Miracle-dz/UniFAR-FTUs huggingface dataset:
Pareto RBF network ensemble using multi-objective evolutionary computation

In this paper, evolutionary multi-objective selection method of RBF networks structure is considered. The candidates of RBF network structure are encoded into the chromosomes in GAs. Then, they evolve toward Pareto-optimal front defined by several objective functions concerning with model accuracy and model complexity. An ensemble network constructed by such Pareto-optimal models is also considered in this paper. Some numerical simulation results indicate that the ensemble network is much robust for the case of existence of outliers or lack of data, than one selected in the sense of information criteria.
A Technical Review on Adaptive Algorithms on Various Aspects in Communication

Speech enhancement is an important research area with numerous applications ranging from hearing aids, to coding and automatic recognition of speech signals. In this survey paper we focus on enhancement of the audio signal from Noise with help of various Adaptive Algorithm techniques. The LMS, NLMS, VSLMS and RLS algorithms comparative analysis was
Multi-robot path-planning using artificial bee colony optimization algorithm

Path-planning is an interesting problem in mobile robotics. This paper proposes an alternative approach to path-planning of mobile robots using the artificial bee colony (ABC) optimization algorithm. The problem undertaken here attempts to determine the trajectory of motion of the robots from predefined starting positions to fixed goal positions in the world map with an ultimate objective to minimize the path length of all the robots. A local trajectory planning scheme has been developed with ABC optimization algorithm to optimally obtain the next positions of all the robots in the world map from their current positions, so that the paths to be developed locally for n-robots are sufficiently small with minimum spacing with the obstacles, if any, in the world map. Experiments reveal that the proposed optimization scheme outperforms two well-known algorithms with respect to standard metrics, called average total path deviation and average uncovered target distance.
Development and Evaluation of a Duplex Real-Time PCR Assay With a Novel Internal Standard for Precise Quantification of Plasma DNA

Background Circulating levels of cell-free DNA increase in many pathologic conditions. However, notable discrepancies in the quantitative analysis of cell-free DNA from a large number of laboratories have become a considerable pitfall, hampering its clinical application. Methods We designed a novel recombinant DNA fragment that could be applied as an internal standard in a newly developed and validated duplex real-time PCR assay for the quantitative analysis of total cell-free plasma DNA, which was tested in 5,442 healthy adults and 200 trauma patients. Results Compared with two traditional methods, this novel assay showed a lower detection limit of 0.1 ng/mL, lower intra- and inter-assay CVs, and higher accuracy in the recovery test. The median plasma DNA concentration of healthy males (20.3 ng/mL, n=3,092) was significantly higher than that of healthy females (16.1 ng/mL, n=2,350) (Mann-Whitney two-sample rank sum test, P<0.0001). The reference intervals of plasma DNA concentration were 0-45.8 ng/mL and 0-52.5 ng/mL for healthy females and males, respectively. The plasma DNA concentrations of the majority of trauma patients (96%) were higher than the upper normal cutoff values and were closely related to the corresponding injury severity scores (R2=0.916, P<0.0001). Conclusions This duplex real-time PCR assay with a new internal standard could eliminate variation and allow for more sensitive, repeatable, accurate, and stable quantitative measurements of plasma DNA, showing promising application in clinical diagnosis.
Learning attentional mechanisms for simultaneous object tracking and recognition with deep networks

We propose a novel attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of the human perceptual system, the model consists of two interacting pathways: ventral and dorsal. The ventral pathway models object appearance and classiﬁcation using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of retinal images; with decaying resolution toward the periphery of the gaze. The dorsal pathway models the location, orientation, scale and speed of the attended object. The posterior distribution of these states is estimated with particle ﬁltering. Deeper in the dorsal pathway, we encounter an attentional mechanism that learns to control gazes so as to maximize different objectives. Here we demonstrate the method when the objective is to minimize the uncertainty in the posterior distribution of the states. The approach is modular (with each module easily replaceable with more sophisticated algorithms), straightforward to implement, practically efﬁcient, and works well in simple video sequences.
Accelerating Fully Homomorphic Encryption over the Integers with Super-size Hardware Multiplier and Modular Reduction

