Miracle-dz/UniFAR-FTUs huggingface dataset:
The Journal of Mixed Methods Research Starts a New Decade: Perspectives of Past Editors on the Current State of the Field and Future Directions

In this last editorial of 2017, and in celebration of the first decade of mixed methods research scholarship in the Journal of Mixed Methods Research (JMMR), we provide special commentary from the former editors. In the January issue, we examined language of the field and principles for adding new language and divesting of old language as the mixed methods research field develops (Fetters & Molina-Azorin, 2017a). In the April issue, we described the main features of editorials and manuscripts published in the first 10 years of JMMR (2007-2016), the multidisciplinary and international characteristics of JMMR, and JMMR’s most cited and mostread articles (Molina-Azorin & Fetters, 2017). In the July issue, we introduced the integration trilogy and the need to consider integration systematically through all dimensions of philosophy, methodology, and methods (Fetters & Molina-Azorin, 2017b). For a substantive contribution to the field of mixed methods research, we are pleased to bring in this October issue the perspectives of the former editors of JMMR relative to developments in the field, current challenges, recommendations for learning mixed methods research, future directions, and the role of JMMR moving forward. As per our commitment in the first editorial, we begin this editorial by discussing key features of the articles appearing in this issue.
A novel approach to real-time RTI based distributed simulation system

HLA, a DoD high level architecture, is emerging as a standard platform for large-scale distributed interactive simulation systems. It mainly emphasizes on the reusability and interoperability of different simulation components. HLA was well designed and has been proved to have the ability to guarantee the desirable properties of reusability and the interoperability in distributed simulation systems from several aspects. However, HLA shows an apparent weaknesses when it comes to real-time simulation applications. Indeed, there are no specific rules provided with respect to real-time applications in HLA specifications. Therefore, there has not been any widely accepted real-time implementation specification for the HLA run-time infrastructure (HLA/RTI). In this paper, we propose a novel real-time RTI architecture which addresses the weakness of HLA/RTI in terms of real-time applications. We discuss our design and the implementation of our real-time RTI architecture with an emphasis on the real-time RTI end-system.
PACMA : An Adaptive Symmetric Cryptographic Algorithm for Parallel Computing Environments

Traditional symmetric cryptographic algorithms are designed considering the cryptographic complexities rather than the processing capacities of the modern computing systems. Hence the performances of these algorithms are inconsistent and vary enormously when implemented in different computing systems with different processing capabilities. Parallelized adaptive cryptographic algorithms that can fit into the nature of the processing devices are the need of the hour. “Parallelized Adaptive Cipher with Modular Arithmetic” (PACMA) is a different class of cryptographic algorithm involving modular arithmetic concepts, which addresses these issues effectively. The size of the key and the plain text blocks are each 1024-bits. This symmetric block cipher is made adaptive by incorporating flexibility in the size of the key and plain text sub-blocks and the number of rounds. Level of Intra-packet parallelization, variety in grain size and the required security strength are achieved by suitably deciding the sub-block size. Flow of the algorithm is made dynamic by determining the execution steps through each key value at the runtime. The performance of the PACMA is analyzed with implementations in shared memory parallel programming environment using OpenMP, Java Threads and MPI.
Exploiting Prolog and NLP Techniques for Matching Ontologies and for Repairing Correspondences

Providing efficient ontology matching algorithms is one of the means for pursuing semantic interoperability. In this paper we discuss an algorithm that exploits natural language processing techniques for matching ontologies and that post-processes the obtained alignment in order to find semantic inconsistencies. The algorithm has been entirely implemented in Prolog, whose usefulness was mainly evident in the post-processing phase. A careful analysis of the recent state- of-the art witnesses the originality of our matching algorithm which is based on the "Adapted Lesk Algorithm" for word sense disambiguation. The experiments we carried out, although in their early stages, are encouraging.
L1-norm multi-frame super-resolution from images with zooming motion

