We present 'The Ancient Theater of Philippi,' a 3D gamified educational tool that integrates an online platform for distance learning with a virtual environment aimed at interactive learning. We introduce the educational opportunity, design and development considerations and showcase the tool's functionality and use cases for students and educators. � 2022 IEEE.

[No abstract available]

Although road length and extent have dramatically increased in Cyprus by 88% over the last 20 years, this has not been followed by studies looking at the impacts of roads on biodiversity on the island, a global biodiversity hotspot. To address the lack of adequate information on road impacts on biodiversity, the Cyprus Roadkill Observation System (CyROS) www.cyroadkills.org was launched in 2017. CyROS is a citizen science approach that uses the Google Earth Engine and smart phone applications to collect citizens' observation of dead animals on road network. Preliminary results of this new system demonstrate that reptiles (including endemic and rare species included in the EU Habitats Directive) are the animal group most affected by roads. This corroborates results from similar studies which point to the susceptibility of this taxonomic group to road-induced impacts. We combined reptile records from the CyROS database with data on road mortality from the Herpetological Repository of Cyprus (www.herprepository.org), a citizen science website launched in 2013 to record live and dead reptile and amphibian sightings throughout the island. We used KDE+ based on kernel density estimation to evaluate hotspots of reptile roadkills. A total of 196 roadkills were identified, belonging to 11 different species, of the 19 terrestrial reptiles of the island. The number of observations recorded so far is not related to the frequency of road use, road type or geographic location. Fourteen hotspots of varying length and significance were identified. This collaborative approach has so far engaged four government departments and 100 volunteer scientists, and is the first effort to understand the impact of Cyprus' extensive road network on the island's reptiles. It has revealed the importance of examining transportation ecology on small islands with rapid urban and road expansion such as Cyprus. � 2018 SPIE.

Wireless Sensor Networks (WSNs) are supporting the operation of a variety of critical infrastructures. In order to secure the operation of WSNs, appropriate security protocols have been specified supporting different operational objectives and security features. Often, it is challenging to identify the protocols� key operation and key features due to various reasons such as the lack of expert knowledge and the complexity of protocols. This can limit the ability of researchers to identify protocols of interest and apply them at a specific setup. This challenge is addressed by designing a platform to classify a wide-range of security protocols in WSNs, to highlight their key features and to guide users through an interactive and user-friendly approach to select protocols of interest. An appropriate proof-of-concept has been developed. � ISD 2017.

We present a system for parsing advice offered by a human to a machine. The advice is given in the form of conditional sentences in natural language, and the system generates a logic-based (machine-readable) representation of the advice, as appropriate for use by the machine in a downstream task. The system utilizes a �white-box� knowledge-based translation policy, which can be acquired iteratively in a developmental manner through a coaching process. We showcase this coaching process by demonstrating how linguistic annotations of sentences can be combined, through simple logic-based expressions, to carry out the translation task. � 2024 by SCITEPRESS � Science and Technology Publications, Lda.

Data generation through crowdsourcing has become a common practice for building or augmenting an Artificial Intelligence (AI) system. These systems often reflect the stereotypical behaviors expressed by humans through the reported data, which can be problematic, especially when dealing with sensitive tasks. One such task is the interpretation of images depicting people. In this work, we evaluate a crowdsourcing approach aimed at identifying the stereotypes conveyed in the collected annotations on people images. By including closed-ended, categorical responses as well as open-ended tags during the data collection phase, we can detect potentially harmful crowd behaviors. Our results suggest a means to assess descriptive tags, as to their alignment with stereotypical beliefs related to gender, age, and body weight. This study concludes with a discussion on how our analytical approach can be applied to pre-existing datasets with similar characteristics or to future knowledge being crowdsourced such as to audit for stereotypes. � The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Past work on the task of identifying the geographic focus of news-stories has established that state-of-the-art performance can be achieved by using existing crowdsourced knowledge-bases. In this work we demonstrate that a further refinement of those knowledge-bases through an additional round of crowdsourcing can lead to improved performance on the aforementioned task. Our proposed methodology views existing knowledge-bases as collections of arguments in support of particular inferences in terms of the geographic focus of a given news-story. The refinement that we propose is to associate these arguments with weights - computed through crowdsourcing - in terms of how strongly they support their inference. The empirical results that we present establish the superior performance of this approach compared to the one using the original knowledge-base. � 2021 by SCITEPRESS - Science and Technology Publications, Lda.

A central goal of Artificial Intelligence is to create systems that embody commonsense knowledge in a reliable enough form that it can be used for reasoning in novel situations. Knowledge Infusion is an approach to this problem in which the commonsense knowledge is acquired by learning. In this paper we report on experiments on a corpus of a half million sentences of natural language text that test whether commonsense knowledge can be usefully acquired through this approach. We examine the task of predicting a deleted word from the remainder of a sentence for some 268 target words. As baseline we consider how well this task can be performed using learned rules based on the words within a fixed distance of the target word and their parts of speech. This captures an approach that has been previously demonstrated to be highly successful for a variety of natural language tasks. We then go on to learn from the corpus rules that embody commonsense knowledge, additional to the knowledge used in the baseline case. We show that chaining learned commonsense rules together leads to measurable improvements in prediction performance on our task as compared with the baseline. This is apparently the first experimental demonstration that commonsense knowledge can be learned from natural inputs on a massive scale reliably enough that chaining the learned rules is efficacious for reasoning. Copyright � 2008, Association for the Advancement of Artificial Intelligence.

