Hand Commanded Machine Interface (Data Glove)

Human-Robot interface is a methodology that is heavily utilized in industrial and commercial applications such as to fulfill user needs. Different types of robotic frameworks are designed to fulfill those needs. The aim of the work presented in this paper is to simulate a robotic movement that emulates the movement of a human hand (i.e. arm and fingers section). The robotic hand comprises of three fingers. The main objective of this robotic hand simulation is to design a link between the pre designed data glove (FYPI) to a robotic hand and imitates the movement of a human hand. This project presents a new simulation methodology for the human-machine interface of robotic frameworks and control systems. The interface uses dynamic hand gestures, relative arm position estimation in order to have control of the Humanoid Robot used (NAO) and provides a control and visualization interface between a human and NAO. All the Important aspects to develop such an interface; image processing techniques, object tracking, colour tracking, motion detection and software filters; contrast, brightness and saturation have been explored. Seeking high ratio of faster and correct tracking have been achieved from the experiments. Using "Python" and some other programming IDEs (integrated developing environments), Software simulation and hardware implementation to show the behavior of the Data Glove will be carried

DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language Models

A few benchmarking datasets have been released to evaluate the factual knowledge of pretrained language models. These benchmarks (e.g., LAMA, and ParaRel) are mainly developed in English and later are translated to form new multilingual versions (e.g., mLAMA, and mParaRel). Results on these multilingual benchmarks suggest that using English prompts to recall the facts from multilingual models usually yields significantly better and more consistent performance than using non-English prompts. Our analysis shows that mLAMA is biased toward facts from Western countries, which might affect the fairness of probing models. We propose a new framework for curating factual triples from Wikidata that are culturally diverse. A new benchmark DLAMA-v1 is built of factual triples from three pairs of contrasting cultures having a total of 78,259 triples from 20 relation predicates. The three pairs comprise facts representing the (Arab and Western), (Asian and Western), and (South American and Western) countries respectively. Having a more balanced benchmark (DLAMA-v1) supports that mBERT performs better on Western facts than non-Western ones, while monolingual Arabic, English, and Korean models tend to perform better on their culturally proximate facts. Moreover, both monolingual and multilingual models tend to make a prediction that is culturally or geographically relevant to the correct label, even if the prediction is wrong.Comment: Accepted to ACL 2023 (Findings

ALDi: Quantifying the Arabic Level of Dialectness of Text

Transcribed speech and user-generated text in Arabic typically contain a mixture of Modern Standard Arabic (MSA), the standardized language taught in schools, and Dialectal Arabic (DA), used in daily communications. To handle this variation, previous work in Arabic NLP has focused on Dialect Identification (DI) on the sentence or the token level. However, DI treats the task as binary, whereas we argue that Arabic speakers perceive a spectrum of dialectness, which we operationalize at the sentence level as the Arabic Level of Dialectness (ALDi), a continuous linguistic variable. We introduce the AOC-ALDi dataset (derived from the AOC dataset), containing 127,835 sentences (17% from news articles and 83% from user comments on those articles) which are manually labeled with their level of dialectness. We provide a detailed analysis of AOC-ALDi and show that a model trained on it can effectively identify levels of dialectness on a range of other corpora (including dialects and genres not included in AOC-ALDi), providing a more nuanced picture than traditional DI systems. Through case studies, we illustrate how ALDi can reveal Arabic speakers' stylistic choices in different situations, a useful property for sociolinguistic analyses.Comment: Accepted to EMNLP 202

Double Skin Façade Adoption Influencing Ventilation Performance in Educational Buildings

Double skin façades are adaptive envelopes that aim to improve building energy consumption and comfort performance. Their adaptive principle relies on the dynamic management of the cavity's ventilation flow and the shading devices, which can integrate with the environmental systems. This research demonstrates the possibility of modifying existing buildings to study unconventional building envelope solutions. Scenarios A and B show insignificantly decreasing measures in air velocity, relative humidity, and air temperature; however, Scenario C shows the most significant changes, the average air velocity increases by 45%, the air temperature drops between 5 to 8%, and the relative humidity drops between 5 to 8%. The utilization of DSF can be used to reduce solar heat gain, enhance natural ventilation, and mitigate the inefficiencies of mechanical ventilation in educational buildings. The Double Skin Façade is effective in improving parameters that are in accordance with thermal comfort

ALDi: Quantifying the Arabic Level of Dialectness of Text

Transcribed speech and user-generated text in Arabic typically contain a mixture of Modern Standard Arabic (MSA), the standardized language taught in schools, and Dialectal Arabic (DA), used in daily communications. To handle this variation, previous work in Arabic NLP has focused on Dialect Identification (DI) on the sentence or the token level. However, DI treats the task as binary, whereas we argue that Arabic speakers perceive a spectrum of dialectness, which we operationalize at the sentence level as the Arabic Level of Dialectness (ALDi), a continuous linguistic variable. We introduce the AOC-ALDi dataset (derived from the AOC dataset), containing 127,835 sentences (17% from news articles and 83% from user comments on those articles) which are manually labeled with their level of dialectness. We provide a detailed analysis of AOC-ALDi and show that a model trained on it can effectively identify levels of dialectness on a range of other corpora (including dialects and genres not included in AOC-ALDi), providing a more nuanced picture than traditional DI systems. Through case studies, we illustrate how ALDi can reveal Arabic speakers' stylistic choices in different situations, a useful property for sociolinguistic analyses

AndroShield:automated Android applications vulnerability detection, a hybrid static and dynamic analysis approach

The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution

Hand Commanded Machine Interface (Data Glove)

Human-Robot interface is a methodology that is heavily utilized in industrial and commercial applications such as to fulfill user needs. Different types of robotic frameworks are designed to fulfill those needs. The aim of the work presented in this paper is to simulate a robotic movement that emulates the movement of a human hand (i.e. arm and fingers section). The robotic hand comprises of three fingers. The main objective of this robotic hand simulation is to design a link between the pre designed data glove (FYPI) to a robotic hand and imitates the movement of a human hand. This project presents a new simulation methodology for the human-machine interface of robotic frameworks and control systems. The interface uses dynamic hand gestures, relative arm position estimation in order to have control of the Humanoid Robot used (NAO) and provides a control and visualization interface between a human and NAO. All the Important aspects to develop such an interface; image processing techniques, object tracking, colour tracking, motion detection and software filters; contrast, brightness and saturation have been explored. Seeking high ratio of faster and correct tracking have been achieved from the experiments. Using "Python" and some other programming IDEs (integrated developing environments), Software simulation and hardware implementation to show the behavior of the Data Glove will be carried

Information technology in the British and Irish undergraduate accounting degrees

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Using an online questionnaire and a series of semi-structured interviews, this study seeks the perceptions of accounting educators and professional accounting bodies in the UK and Ireland on the status quo of technological developments within accounting curricula and the factors influencing this status quo. Findings suggest a fairly widespread view that technological developments represent an important area that should be covered across accounting curricula, to expose changes in the marketplace and to enhance the employability of graduates. However, it is still a peripheral component in accounting curricula, with no clear agenda for change. Professional accounting bodies seem to play a hegemonic inhibiting role through accreditation requirements although other inhibitors were reported such as lack of competent/interested staff and lack of time/space in already overloaded syllabi