Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding maximal cliques in massive networks by H*-graph

Maximal clique enumeration (MCE) is a fundamental problem in graph theory and has important applications in many areas such as social network analysis and bioinformatics. The problem is extensively studied; however, the best existing algorithms require memory space linear in the size of the input graph. This has become a serious concern in view of the massive volume of today's fast-growing network graphs. Since MCE requires random access to different parts of a large graph, it is difficult to divide the graph into smaller parts and process one part at a time, because either the result may be incorrect and incomplete, or it incurs huge cost on merging the results from different parts. We propose a novel notion, H*-graph, which defines the core of a network and extends to encompass the neighborhood of the core for MCE computation. We propose the first external-memory algorithm for MCE (ExtMCE) that uses the H*-graph to bound the memory usage. We prove both the correctness and completeness of the result computed by ExtMCE. Extensive experiments verify that ExtMCE efficiently processes large networks that cannot be fit in the memory. We also show that the H*-graph captures important properties of the network; thus, updating the maximal cliques in the H*-graph retains the most essential information, with a low update cost, when it is infeasible to perform update on the entire network.
What about ethics? Developing qualitative research in confinement settings

The main purpose of this article is to discuss some ethical-methodological issues associated with scientific research in confinement settings, particularly those that result from the relationship with the confined individual in the framework of qualitative research. Basing the reflection on empirical research developed by both authors in Portuguese confinement settings – prisons and youth educational centres – we examine the significant challenges and dilemmas this type of research entails, exploring the interface between procedural ethics and ethics in practice at three points in the analytical process: before, during and after data collection. This article illustrates the interplay between formal and informal procedures, and between the initial distancing and strangeness when making contact with confinement settings and their social actors and the institutional and relational dynamics that become ingrained in our everyday practice. Our goal is to give visibility to these institutional and relational dynamics and to reflect on the challenges experienced by those who enter confinement settings to do research, in an effort to make the research process more transparent and at the same time more reflexive. We end our reflection advocating more ethically committed and critical scientific research.
T UBE : a Prototype-Based Programming Environment

—Handling the growing complexity of software is one of the biggest challenges for the software industry. Two common answers to this challenge are: raising the level of abstraction (i. e., reducing complexity) and improving human understanding by improving the organization of software (managing complexity). It is our contention that if one gives developers tools with which they can better structure their software, one can reach both goals. Our programming language and integrated programming environment T UBE is such a tool and is based on hypergraphs, in their incarnation Topic Maps . The relational nature of Topic Maps makes them an ideal candidate for structuring a software system. Topic Maps give us powerful ways of annotating, documenting, querying and presenting T UBE programs. It also allows us to integrate all artifacts that are part of the system. Moreover, T UBE is a prototype-based object-oriented language which leads to a more direct, concrete and interactive style of programming in it, further increasing productivity.
Storage Security Zoomable User Interface ( ZUI ) ▶ Zooming Techniques Zooming Techniques

Definition Zooming facilitates data presentation on limited screen real-estate by allowing the users to alter the scale of the viewport such that it shows a decreasing fraction of the information space with an increasing magnification. Hence the system may first present a global overview of the information space for the benefit of orientation, and in a second step, the users can then dynamically re-allocate the screen space based on the information objects they are interested in. A navigation technique commonly used in conjunction with zooming is panning: a movement of the viewport over the information space at a constant scale.
The contribution of cognitive abilities and general language proficiency to explaining listening comprehension in English as a foreign language

Abstract The present study aimed to investigate the relationship between a number of cognitive abilities (auditory/visual attention, processing speed, and fluid intelligence) and listening comprehension. A total of 97 undergraduate EFL students participated in the study. Tests of auditory and visual attention, processing speed, verbal and nonverbal fluid intelligence, overall English language proficiency, and listening comprehension were administered to the participants. The results indicated that the measure of English proficiency had the highest correlation with the listening test followed by measures of attention and processing speed. Hierarchical regression analysis indicated that 17% of the variance in listening comprehension was explained by the cognitive factors. The addition of second language proficiency to the regression model in the second step added another 25% to the total variance explained. That is, foreign language proficiency, after controlling for cognitive abilities, can explain 25% of the variance in listening comprehension. These findings suggest that understanding when listening in L2 involves cognitive information processing skills in addition to proficiency in that language.
Robust sensor scheduling via iterative design for parameter estimation of distributed systems

