Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of airline exercises prescribed to avoid deep vein thrombosis using fiber Bragg grating sensors

Abstract. Various leg exercises have been recommended to prevent deep vein thrombosis (DVT), a condition where a blood clot forms in the deep veins, especially during long-haul flights. Accessing the benefit of each of these exercises in avoiding the DVT, which can be fatal, is important in the context of suggesting the correct and the most beneficial exercises. Present work aims at demonstrating the fiber Bragg grating (FBG)–based sensing methodology for measuring surface strains generated on the skin of the calf muscle to evaluate the suggested airline exercises to avoid DVT. As the dataset in the experiment involves multiple subjects performing these exercises, an inertial measurement unit has been used to validate the repetitiveness of each of the exercises. The surface strain on the calf muscle obtained using the FBG sensor, which is a measure of the calf muscle deformation, has been compared against the variation of blood velocity in the femoral vein of the thigh measured using a commercial electronic-phased array color Doppler ultrasound system. Apart from analyzing the effectiveness of suggested exercises, a new exercise which is more effective in terms of strain generated to avoid DVT is proposed and evaluated.
Methods for the economic evaluation of health care programmes, 3rd ed

M E Drummond, M J Sculpher, G W Torrance GW, et al. Oxford University Press, 2005. ISBN 0-19-852945-7 A sketchy outline to the main economic theories forms the introduction to this book. The authors skillfully invoke those theories in the end only to point out that the economic evaluation primarily serves as a pragmatic aid to decision making. The “blue book” systematises and summarises recent knowledge on the main types of economic evaluations, thereby providing a useful overview including sources of further readings. Economic evaluation is defined as a comparative analysis of alternative courses of action in terms of both their costs and consequences. The authors make clear that the subject matter of economics is the deployment of real resources whether they cost money or …
Generalized variable structure model reference adaptive control of one-link artificial muscle manipulator in two operating modes

The classical techniques of linear and nonlinear control of the non-constant parameters systems or nonlinear systems (the case of robots) do not often give very acceptable results. This is due to the margin between the control system and the system model chosen to elaborate the control of the actuator. The adaptive control represents a solution to the modelling gaps because the exact knowledge of the system is not required. This control methodology is essentially based on the choice of the adaptive mechanism for the parameters controller. In this paper, the adaptive mechanism is chosen as a sliding regime for a dynamic variable structure. The association of the two types of control law gives a new type of control law, named the generalized variable structure model reference adaptive control. <<ETX>>
Understanding Reading: A Model of Meaningful Reading

A theory of reading provides the foundation for reading research, instruction and remediation. The Simple View of Reading (SVR) forms the basis of many present day understandings of reading. However, this and other current reading theories seem to be missing significant components that can contribute to a more holistic understanding of the reading process and subsequent models of reading. An incomplete understanding of the reading process can hinder efforts at targeted interventions and the full development of student abilities. This article serves to consolidate several ideas about reading to create a comprehensive, and thus more useful, Model of Meaningful Reading.
Chaotic routing: design and implementation of an adaptive multicomputer network router

A crucial component of a massively parallel multicomputer is the interconnection network which links all of the nodes of the computer together. This network provides the primary method of communication between the hundreds or thousands of processing nodes and is, thus, critical to the successful operation of the multicomputer. Current state-of-the-art interconnection networks use simple, oblivious routing techniques which achieve very good performance when loading is light, but do not perform well in the presence of non-uniform congestion or faults. Chaotic routing, a non-minimal adaptive routing technique, provides a mechanism which takes into account the presence of congestion and faults when choosing a path for a message and can, thus, achieve better performance. Chaotic routing is shown to be applicable to all finite-sized networks of bounded degree with bi-directionally connected links. A design for a chaotic router is presented which includes both the features of virtual cut-through routing and the advantages of internal non-blocking buffering. This design allows messages to follow minimum-latency paths when network loading is light, and provides sufficient buffering to ensure high throughput when the network is heavily loaded. The resulting design is compared with other minimal and non-minimal adaptive designs, as well as with oblivious routing. Chaotic routing is shown to be superior to most other adaptive designs due to its lower design complexity, and simulations are used to compare it with oblivious and deflection routing. The simulations show that, for mesh-connected networks, chaotic routing performs only slightly better than oblivious routing. However, for torus- and hypercube-connected networks, chaotic routing is superior to oblivious and deflection routing, achieving much higher throughput and lower latency. To demonstrate the feasibility of chaotic routing, a prototype chaos router chip is presented. This chip, fabricated in 1.2$\mu$m CMOS, implements a two-dimensional chaos router. The chip, in simulations, operates at 66MHz, with the speed being limited only by the speed at which the 5V CMOS pads can be switched. Thus, the chip should operate at the same clock speed as oblivious routers using the same technology, giving an equivalent bandwidth capability.
Design and Implementation of Microcomputer based Device for Measuring Various Physiological Parameters

