Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Management Analysis: An Empirical Study Using Bivariate GARCH

This study employs a bivariate asymmetric GARCH model to reveal the hidden dynamics price changes and volatility among the emerging markets of Thailand and Malaysian after the Asian financial crisis from January 2001 to December 2008. Our results indicated that the equity markets are sharing the common information (shock) that transmitted among each others. These empirical findings are used to demonstrate the importance of shock and volatility dynamic transmissions in the cross-market hedging and market risk. Keywords— multivariate ARCH, structural change, value at risk.
On reliability of clock-skew-based remote computer identification

Clocks have a small in-built error. As the error is unique, each clock can be identified. This paper explores remote computer identification based on the estimation of clock skew computed from network packets. The previous knowledge of the method is expanded in various ways: (1) we argue about the amount of data that is necessary to get accurate clock skew estimation, (2) the study of different time stamp sources unveils several irregularities that hinders the identification, and (3) the distribution of clock skew in real network makes the precise identification hard or even impossible.
Cognitive and neural mechanisms of inflectional morphology processing : Studies of native speakers and second language learners of Swedish

The present dissertation investigates inflectional morphology processing in native speakers and second language (L2) learners of Swedish. Results of Study 1 suggest that two separate neural mechanisms might be available for native comprehension of inflected words, as reflected in event-related brain potentials obtained for visually presented verb forms. Overregularized verbs (e.g. *bar+de ‘bear + past tense’) yielded a left anterior negativity (LAN), indicating decompositional processing of the regular tense inflection versus whole-word retrieval of correct irregular verb forms (e.g. bar ‘bore’). Enhanced long-range neural oscillatory phase synchrony observed for familiar irregular words potentially reflected increased engagement of the ventral language processing stream during whole-word access. As Swedish is characterized by a predictive association between specific word stem tones and upcoming suffixes, facilitating speech processing, Study 2 examines the integration of tonal cues into the native morphological system. Correlational analysis was conducted between cortical thickness in selected brain regions and individual participants’ response time patterns for suffix recognition following the tonal cue in real words (e.g. hattAccent 1+en ‘hat+sg’) and pseudowords (e.g. kvutAccent 1+en ‘kvut+sg’). Results suggest that the left planum temporale might play a role when tones are accessed as part of whole-word memory representations, whereas the pars opercularis of the left inferior frontal gyrus could potentially support rule-based decompositional analysis of cued suffixes when no stored full-form representations are present. Study 3 focuses on the L2 acquisition of the tonal aspects of Swedish inflectional morphology. Response time patterns to inflected verbs indicate facilitated processing of word endings validly cued by the preceding stem tone in proficient L2 learners of Swedish, who had not received any explicit information about the tested L2 regularity. As these results suggested gradual and slow implicit acquisition of tone-suffix associations through exposure to L2 input, Study 4 explores possibilities of training the L2 feature at earlier stages of learning. Performance data collected during a two-week-period of training with a game prototype show gradually faster and more accurate responses to suffixes cued by preceding tones, indicating that low proficient learners start to integrate Swedish word accents into their L2 morphological processing system. (Less)
Performance sensitivity of routing algorithms with various models of wireless sensor networks

Routing algorithms for wireless sensor networks are often evaluated through simulations in order to measure the lifetime of the network and the efficiency of the algorithm regarding energy consumption. Some particular communication models with their parameters' set are used to implement the simulation and are rarely identical. We exhibit two kinds of performance sensitivity regarding simulations: the first one concerns the communication model itself and the second one is generated by the parameters of the communication model. We provide a generalized model that covers very different situations and we state the routing problem as a linear programming problem in order to measure the absolute efficiency of the algorithms with different models and parameter values. Our experiments run using two routing protocols, LEACH and Flow Augmentation, showed that different models or parameters can lead to significantly different results and conclusions. We tried to characterize the origin of the performance sensitivity in each case.
Term clouds as surrogates for user generated speech

User generated spoken audio remains a challenge for Automatic Speech Recognition (ASR) technology and content-based audio surrogates derived from ASR-transcripts must be error robust. An investigation of the use of term clouds as surrogates for podcasts demonstrates that ASR term clouds closely approximate term clouds derived from human-generated transcripts across a range of cloud sizes. A user study confirms the conclusion that ASR-clouds are viable surrogates for depicting the content of podcasts.
Testing k-colorability

Let G be a graph on n vertices and suppose that at least $\epsilon n^2$ edges have to be deleted from it to make it k-colorable. It is shown that in this case most induced subgraphs of G on $c k\,{\rm ln}\,k/ \epsilon^2$ vertices are not k-colorable, where c > 0 is an absolute constant. If G is as above for k=2, then most induced subgraphs on $\frac{({\rm ln} (1/\epsilon))^b}{\epsilon}$ are nonbipartite, for some absolute positive constant b, and this is tight up to the polylogarithmic factor. Both results are motivated by the study of testing algorithms for k-colorability, first considered by Goldreich, Goldwasser, and Ron in, [J. ACM, 45 (1998), pp. 653--750], and improve the results in that paper.
Unstable Communities in Network Ensembles