This paper proposes a new image super-resolution (SR) approach to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images with zooming motion. Most conventional SR image reconstruction methods assume that the motion among different images consists of only translation and possibly rotation. This in-plane motion model, however, is not practical in some applications, when relative zooming exists among the acquired LR images. In view of this, this paper presents a new SR method that addresses a motion model including both in-plane motion (e.g. translation and rotation) and zooming motion. Based on this model, a maximum a posteriori (MAP) based SR algorithm using L1-norm optimization is proposed. Experimental results show that the proposed algorithm based on the new motion model performs well in terms of visual evaluation and quantitative measurement.
Sample consensus fitting of bivariate polynomials for initializing EM-based modeling of smooth 3D surfaces

This paper presents a method for finding the largest, connected, smooth surface in noisy depth images. The formulation of the fitting in a Sample Consensus approach allows the use of RANSAC (or any other similar estimator), and makes the method tolerant to low percentage of inliers in the input. Therefore it can be used to simultaneously segment and model the surface of interest. This is important in applications like analyzing physical properties of carbon-fiber-reinforced polymer (CFRP) structures using depth cameras. Employing bivariate polynomials for modeling turns out to be advantageous, allowing to capture the variations along the two principle directions on the surface. However, fitting them efficiently using RANSAC is not straightforward. We present the necessary pre- and post-processing, distance and normal direction checks, and degree optimization (lowering the order of the polynomial), and evaluate how these improve results. Finally, to improve the initial estimate provided by RANSAC and to stabilize the results, an Expectation Maximization (EM) strategy is employed to converge to the best solution. The method was tested on high-quality data and as well on real-world scenes captured by a RGB-D camera.
Dynamic HMM selection for continuous speech recognition

In this paper we propose a dynamic model selection technique based on hidden model sequences (HMS). HMS modelling assumes, that not only the actual state sequence is unknown, but also the model sequence given a particular sentence. This allows more than one model to be used for a particular phone in a certain context. The most appropriate model is determined locally rather than a priori globally by the acoustic probability of that model together with a probability that this model is produced in a particular phone (or model) context. Experiments on the Resource Management corpus show signiicant improvements in word error rate over phonetically model{ and state{tied triphone hidden Markov models (HMMs). Initial results on the Switchboard corpus also show improvements on a much more diicult task.
Multidimensional Sparse Array Storage for Data Analytics

A relational table over a set of attributes can be mapped onto a multi-dimensional array and stored as such. Such a conceptual view of relations lends itself to easy formulations of numerous analytical algorithms. This is the view taken in the representation of relations in data-warehousing to support On-Line Analytical Processing (OLAP). The main drawback of such a storage scheme is that the equivalent array is typically a highly sparse multi-dimensional array with dominating null entries, and requires a storage scheme with high compression scheme that retains the significant non-null elements. We introduce, analyse and compare the performances of some storage schemes for Multi-Dimensional Sparse Arrays (MDSAs). We first introduce a previously known method called Bit Encoded Sparse Storage (BESS) and then introduce four new storage schemes namely, Patricia trie compressed storage (PTCS), extended compressed row storage (xCRS), bit encoded compressed row storage (BxCRS) and a hybrid storage scheme (Hybrid), that combines the two methods of BESS and xCRS. The performances of these storage schemes are compared with respect to their compression ratios and computational efficiency for accessing an element, retrieving sub-array elements and computing aggregate functions and other analytic functions. We focus primarily on the aggregate function of summation of sub-array elements in this paper. The results show that xCRS, BxCRS, Hybrid and BESS can achieve compression ratios of less than 40% for MDSAs with more than 80% sparsity. The BESS storage scheme gives the best performance in computing multi-dimensional aggregates, for varying sparsity and dimensionality, compared with the other schemes. The key virtue of PTCS is that it is the only scheme that allows for insertions and deletions without reorganising the entire storage previously allocated.
A novel text subject extraction method

