Miracle-dz/UniFAR-FTUs huggingface dataset:
Partitioning multicast MANETs

MANETs (Mobile Ad-hoc Networks) are autonomous networks operating either in isolation or as stub networks connected to a fixed network. They do not necessarily rely on existing infrastructures. When the mobile nodes need one-to-many or many-to-many communication then multicasting is employed. Two of the best multicast protocols, MAODV (Multicast Ad-hoc On-demand Distance Vector Routing Protocol) and ODMRP (On Demand Multicast Routing Protocol), are compared. The performance measures to be evaluated are the PDR (Packet Delivery Ratio) and the Latency. This paper investigates two problems: i) does nodes partitioning into sub-groups improve performance? and ii) what is the best number of sub-groups? Three different movement scenarios are examined: i) Random movement, where all nodes move unpredictably in the area, ii) Directed movement, where the nodes move freely along the x-axis but directionally from y(0, 0) to y(0, 2000) along the y-axis, and iii) Directed II, where the nodes move as in the Directed movement and the subgroup leaders communicate with each other. Two different speed values are also examined: i) 1 m/s (average walking speed), and ii) 10 m/s (slow vehicle speed).
Is There a Caring Perspective in Garden/Patio Design in Elderly Care? A Description and a Comparison of Residents’ and Staff Members’ Perceptions of These Outdoor Spaces

This article aims to describe the characteristics of and design elements in gardens/patios at 87 residential living homes for older people and to describe and compare residents’ and staff members’ perceptions of outdoor spaces. The aim was also to investigate factors related to residents' satisfaction with and stays in the garden/patio. The result showed that many gardens/patios had several recommended design elements and, at the same time, obvious deficiencies. The residents (n = 415) valued various aspects of the garden/patio more highly than did the staff (n = 667). Managers responsible for residential living homes for elderly people should take measures to ameliorate deficiencies and deficient elements in design and accessibility that are lacking so that the garden/patio can be used as an important health promotion resource in the care of elderly people. Residents and staff are important actors in such development work.
Belief systems enforcing female genital mutilation in Europe

Purpose – Despite numerous studies on FGM, little is known about belief systems that support FGM in the EU. The purpose of this paper is to explore the dynamic nature of belief systems and enforcement mechanisms that perpetuate FGM among three African migrant communities in the EU. Design/methodology/approach – This paper is based on data collected through community-based participatory action research in three communities: Eritrean and Ethiopian community in Palermo, Italy; Guinea Bissauan community in Lisbon, Portugal; and Senegalese and Gambian community in Banyoles, Spain. A total of 24 FGDs and 70 in-depth narrative interviews were conducted for the research. Findings – The research finds that belief systems supporting the practice of FGM among African migrants in the European diaspora are similar to those in their home countries. Beliefs structured around religion, sexuality, decency, marriage and socialisation are particularly significant in perpetuating FGM in the study migrant communities. These a.
Scattering matrix approach for the design of microwave filters

A synthesis procedure, based on a distributed parameter model, for the design of microwave filters is presented in this paper. The frequency response of the filter is described in terms of the characteristic polynomial T/sub 21/=S/sub 11//S/sub 21/ where S/sub 11/ and S/sub 21/ are the scattering parameters of the filter. Starting from the desired polynomial T/sub 21/, the design scheme directly yields the scattering parameters of the various junctions, which can be realized by any kind of discontinuity. The capability of synthesizing an arbitrary frequency response allows one to introduce the concept of a "predistorted" characteristic polynomial in order to compensate for the degradations caused by multimodal interactions, frequency dispersion, etc. Comparison with measured data of a Chebyshev-like eight-pole E-plane filter confirms the validity of the method also in the presence of losses.
Effects of Varying the Threshold of Alarm Systems and Task Complexity on Human Performance and Perceived Workload

Using a dual-task paradigm, we examined how alarm system detection threshold and task complexity affected human performance and perceived workload. We hypothesized that using an alarm system would improve task performance and lower perceived workload, particularly when task complexity was high and at the medium threshold level. Twenty-one students from Old Dominion University participated in this study. Results showed that alarm use improved performance during low task complexity. For high task complexity, improvement was accomplished only when alarm system threshold was low or intermediate. Results also indicated that changing the alarm system threshold affected performance only under high task load conditions. Optimal performance was achieved by setting the threshold of the alarm system at its lowest level. The use of alarms reduced workload under both low and high task complexity levels, but only when the threshold was high.
Assessment of Dietary Intakes and Food habits in Female Adolescents with Eating Disorders

