Miracle-dz/UniFAR-FTUs huggingface dataset:
New Optimal Cyclic Locally Recoverable Codes of Length $n=2(q+1)$

Locally recoverable codes are very important due to their applications in distributed storage systems. In this paper, by using cyclic codes, we construct two classes of optimal q-ary cyclic <inline-formula> <tex-math notation="LaTeX">$(\text {r}, \delta _{1})$ </tex-math></inline-formula> locally recoverable codes with parameters <inline-formula> <tex-math notation="LaTeX">$[2(\text {q}+1), 2(\text {q}+1)-2\delta _{1}, \delta _{1}+2]_{\text {q}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> is an odd prime power and <inline-formula> <tex-math notation="LaTeX">$\text {r}+\delta _{1}-1=\text {q}+1$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$(\text {r}, \delta _{2})$ </tex-math></inline-formula> locally recoverable codes with parameters <inline-formula> <tex-math notation="LaTeX">$[2(\text {q}+1), 2(\text {q}+1)-4\delta _{2}+2, \delta _{2}+2]_{q}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\text {q}\geq 7$ </tex-math></inline-formula> is an odd prime power, <inline-formula> <tex-math notation="LaTeX">$\text {q}\equiv 3~ (mod~ 4)$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$r+\delta _{2}-1=\frac {\text {q}+1}{2}$ </tex-math></inline-formula>. Compared with the known cyclic and constacyclic <inline-formula> <tex-math notation="LaTeX">$(\text {r}, \delta)$ </tex-math></inline-formula> locally recoverable codes, our construction yields new optimal cyclic <inline-formula> <tex-math notation="LaTeX">$(\text {r}, \delta)$ </tex-math></inline-formula> locally recoverable codes.
Tracking Hand Trajectory as a Preliminary Study for Hand Hygiene Stages

The process of hand washing involves complex hand movements. There are six principal sequential steps for washing hands as per the World Health Organisation (WHO) guidelines. In this work, a preliminary analysis was undertaken in order to develop an automated image processing system for tracking and classification of two-handed dynamic gestures involved in hand washing. To facilitate this study, videos of healthcare workers who were engaged in washing hands were sourced from the internet. The videos were analysed in order to extract the unique features of two-handed gestures associated with all hand hygiene (HH) stages. The combination of these unique features can be used to detect each HH stage. In the video recordings, the hand trajectory was found to be linear or circular for all six HH stages. In this paper, we attempt to track hand trajectory with the help of 2.5 Mega Pixel ELP-USB cameras and using an image processing approach for skin detection and the contour-centroid detection method. The YCbCr colour space is invariant to illumination intensity and therefore it was selected for the skin detection method. This work concludes that cameras are suitable for tracking one hand movementlinear and circular motion as a preliminary work and can be further expanded for detecting two hand movements in hand washing.
GASIM: The Gate Array Graphical Simulator for FPGA Architecture Learning

Field Programmable Gate Arrays, also called FPGA, are the main contemporary tool for digital systems learning and development. Its benefits are more than evident, but also are its internal complexity. The widespread use of FPGA devices has created a necessity for understanding the inner logic using diagrams or theoretical explanations. This paper presents an innovative approach for FPGA basic architecture learning called GASIM. A conceptually complete model for a simple but general 2-D graphical FPGA has been designed and implemented in such a way that is easy to understand, aesthetically appealing from the student’s point of view, and also easy to use by any teacher. The aim of this kind of design is to fill the conceptual gap we have found while teaching FPGA based courses. The final tool is a 2-D modular graphical library for a standalone logic simulator that not only allows teachers and students to program an FPGA model and simulate every aspect of its internal logic complexities but also to construct their own limited, but complete FPGA structure using drag and drop graphical modules and programmable interconnections. Our study provides a technical description of the tool, and our first results in the classroom.
CANCER DETECTION USING THE APPLICATION OF WEINER FILTERING AND MULTI SUPPORT VECTOR SCHEME

- MRI image capturing process is such that they are prone to unidentified noise. MRI image is used to diagnose a disease. Clarity is utmost necessity in such environment. Noises which can corrupt the image involve salt and pepper, Gaussian, shot noise etc. detection and removal of such noise from within the image is critical. Improving image quality is aim of this paper. Buffer is implemented to remove overlapped pixels and then wiener filter is implemented to smoothen the image. The simulation is conducted in MATLAB and noise is introduced using point spread function (PSF). Noise introduction is the first step after loading MRI image. Buffer mechanism implements uniqueness and size is reduced considerably. Weiner filter application is last step in the modified approach leading to smoothness in image. Obtained results prove the worth of study.
CTScanTool, a semi-automated organ segmentation tool for radiotherapy treatment planning