Word segmentation or word extraction is always the first step of subject extraction. For no intervals between words, word segmentation of Chinese text is rather complicated. A novel text subject extraction method based on contextual cooccurrence is put forward, and an approach of extracting subject sentence from Chinese text using character contextual cooccurrence data is described. The new approach has fast speed and can skip the segmentation. It also can be applied in multistyle text. The result of three experiments shows that the approach gains high accuracy in multistyle text, 77.19% in news text. Comparative experiment shows that there was no loss in accuracy.
Kinematics analysis and simulation of a new parallel mechanism with two translational and one rotational outputs

Based on the single-opened-chain (SOC) unit theory, a new parallel mechanism with two translational and one rotational (2T1R) degrees of freedom is put forward. Both the forward and the inverse analytical solutions of the moving platform's positions are derived. The virtual prototype model of the mechanism is established by use of the ADAMS software. According to the simulation of the virtual prototype, it is shown that the moving platform has two translational and one rotational kinematic output characteristic. At the same time, simulation datum verifies the correctness of kinematics analysis. The singularity of the mechanism is analyzed.
Poster: Getting All Your Bats in a Row: Optimizing Layout in Chronophotographic Style Visualizations

Chronophotography, a concurrent depiction of multiple states of an object photographed at uniform time intervals in the same visual context, can serve as a valuable technique to give an overview of a dynamic process (e.g. motion) in a static illustration. In this poster we present an algorithm that generates chronophotograpically styled visualizations from large sets of still images, while reducing occlusion and aiming to increase efficient use of the available space.
Can Temporal Repetitive Transcranial Magnetic Stimulation be Enhanced by Targeting Affective Components of Tinnitus with Frontal rTMS? A Randomized Controlled Pilot Trial

Objectives: Low-frequency repetitive transcranial magnetic stimulation (rTMS) of the temporal cortex has been investigated as a new treatment tool for chronic tinnitus during the last years and has shown moderate efficacy. However, there is growing evidence that tinnitus is not a pathology of a specific brain region, but rather the result of network dysfunction involving both auditory and non-auditory brain regions. In functional imaging studies the right dorsolateral prefrontal cortex has been identified as an important hub in tinnitus related networks and has been shown to particularly reflect the affective components of tinnitus. Based on these findings we aimed to investigate whether the effects of left low-frequency rTMS can be enhanced by antecedent right prefrontal low-frequency rTMS. Study Design: Fifty-six patients were randomized to receive either low-frequency left temporal rTMS or a combination of low-frequency right prefrontal followed by low-frequency left temporal rTMS. The change of the tinnitus questionnaire (TQ) score was the primary outcome, secondary outcome parameters included the Tinnitus Handicap Inventory, numeric rating scales, and the Beck Depression Inventory. The study is registered in clinicaltrials.gov (NCT01261949). Results: Directly after therapy there was a significant improvement of the TQ-score in both groups. Comparison of both groups revealed a trend toward more pronounced effects for the combined group (effect size: Cohen’s d = 0.176), but this effect did not reach significance. A persistent trend toward better efficacy was also observed in all other outcome criteria. Conclusion: Additional stimulation of the right prefrontal cortex seems to be a promising strategy for enhancing TMS effects over the temporal cortex. These results further support the involvement of the right DLPFC in the pathophysiology of tinnitus. The small effect size might be due to the study design comparing the protocol to an active control condition.
α7 nicotinic acetylcholine receptors: regulation of plasticity and network activity in the prelimbic cortex