There are many conventional devices used for individual measurement of Blood Pressure, Heart Rate and Body Temperature. The Devices used in the measurement are such as sphygmomanometer, oscillometric, etc (for blood pressure measurement), stethoscope, pulse meter, etc (for heart rate measurement), thermometer, forehead temperature strips, etc (for temperature measurement). In the present Scenario there is no device available which can measure all these parameters by a single unit at home. Only one parameter or two at the most can be measured with a single device. So there is an essential need to develop such an integrated measuring instrument. This present paper describes the device which can measure all these parameters. The present device is implemented with use of microcontroller and GSM module, which can transmit the data through SMS to the doctor.
Algorithms for structural variation discovery and protein-protein interaction prediction

This thesis has two main parts. In the first part, we will give an introduction on human genomic sequences, next-generation sequencing technologies, the structural differences among genomes of different individuals, and the 1000 Genomes Project. We will then discuss the problems of finding novel sequence insertions and mobile element insertions (e.g. Alu elements) in sequenced genomes. To identify those genomic variations with much higher accuracy than what is currently possible, we propose to move from the current model of (1) detecting genomic variations in individual nextgeneration sequenced (NGS) donor genomes independently, and (2) checking whether two or more donor genomes, indeed, agree or disagree on the variationswe will call this model the independent structural variation detection and merging (ISV&M) framework. As an alternative, we propose a new model in which genomic variation is detected among multiple genomes simultaneously. The second part of the thesis focuses on a different project which is concerned with gene tree alignment. The aim is to present the first efficient approach to the problem of determining the interaction partners among protein/domain families. This is a hard computational problem, in particular in the presence of paralogous proteins. We devise a deterministic algorithm which directly maximizes the similarity between two leaf labeled trees with edge lengths, obtaining a score-optimal alignment of the two trees in question.
Merging of Ontologies - Conceptual Design Issues

An approach to merging ontologies is presented that integrates financial knowledge in Decision Support Systems. The process of ontology merging is supported by an editor Protégé, and illustrated by examples extracted from financial information systems. It is shown how the correspondences between concepts, properties, and relations in the various ontologies can be processed. However not all problems of ontology integration may be resolved automatically. Therefore a number of cases of manager involvement in the merging process are considered.
An Information Retrieval System on Thailand Tourism Community Websites

Nowadays, there are many sharing travel knowledge on the internet such as community web-boards which allows most people prefer to post their travel experience. There are many content that implicated in the hotel, the restaurant, or even the travel route. Because of the Internet includes many review articles that makes the tourists spend a lot of time reading and summarizing articles. This research proposed the information retrieval model to search related articles to user needs. Moreover, the system tries to summarize the content related to tourism in the post to continue to know. In this research, we use vector space model to represent the keywords that are the social media content and applies the cosine similarity to indicate the ranking of related articles. The results are travel review article to guide the attractions, accommodations and location that help to plan the user's travel. The experiment results show that our tourism web-boards retrieval provides relevant information on high performance.
First International Workshop on Professional Search

In this report we describe the outcome of the First International Workshop on Professional Search, held in co-location with SIGIR 2018. The workshop addressed the specific requirements and challenges of professional search, as opposed to web search. The workshop included a survey held among 113 professional searchers, two keynote talks, six short paper talks, and two break-out sessions. From all parts of the workshop, we concluded that (1) we can define professional search with a set of common characteristics; but (2) there are definitely differences between domains; (3) we are in need of more knowledge about the search process in different domains, and (4) we need to collect data, not only Cranfieldstyle test collections, but also user observation data that help us understand the challenges and requirements of professional search better. Currently, there are many open problems that potentially have an impact on the IR community beyond the professional domains. We therefore call the scientific IR community to address professional search more actively.
Towards Tweet Content Suggestions for Museum Media Managers