Background and Objectives: Eating disorders are psychological syndromes which characterized by abnormal or disturbed food habits resulted from intense fear of obesity and may include negative effects on people physical or mental health. This cross sectional study was carried out to assess dietary intakes and food habits in female adolescents with eating disorders in Tehran, Iran. Materials and Methods: Totally, 231 female adolescent with eating disorders were selected from a total number of 2766 students from five districts in Tehran using multistage clustered sampling in a 2-phase procedure. All affected students and 284 non-affected students filled demographic and food habit questionnaires (modified version) for adolescents and then were interviewed for the 70-item non-quantitative food frequency by expert nutritionists. Results: Study of dietary patterns in adolescents showed that the most common meal pattern (30%) in nonaffected group included three main meals and two snacks. The highest frequent patterns in partial syndrome group (33.5%) and bulimia nervosa (28.1%) included two main meals daily. The missing dinner in partial syndrome and bulimia nervosa groups were significantly higher than that in non-affected group (P<0.05). The highest frequency of drinking water (4–6 cups/day) was seen in partial syndrome group (41%). This included 3–2 cups/day (39% and 42.3%) in bulimia nervosa and non-affected groups, respectively. A significant difference was seen between these groups (P = 0.037). Consumption of all cereals and vegetables in the affected group was significantly lower than that in non-affected group (P < 0.05). Study of lipid intake status in the participants showed that the frequency of low-fat dairy products in affected groups (25% and 35.8% in partial syndrome and bulimia nervosa, respectively) was significantly higher than that in non-affected group (12.7%). Conclusions: Study demonstrated that missing dinner and drinking higher volumes of water with decreased cereal and low-fat dairies intakes were the major techniques used by the adolescents to reduce the calorie intake. Despite of these differences, the overall food habits in the affected group do not seem different significantly from those in non-affected groups.
Characterization of intracellular protein aggregates

Durant les ultimes decades, l'agregacio de proteines ha esdevingut un tema d’investigacio molt dinamic que s'esten transversalment per diferents camps de recerca, incloent la bioquimica, la biotecnologia, la biomedicina i la nanotecnologia. D'una banda, l'acumulacio de proteines en diposits amiloids insolubles constitueix una caracteristica comuna de molts trastorns humans, coneguts com a malalties conformacionals. D'altra banda, des d'un punt de vista biotecnologic, l’agregacio proteica representa un obstacle habitual en la produccio de proteines recombinants, que en general s'acumulen en forma de cossos d’inclusio intracel·lulars. Malgrat que els cossos d’inclusio han estat tradicionalment considerats particules desestructurades i amb molt poc interes, nombroses evidencies indiquen que aquests agregats contenen estructura de tipus amiloid, la qual cosa aplana el cami per a emprar-los en l'estudi de l’agregacio amiloid. La tesi que aqui es presenta recapitula la feina pertanyent a una serie de publicacions relatives a l’agregacio amiloid de proteines en l'espai intracel·lular. En tres d'aquests treballs, s'utilitzen tres models cel·lulars diferents filogeneticament distants (bacteris, llevats i plantes) per abordar l’estudi de la formacio de diposits proteics amb l'objectiu de caracteritzar-los i analitzar-ne el seu impacte en el metabolisme cel·lular. En una publicacio complementaria, explotem els agregats bacterians per tal de desenvolupar un assaig de screening in vitro per trobar moduladors de l’agregacio amiloid. Finalment, tambe s'inclouen dues obres de revisio sobre la deposicio de proteines en bacteris i el paper d’aquest organisme com a model per a l'estudi de l'agregacio amiloid. Les dades obtingudes de tots aquests estudis indiquen que l'agregacio en conformacio amiloid es una propietat generica dels polipeptids i un fenomen omnipresent a la natura. No obstant, l'aparicio d'aquests diposits en l'entorn cel·lular pot anar acompanyada d'un cert grau de toxicitat. Aqui, analitzem l'efecte d'envelliment promogut pels cossos d'inclusio intracel·lulars en cel·lules bacterianes. A mes, descrivim com les cel·lules procariotes i eucariotes estan dotades d'una poderosa maquinaria de qualitat proteica que permet fer front a aquesta situacio perjudicial. Addicionalment, la caracteritzacio en profunditat dels agregats proteics en bacteris i del seu proces de formacio permet utilitzar-los com a eina en la recerca d'inhibidors de l'agregacio amiloid amb potencial interes biomedic i farmaceutic. En general doncs, aquesta tesi aprofundeix en l'estudi de l'agregacio amiloid de proteines i amplia el coneixement per a emprar organismes simples com models cel·lulars rellevants.
Collaboration of Smart IoT Devices Exemplified With Smart Cupboards