Ensembles of graphs arise in several natural applications. Many techniques exist to compute frequent, dense subgraphs in these ensembles. In contrast, in this paper, we propose to discover maximally variable regions of the graphs, i.e., sets of nodes that induce very different subgraphs across the ensemble. We first develop two intuitive and novel definitions of such node sets, which we then show can be efficiently enumerated using a level-wise algorithm. Finally, using extensive experiments on multiple real datasets, we show how these sets capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.
Anti-EGFR function of EFEMP1 in glioma cells and patient prognosis

EGFR is one of the key oncogenes subjected to targeted therapy for several cancers, as it is known to be amplified and/or mutated in up to 40% of malignant gliomas. EFEMP1, a fibulin-like extracellular protein, exerts both tumor suppressive and oncogenic effects in various cancers and glioma cell models. Although EFEMP1's anti-cancer activity has most commonly been attributed to its anti-angiogenic effects, we showed for gliomas that EFEMP1's binding to EGFR accounts for its suppression of the intracranial tumorigenicity of glioma cells expressing high levels of EGFR. In gliomas where EFEMP1 expression, and thus the anti-EGFR effect of EFEMP1, was suppressed, heightened levels of EGFR expression were associated with unfavorable patient outcomes in prognostic models. Results from the current study clearly demonstrate the impact that the anti-EGFR function of EFEMP1 has on the expression of EGFR and patient prognosis. A glioma prognostic model also suggests EFEMP1's context-dependent oncogenic function in gliomas expressing low levels of EGFR. Hence the level of EFEMP1 expression may have a predictive value for choosing patients for anti-EGFR therapy.
Análisis de vulnerabilidades de sistemas web en desarrollo y en producción

La realización de pruebas de penetración permiten detectar las vulnerabilidades de los sistemas de información, en el caso particular del proyecto de investigación y desarrollo que se lleva a cabo en UTN FRC, se han realizado acciones de esta metodología en sistemas web que desarrollan los estudiantes del último año de la carrera de Ingeniería en Sistemas de Información y con acciones similares que se realizaron en Sistemas web en producción de empresas privadas nacionales e internacionales.
The Influence of Communication Skills towards Job Performance of The State Civil Apparatus

Communication skills are the basic skills needed by each ASN in performing its job duties. The elements of communication skills that are studied include the skills of speaking, writing, reading, listening, and using media. This study aims to: (1) describes ASN communication skills; (2) analyze the factors that are related and affect the elements of ASN communication skills; and (3) analyze the influence of the elements of communication skills to the achievement of ASN job performance. The sample used was 380 respondents, taken using random proportional stratified sample method. This study uses quantitative approach and is strengthened by qualitative through interview result, focus group discussion, and field observation. The results show that ASN communication skills are in sufficient category, with the order of the highest being the skills in using media, listening, speaking, reading, and writing. Factors that have a positive and significant effect on all elements of communication skills are individual characteristics of employees and work motivation. Communication skills proved to have a positive and significant impact on ASN job performance, with the strongest predictor being skills in using media.
SENESCENCE OF CUT LEAVES OF Zantedeschia aethiopica AND Z. elliottiana. PART I. CHLOROPHYLL DEGRADATION

Chlorophyll degradation occurring during leaf senescence in under control of plant hormones. Changes in the chlorophyll content and the effects of BA (benzyladenine) and GA3 (gibberellic acid) on this process were analyzed during senescence of cut leaves of Zantedeschia aethiopica Spr. and Zantedeschia elliottiana Engl. , two species grown for the florists' green. Both growth regulators were applied as 24 h pulse treatment: 0.25 mmol·dm -3 and 0.1 mmol·dm -3 for GA3 and BA, respectively. Gibberellic acid was more effective than benzyladenine in delaying senescence of both Zantedeschia species leaves by retarding chlorophyll loss. A standard preservative solution used to prolong longevity of cut flowers (8-HQC /citrate of hydroxyquinoline/ +2% S /sucrose/) radically accelerated chlorophyll loss of Z. aethiopica leaves but had no effect on pigment degrada- tion in leaves of Z. elliottiana. However, in both species the preservative diminished the positive effect of GA3 treatment on final chlorophyll content.
InterPoll : Crowd-Sourced Internet Polls ( Done Right ) MSR-TR-2014-3