Abnormalities in the connectivity and activity of the prefrontal cortex (PFC) is the cause of many of the symptoms of neuropsychiatric disorders such as schizophrenia and Alzheimer’s disease. The PFC relies on a complex regulation of network activity and synaptic plasticity for healthy PFC function. These fundamental processes can be modulated by the neuromodulator acetylcholine, acting at α7 nicotinic acetylcholine receptors (α7 nAChRs), a system also compromised in neuropsychiatric disorders. Despite the evidence that α7 nAChRs are essential for healthy PFC function relatively little is known about how activity at this receptor can modulate the fundamental network activity and synaptic plasticity within the PFC. This thesis aims to address some of these issues by using brain slice electrophysiology to measure network activity and synaptic plasticity in response to α7 nAChR activity within the prelimbic cortex (PrL) of C57BL/6J mice. Extracellular field recordings revealed that the selective α7 nAChR antagonist MLA, can reduce and enhance the levels of stimulus- induce long-term potentiation (LTP) and depression (LTD) respectively. In contrast global activation of α7 nAChRs with the selective α7 nAChRs agonist PNU-282987 and positive allosteric modulator PNU-120596 also reduced the levels of LTP. To provide a mechanism for these observations, whole-cell patch clamp recordings were carried out. These experiments revealed that α7 nAChRs reside presynaptically on glutamate inputs and somatodendritically on non-fast-spiking inhibitory interneurons enabling them to enhance both excitation and inhibition in a dynamic way. Further work demonstrated that tonic endogenous ACh acting at α7 nAChRs preferentially enhances excitation rather than inhibition. To further investigate if presynaptic α7 nAChRs were expressed selectively on a subset of the many afferent fibres connecting to the PrL, optogenetic methodologies were used to selectively evoke glutamate release from discrete afferent inputs, these experiments revealed α7 nAChRs may potentially reside on thalamic inputs to the PrL.
Comparing spectrum estimators in speaker verification under additive noise degradation

Different short-term spectrum estimators for speaker verification under additive noise are considered. Conventionally, mel-frequency cepstral coefficients (MFCCs) are computed from discrete Fourier transform (DFT) spectra of windowed speech frames. Recently, linear prediction (LP) and its temporally weighted variants have been substituted as the spectrum analysis method in speech and speaker recognition. In this paper, 12 different short-term spectrum estimation methods are compared for speaker verification under additive noise contamination. Experimental results conducted on NIST 2002 SRE show that the spectrum estimation method has a large effect on recognition performance and stabilized weighted LP (SWLP) and minimum variance distortionless response (MVDR) methods yield approximately 7 % and 8 % relative improvements over the standard DFT method at -10 dB SNR level of factory and babble noises, respectively in terms of equal error rate (EER).
Sparse Modeling for Artist Identification: Exploiting Phase Information and Vocal Separation

As artist identification deals with the vocal part of music, techniques such as vocal sound separation and speech feature extraction has been found relevant. In this paper, we argue that the phase information, which is usually overlooked in the literature, is also informative in modeling the voice timbre of a singer, given the necessary processing techniques. Specifically, instead of directly using the raw phase spectrum as features, we show that significantly better performance can be obtained by learning sparse features from the negative derivative of phase with respect to frequency (i.e., group delay function) using unsupervised feature learning algorithms. Moreover, better performance is achieved by using singing voice separation as a pre-processing step, and then learning features from both the magnitude spectrum and the group delay function. The proposed system achieves 66% accuracy in identifying 20 artists from the artist20 dataset, which is better than a prior art by 7%.
Bioprospecting Potential of the Soil Metagenome: Novel Enzymes and Bioactivities

The microbial diversity in soil ecosystems is higher than in any other microbial ecosystem. The majority of soil microorganisms has not been characterized, because the dominant members have not been readily culturable on standard cultivation media; therefore, the soil ecosystem is a great reservoir for the discovery of novel microbial enzymes and bioactivities. The soil metagenome, the collective microbial genome, could be cloned and sequenced directly from soils to search for novel microbial resources. This review summarizes the microbial diversity in soils and the efforts to search for microbial resources from the soil metagenome, with more emphasis on the potential of bioprospecting metagenomics and recent discoveries.
2 2 Ju n 20 05 GLOBAL OPTIMIZATION IN INVERSE PROBLEMS : A COMPARISON OF KRIGING AND RADIAL BASIS FUNCTIONS