A headspace high sorptive extraction (HS-HiSorb) Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS) method was developed and optimized for the determination of the volatile profile of extra virgin olive oil (EVOO). The HS-HiSorb extraction parameters of temperature, sample mass, stirring rate, and adsorption time were optimized by applying the one-factor-at-a-time (OFAT) approach. A total of 21 multi-varietal olive oil samples were collected from four different olive mills in Cyprus during the harvesting period 2020-2021. Seventy-six volatile organic compounds (VOCs) were identified and semi-quantified, belonging to several chemical categories such as hydrocarbons (31) three of which are terpenes, aldehydes (22), carboxylic acids (6), ketones (5), esters (4), alcohols (3), ethers (2), furans (2), and others (1). Aldehydes (40.20%) and hydrocarbons (41.08%) represented the main components of olive oil's volatile profile. The overall concentrations of VOCs in the samples ranged from 8.73 to 39.81mg/kg. The HiSorb-TD-GC-MS method was evaluated in terms of repeatability and linearity for selected VOCs. Repeatability was performed at three different concentrations (1, 10, and 100ppbv), and the relative standard deviation (RSD) ranged from 2.21 to 15.86%. The calibration curves of (E)-2-hexenal, 1-penten-3-one, nonanal, and hexanal were developed to evaluate the linearity range. The results were satisfactory, with the correlation coefficient (R2) greater than 0.98. Finally, the limitations of the method are mentioned and discussed. � 2023 IUPAC & De Gruyter. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. For more information, please visit: http://creativecommons.org/licenses/by-nc-nd/4.0/.

This work presents a knowledge acquisition platform and a certain game developed on that platform for endowing machines with common sense, by following a hybrid approach that combines crowdsourcing techniques, knowledge engineering, and automated reasoning. Short narratives are presented to players, who are asked to combine fragments of text into rules that would correctly answer a given question, to evaluate the appropriateness of gathered rules, and to resolve conflicts between them by assigning priorities. The text fragments that are used are a priori translated by a knowledge engineer into a machine-readable predicate form. Players are rewarded based not only on their inter-agreement (as in most games with a purpose) but also based on the objective ability of the rules to answer questions correctly, as determined by an underlying reasoning engine. Beyond discussing the knowledge acquisition platform and the game design, we analyze the common sense that has been gathered during the deployment of the game over a five-month period and we use the acquired knowledge to answer questions on unknown stories. � 2016 The authors and IOS Press.

Cybersecurity education and training activities are essential to empower end users to take informed decisions and address cyber threats. An ongoing problem that still troubles the cybersecurity community is the selection of weak passwords. Users keep using weak passwords, cultivating the risk of compromisation. Users often choose passwords that appear to be strong. This creates a false sense of security as users have the belief that their passwords cannot be guessed. Unfortunately, given that attackers are aware of the users' habits, they often recover users' passwords. Therefore, it is imperative to educate people about the bad password construction strategies and empower them to select stronger passwords. Educational activities should be enhanced by integrating practical aspects that will assist the users to realize the problem. This work identifies and combines a range of bad password construction strategies and designs a relevant tool to practically demonstrate the strategies to the users. � 2018 IEEE.

In this article, we present our work on developing and using a crowdsourcing platform for acquiring commonsense knowledge aiming to create machines that are able to understand stories. More specifically, we present a platform that has been used in the development of a crowdsourcing application and two Games With A Purpose. The platform�s specifications and features are presented along with examples of applying them in developing the aforementioned applications. The article concludes with pointers on how the crowdsourcing platform can be utilized for language learning, referencing relevant work on developing a prototype application for a vocabulary trainer. � 2019 CEUR-WS. All rights reserved.

We report on an ongoing research program to develop a formal framework for automated narrative text comprehension, bringing together know-how from research in Artificial Intelligence and the Psychology of Reading and Comprehension. It uses argumentation to capture appropriate solutions to the frame, ramification, and qualification problems, and their generalizations as required for text comprehension. In this first part of the study we concentrate on the central problem of integration of the explicit information from the text narrative with the reader's implicit commonsense world knowledge, and the associated tasks of elaboration and revision. Copyright � 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Text-based passwords are still one of the main techniques to authenticate the users. Although a variety of measures (e.g. awareness activities, password-strength checkers, password-composition policies, etc.) are taken to prevent users from selecting weak passwords, the problem remains. A main factor that leads to weak passwords is the lack of awareness on what constitutes a strong password. Organizations should assess the users' ability to construct a strong password through the assessment of their password's strength, and taking into consideration the users' practices that are typically applied when selecting a password. In this way, organizations can be aware of the situation, that is, if their users follow good or bad password construction practices. Depending on the practice utilized, the organization's security level can be affected. Bad password construction practices can lead to weak passwords which can increase the risk of unauthorized access. Therefore, organizations should target for good practices to be utilized by their users in an effort to decrease the possibility of unauthorized access. A typical way to assess a password's strength is by trying to crack it using password cracking tools. An assessor, e.g. system administrator, requires a fair amount of knowledge on how password cracking tools operate and need to be configured. Also, it is essential to be aware of the bad practices that users typically utilize. Such knowledge is not always present. Furthermore, these tools and their respective graphical user interface, have not been designed with the objective of assessing the users' awareness level against bad password construction practices. This paper proposes a conceptual architecture to assist in designing a situation-Aware user interface to assess users' ability to construct a password that is not easily crackable. An initial mock prototype has been developed to realize the proposed architecture and identify the main features of the user interface. � 2017 IEEE.