The goal of radiation therapy is to eradicate tumors while sparing normal tissues from radiation damage. Therefore, accurate definition of tumor and organ boundaries is important for achieving good treatment outcome. Segmentation of tumors and organs at risk is a standard procedure done by radiation oncologists for radiation therapy treatment planning, for which computed tomography (CT) images are commonly used. In this paper, we present the development of CTScanTool, the organ segmentation tool with a graphical user interface, allowing semi-automated and manual delineation. The proposed semi-automated segmentation method is based on the combined techniques of region growing, morphological reconstruction, and watershed transform algorithms. The semi-automated segmentation results of CT images were compared with those contoured by experienced radiation oncologists. In the experiment, we investigated the combined segmentation method on brain, lung and kidney tissues. In all cases, the true positive rate (sensitivity) was higher than 95%. Jaccard indices and positive predictive values (precision) were higher than 91% for brain and lung tissues and better than 81% for kidney tissues. In the near future, CTScanTool will be incorporated in the Proton pencil beam Scanning treatment PLANning system (PSPLAN) used for research and education in proton therapy.
Trust Prediction via Matrix Factorisation

In this article, we propose the PTP-MF (Pairwise Trust Prediction through Matrix Factorisation) algorithm, an approach to predicting the intensity of trust and distrust relations in Online Social Networks (OSNs). Our algorithm maps each OSN user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and it computes the trust a user i places in a user j as the dot product of trustor profile of i and the trustee profile of j. The PTP-MF algorithm incorporates also biases in trustor and trustee behaviour to make more accurate predictions. Experiments on four real-life datasets indicate that the PTP-MF algorithm significantly outperforms other methods in accuracy and it showcases a high scalability.
S-MANAGE Protocol for Provisioning IoT Applications on Demand

Internet of Things (IoT)-based services have started making an impact in various domains, such as agriculture, smart farming, smart cities, personal health, and critical infrastructures. Sensor/IoT devices have become one of the indispensable elements in these IoT systems and services. However, their development is restricted by the rigidity of the current network infrastructure, which accommodates heterogeneous physical devices. Software-Defined Networking-Network Functions Virtualization (SDN-NFV) has emerged as a service-enabling solution, supporting network and network function programmability. Provisioning IoT applications on demand is a natural application of programmability. However, these technologies cannot be directly deployed in the sensing/monitoring domain due to the differences in the functionality of SDN network devices and sensor/IoT devices, as well as the limitation of resources in IoT devices. This paper proposes an S-MANAGE protocol that preserves the SDN-NFV paradigm but provides a practical solution in controlling and managing IoT resources for provisioning IoT applications on demand. S-MANAGE is proposed as a new southbound protocol between the software-defined IoT controller and its IoT elements. The paper presents the design of S-MANAGE and demonstrates its use in provisioning IoT services dynamically.
Rethinking the Science of Statistical Privacy

Nowadays, more and more data, such as social network data, mobility data, business data, medical data, are shared or made public to enable real world applications. Such data is likely to contain sensitive information and thus needs to be obfuscated prior to release, to protect privacy. However, existing statistical data privacy mechanisms in the security community have several weaknesses: 1) they are limited in protecting sensitive information in the static scenario, and can not be generally applied to accommodate temporal dynamics. With the increasing development of data science, a large amount of sensitive data such as personal social relationships are becoming public, making the privacy concerns of a time series of data more and more challenging; 2) these privacy mechanisms do not explicitly capture correlations, leaving open the possibility of inference attacks. In many real world scenarios, the data tuple dependence/correlation occurs naturally in datasets due to social, behavioral and genetic interactions between users; 3) there are very few practical guidelines on how to apply existing statistical privacy notions in practice, and a key challenge is how to set an appropriate value for the privacy parameters. In this thesis, we aim to overcome these weaknesses to provide privacy guarantees for protecting dynamic data structures, dependent (correlated) data structures. We also aim to discover useful and interpretable guidelines for selecting proper values of parameters in the state-of-the-art privacy-preserving frameworks. Furthermore, we investigate how an auxiliary information – in the form of prior distribution of the database and correlation across records and time – can influence the proper choice of the privacy parameters. Specifically, we 1) first propose the design of a privacy-preserving system called LinkMirage, that mediates access to dynamic social relationships in social networks, while effectively supporting social graph-based data analytics; 2) explicitly incorporate structural properties of data into current differiii ential privacy metrics and mechanisms, to enable privacy-preserving data analytics for dependent/correlated data; and 3) finally provide a quantitative analysis of how hypothesis testing can guide the choice of the privacy parameters in an interpretable manner for differential privacy and other statistical privacy frameworks. Overall, our work aims to place the field of statistical data privacy on a firm analytic foundation that is coupled with the design of practical systems.
Question and Accepted Answer Recommendation System with a Generic Knowledge Mining Visualization Tool