The problem of selection of measurement data provided by sensor array so as to maximize the accuracy of parameter estimation of a distributed system defined in a given multidimensional domain is discussed. Usually, when designing an identification experiment for nonlinear models, the uncertainty of nominal parameters has to be taken into account. In particular, an iterative scheme for parameter estimation is proposed enhanced with sequential experimental design techniques where there is no particular information about the parameter distribution. The setting examined here correspond to situation where from among the observations provided by nodes of given sensor array the most informative measurements have to be selected in order to provide an update of the parameter estimates. Finally, a proposed approach is verified by a computer simulation regarding heat transfer problem.
Automatic Detection of Melanoma Skin Cancer using Texture Analysis

Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.
Merging Color Shift Keying and complementary Pulse Position Modulation for visible light illumination and communication

The ever increasing need to be able to take advantage of broadband services without the need to increase the electromagnetic pollution, has led the scientific community, in recent years, to look for alternatives to the use of a radio frequency communication. From this, it stems the need of budding paradigm of visible light communications. In the context of the activities of the IEEE 802.15. 7 Task Group, a new modulation format named Color Shift Keying (CSK), based on sending signals spaced in the domain of the wavelength able to both support the communication and the illumination of indoor environments has been tackled. In this paper, a transmission scheme based on the use of the CSK modulation which also makes use of a modulation format that descends from the Pulse Position Modulation (PPM) has been proposed. The aim of this contribution is also proposing the receiver architecture for that kind of transmission and then evaluate its performance in terms of Bit Error Rate (BER) of Transmission Rate by performing also comparisons with the literature. The proposed scheme is robust with respect to optical interference and presents high rate and low BER at the cost of a bit complexity increasing with respect to other approaches.
Program phase detection in heterogeneous multi-core processors

In present study, in order to improve the performance and reduce the amount of power which is dissipated in heterogeneous multicore processors, the ability of detecting the program execution phases is investigated. The program's execution intervals have been classified in different phases based on their throughput and the utilization of the cores. The results of implementing the phase detection technique are investigated on a single core processor and also on a multi-core processor. To minimize the profiling overhead, an algorithm for the dynamic adjustment of the profiling intervals is presented. It is based on the behavior of the program and reduces the profiling overhead more than three fold. The results are obtained from executing multiprocessor benchmarks on a given processor. In order to show the program phases clearly, throughput and utilization of execution intervals are presented on a scatter plot. The results are presented for both fixed and variable intervals.
ECG DeNoising using thresholding based on Wavelet transforms

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Noise severely limits the utility of the recorded ECG and thus need to be removed, for better clinical evaluation. Donoho and Johnstone [4, 5, 10] proposed wavelet thresholding denoising method based on discrete wavelet transform (DWT) with universal threshold is suitable for non-stationary signals such as ECG signal. In the present paper a new thresholding technique is proposed for de-noising of ECG signal. This new de-noising method is called as improved thresholding de-noising method could be regarded as a compromising between hardand soft-thresholding de-noising methods. The proposed method selects the best suitable wavelet function based on DWT at the decomposition level of 5, using mean square error (MSE) and output SNR. The advantage of the improved thresholding de-noising method is that it retains both the geometrical characteristics of the original ECG signal and variations in the amplitudes of various ECG waveforms effectively. The experimental results indicate that the proposed method is better than traditional wavelet thresholding de-noising methods in the aspects of remaining geometrical characteristics of ECG signal and in improvement of signal-tonoise ratio (SNR).
Explaining microfinance's resilience: the case of microfinance in Australia

ABSTRACT Microfinance has been unsuccessfully deployed as a poverty reduction strategy in the Global South. However, despite advocates acknowledging this shortcoming, the sector continues to grow. Moreover, over the past decade it has expanded its geographic scope, targeting poor communities in the Global North under the banner of financial inclusion. This article investigates the growth of the Australian sector. Analyzing the impacts of reforms to Australia's social security system alongside the political economy of the microfinance industry, it argues the debtfare state in Australia has provided fertile ground for microfinance's expansion. Documenting the advocacy strategies and tools to target vulnerable communities used by microfinance organizations and corporate partners, it charts how government support and subsidies have created a favourable regulatory environment, driving the sector's growth. This has benefitted these coalitions while displacing other forms of social security, despite the lack of robust evidence demonstrating microfinance's benefits for the poor.
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders

