information is stored in the form of blocks in many databases which is connected with the chains and it doesn’t require any third-party users for permission. Benefits of blockchain is that it is immutable and secure. It is decentralized and distributed. We can use Quick Response [1] (QR) code or an encrypted unique code which is a very efficient technique to detect fake product. When the QR code is scanned or the unique code in entered, it will redirect us to the blockchain containing the information of the product and provides us the details of the manufacturer and information of owner to make decision easy for buyer if they are looking to buy the product. Keywords: Blockchain, Fake Product, Ownership, QR code, Anti-counterfeit
Distilling Knowledge for Empathy Detection

Empathy is the link between self and others.Detecting and understanding empathy is a key element for improving human-machine interaction.However, annotating data for detecting empathy at a large scale is a challenging task.This paper employs multi-task training with knowledge distillation to incorporate knowledge from available resources (emotion and sentiment) to detect empathy from the natural language in different domains.This approach yields better results on an existing news-related empathy dataset compared to strong baselines.In addition, we build a new dataset for empathy prediction with finegrained empathy direction, seeking or providing empathy, from Twitter.We release our dataset for research purposes.
Modeling of "information bubbles" in the global information space

The article uses the Sedov-Taylor function to model "information bubbles" formed in the global information space due to information attacks.The authors identify the most relevant determinants that describe information activities related to cyber threats and reactions of economic agents in the global digital economic space.The article hypothesizes about the emergence of "information bubbles" due to increases in information activities and their rupture due to
Joint Passage Ranking for Diverse Multi-Answer Retrieval

We study multi-answer retrieval, an underexplored problem that requires retrieving passages to cover multiple distinct answers for a given question.This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer.In this paper, we introduce JPR, the first joint passage retrieval model for multi-answer retrieval.JPR makes use of an autoregressive reranker that selects a sequence of passages, each conditioned on previously selected passages.JPR is trained to select passages that cover new answers at each timestep and uses a tree-decoding algorithm to enable flexibility in the degree of diversity.Compared to prior approaches, JPR achieves significantly better answer coverage on three multianswer datasets.When combined with downstream question answering, the improved retrieval enables larger answer generation models since they need to consider fewer passages, establishing a new state-of-the-art.
A Privacy-Preserving Incentive Mechanism for Data Offloading in Satellite-Terrestrial Crowdsensing

Data offloading algorithm is the foundation of urban Internet of Things, which has gained attention for its large size of user engagement, low cost, and wide range of data sources, replacing traditional crowdsensing in areas such as intelligent vehicles, spectrum sensing, and environmental surveillance. In data offloading tasks, users’ location information is usually required for optimal task assignment, while some users in remote areas are unable to access base station signals, making them incapable of performing sensing tasks, and at the same time, there are serious concerns about users’ privacy leakage about their locations. Until today, location protection for task assignment in data offloading has not been well explored. In addition, existing privacy protection algorithms and data offloading task assignment mechanisms cannot provide personalized protection for different users’ privacy protection needs. To this end, we propose an algorithm known as differential private long-term privacy-preserving auction with Lyapunov stochastic theory (DP-LAL) for data offloading based on satellite-terrestrial architecture that minimizes the total payment. This not only gives an approximate optimal total payment in polynomial time but also improves the issue of poor signal in remote areas. Meanwhile, satellite-terrestrial data offloading architecture integrates wireless sensor networks and cloud computing to provide real-time data processing. What is more, we have considered long-term privacy protection goals. We employ reverse combinatorial auction and Lyapunov optimization theorem to jointly optimize queue stability and total payment. More importantly, we use Lyapunov optimization theorem to jointly optimize queue stability and total payment. We prove that our algorithm is of high efficiency in computing and has good performance in various economic attributes. For example, our algorithms are personally rational, budget-balanced, and true to the buyer and seller. We use large-scale simulations to evaluate the proposed algorithm, and compare our algorithm with existing algorithms, our algorithm shows higher efficiency and better economic properties.
Probing Across Time: What Does RoBERTa Know and When?

Models of language trained on very large corpora have been demonstrated useful for natural language processing.As fixed artifacts, they have become the object of intense study, with many researchers "probing" the extent to which they acquire and readily demonstrate linguistic abstractions, factual and commonsense knowledge, and reasoning abilities.Recent work applied several probes to intermediate training stages to observe the developmental process of a large-scale model (Chiang et al., 2020).Following this effort, we systematically answer a question: for various types of knowledge a language model learns, when during (pre)training are they acquired?Using RoBERTa as a case study, we find: linguistic knowledge is acquired fast, stably, and robustly across domains.Facts and commonsense are slower and more domainsensitive.Reasoning abilities are, in general, not stably acquired.As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.