With the increasing software developer community, questions answering (QA) sites, such as StackOverflow, Quora, Yahoo Answer etc. have been gaining its popularity. Hence, in recent years, millions of users are posting in StackOverflow. With the increase of users their question/answer a copious amount of data is generated, which attracts both the research community to utilize this information for extracting knowledge and the industry for developing the knowledge based systems. Since it is really difficult to find a question that relates one’s expertise area in order to gain some extra reputation or bounty points, we have built a personalized recommender system named PRISM that will recommend posts to a user after login based on their expertise. We have also implemented our recommendation system web application which is currently deployed in our localhost. Again it takes an enormous amount of effort to find out the suitable answer of a question. Propitiously, StackOverflow allows their community members to label an answer as an accepted answer. However, in the most of the questions answers are not marked as accepted answers. Therefore, there is a need to build a recommender system which can accurately suggest the most suitable answer to the questions. Contrary to the existing systems, in this work, we have utilized the textual features of the answers comments with the other metadata of the answers to build a recommender system for predicting the accepted answer called RAiTA. We have also deployed our recommendation system web application, which is publicly accessible at http://210.4. 73.237:8888/ Visualization of data, pattern mining from datasets and analyzing data drift for the different features are three highly used applications of machine learning and data science fields. A generic web-based tool integrated with such features will provide prodigious support for pre-processing the dataset and thus extracting accurate information. In this work, we propose such a data visualization tool, named VIM which is publicly accessible at http://210.4. 73.237:9999/ In memory of our family,friends and our parents.
Learning Analytics or Educational Data Mining? This is the Question...

In full expansion, a vital area such as education could not remain indifferent to the use of information and communication technology. Over the past two decades we have witnessed the emergence and development of e-learning systems, the proliferation of MOOCs, and generally the rise of Technology Enhanced Education. All of these contributed to generation and storage of unprecedented volumes of data concerning all areas of learning. At the same time, domains such as data mining and big data analytics have emerged and developed. Their applications in education have spawned new areas of research such as educational data mining or learning analytics. As an interdisciplinary research area Educational Data Mining (EDM) aims to explore data from educational environment to build models based on which students' behavior and results are better understood. In fact, EDM is a complex process that consists of a few steps grouped in three stages: data preprocessing, modelling and postprocessing. It transforms raw data from educational environments in useful information that could influence in a positive way the educational process. According to Society for Learning Analytics Research (SoLAR) which took over the wording of the first International Conference on Learning Analytics and Knowledge, learning analytics is ”the measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs” (Siemens, 2011). This paper proposes a comparative study of the two concepts: EDM and learning analytics. Due to certain voices in the scientific environment that claim that the two terms refer to the same thing, we want to emphasize the similarities and differences between them, and how each one can serve to raise the quality in educational processes.
Low Reynolds Number Flow past a Body with Point Symmetry

Low Reynolds number flow of an incompressible fluid past a body is studied by solving the Navier-Stokes equations, on the basis of the method of matched asymptotic expansions. It is shown that, when the shape of the body is symmetric with respect to a point, the force on the body is determined to the order of R e 2 log R e , where R e denotes the Reynolds number, simply from a knowledge of the force on the body according to Stokes' approximation.
INFLUENCE OF DIMENSIONAL DEVIATIONS OF MECHANICAL ELEMENTS IN THE KINEMATIC STRUCTURE OF SCARA INDUSTRIAL ROBOTS ON POSITIONING PRECISION AND TRAJECTORY ACCURACY

The purpose of this article is to present the research on the influence of the dimensional deviations of the mechanical elements in the kinematic structures of the SCARA industrial robots regarding the positioning precision and trajectory error. In this article the direct kinematics method is used to calculate the positioning of the end-effector and generate the trajectory simulation with and without dimensional deviations.
The Psychoacoustic Properties Of Sound: An Introduction

This study presents a general review on four psychoacoustic properties of sound: pitch, loudness, duration and timbre. Most studies in this domain provide an extensive detail on the topic; however, this article presents a perspective to the beginner on how sounds and/or music can be influenced by these properties. This article has made an effort to provide a preliminary understanding of the domain by highlighting concepts associated closely with cognition of music. The present study is largely confined to four major psychoacoustic properties of sound and their effect on music perception and cognition. A review of existing literature was performed with the purpose of providing insight into the topic under study.
Enhancing education and training through data-driven adaptable games in flipped classrooms