This paper describes the University of Shefﬁeld’s submission to the SemEval 2016 Twitter Stance Detection weakly supervised task (SemEval 2016 Task 6, Subtask B). In stance detection, the goal is to classify the stance of a tweet towards a target as “favor”, “against”, or “none”. In Subtask B, the targets in the test data are different from the targets in the training data, thus rendering the task more challenging but also more realistic. To address the lack of target-speciﬁc training data, we use a large set of unlabelled tweets containing all targets and train a bag-of-words autoencoder to learn how to produce feature representations of tweets. These feature representations are then used to train a logistic regression clas-siﬁer on labelled tweets, with additional features such as an indicator of whether the target is contained in the tweet. Our submitted run on the test data achieved an F1 of 0.3270.
A knowledge-based approach in video frame processing using Iterative Qualitative Data Analysis

The ability to acquire, identify and represent the knowledge that a human expert has about a particular domain is a powerful key method in the development of a knowledge-based computer system. This paper demonstrates a methodology for acquiring and analyzing data based on semi-structured interview responses conducted upon human experts. Human experts are asked to determine the acceptability of an image containing person(s) in a sequence of images. Different experts may have different judgments and collectively the image values or attributes from their subjective judgment may contribute to the main factors of consideration in determining the overall image acceptability. The aim of this paper is to identify the most appropriate image attributes used by human experts during an image selection task which is in line with our research objectives. We discuss the knowledge acquisition task by adopting the Iterative Qualitative Data Analysis (IQDA) approach and represent the knowledge into a set of filtering attributes.
Analytical performance analysis of mesh network-on-chip based on network calculus

The design of on chip interconnection architecture (NoC) should carefully take on consideration both hardware and communication constraints in order to built up a system that meets quality of service requirements. In NoC architecture, the on chip switch available hardware and software resources drive up the global performances of communication processes. Therefore it is crucial, before the physical design process, to carry out the required capacities such as buffer depth and management-tasks of a flit. In fact, one of the most critical parameters that can affect communication characteristics are the available memory space in addition to flit-time processing according to a given scheduling approach. This paper deals with these concepts. It presents a study of NoC switch using Network Calculs (NC) theory. It provides an analytic model of the internal on chip switch architecture to study the performance with a mathematical approach. This helps to specify the best physical and logical characteristics that can achieve enhanced performances.
Training the future workforce through task curation in an OSS ecosystem

Volunteers to Open Source Software (OSS) projects contribute not only to help creating software that they use, but also to gain skills and enrich their expertise and resumes. However, newcomers to OSS face several challenges when joining a project. Particularly, they do not know where to start, or choose tasks that they can be successful at. Here, we describe our vision towards BugExchange, a system that curates tasks from OSS projects and helps train newcomers. While evaluating and executing these tasks, newcomers can gain an understanding about the project, its technology, and concepts. There are many challenges in designing such a system. For example, identifying the information needs of newcomers, creating task recommendations that match newcomers’ skills and career goals, and providing mentoring and networking support. We plan to leverage our previous work to conceive and prototype our system, which will include multiple research lines. BugExchange has the potential to improve newcomer learning experiences, reduce dropouts, and foster community building.
D-terms from generalized NS-NS fluxes in type II

Orientifolds of type II string theory admit a certain set of generalized NS-NS fluxes, including not only the three-form field strength H, but also metric and non-geometric fluxes, which are related to H by T-duality. We describe in general how these fluxes appear as parameters of an effective = 1 supergravity theory in four dimensions, and in particular how certain generalized NS-NS fluxes can act as charges for R-R axions, leading to D-term contributions to the effective scalar potential. We illustrate these phenomena in type IIB with the example of a certain orientifold of T6/4.
Learning about concepts through everyday language interactions in preschools