Cultural Heritage institutions are embracing social technologies in the attempt to provide an effective communication towards citizens. Although it seems easy to reach millions of people with a simple message posted on social media platforms, media managers know that practice is different from theory. Millions of posts are competing every day to get visibility in terms of likes and retweets. The way text, images, hashtags and links are combined together is critical for the visibility of a post. In this paper, we propose to exploit machine learning techniques in order to predict whether a tweet will likely be appreciated by Twitter users or not. Through an experimental assessment, we show that it is possible to provide insights about the tweet features that will likely influence its reception/recommendation among readers. The preliminary tests, performed on a real-world dataset of 19, 527 museum tweets, show promising accuracy results.
Studying micro-communities in Facebook Communities

In the visionary view of the future Internet, named the Next Generation Internet, a current idea is to have a user-centric approach where human behavior models will be used to define the networks or to manage services. During the last years, a great trend in current Social Media platforms is to offer the opportunity to establish and join groups of people online. Despite human behaviour in current Online Social Media have been studied in depth, characteristics of these aggregations of people in content-based communities are still unknown. In this paper, we propose an evaluation of micro-communities of users inside the big network of Facebook groups to understand how and when users are active, and to evaluate the evolution of these micro-communities over time. Results show that almost all groups showed interactions-based communities. We found out that in all cases there is one massive core community which attracts small communities.
Evaluating a multi-avatar game for speech therapy applications

In this paper, we propose a new set of experiments to further evaluate the performance of a previously presented system based on an adaptive strategy for stimuli selection masked behind a gamified activity. This involves two virtual agents creating a social setting designed to support a narrative to engage young children. With respect to previously obtained results, we further evaluate the quality of the support for diagnosis purposes and we present a first investigation concerning the applicability of the system for therapeutic goals.
Image-Based Hoax Detection

In the last few years, the impact of information spread through online social networks has continuously grown. For this reason, understanding the trustworthiness of news has become one of the most important challenges for an Internet user, especially during crisis events or in political, health and social issues. As part of a more comprehensive project for the detection of fake news, this paper proposes a machine learning method to evaluate the trustworthiness of a piece of information especially considering its associated image. In the work described in this paper, the training and test datasets have been first collected from the web, downloading more than 1000 images related to trusted and fake Facebook pages. All collected images have been processed using the Google Vision online service for extracting their specific internal details. For each image, various kinds of features have been considered, including its color composition, the recognized objects, the list of sites in which it is published, and eventually the contained text. These details have been then used for training a classifier using different algorithms which allowed us to reach an accuracy of about 85% in hoax identification. Future research will focus on social-network information related to images, to improve the system accuracy and acquire more knowledge about various types of news spread online.
Facial-based Intrusion Detection System with Deep Learning in Embedded Devices

With the advent of deep learning based methods, facial recognition algorithms have become more effective and efficient. However, these algorithms have usually the disadvantage of requiring the use of dedicated hardware devices, such as graphical processing units (GPUs), which pose restrictions on their usage on embedded devices with limited computational power. In this paper, we present an approach that allows building an intrusion detection system, based on face recognition, running on embedded devices. It relies on deep learning techniques and does not exploit the GPUs. Face recognition is performed using a knn classifier on features extracted from a 50-layers Residual Network (ResNet-50) trained on the VGGFace2 dataset. In our experiment, we determined the optimal confidence threshold that allows distinguishing legitimate users from intruders. In order to validate the proposed system, we created a ground truth composed of 15,393 images of faces and 44 identities, captured by two smart cameras placed in two different offices, in a test period of six months. We show that the obtained results are good both from the efficiency and effectiveness perspective.
Report on the DATA

The increasing availability of structured data on the web makes searching for data an important and timely topic. This report presents the motivation, output, and research challenges of the second DATA:SEARCH workshop which was held in conjunction with SIGIR 2018, in Ann Arbor, Michigan. This workshop explored challenges in data search, with a particular focus on data on the web. The aim was to share and establish links between different perspectives on search and discovery for different kinds of structured data, which can potentially inform the design of a wide range of information retrieval technologies. The DATA:SEARCH workshop tries to bring together communities interested in making the web of data more discoverable, easier to search and more user friendly.
Social Networks Serving Web Feeds: An Approach for Web Feed Enrichment