The variety of smart things connected to Internet hampers the possibility of having a stand-alone solution for service-centric provisioning in the Internet of Things (IoT). The different features of smart objects in processing capabilities, memory, and size make it difficult for final users to learn the installation and usage of all these devices in collaboration with other IoT objects, hindering the user experience. In this context, we propose a collaboration mechanism for IoT devices based on the multi-agent systems with mobile agents. This paper illustrates the current approach with smart cupboards for potentially tracking memory losses. The user study revealed that users found working products of this approach usable, easy-to-learn and useful, and they agreed that the current approach could provide a high quality of experience not only in the specific case of service-centric IoT devices for tracking memory losses but also in other domains. The learning capability by means of this approach was showed with significant reductions of reaction times and number of errors over the first and second tests with the current approach. System response times were appropriate for both continuous rendering and presenting the classification results. The usage of RAM memory was also adequate for the common actual devices.
Music-Induced Brain Functional Connectivity Using EEG Sensors: A Study on Indian Music

Music is promoted in all traditions for the well-being of the listener. The 2-D emotion models and their effects on the brain were analyzed while listening to selected raga and at Rest. This paper visualized the functional connectivity of emotional responses to Indian Dharmavathi raga, thereby locating the similar regions across the electroencephalogram (EEG) sensor pairs. The international 10–20 sensor placement system was followed for recording EEG signals. The stimulus-based functional associations were noted, which help in investigating the neuronal synchronization. Asymmetrical alpha band energy was noticed in the frontal locations where the energy was low at F3 and F7 positions compared with F4 and F8 sensor positions. This indicates that it induces a positive emotion. The relative theta band energy at frontal locations was increased during Task (listening to Dharmavathi raga) as compared with Rest (silence) condition. The relative energy of beta band at frontal locations was significantly decreased after listening to raga. While listening to Dharmavathi raga, alpha band energy on the left frontal region was appeared to be low, and high energy was observed on the right frontal region. The raga played was evoked an increased bi-frontal theta response, which indicates that the raga is pleasant and induces mood change after listening.
A mathematical model of Chagas disease infection predicts inhibition of the immune system

The protozoan parasite Trypanosoma cruz causes the Chagas disease, which final outcome can be morbidity or death. The complexity of this infection is due to the many kinds of players involved in the immune response and to the variety of host cells targeted by the parasite. We built an ordinary differential equation model which includes aspects of innate and adaptive immune response to study the T. cruzi infection. The model also includes cardiomyocytes to represent how the infection affects the heart. We used parasitemia experimental data of infected wild-type mice to estimate the model parameters. We investigated how the number of parasites and infected cardiomyocytes were affected by changes of parameters controlling the survival rates of the parasite. We thus introduce a 20% variation in either macrophages, $\mathrm {C}\mathrm {D}\mathrm {S}^{+}\mathrm {T}$ cells, or anti- parasite specific antibody activity. This resulted in a change of the parasitemia as expected, and produced a broader variation in the number of parasites around the peak of parasitemia. Moreover, the same three model modifications were enabled one at a time to simulate a knockout effect in the host. The results of the knockout effects were a faster parasite growth and death of the host in all three cases, in agreement with in vivo experimental data. The model also is corroborated by in vivo data from the literature where the inhibition of macrophages, antibody, or CTL is not compensated by the other parasite killing mechanisms, and as a result lead to death of the host. Altogether these results indicate that the immune system plays a crucial role in controlling T. cruzi infection and impairment of one modality of action greatly reduces its efficiency and results in a much larger extensionof the infection of cardiomyocytes.
Detecting Serendipitous Drug Usage in Social Media with Deep Neural Network Models

Serendipitous drug usage refers to unexpected relief of comorbid diseases or symptoms when patients take a drug for another common or known indication. In the history of drug discovery, serendipity has contributed significantly to new and successful indications for many drugs. Our previous research has identified patient reported serendipitous drug usage in social media. If such information could be computationally identified in social media, it could be helpful for generating and validating drug-repositioning hypotheses. In this study, we framed detection of serendipitous drug usage in social media as a binary classification problem and investigated deep neural network models as a solution. We constructed word-embedding features from drug-review posts in the patient forum of WebMD, using the word2vec algorithm. We adopted the convolutional neural network (CNN), long short-term memory network (LSTM), and convolutional long short-term memory network (CLSTM) and redesigned them by adding contextual information that we extracted from drug-review posts, information filtering tools, medical ontology, and medical knowledge. We trained, tuned, and evaluated our deep neural network models on a gold standard dataset containing 15,714 sentences, of which 447 contained serendipitous drug usages. Additionally, we compared our deep neural networks to support vector machine, random forest, and AdaBoost.M1 algorithms. The results showed that adding context information helped to reduce the false-positive rate of deep neural network models. In the presence of an extremely imbalanced dataset and limited instances of serendipitous drug usage, deep neural network models did not outperform other machine learning models with n-gram and context features. However, deep neural network models could more effectively utilize word embedding in feature construction. This advantage made deep neural networks worthy of further investigation and improvement.
Finite-time Anti-synchronization of Stochastic Delayed Memristive Neural Networks