Background. In several Nordic countries, the pedagogy in preschools has a social pedagogical ideal. The focus is on development of social competence, aiming to empower children. There is only minimal focus on teaching and academic learning. The aim of this study is to investigate what kind of support children’s concept formation can receive when children are engaged in everyday language interactions with preschool teachers in Norway. Theoretically, the article is based on theories developed from Vygotsky’s (1987) perspectives on language as a mediating tool. Design. Two classrooms with two preschool teachers and 18 children in each class participated in the study. The preschool was chosen because it especially focused on children’s language learning. Method. This study is a qualitative study based on video-taped observations in one preschool, and the data are video-taped observations of language interactions between two preschool teachers and children in two preschool classrooms. Most language interactions in Norway occur in everyday conversations such as play, art activities and meals. Results. The teachers interacted with the children around topics that engage the children and topics they took initiative to talk about. The teachers invited the children in warm ways to use language to make meaning of the shared topic. However, they seldom presented supplementary concepts or expanded the children’s concept understanding with their own knowledge. Conclusion. The social pedagogical ideal may have made them associate such sharing of knowledge with teaching.
Maneuvering target tracking based on combined stochastic differential equations and garch process

In this paper, we propose a new model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation (SDE) to model acceleration of a maneuvering target with GARCH process as the model for the SDE volatility. The proposed tracking algorithm operates in both the non-manoeuvring and the manoeuvring modes and the manoeuvre detection procedure is eliminated. In the proposed scheme, the original state and stochastic volatility are estimated simultaneously with a particle filter. We show analytically that target tracking performance by considering GARCH acceleration model is improved. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through a simulation study.
Uniqueness of tachyonic solitons

It has been conjectured that condensation of tachyons on a bosonic D-brane gives rise to vacuum/soliton solutions which are independent of the initial magnetic field on the D-brane. We present evidence for this conjecture using results from two-dimensional conformal field theory. In particular we identify a continuous path in the configuration space of open string fields which interpolates between D-brane configurations with two different quantized magnetic flux.
Appropriate Unlabeled Data , Post-processing and Voting can Improve the Performance of a NER System

This paper reports how the appropriate unlabeled data, post-processing and voting can be effective to improve the performance of Named Entity Recognition (NER) system. The proposed method is tested with Bengali and based on the classifiers such as Conditional Random Field (CRF) and Support Vector Machine (SVM). The training set consists of approximately 272K wordforms and annotated with the four major named entity (NE) tags such as Person, Location, Organization and Miscellaneous tags. Apart from the different orthographic word level features, we have also presented two semi-supervised learning techniques in order to improve the performance of each classifier. We have shown that simply relying upon the use of large corpora for training is not in itself sufficient. We describe the measures to automatically select effective documents and sentences from unla-beled data. In addition, we have developed two techniques to post-process the output of each of the models in order to reduce the errors and to improve the performance. Finally , we have applied weighted voting approach to combine the models. Experimental results show the effectiveness of the proposed approach with the overall average recall, precision, and f-score values of 93.72%, 90.83%, and 92.25%, respectively, which shows an improvement of 16.9% in f-score over the least performing baseline CRF based system and an improvement of 14.89% in f-score over the best performing baseline SVM based system.
Medical Smart Textiles Based on Fiber Optic Technology: An Overview

The growing interest in the development of smart textiles for medical applications is driven by the aim to increase the mobility of patients who need a continuous monitoring of such physiological parameters. At the same time, the use of fiber optic sensors (FOSs) is gaining large acceptance as an alternative to traditional electrical and mechanical sensors for the monitoring of thermal and mechanical parameters. The potential impact of FOSs is related to their good metrological properties, their small size and their flexibility, as well as to their immunity from electromagnetic field. Their main advantage is the possibility to use textile based on fiber optic in a magnetic resonance imaging environment, where standard electronic sensors cannot be employed. This last feature makes FOSs suitable for monitoring biological parameters (e.g., respiratory and heartbeat monitoring) during magnetic resonance procedures. Research interest in combining FOSs and textiles into a single structure to develop wearable sensors is rapidly growing. In this review we provide an overview of the state-of-the-art of textiles, which use FOSs for monitoring of mechanical parameters of physiological interest. In particular we briefly describe the working principle of FOSs employed in this field and their relevant advantages and disadvantages. Also reviewed are their applications for the monitoring of mechanical parameters of physiological interest.