We study global optimization (GOP) in the framework of non-linear inverse problems with a unique solution. These problems are in general ill-posed. Evaluation of the objective function is often expensive, as it implies the solution of a non-trivial forward problem. The ill-posedness of these problems calls for regularization while the high evaluation cost of the objective function can be addressed with response surface techniques. The global optimization using Radial Basis Function (RBF) as presented by Gutmann [4] is a response surface global optimization technique with regularizing aspects. Alternatively, several publications put forward global optimization using a probabilistic approach based upon Kriging as an efficient technique for non-linear multi modal objective functions, thereby providing a credible stopping rule [5]. After comparing both concepts, we argue that in case of non-linear inverse problems an adaptation of the RBF algorithm seems to be the most promising approach.
Effects of Computer Game-based Instruction on Students' Programming Achievement in Taiwan

The primary purpose of this study was to investigate the effects of computer game-based instruction on students’ programming achievement. This quasi-experiment proceeded in 2007. Participants were 146 technology college management information system students enrolled in a Visual Basic programming class. Students in four intact classes were assigned one of two instructional methods and received a ten-week treatment. Experiment groups were taught by computer game-based instruction, the control group received traditional programming instruction. Overall, the results showed that computer game-based instruction has a great potential in facilitating student’s problem-solving ability and improving programming achievement. The implications were discussed in the paper.
Chronic pain in multiple sclerosis: Is there also fibromyalgia? An observational study

Background Chronic pain is common in persons with multiple sclerosis (MS), but the co-morbidity of fibromyalgia (FM) has yet to be investigated in MS. Objectives of the study were to evaluate, among the various types of chronic pain, the frequency of FM in MS and its impact on MS patients’ health-related quality of life (HRQoL). Material/Methods 133 MS patients were investigated for the presence and characterization of chronic pain within 1 month of assessment. A rheumatologist assessed the presence FM according to the 1990 ACR diagnostic criteria. Depression, fatigue, and HRQoL were also assessed by means of specific scales. Results Chronic pain was present in 66.2% of patients (musculoskeletal in 86.3%; neuropathic in 13.7%; absent in 33.8% [called NoP]). Pain was diagnosed with FM (PFM+) in 17.3% of our MS patients, while 48.9% of them had chronic pain not FM type (PFM−); the prevalence of neuropathic pain in these 2 sub-groups was the same. PFM+ patients were prevalently females and had a higher EDSS than NoP. The PFM+ patients had a more pronounced depression than in the NoP group, and scored the worst in both physical and mental QoL. Conclusions In our sample of MS patients we found a high prevalence of chronic pain, with those patients displaying a higher disability and a more severe depression. Moreover, FM frequency, significantly higher than that observed in the general population, was detected among the MS patients with chronic pain. FM occurrence was associated with a stronger impact on patients’ QoL.
Influence of Steroid Hormone Signaling on Life Span Control by Caenorhabditis elegans Insulin-Like Signaling

Sterol-sensing nuclear receptors and insulin-like growth factor signaling play evolutionarily conserved roles in the control of aging. In the nematode Caenorhabditis elegans, bile acid-like steroid hormones known as dafachronic acids (DAs) influence longevity by binding to and regulating the activity of the conserved nuclear receptor DAF-12, and the insulin receptor (InsR) ortholog DAF-2 controls life span by inhibiting the FoxO transcription factor DAF-16. How the DA/DAF-12 pathway interacts with DAF-2/InsR signaling to control life span is poorly understood. Here we specifically investigated the roles of liganded and unliganded DAF-12 in life span control in the context of reduced DAF-2/InsR signaling. In animals with reduced daf-2/InsR activity, mutations that either reduce DA biosynthesis or fully abrogate DAF-12 activity shorten life span, suggesting that liganded DAF-12 promotes longevity. In animals with reduced DAF-2/InsR activity induced by daf-2/InsR RNAi, both liganded and unliganded DAF-12 promote longevity. However, in daf-2/InsR mutants, liganded and unliganded DAF-12 act in opposition to control life span. Thus, multiple DAF-12 activities influence life span in distinct ways in contexts of reduced DAF-2/InsR signaling. Our findings establish new roles for a conserved steroid signaling pathway in life span control and elucidate interactions among DA biosynthetic pathways, DAF-12, and DAF-2/InsR signaling in aging.