Social networks constitute an important medium for social interaction where people communicate and formulate relationships in a way similar to what they do in real life. The analysis of the users� relationships in social networks can lead to new insights into human social behavior. Tie strength constitutes a core aspect of social relationships, which represents the importance of a relationship and the closeness of individuals. Understanding the key features of tie strength in social networks can assist in formulating more efficient user-centric services. This survey paper examines the advances in the area of the analysis of tie strength in social networks. We study the dimensions of tie strength and review the key predictive features for each dimension. We, then, undertake a comparative study of methodologies to model tie strength and examine the key findings. Finally, we discuss open issues and challenges in specifying tie strength. � 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Among the issues faced by an intelligent agent, central is that of reconciling the, often contradictory, pieces of knowledge - be those given, learned, or sensed - at its disposal. This problem, known as knowledge qualification, requires that pieces of knowledge deemed reliable in some context be given preference over the others. These preferences are typically viewed as encodings of reasoning patterns; so, the frame axiom can be encoded as a preference of persistence over spontaneous change. Qualification, then, results by the principled application of these preferences. We illustrate how this can be naturally done through argumentation, by uniformly treating object-level knowledge and reasoning patterns alike as arguments that can be defeated by other stronger ones. We formulate an argumentation framework for Reasoning about Actions and Change that gives a semantics for Action Theories that include a State Default Theory. Due to their explicit encoding as preferences, reasoning patterns can be adapted, when and if needed, by a domain designer to suit a specific application domain. Furthermore, the reasoning patterns can be defeated in lieu of stronger external evidence, allowing, for instance, the frame axiom to be overridden when unexpected sensory information suggests that spontaneous change may have broken persistence in a particular situation.

Among the issues faced by an intelligent agent, central is that of reconciling the, often contradictory, pieces of knowledge-be those given, learned, or sensed-at its disposal. This problem, known as knowledge qualification, requires that pieces of knowledge deemed reliable in some context be given preference over the others. These preferences are typically viewed as encodings of reasoning patterns; so, the frame axiom can be encoded as a preference of persistence over spontaneous change. Qualification, then, results by the principled application of these preferences. We illustrate how this can be naturally done through argumentation, by uniformly treating object-level knowledge and reasoning patterns alike as arguments that can be defeated by other stronger ones. We formulate an argumentation framework for Reasoning about Actions and Change that gives a semantics for Action Theories that include a State Default Theory. Due to their explicit encoding as preferences, reasoning patterns can be adapted, when and if needed, by a domain designer to suit a specific application domain. Furthermore, the reasoning patterns can be defeated in lieu of stronger external evidence, allowing, for instance, the frame axiom to be overridden when unexpected sensory information suggests that spontaneous change may have broken persistence in a particular situation. � 2019 10th International Symposium on Logical Formalizations of Commonsense Reasoning, Commonsense 2011 - Proceedings. All rights reserved.

Journalists and researchers alike have claimed that IR systems are socially biased, returning results to users that perpetuate gender and racial stereotypes. In this position paper, I argue that IR researchers and in particular, evaluation communities such as CLEF, can and should address such concerns. Using as a guide the Principles for Algorithmic Transparency and Accountability recently put forward by the Association for Computing Machinery, I provide examples of techniques for examining social biases in IR systems and in particular, search engines. � Springer Nature Switzerland AG 2018.

We study the adoption of translation support technologies by professors at a multilingual university, using the framework of the Technology Adoption Model (TAM). TAM states that a user's perceived usefulness and ease of use for the technology ultimately determines her actual use of it. Through a survey and a set of interviews with our subjects, we find that there is evidence for TAM in the context of translation support tools. However, we also find that user adoption of these tools is a bit more complicated. Users who are able to successfully employ these tools have not only developed strategies to overcome their inaccuracies (e.g. by post-editing machine translated text), they also often compensate for the weaknesses of a given technology by combining the use of multiple tools. � Springer-Verlag Berlin Heidelberg 2007.

Collaboration in nature is often illustrated through the collective behavior of ants. Although entomological studies have shown that certain goals are well-served by this collective behavior, the extent of what such a collaboration can achieve is not immediately clear. We extend past work that has argued that ants are, in principle, able to collectively compute logical circuits, and illustrate through a simulation that indeed such computations can be carried out meaningfully and robustly in situations that involve complex interactions between the ants. � 2012 Massachusetts Institute of Technology.