The Flipped Classroom (FC) is a set of pedagogical approaches that move the information transmission out of class and exploit class time for active and/or peer learning activities. In this context, students are required to engage with pre- and/or post-class activities in order to prepare themselves for class work. The FC instruction method has already been used in conjunction with other learning strategies. This theoretical paper presents the first developmental steps of a research project, which aims at building the FC through a fully bespoke and personalized experience, by using data-driven adaptable games and problem-based learning elements to improve the learning experience. The project will develop a gaming platform that will support the whole FC in a cyclical perspective, and aims to use the resources of gamification in a more significant manner that could go beyond score tracking and badges. Moreover, the problem-based learning approach will be used to better frame the learning activities included in FCs, while learning analytics features will provide adaptable learning pathways. The potential of this approach is to build a better FC experience for all the stakeholders. Students will be given more agency to calibrate their learning experience, while educators can monitor the students’ progress more effectively and adjust their learning activities accordingly. Finally, researchers will get better insight into the FC learning process, and the mechanics, which contribute to optimize the learning experience.
Loquat: An Interactive System Design for Location-Aware Query Autocompletion

—Query autocompletion (QAC) is the feature to provide the intended possible candidate completions given some initial prefixes from users. By applying QAC techniques, users are assisted in formulating queries and saving input keystrokes. Due to the convenience it brings to users, QAC has been adopted in many real-world applications, including search engines, integrated development environments (IDEs), and mobile devices. With the growing popularity of mobile devices, a recent trend is to integrate query autocompletion into location-based services, such as Web mapping and spatial keyword search. In this paper, we present an interactive system of location-aware query autocompletion called Loquat, which provides a graphical interface to help users easily formulate their location-aware queries. We develop novel index structures and search algorithms to make such an interactive system work efficiently. We extend our system to support fuzzy search and multiple-keyword search. We also define a new ranking function taking fuzzy threshold value into consideration. The experiments on two real-life datasets verify the efficiency and its interactive usability of our system.
FROM POINT CLOUD TO EXISTING BIM FOR MODELLING AND SIMULATION PURPOSES

Abstract. Many BIM experts agree that employing BIM for new construction is an easy task. However, applying BIM to existing construction will be difficult but is more suitable for heritage buildings cases. These heritage buildings have unique façades and architectural vocabulary, which are of special interest. Furthermore, studying these architecture heritages require some advanced tools in order to understand and analyse their structure, components, and design. Relying only on traditional methods is not adequate, especially for architectural engineers and experts who need digital representations of architectural heritage in order to draw a complete image of any aspect of the project. Moreover, lots of these heritage architectural elements are not documented or provided in the digital architectural libraries, which in turn requires advanced and easy access methods and tools that can extract basic information professionally and explain the essence of heritage. BIM has emerged as an efficient solution that could possibly help in analysing architectural heritage through effective learning processes. Existing BIM is characterised by their ability to create and operate within a digital database of any existing by 3-D laser through scanning the building and transforming it into point-cloud as digital data, so that engineers and experts can work on existing and buildings via the BIM software. As with many heritage buildings in the world, many of the heritage buildings in the Historic district of Jeddah city, Saudi Arabia, face serious issues in terms of conservation, restoration, documentation, managing, recording, and monitoring of these valuable heritage buildings. Therefore, this paper will examine and evaluate the use of BIM in modelling and for simulation purposes, (e.g. structure and energy simulation) with regard to one of the existing heritage buildings in the Historic district of Jeddah.
COMPARATIVE ANALYSIS OF TWITTER ANALYTICS OF IFLA AND OCLC TWEETS USING UNION METRICS TWEET REACH ONLINE TOOL – A PILOT STUDY

Twitter Analytics provides information that can help create meaningful tweets that will resonate with the target audience. Many businesses and professionals use Twitter to connect with and connect with their customers and colleagues. Benefits of Twitter include reaching out to a wider audience and enabling two-way communication with customers and followers. The loss involves possible negative feedback. All Twitter users can get a detailed report on their followers and personalized tweets, including impressions, clicks and engagement. This pilot study conducts to know the library and science professional association of IFLA and OCLC (OCLC is a global library cooperative that provides shared technology services, original research and community programs for its membership and the library community at large) how to reach the world wide via twitter, to find the reach among the LIS professionals, number of following / followers, Tweets, retweets, reply activity and impressions of previous 100 tweets using the Union Metric Tweet reach online Tool.