Crowd-sourcing is increasingly being used for providing answers to online polls and surveys. However, existing systems, while taking care of the mechanics of attracting crowd workers, poll building, and payment, provide little that would help the survey-maker or pollster to obtain statistically significant results devoid of even the obvious selection biases. This paper proposes InterPoll, a platform for programming of crowd-sourced polls. Polls are expressed as embedded LINQ queries, whose results are provided to the developer. InterPoll supports reasoning about uncertainty, enabling t-tests, etc. on random variables obtained from the crowd. InterPoll performs query optimization, as well as bias correction and power analysis, among other features. Making InterPoll queries part of the surrounding program allows for optimizations that take advantage of the surrounding code context. The goal of InterPoll is to provide a system that can be reliably used for research into marketing, social and political science questions. This paper highlights some of the existing challenges and how InterPoll is designed to address most of them. We outline some of the optimizations and give numerous motivating examples designed to illustrate our system design. Note that this paper is an outline of our vision — we deliberately focus on examples and motivation and leave a detailed technical treatment for future work.
Investigating audio classification to automate the trimming of recorded lectures

With the demand for recorded lectures to be made available as soon as possible, the University of Cape Town (UCT) needs to find innovative ways of removing bottlenecks in lecture capture workflow and thereby improving turn-around times from capture to publication. UCT utilises Opencast, which is an open source system to manage all the steps in the lecture-capture process. One of the steps involves manual trimming of unwanted segments from the beginning and end of video before it is published. These segments generally contain student chatter. The trimming step of the lecture-capture process has been identified as a bottleneck due to its dependence on staff availability. In this study, we investigate the potential of audio classification to automate this step. A classification model was trained to detect 2 classes: speech and non-speech. Speech represents a single dominant voice, for example, the lecturer, and non-speech represents student chatter, silence and other environmental sounds. In conjunction with the classification model, the first and last instances of the speech class together with their timestamps are detected. These timestamps are used to predict the start and end trim points for the recorded lecture. The classification model achieved a 97.8% accuracy rate at detecting speech from non-speech. The start trim point predictions were very positive, with an average difference of -11.22s from gold standard data. End trim point predictions showed a much greater deviation, with an average difference of 145.16s from gold standard data. Discussions between the lecturer and students, after the lecture, was predominantly the reason for this discrepancy.
Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles

Plug-in hybrid electric vehicles (PHEVs) are currently recognized as a promising solution for reducing fuel consumption and emissions due to the ability of storing energy through direct connection to the electric grid. Such benefits can be achieved only with a supervisory energy management strategy that optimizes the energy utilization of the vehicle. This control problem is particularly challenging for PHEVs due to the possibility of depleting the battery during usage and the vehicle-to-grid interaction during recharge. This paper proposes a model-based control approach for PHEV energy management that is based on minimizing the overall CO2 emissions produced-directly and indirectly-from vehicle utilization. A supervisory energy manager is formulated as a global optimal control problem and then cast into a local problem by applying the Pontryagin's minimum principle. The proposed controller is implemented in an energy-based simulator of a prototype PHEV and validated on experimental data. A simulation study is conducted to calibrate the control parameters and to investigate the influence of vehicle usage conditions, environmental factors, and geographic scenarios on the PHEV performance using a large database of regulatory and “real-world” driving profiles.
Which Regular Expression Patterns Are Hard to Match?

Regular expressions constitute a fundamental notion in formal language theory and are frequently used in computer science to define search patterns. In particular, regular expression matching and membership testing are widely used computational primitives, employed in many programming languages and text processing utilities. A classic algorithm for these problems constructs and simulates a non-deterministic finite automaton corresponding to the expression, resulting in an O(m n) running time (where m is the length of the pattern and n is the length of the text). This running time can be improved slightly (by a polylogarithmic factor), but no significantly faster solutions are known. At the same time, much faster algorithms exist for various special cases of regular expressions, including dictionary matching, wildcard matching, subset matching, word break problem etc. In this paper, we show that the complexity of regular expression matching can be characterized based on its depth (when interpreted as a formula). Our results hold for expressions involving concatenation, OR, Kleene star and Kleene plus. For regular expressions of depth two (involving any combination of the above operators), we show the following dichotomy: matching and membership testing can be solved in near-linear time, except for "concatenations of stars", which cannot be solved in strongly sub-quadratic time assuming the Strong Exponential Time Hypothesis (SETH). For regular expressions of depth three the picture is more complex. Nevertheless, we show that all problems can either be solved in strongly sub-quadratic time, or cannot be solved in strongly sub-quadratic time assuming SETH. An intriguing special case of membership testing involves regular expressions of the form "a star of an OR of concatenations", e.g., [a|ab|bc]*. This corresponds to the so-called word break problem, for which a dynamic programming algorithm with a runtime of (roughly) O(n √m) is known. We show that the latter bound is not tight and improve the runtime to O(n m0.44.).
Efficient Mining of Frequent Closures with Precedence Links and Associated Generators