A fully homomorphic encryption (FHE) scheme is envisioned as being a key cryptographic tool in building a secure and reliable cloud computing environment, as it allows arbitrarily evaluation of a ciphertext without revealing the plaintext. However, existing FHE implementations remain impractical due to their very high time and resource costs. Of the proposed schemes that can perform FHE to date, a scheme known as FHE over the integers has the advantage of comparatively simpler theory, as well as the employment of a much shorter public key making its implementation somewhat more practical than other competing schemes. To the author’s knowledge, this paper presents the first hardware implementations of encryption primitives for FHE over the integers using FPGA technology. First of all, a super-size hardware multiplier architecture utilising the Integer-FFT multiplication algorithm is proposed, and a super-size hardware Barrett modular reduction module is designed incorporating the proposed multiplier. Next, two encryption primitives that are used in two schemes of FHE over the integers are designed employing the proposed super-size multiplier and modular reduction modules. Finally, the proposed designs are implemented and verified on the Xilinx Virtex-7 FPGA platform. Experimental results show that the speed improvement factors of up to 44.72 and 54.42 are available for the two FHE encryption schemes implemented in FPGA when compared to the corresponding software implementations. Meanwhile, the performance analysis shows that further improvement is speed of these FHE encryption primitives may still be possible.
IT Governance Theories and the Reality of SMEs: Bridging the Gap

The need to effectively manage IT resources such that they enhance the business value of firms makes IT governance (ITG) an important issue for both IS researchers and practitioners. The purpose of this paper is to build a conceptual framework for ITG in small and medium-sized enterprises (SMEs). We first analyze the main theories applied in ITG research, and confront them with the specificities of SMEs. We then highlight the limits of those theories in SMEs context and discuss adaptations needed or alternative theories in such context. The resulting framework is then applied to generate a set of six research propositions on ITG in SMEs.
Discovery of Approximate Differential Dependencies

Differential dependencies (DDs) capture the relationships between data columns of relations. They are more general than functional dependencies (FDs) and and the difference is that DDs are defined on the distances between values of two tuples, not directly on the values. Because of this difference, the algorithms for discovering FDs from data find only special DDs, not all DDs and therefore are not applicable to DD discovery. In this paper, we propose an algorithm to discover DDs from data following the way of fixing the left hand side of a candidate DD to determine the right hand side. We also show some properties of DDs and conduct a comprehensive analysis on how sampling affects the DDs discovered from data.
Aggregation of Information About the Cross Section of Stock Returns: A Latent Variable Approach

We propose a new approach for estimating expected returns on individual stocks from a large number of firm characteristics. We treat expected returns as latent variables and apply the partial least squares (PLS) estimator that filters them out from the characteristics under an assumption that the characteristics are linked to expected returns through one or few common latent factors. The estimates of expected returns constructed by our approach from 26 firm characteristics generate a wide cross-sectional dispersion of realized returns and outperform estimates obtained by alternative techniques. Our results also provide evidence of commonality in asset pricing anomalies.
Keyword and keyphrase extraction from single Hindi document using statistical approach

In this paper we propose an unsupervised, domain independent as well as corpus independent approach for automatic keyword extraction. In second part of the paper we have suggested an extension of the approach to extract keyphrases from the document. The approach is general and can be applied to any language. However, we have tested the approach on Hindi language. Our approach combines the information contained in frequency and spatial distribution of a word in order to extract keywords from a document. Our work is especially significant in the light that it has been implemented and tested on Hindi which is a resource poor and underrepresented language.
e-Silkroad: a sample of combining social media with cultural tourism

With the development of Web2.0, very large scale resources of multimedia have emerged in the internet. In this paper, we present a novel framework of building a tour guide based on the online knowledge resources, e.g., e-Silkroad, a photographic guide of traditional Silkroad. The tour guide is jointly established by text information from Wikipedia and images from flickr website. Our method starts from a keyword "silkroad" in Wiki and typical cities are extracted and regarded as the key threads of the guide. Then a great number of images and their description tags are downloaded from Flickr website. To highlight the most interesting place and more active tourist, the framework computes the hot spots and photographers in the dataset. To introduce each place along the silkroad, all the images are classified into four categories by its content, including person, food, man-made, and sights. Finally, the images are registered into Google Maps according to the geog-tag descriptions along silk routes to generate e-Silkroad. In our evaluation experiment, 20676 images were downloaded from 35 key cities along silkroad. Experimental results show that it is effective from social media to cultural tourism under the connected environment.
Using social annotations to enhance document representation for personalized search

In this paper, we present a contribution to IR modeling. We propose an approach that computes on the fly, a Personalized Social Document Representation (PSDR) of each document per user based on his social activities. The PSDRs are used to rank documents with respect to a query. This approach has been intensively evaluated on a large public dataset, showing significant benefits for personalized search.
Decreased Phosphatase Activity, Increased Ca2+ Sensitivity, and Myosin Light Chain Phosphorylation in Urinary Bladder Smooth Muscle of Newborn Mice