A web feed is a new kind of web services that was created in the past two decades to keep remote users updated with the latest news without having to crawl individual web sites. Currently, the existing web feed services get their information from one source (the website hosting the feed) in a standardized format that is normally structured as follows: title, description, link, image and date. In this paper, we propose a new method called NERVES (social Networks sERVing wEb feedS) that connects web feeds to a Social Networking Sites (SNS), and aims at (1) keeping users aware of the wider context taking into account both the source of the feed and the SNS. (2) Our approach enriches existing feeds with extra information harvested from the social web. We validated the efficiency and accuracy of our approach on public data and report on empirical results yielding an accuracy of 94%.
Function Point Method Based on Hierarchical Convolutional Neural Network

Function Point method is vulnerable to personal knowledge and experience in software requirements assessment. Combined with JIRA-the work item tool, CNN method is used to extract the feature value of each item, through pooling layer compression and fully connected layer classification. The function classification and function point count of the item are finally determined, and the total function point count are obtained by weighting the item level. It is shown that this method is not easy to produce excessive deviation and can be used to assist prediction.
Genome-Wide Analysis of the Cytochromes P450 Gene Family in Aspergillus Oryzae

Cytochromes P450 gene family has been shown to play significant roles in various physiological processes of Aspergillus oryzae, including the growth and development, abiotic and biotic stress responses and stress signaling. In this study, the members of CYP gene family in Aspergillus oryzae were identified and analyzed on the whole genome level by means of bioinformatics. The phylogenetic tree, gene structure and motifs were analyzed. The results showed that 58 CYP gene family members were identified in A. oryzae. The expression analysis of AoCYP under different salt environments and at different growth stages showed the distribution of the expression in different variables. The results of this study will lay a theoretical foundation for the cloning of the members of the CYP gene family and the identification of their functions. It will also provide reference information for studying the growth stages and osmotic pressure regulation mechanism of A. oryzae.
Constrained Attribute Selection for Stress Detection Based on Physiological Signals

We present a constrained attribute selection method that makes use of feature assessment based on the Fisher's separation criterion followed by variety reduction post-processing. The post-processing incorporates task-specific constrain into the feature selection process, as this is expected to facilitate the subsequent data modeling and classification stages. Here we validate the proposed method in an experimental setup oriented towards acute stress detection based on physiological signals. The experimental results support that the proposed method brings advantage, when compared to three other cases: (i) the full set of features, (ii) a subset selected based on prior knowledge, (iii) and a subset selected based solely on Fisher's separation criterion.
3D Human Pose Estimation from RGB-D Images Using Deep Learning Method

In human activity recognition system, detecting the human and estimating the pose of 2D or 3D human correctly is critical issue. In this paper, we propose a novel approach for 3D human pose estimation from RGB-D CCTV images based on a deep learning approach. By using the RGB-D model rather than the conventional RGB image which has a limitation in detecting object due to the lack of topological information, we can resolve the self-occlusion problem and improve the object detection ratio from efficiently. Subsequently, the position of a human joint is localized with a Convolutional Neural Network (CNN) from the detected person. In this phase, we utilize CPM (Convolutional Pose Machine), to generate belief maps to predict the positions of key-point representing human body parts and estimate 2D human pose by detected key-points. In final stage, we estimate 3D human pose from the 2D joint information based on Deep Neural Network (DNN). From the experiment, we prove that the proposed method detects human objects robustly in occlusion and the estimated 3D human pose are very accurate comparing the previously introduced methods. As for the future work, the estimated 3D human pose will be used for human activity recognition.
Design and evaluation of an exergame for motor-cognitive training and fall prevention in older adults

Exergames have emerged as a strategy to promote physical activity and cognitive tasks through entertainment, being a promising solution for multicomponent training and fall prevention in older adults. Even though seniors may enjoy playing, game design is often not suited to this age group, which hinders interaction and user experience. Therefore, we developed a motion-based interactive game for motor-cognitive training in older adults, based on typical exergames, dance and Tai Chi. New forms of interaction based on inertial sensors and movement were included and tested with eight older adults in multiple sessions, during which usability and user experience were evaluated. People found the exergame fun, even though some challenges related to interaction were reported. Despite the challenges, people learned how to use the system and interaction has gradually became easier. In-game strategies to support interaction are required to encourage autonomous use devoid of supervision.
Automatic processing and classification of citizens' reports

We present a comprehensive framework for managing reports sent to the local government by citizens through well-known instant messaging apps. It leverages a combined use of Web systems and automated bots, based on Machine Learning techniques. This project has been developed in collaboration with the local administration of Montecchio Emilia (Italy). The results show that an automatic classification system of this kind can reach very good levels of accuracy, also above 90%.