The effective construction of many association rule bases require the computation of frequent closures, generators, and precedence links between closures. However, these tasks are rarely combined, and no scalable algorithm exists at present for their joint computation. We propose here a method that solves this challenging problem in two separated steps. First, we introduce a new algorithm called Touch for finding frequent closed itemsets (FCIs) and their generators (FGs). Touch applies depth-first traversal, and experimental results indicate that this algorithm is highly efficient and outperforms its levelwise competitors. Second, we propose another algorithm called Snow for extracting efficiently the precedence from the output of Touch. To do so, we apply hypergraph theory. Snow is a generic algorithm that can be used with any FCI/FG-miner. The two algorithms, Touch and Snow, provide a complete solution for constructing iceberg lattices. Furthermore, due to their modular design, parts of the algorithms can also be used independently.
RF MEMS variable matching networks for multi-band and multi-mode GaN power amplifiers

This work presents Radio-Frequency Microelectromechanical-System (RF MEMS) based tunable matching networks for a multi-band Gallium Nitride (GaN) power amplifier application. In the frequency range from 3.5 GHz to 8.5 GHz a return loss of 5 dB to 10 dB was measured for the input network, matching impedances close to the border of the smith chart. For the output matching network return loss of 10 dB to 20 dB and insertion loss of 1.3 dB to 2 dB were measured. The matching networks can tune the power amplifier to four different operating frequencies, as well as changing the transistor's mode of operation from maximum delivered output power to maximum Power Added Efficiency (PAE), while keeping the operating frequency constant.
A learning approach to optimize walking cycle of a passivity-based biped robot

A learning mechanism based on Powell's optimization algorithm is proposed to optimize walking behavior of a passivity based biped robot. To this end, a passivity-based biped robot has been simulated in MSC ADAMS and a control policy inspired from humanoid walking is adopted for a stable walking of the robot. Linear controllers try to control the joints of robot in each walking phase to implement the gait proposed by the control policy. Learning is employed using Powell's optimization algorithm to adjust the control parameters so that the robot enters to an optimum limit cycle in a finite time. The fitness function is defined to evaluate the robot's optimum behavior. The results are verified by simulations in SIMULINK+ADAMS.
Four SNPs in the CHRNA3/5 Alpha-Neuronal Nicotinic Acetylcholine Receptor Subunit Locus Are Associated with COPD Risk Based on Meta-Analyses

Background Several single nucleotide polymorphisms (SNPs) in an α-neuronal nicotinic acetylcholine receptor subunit (CHRNA3/5) were identified to be associated with chronic obstructive pulmonary disease (COPD) in a study based on a Norwegian population. However, results from subsequent studies have been controversial, particularly in studies recruiting Asians. In the present study, we conducted a comprehensive search and meta-analyses to identify susceptibility SNPs for COPD in the CHRNA3/5 locus. Methods A comprehensive literature search was conducted to find studies that have reported an association between SNPs in the CHRNA3/5 locus and COPD risk. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for each SNP were calculated with the major allele or genotype as the reference group. The influence of individual studies on pooled measures was assessed, in addition to publication bias. Results A total of 12 articles with 14 eligible studies were included in this analysis. Association between 4 SNPs in the CHRNA3/5 locus and COPD was evaluated and included rs1051730, rs8034191, rs6495309, and rs16969968. Significant associations between the 4 SNPs and COPD were identified under allele (rs1051730: OR = 1.14, 95%CI = 1.10–1.18; rs8034191: OR = 1.29, 95%CI = 1.18–1.41; rs6495309: OR = 1.26, 95%CI = 1.09–1.45; rs16969968: OR = 1.27, 95%CI = 1.17–1.39) and genotype models. Subgroup analysis conducted for rs1051730 showed a significant association between this SNP and COPD risk in non-Asians (OR = 1.14, 95%CI = 1.10–1.18), but not Asians (OR = 1.23, 95%CI = 0.91–1.67). Rs1051730 and rs6495309 were also significantly associated with COPD after adjusting for multiple variables, including age and smoking status. Conclusion Our results indicate that 4 SNPs in the CHRNA3/5 locus are associated with COPD risk. Rs1051730 was particularly associated with COPD in non-Asians, but its role in Asians still needs to be verified. Additional studies will be necessary to assess the effect of rs6495309 on COPD. Although rs1051730 and rs6495309 were shown to be independent risk factors for COPD, validation studies should be performed.
MGF-based performance analysis of selection diversity with switching constraints in nakagami fading

This paper focuses on the performance evaluation of selection combining (SC) diversity with switching constraints over independent non-identically distributed (i.n.i.d.) Nakagami-m fading channels. New closed-form expressions for the moment generating function (MGF) of the SC output signal-to-noise ratio (SNR) in Nakagami-m fading channels, as a function of channel decorrelation, have been obtained. These expressions have been the starting point to derive performance measures like average combined SNR, outage probability, and average bit error probability of various modulation schemes