In this paper, the finite time anti-synchronization of stochastic memristive neural network has been investigated under the framework of Filippov sense. To obtain the anti-synchronization, firstly, we discussed both continuous and discontinuous control, respectively. Then, the new switching controller is presented considering the characteristic of continuous and discontinuous controllers. Correspondingly, some useful results are established, meanwhile, a numerical example is given to illustrate the effectiveness of obtained results.
Trajectory Cluster Lifecycle Analysis: An Evolutionary Perspective

Cluster analysis has helped to uncover changes over time in numerous studies on the dynamics of entities such as people and groups of animals in areas such as human mobility, health, transportation, commerce, and ecology. However, there is a lack of methods that focus on aspects related to the cluster lifecycle, including dynamic analyses on how clusters are formed, change, and disappear. Specifically, how objects enter and exit from the clusters, and how clusters are (de-)composed to form new clusters. In this paper, we introduce our work in progress about an approach to trajectory cluster lifecycle analysis based on big data that supports an evolutionary analysis of clusters throughout their lifecycle. The knowledge that can be captured as a result of such novel forms of analysis will advance the state of the art in a wide range of applications that require information about cluster evolution, and thus provide deeper insights on cluster genesis, existence, and disappearance.
Projection-SVM: Distributed Kernel Support Vector Machine for Big Data using Subspace Partitioning

The training of kernel support vector machine (SVM) is a computationally complex task for large datasets where the number of samples ranges in millions. This is because kernel matrix (in general not sparse) is both computation expensive and memory intensive. Existing methods hardly achieve a linear scale and suffer from high approximation loss. We propose Projection-SVM, a distributed implementation of kernel support vector machine for large datasets using subspace partitioning. In subspace partitioning, a decision tree is constructed on projection of data along the direction of maximum variance (i.e., dominant eigenvector) to obtain smaller partitions (i.e., subspaces) of the dataset. On each of these partitions, a kernel SVM is trained independently over a cluster thereby reducing the overall training time. Also, it results in reducing the prediction time significantly. We demonstrate the efficacy of the proposed approach on eight standard large datasets from various application domains, namely, mnist8m, kddcup99, webspam, etc. where Projection-SVM is on an average 150 times faster than sequential SVM while maintaining the classification accuracy. The experimental results also show the superiority of the Projection-SVM over the state-of-the-art approaches for distributed kernel SVMs, such as DCSVM, CASVM, and DTSVM.
A Bandwidth-Efficient Middleware for Encrypted Deduplication

Data deduplication is a vital component of current cloud storage systems for optimized space utilization. However, users cannot fairly enjoy the storage savings of deduplication. Uploading two identical files consumes twice the storage quota from a user's account, but the server may store one file copy only. In this paper, we design and implement a middleware system, namely UWare. It brings storage and bandwidth savings back to users, while preserving user data privacy. UWare starts from the message-locked encryption for efficient deduplication over encrypted data, and initiates the endeavor in leveraging the similarity characteristics of block-level deduplication to balance the effectiveness of secure deduplication and system efficiency. Also, UWare patches a practically feasible side-channel threat when deploying the proof-of-ownership protocol, i.e., hiding the existence of a target file during the protocol execution. We implement a prototype and use a real-world dataset to demonstrate that UWare can save about 30% storage and bandwidth cost for users, and reduce over 80% memory space consumption compared to the secure block-level deduplication.
Polypheny-DB: Towards a Distributed and Self-Adaptive Polystore

Cloud providers are more and more confronted with very diverse and heterogeneous requirements their customers impose on the management of data. First, these requirements stem from service-level agreements that specify a desired degree of availability and a guaranteed latency. As a consequence, Cloud providers replicate data across data centers or availability zones and/or partition data and place it close to the location of their customers. Second, the workload at each Cloud data center or availability zone is diverse and may significantly change over time – e. g. , an OLTP workload during regular business hours and OLAP analyzes over night. For this, polystore and multistore databases have recently been introduced as they are intrinsically able to cope with such mixed and varying workloads. While the problem of heterogeneous requirements on data management in the Cloud is either addressed at global level by replicating and partitioning data across data centers or at local level by providing polystore systems in a Cloud data center, there is no integrated solution that leverages the benefits of both approaches. In this paper, we present the Polypheny-DB vision of a distributed polystore system that seamlessly combines replication and partitioning with local polystores and that is able to dynamically adapt all parts of the system when the workload changes. We present the basic building blocks for both parts of the system and we discuss open challenges towards the implementation of the Polypheny-DB vision.