Miracle-dz/UniFAR-FTUs huggingface dataset:
Slider : Incremental Sliding-Window Computations for Large-Scale Data Analysis

Sliding-window computations are widely used for data analysis in networked systems. Such computations can consume significant computational resources, particularly in live systems, where new data arrives continuously. This is because they typically require a complete re-computation over the full window of data every time the window slides. Therefore, sliding-window computations face important scalabi lity problems. In this paper, we propose techniques for improving the scalability by performing sliding-window computations incrementally. In this paradigm, when some new data is added at the end of the window or old data dropped from its beginning, the output is updated efficiently by reus ing previously run sub-computations, avoiding a complete recomputation. To realize this approach, we propose Slider, a novel framework that supports incremental sliding-window computations transparently and efficiently by leveraging s elfadjusting computations principles of dynamic dependence graphs and change propagation. We implemented Slider based on the Hadoop MapReduce framework with a declarative SQL-like query interface, and evaluated it with a variety of applications and real world case studies from networked system. Our results show significant performance improvements for large-scale sliding-window computations withou t any modifications to the existing application code.
Sensors, programming and devices in Art Education sessions. One case in the context of primary education / Sensores, programación y dispositivos en sesiones de Educación Artística. Un caso en el contexto de Educación Primaria

Abstract The pedagogical integration of computing is interesting in educational contexts based on the contributions of Seymour Papert and Wing’s concept of computational thinking. Integrating arts in education can lead to the design of activities using Scratch combined with devices. The main goal is to evaluate the integration of computational thinking in art education making use of technological resources, sensor cards and minicomputers, with a student-centred pedagogical approach. This research assesses the results of a control group of 35 students and an experimental group of 109 students in four different schools, using Mann-Whitney’s U-test for independent samples assessing ‘Active Learning’, ‘computational concepts’ and ‘fun’ scales. Applying data triangulation, and consistent with design-based research, the results of interviews and focus groups reinforced the results obtained in the aforementioned test, providing validity to the study. There are advantages regarding student interest, motivation and commitment related to programming technologies in art and education, particularly pedagogical sessions with music. Handling devices, sensors and Raspberry Pi provides participating students with a factor of commitment and enthusiasm, with significant improvements. Working with coding and devices brings an additional advantage in the development of computational thinking and digital competence. The results show an increase in creativity and artistic competence related to the ability to create music from the activities and technological resources described in the technological intervention.
Market segmentation for product family positioning based on fuzzy clustering

To compete in the marketplace, manufacturers have been seeking for expansion of their product lines by providing product families. One difficulty for product family positioning is that diverse customer needs can no longer be satisfied by a mass marketing approach. Realizing the importance of customer purchase behaviours for product family positioning, this paper proposes a fuzzy clustering-based market segmentation approach. With a focus on engineering characteristics, the fuzzy clustering-based market segmentation helps plan the right products to target segments effectively and efficiently. An application of the proposed methodology in a consumer electronics company producing vibration motors is reported. The evaluation of the proposed methodology is also discussed.
Actors in the Classroom: The Dramatic Pedagogy Model of Counselor Education

ABSTRACT Role playing is a fundamental component in the training of counselors to use counseling skills. However, role play activities commonly involve counseling students as clients or actors and hence have notable limitations. One training method widely practiced in the medical profession is the use of professional actors as patients. Although there are some examples in the literature of studies that have used actors for counselor training, most address only a specific topic or skill set. In this article, the authors propose the dramatic pedagogy model (DPM) as a comprehensive model for implementing client actors into the education of counselors. The DPM includes a set of pedagogical techniques and a structure for preparing actors and curriculum.
LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient

This paper describes the system used by the LIPN team in the task 10, Multilingual Semantic Textual Similarity, at SemEval 2014, in both the English and Spanish sub-tasks. The system uses a support vector regression model, combining different text similarity measures as features. With respect to our 2013 participation, we included a new feature to take into account the geographical context and a new semantic distance based on the Bhattacharyya distance calculated on co-occurrence distributions derived from the Spanish Google Books n-grams dataset.
Scheduling Globally Asynchronous Locally Synchronous Programs for Guaranteed Response Times

Safety-critical software systems need to guarantee functional correctness and bounded response times to external input events. Programs designed using reactive programming languages, based on formal mathematical semantics, can be automatically verified for functional correctness guarantees. Real-time guarantees on the other hand are much harder to achieve. In this article we provide a static analysis framework for guaranteeing response times for reactive programs developed using the Globally Asynchronous Locally Synchronous (GALS) model of computation. The proposed approach is applicable to scheduling of GALS programs for different target architectures with single or multiple processors or cores. A Satisfiability Modulo Theory (SMT) formulation in the quantifier free linear real arithmetic (QF_LRA) logic is used for scheduling. A novel technique to encode rendezvous used in synchronization of globally asynchronous processes in the presence of locally synchronous parallelism and arbitrary preemption into QF_LRA logic is presented. Finally, our SMT formulation is shown to produce schedules in reasonable time.
R-COM-MTDP: Comparing and Forming Plans for Team Formation in Multiagent Systems

Team formation, i.e., allocating agents to roles within a team or subteams of a team, and the reorganization of a team upon team member failure or arrival of new tasks are critical aspects of teamwork. Despite significant progress, research in multiagent team formation and reorganization has failed to provide a rigorous analysis of the computational complexities of the approaches proposed or their degree of optimality. This shortcoming has hindered quantitative comparisons of approaches or their complexity-optimality tradeoffs, e.g., is the team reorganization approach in practical teamwork models such as STEAM optimal in most cases or only as an exception? To alleviate these difficulties, this paper presents R-COM-MTDP, a formal model based on decentralized communicating POMDPs, where agents explicitly take on and change roles to (re)form teams. R-COM-MTDP significantly extends an earlier COM-MTDP model, by analyzing how agents’ roles, local states and reward decompositions gradually reduce the complexity of its policy generation from NEXP-complete to PSPACE-complete to P-complete. We also encode key role reorganization approaches (e.g., STEAM) as R-COM-MTDP policies, and compare them with a locally optimal policy derivable in R-COM-MTDP, thus, theoretically and empirically illustrating the complexity-optimality tradeoffs.
Feedback Analysis of Interaction between Urban Densities and Travel Mode Split

A feedback simulation model based on radial basis function neural networks is newly developed in this research to analyse the interaction between urban densities and travel mode split. The changes of populations, road mileages, travel mode split, and so on of the enlarging urbanized areas of different cities in China are studied for the trainings of the radial basis function neural networks constituting the proposed feedback model. Furthermore, the effect of different development policies for Beijing on distinct indicators of the urban density and trip shares of various travel modes is also evaluated by the newly developed model. It is found that stopping the quick urban sprawl of Beijing is the most important for the sustainable development of its urban transport. It is confirmed that the newly developed model is able to rationally explain the interactive correlation between urban densities and travel mode split of a city for its different development plans. (Received, processed and accepted by the Chinese Representative Office.)
A Multiple Hypothesis Tracking Method with Fragmentation Handling

In this paper, we present a new multiple hypothesestracking (MHT) approach. Our tracking method is suitablefor online applications, because it labels objects at everyframe and estimates the best computed trajectories up tothe current frame. In this work we address the problems ofobject merging and splitting (occlusions) and object fragmentations.Object fragmentation resulting from imperfectbackground subtraction can easily be confused with splittingobjects in a scene, especially in close range surveillanceapplications. This subject is not addressed in mostMHT methods. In this work, we propose a framework forMHT which distinguishes fragmentation and splitting usingtheir spatial and temporal characteristics and by generatinghypotheses only for splitting cases using observation inlater frames. This approach results in a more accurate dataassociation and a reduced size of the hypothesis graph. Ourtracking method is evaluated with various indoor videos.
Stilbene Induced Inhibition of Androgen Receptor Dimerization: Implications for AR and ARΔLBD-Signalling in Human Prostate Cancer Cells