Developmental changes in the regulation of smooth muscle contraction were examined in urinary bladder smooth muscle from mice. Maximal active stress was lower in newborn tissue compared with adult, and it was correlated with a lower content of actin and myosin. Sensitivity to extracellular Ca2+ during high-K+ contraction, was higher in newborn compared with 3-wk-old and adult bladder strips. Concentrations at half maximal tension (EC50) were 0.57 ± 0.01, 1.14 ± 0.12, and 1.31 ± 0.08 mM. Force of the newborn tissue was inhibited by ∼45% by the nonmuscle myosin inhibitor Blebbistatin, whereas adult tissue was not affected. The calcium sensitivity in newborn tissue was not affected by Blebbistatin, suggesting that nonmuscle myosin is not a primary cause for increased calcium sensitivity. The relation between intracellular [Ca2+] and force was shifted toward lower [Ca2+] in the newborn bladders. This increased Ca2+ sensitivity was also found in permeabilized muscles (EC50: 6.10 ± 0.07, 5.77 ± 0.08, and 5.55 ± 0.02 pCa units, in newborn, 3-wk-old, and adult tissues). It was associated with an increased myosin light chain phosphorylation and a decreased rate of dephosphorylation. No difference was observed in the myosin light chain phosphorylation rate, whereas the rate of myosin light chain phosphatase–induced relaxation was about twofold slower in the newborn tissue. The decreased rate was associated with a lower expression of the phosphatase regulatory subunit MYPT-1 in newborn tissue. The results show that myosin light chain phosphatase activity can be developmentally regulated in mammalian urinary bladders. The resultant alterations in Ca2+ sensitivity may be of importance for the nervous and myogenic control of the newborn bladders.
Automatic Facial Expression Recognition Using SVM Based on AAMs

An automatic facial expression recognition method is proposed to effectively recognize facial expression without any region unrelated to facial region. Support Vector Machine (SVM) is applied to recognize facial expression by Gabor features extracting using Gabor wavelet transformation after separate facial region from images Based on Active Appearance Models (AAMs), which reduce influence of illumination and pose. The feasibility and effectiveness of this system are verified by multiple experiments, and satisfied results are achieved.
AdaptCache: Adaptive Data Partitioning and Replication for Distributed Object Caches

This paper presents the AdaptCache project. AdaptCache is an adaptive caching middleware for application servers that monitors the current workload and generates policies to distribute and/or replicate objects and requests among the local caches of application servers so that most requests can be executed on locally cached objects and, at the same time, the load will be evenly distributed among servers. The project is divided into two main phases. The first one, which is described in detail in this paper, tackles the problem of dynamically distributing objects and requests for volatile and fluctuating e-commerce applications. Several data distribution approaches based on graph partitioning are proposed. The approaches are compared using the YCSB and RUBiS benchmarks showing that AdaptCache is able to dynamically capture various workload changes and react quickly to these changes. The second phase of the AdaptCache project explores data replication for distributed object caches. It discusses the advantages of object replication such as increased locality but also possible overheads due to consistency requirements and space limitations. Any dynamic replication solution must take these issues into account.
A Web-based Intelligent Learning Environment for Digital Systems*

Many recent web-based teaching and learning environments that involve the use of fixed sets of problems are unable to accurately measure a student's understanding about the subject matter. Such systems do not contribute significantly to the learning process. A good pedagogical framework is needed to develop effective web-based teaching and learning systems. This paper presents just such an effective system, which is designed for a specific engineering subject, Digital Logic Systems Design. Our system is called Web-based Intelligent Learning Environment for Digital System or WILEDS. WILEDS has been built on a robust framework of intelligent tutoring systems realised on a three-tier Java client-server architecture. It comprises five components: a communications module, a pedagogical module, a student model, an expert model and the domain knowledge. WILEDS incorporates the entire process needed to assess students, starting from automatically generating problems to checking the students' solutions and keeping track of their progress.
Age Differences in Arousal, Perception of Affective Pictures, and Emotional Memory Enhancement: Appraisal, Electrodermal Activity, and Imaging Data: (529972013-001)

In contrast to effortful cognitive functions, emotional functioning may remain stable or even be enhanced in older adults. It is unclear how affective functions in aging correspond to subjective experiences and physiological changes. In Study I, ratings of emotional intensity and neural activity to facial expressions using functional magnetic resonance imaging (fMRI) were analyzed in younger and older adults. Negative expressions resulted in increased neural activity in the right amygdala and hippocampus in younger adults, and increased activation in the right insular cortex in older adults. There were no age differences in subjective ratings. In Study II, subjective ratings of, and skin conductance response (SCR) to, neutral and negative pictures were studied. The ratings of negative pictures were higher for older adults compared to younger adults. SCRs increased in both age groups for the negative pictures, but magnitude of SCRs was significantly larger in younger adults. Finally, in Study III, emotional memory after a one-year retention interval was tested. The memory performance of both age groups was higher in response to negative pictures compared to neutral ones, although the performance was generally higher for younger adults. SCR at encoding was the better arousal predictor for memory, but only in younger adults. The results indicate age-related changes in affective processing. Age differences may involve a gradual shift from bottom-up processes, to more top-down processes. The results are discussed in a wider lifespan perspective taking into consideration the accumulated life experience of older adults.
Fractal Analysis Reveals Reduced Complexity of Retinal Vessels in CADASIL

The Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) affects mainly small cerebral arteries and leads to disability and dementia. The relationship between clinical expression of the disease and progression of the microvessel pathology is, however, uncertain as we lack tools for imaging brain vessels in vivo. Ophthalmoscopy is regarded as a window into the cerebral microcirculation. In this study we carried out an ophthalmoscopic examination in subjects with CADASIL. Specifically, we performed fractal analysis of digital retinal photographs. Data are expressed as mean fractal dimension (mean-D), a parameter that reflects complexity of the retinal vessel branching. Ten subjects with genetically confirmed diagnosis of CADASIL and 10 sex and age-matched control subjects were enrolled. Fractal analysis of retinal digital images was performed by means of a computer-based program, and the data expressed as mean-D. Brain MRI lesion volume in FLAIR and T1-weighted images was assessed using MIPAV software. Paired t-test was used to disclose differences in mean-D between CADASIL and control groups. Spearman rank analysis was performed to evaluate potential associations between mean-D values and both disease duration and disease severity, the latter expressed as brain MRI lesion volumes, in the subjects with CADASIL. The results showed that mean-D value of patients (1.42±0.05; mean±SD) was lower than control (1.50±0.04; p = 0.002). Mean-D did not correlate with disease duration nor with MRI lesion volumes of the subjects with CADASIL. The findings suggest that fractal analysis is a sensitive tool to assess changes of retinal vessel branching, likely reflecting early brain microvessel alterations, in CADASIL patients.
Functional Diversity of Microbial Communities in Soils in the Vicinity of Wanda Glacier, Antarctic Peninsula

Microbial functional diversity in four soils sampled in the vicinity of Wanda Glacier, Antarctic Peninsula, was determined using Biolog EcoPlates at 5°C and 25°C. Comparisons of the patterns of substrate utilization and the diversity index showed differences in community composition, reflecting the heterogeneous distribution of microorganisms in this environment. Differences in microbial diversity may be related to soil chemical properties. Higher incubation temperature influenced the overall microbial diversity, reducing richness due to the selection of psychrotrophic microorganisms. To our knowledge, this is the first study with microbial communities from Wanda Glacier and contributes to understanding the microbial diversity of Antarctic environments.
Comparative Analysis of Music Recordings from Western and Non-Western traditions by Automatic Tonal Feature Extraction

The automatic analysis of large musical corpora by means of computational models overcomes some limitations of manual analysis, and the unavailability of scores for most existing music makes necessary to work with audio recordings. Until now, research on this area has focused on music from the Western tradition. Nevertheless, we might ask if the available methods are suitable when analyzing music from other cultures. We present an empirical approach to the comparative analysis of audio recordings, focusing on tonal features and data mining techniques. Tonal features are related to the pitch class distribution, pitch range and employed scale, gamut and tuning system. We provide our initial but promising results obtained when trying to automatically distinguish music from Western and non- Western traditions; we analyze which descriptors are most relevant and study their distribution over 1500 pieces from different traditions and styles. As a result, some feature distributions differ for Western and non-Western music, and the obtained classification accuracy is higher than 80% for different classification algorithms and an independent test set. These results show that automatic description of audio signals together with data mining techniques provide means to characterize huge music collections from different traditions and complement musicological manual analyses.
Continuous Rainbow Options in Co-integrated Markets

This paper develops a continuous real option model on the best of two commodities when their price spread follows an arithmetic mean-reverting process. The co-integration of the two variables effectively allows the complexity to be reduced from two sources of uncertainty to only one by focusing on the spread, which is why the model can also be applied to continuous entry/exit problems on a single mean-reverting variable. We provide a quasi-analytical solution for valuing this real rainbow option and the trigger levels when to switch between the two operating modes by incurring a switching cost. All parameters of our solution are estimated from empirical data and consistent riskadjusted discounting is applied. We apply the theoretical model to value a polyethylene plant based on the spread between polyethylene and ethylene. The spread is shown to be stationary and the parameters of the stochastic process are estimated by OLS regression and tested for validity. The sensitivity analysis reveals important implications for the investment timing and operation decisions of investors in a flexible plant, both depending on the extent of mean-reversion in the value-driver.