Background Advanced castration resistant prostate cancer (CRPC) is often characterized by an increase of C-terminally truncated, constitutively active androgen receptor (AR) variants. Due to the absence of a ligand binding domain located in the AR-C-terminus, these receptor variants (also termed ARΔLBD) are unable to respond to all classical forms of endocrine treatments like surgical/chemical castration and/or application of anti-androgens. Methodology In this study we tested the effects of the naturally occurring stilbene resveratrol (RSV) and (E)-4-(2, 6-Difluorostyryl)-N, N-dimethylaniline, a fluorinated dialkylaminostilbene (FIDAS) on AR- and ARΔLBD in prostate cancer cells. The ability of the compounds to modulate transcriptional activity of AR and the ARΔLBD-variant Q640X was shown by reporter gene assays. Expression of endogenous AR and ARΔLBD mRNA and protein levels were determined by qRT-PCR and Western Blot. Nuclear translocation of AR-molecules was analyzed by fluorescence microscopy. AR and ARΔLBD/Q640X homo-/heterodimer formation was assessed by mammalian two hybrid assays. Biological activity of both compounds in vivo was demonstrated using a chick chorioallantoic membrane xenograft assay. Results The stilbenes RSV and FIDAS were able to significantly diminish AR and Q640X-signalling. Successful inhibition of the Q640X suggests that RSV and FIDAS are not interfering with the AR-ligand binding domain like all currently available anti-hormonal drugs. Repression of AR and Q640X-signalling by RSV and FIDAS in prostate cancer cells was caused by an inhibition of the AR and/or Q640X-dimerization. Although systemic bioavailability of both stilbenes is very low, both compounds were also able to downregulate tumor growth and AR-signalling in vivo. Conclusion RSV and FIDAS are able to inhibit the dimerization of AR and ARΔLBD molecules suggesting that stilbenes might serve as lead compounds for a novel generation of AR-inhibitors.
A numerical SC approach for a teleoperated 7-DOF manipulator

To tackle the singularity problem, the SC (singularity-consistent) approach was introduced. It achieves very stable control at and around a singularity with feasible joint velocities and no directional error in the end-effector velocity. This approach is very suited for a direct manual teleoperation system because of its errorless character for the direction. Until now, it was applied for the teleoperation of a nonredundant manipulator. In this paper, the SC approach for a 7-DOF manipulator will be addressed. To derive the whole properties of the SC approach, analytical studies for both the adjoint and the determinant of the Jacobian are necessary. However, it is very difficult to realize this goal, since the kinematics of a 7-DOF manipulator is quite complicated. Therefore, here, we establish a method to apply the SC approach to a 7-DOF manipulator numerically without analyzing the kinematic properties. This method, though, cannot realize the whole properties of the SC approach but a stable control at and around the singularities is achieved which is the most important and demanded property of the SC approach. Moreover, this method can also be applied for any type of articulated manipulator if its Jacobian can be defined. The results of our approach have been confirmed by experiments with graphics model.
Performance Analysis and Energy Optimization of Wake-Up Receiver Schemes for Wireless Low-Power Applications

The use of duty-cycled ultralow-power wake-up receivers (WRxs) can significantly extend a node lifetime in low-power sensor network applications. In the WRx design, both the low-power operation of the WRx and the wake-up beacon (WB) detection performance are of importance. We present a system-level analysis of a duty-cycled WRx design, including an analog front end, a digital baseband, the WB structure, and the resulting WB detection and false-alarm probabilities. We select a low-power WRx design with about two orders of magnitude lower power consumption than the main receiver. The associated cost is an increase in the raw bit error rate (BER), as compared with the main receiver, at the same received power level. To compensate, we use a WB structure that employs spreading. The WB structure leads us to an architecture for the digital baseband with high address-space scalability. We calculate closed-form expressions for detection and false-alarm probabilities. Using these, we analyze the impact of design parameters. The analytical framework is exemplified by the minimization of the WB transmit energy. For this particular optimization, we also show that the obtained results are valid for all transmission schemes with an exponential relationship between the signal-to-noise ratio and the BER, e.g., the binary orthogonal schemes with noncoherent detection used in many low-power applications.
Transliteration Alignment

This paper studies transliteration alignment, its evaluation metrics and applications. We propose a new evaluation metric, alignment entropy, grounded on the information theory, to evaluate the alignment quality without the need for the gold standard reference and compare the metric with F-score. We study the use of phonological features and affinity statistics for transliteration alignment at phoneme and grapheme levels. The experiments show that better alignment consistently leads to more accurate transliteration. In transliteration modeling application, we achieve a mean reciprocal rate (MRR) of 0.773 on Xinhua personal name corpus, a significant improvement over other reported results on the same corpus. In transliteration validation application, we achieve 4.48% equal error rate on a large LDC corpus.
Opportunistic Protocols in Multi-rate Environments

In recent years the research of opportunistic protocols for wireless mesh networks gained lots of attention. A great number of protocols like extreme opportunistic routing (ExOR) and multiuser diversity forwarding (MDF) was proposed. Most of the performance evaluations were conducted in a constant bit-rate environment. This paper presents simulation results of the performance of existing opportunistic protocols as well as a new opportunistic protocol called hybrid opportunistic routing (HOR) in a constant- and multi-rate environment. In a constant rate environment with a slow fading channel ExOR outperforms MDF and HOR by around 20%. This is mainly due to its small signaling overhead. ExOR is also the best choice in a fast fading channel. However, here HOR is able to outperform MDF. In a multi-rate environment our proposed ETT-RCA rate control algorithms outperforms the existing adaptive auto rate fallback (AARF) significantly. AARF is only suitable for short, high quality links. The biggest problem with ExOR is that is cannot be used together with ETT-RCA. In a slow fading channel MDF with ETT-RCA is the best choice. It outperforms ExOR with AARF by multiple times (up to 360%). In a fast fading channel HOR with ETT-RCA is the best choice for medium distances (e.g. 80% and 330% higher throughput than MDF with ETT-RCA and ExOR AARF). Only for very large distances ExOR with AARF is able to offer the highest throughput. Here in the multi-rate environment the degrees of freedom (candidate and bit-rate selection) are too large for ExOR and AARF.
North Indian Classical Music's Singer Identification by Timbre Recognition using MIR Toolbox

Timbre recognition has always been associated with musical instrument identification. In music information retrieval (MIR) community there has always been increasing interest in identifying singers and identifying the musical instruments. There can be unending debate on, whether to consider human throat as a kind of musical instrument generating sound through air pressure and vibration or not. Any musical sound possesses "Timbre", an unidentified and undefined entity which is yet non-tangible that uniquely defines the sound. A lot of research has been done on catching this fuzzy term in terms of its synonyms related to light or texture or so. MIR toolbox of MatLab provides some strong techniques to extract variety of audio attributes/characteristics from an audio file. These attributes are called as "Audio Descriptors". In this paper, we have studied such audio descriptors that fall under the Timbre category and found out the most prominent audio descriptors that plays substantial role in the identification of a singer from North Indian classical music. In this system we have considered Noisy data, Noise filtered data without background instruments and noise free studio recordings with only Tanpura instrument in the background as input. We have found that roll off, brightness, roughness and irregularity are the four strong audio descriptors designated under the Timbre category of MIR Toolbox, plays vital role in the identification of a singer from North Indian Classical Music giving accuracy of identification of 96.66% for three singers trained and tested simultaneously on studio recordings containing Tanpura(a Supportive Musical instrument of the singer) each of 5 sec duration and sampling rate of 11,025Hz, 16 bits with Pulse Code Modulation (PCM) uncompressed file format. The results indicate that a singer can also be treated as a wind type of musical instrument and be successfully identified by recognizing its Timbre.
A Neurofeedback-Based Near-Infrared Spectroscopy Brain-Computer Interface

Near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs) enable individuals to interact with their environment using only cognitive activities. This thesis investigates the development of a more user-friendly, intuitive, and easy to use NIRS-BCI through six research objectives: exploring prescribed and personalized mental task frameworks offline, using researcher-selected tasks to move beyond the binary paradigm, exploring correlations of user characteristics with accuracy, comparing user-selected personalized tasks to prescribed tasks online, weaning off mental tasks to achieve voluntary self-regulation, and applying personalized frameworks to a client case study. Firstly, personalized tasks outperformed prescribed tasks in a five-session offline study conducted on ten able-bodied participants. Specifically, user-selected tasks resulted in significantly higher ease-of-use, while researcher-selected tasks resulted in significantly higher accuracies. The same data were used to show that researcher-selected personalized mental tasks enabled classification in some users beyond a binary BCI paradigm. Accuracy was strongly positively correlated with perceived ease of session, ease of concentration, and enjoyment, but strongly negatively correlated with verbal IQ. In a second study, when comparing two able-bodied groups online (N = 9 and N = 10), the usability of user-selected personalized
Keyword-Base Concept Nets Model for Information Retrieval in the Mobile Cloud

The purpose of the following report is to introduce a model that makes it possible to efficiently search data by using keyword-based concept network for reliable access of information which is rapidly increasing in the mobile cloud. A keyword-based concept network is a method with the application of ontology. However, the proposed model is added by association information between keyword concepts as a method for a user's efficient information retrieval. Furthermore, the proposed concept network consists of the keyword centered concept network, expert-group-recommended field concept network, and process concept network.
ADHD SYMPTOMS AND ITS ASSOCIATION WITH PROSOCIAL AND AGGRESSIVE BEHAVIOURS AT SCHOOL

RESUMEN. Introduccion: Un numero de investigaciones cada vez mayor esta centrando su objeto de analisis sobre el papel que las funciones ejecutivas y los problemas de externalizacion ejercen sobre las conductas prosociales y agresivas de ninos y adolescentes (Bacchini, Affuso & Trotta, 2008). El objetivo de este estudio ha sido analizar la relacion entre la prosocialidad y la agresividad y los sintomas de TDAH: hiperactividad, inatencion e impulsividad. Metodo: Estudiantes de tercer ciclo de educacion primaria y primer ciclo de educacion secundaria (N = 1.853, 47,2% chicas) completaron una cuestionario para evaluar los niveles de prosocialdad y agresividad en el grupo de clase asi como un cuestionario para medir los sintomas del TDAH. Resultados: Los analisis mostraron una asociacion negativa entre la prosocialidad y los factores de los sistomas del trastorno, asi como una relacion positiva entre la agresividad y los mismos factores. Conclusiones: Los resultados mostraron resultados similares a estudios anteriores y destacan la necesidad de analizar con mas detalle la influencia de variables neuropsicologicas como las funciones ejecutivas sobre las conductas sociales.
Community characterization of heterogeneous complex systems

We introduce an analytical statistical method for characterizing the communities detected in heterogeneous complex systems. By proposing a suitable null hypothesis, our method makes use of the hypergeometric distribution to assess the probability that a given property is over-expressed in the elements of a community with respect to all the elements of the investigated set. We apply our method to two specific complex networks, namely a network of world movies and a network of physics preprints. The characterization of the elements and of the communities is done in terms of languages and countries for the movie network and of journals and subject categories for papers. We find that our method is able to characterize clearly the communities identified. Moreover our method works well both for large and for small communities.