Miracle-dz/UniFAR-FTUs huggingface dataset:
Single image super-resolution via learned representative features and sparse manifold embedding

Advances in machine learning technology have made efficient Super-Resolution Image Reconstruction (SRIR) possible. In this paper, we advance a hierarchical support vector machine (HSVM) to learn representative features of both training and test Low-Resolution (LR) image patches. Then a sparse manifold assumption is cast on training patch features to find local HR neighbors for each test LR input. The reconstructed High-Resolution (HR) patches can then be derived via Neighbors Embedding (NE) technology with the help of the HR neighbors from training HR patches, and compensated for the LR images. Some experiments are taken on realizing a 3X amplification of natural images, the recovered results prove its efficiency and superiority to its counterparts visually and qualitatively.
High Prevalence of Hepatitis B Virus Infection in the Age Range of 20-39 Years Old Individuals in Lome

Background: Hepatitis B is a liver infection caused by the hepatitis B virus (HBV). It affects all women and men irrespective of age. Although sub-Saharan Africa is an area of high prevalence of this disease, data on the prevalence of acute and chronic HBV infections in this region remain to be widely documented. Objective: This study aimed to investigate the prevalence of HBV in relation to age in Centre Hospitalier Universitaire Campus (CHU-C), one of the two teaching hospitals of Lome, Togo. Method: The present study is a cross-sectional study about the prevalence of hepatitis B surface antigen (HBsAg) carriage from 2009 to 2011. All study participants were screened for HBsAg at the Immunology laboratory of CHU Campus of Lome. Results: One thousand two hundred individuals were screened for HBsAg from 2009-2011. The overall prevalence of HBV infection was 19.08%. This prevalence was significantly higher in men (25.00%) than women (14.80%). The highest prevalence of HBV was observed in age range of 20-29 years and 30-39 years with respectively 26.33% and 21.67%. The lowest prevalence was 6.08%, found in people over 50 years. Concerning the clinical indication of the test, the prevalence during the clinical abnormalities related to liver (CARL) was the highest (26.21%), followed by the systematic screening (SS) with 20.25% while the pre-operative assessment (POA) showed the lowest prevalence with 5.56%. Conclusion: The study shows the high prevalence of HBsAg carriage in young people. This could be used to enhance prevention and treatment of HBV infection in Togo.
Revitalizing Agricultural Research in the Sahel: A Proposed Framework for Action

The problem which this report seeks to address is the slow rate of technology generation of the agricultural research system in the Sahel. To overcome this problem, the report proposes to strengthen the national agricultural research systems in the region through a three-pronged effort: (a) institutional reforms of the national agricultural research systems to evolve an enabling environment for creativity, innovation and improved performance; (b) new modes of regional cooperation based on the principles of comparative advantage and the relative strengths of national agricultural research systems; and (c) a series of cross-cutting actions to support the revitalized national and regional efforts. It is expected that a more demand-driven national/regional research agenda and more vibrant linkages between scientists and clients will lead to faster rates of technology generation, as was demonstrated by the success of cotton research in the Sahel.
Effecient ECG Signal Classification Using Sparsely Connected Radial Basis Function Neural Network

Precise electrocardiogram (ECG) classification to diagnose patients’ heart condition is essential in getting maximum benefits via effective treatment. Artificial neural network is known to yield good results for classification of “difficult-to-diagnose” signals in medical domain. In this work, the ECG classification is performed using sparsely connected radial basis function neural network (RBFNN). Unlike the fully-connected RBFNN architecture, this type of structure reduces the computational cost and increases the classification accuracy. Only prominent features are necessary to a certain class of ECG during classification task. The ECG signal is obtained from Boston’s Beth Israel Hospital. The processes inc ude signal pre-processing, QRS complex detection, features extraction, determination of important parameter and construction of RBF network. Wavelet decomposition method was used for feature extraction process. Five features to be used are standard deviation of R-R distance, P-wave and R-wave amplitude, QS-wave distance and minimum point of T-wave. The signal is classified into three classes; normal sinus rhythm, malignant ventricular ectopy and atrial fibrillation. The results show that sensitivity value is between 75 to 100 percent while the certainty value is between 91.7 to 100 percent. In addition, this work shows that conventional RBFNN and linear discriminant analysis (LDA) produce less accurate results in terms of overall classification accuracy.
An empirical study of multipass decoding for vietnamese LVCSR

ABSTRACT In this paper, we represent an empirical study of multipass decoding for Vietnamese LVCSR. We report our experiments with N-best, lattice and consensus decoding on the VNBN data. Results from this study indicate that our acoustic model for Vietnamese was precise. The results could be investigated in further steps to improve the performance of our system. Index Terms Vietnamese, Acoustic Model, Language Model, N-best, Word Lattice, Confusion Network. 1. INTRODUCTION Large vocabulary continuous speech recognition (LVCSR) is both a pattern recognition and search problem. As described in [4], the acoustic and language models are built upon a statistical pattern recognition framework. In speech recognition, making a search decision is also referred to as decoding. The decoding process should take into account all available knowledge sources when hypothesising an utterance. Besides the speech signal, and the models of the recognition units, also knowledge about syntax, semantic, and other properties of the natural language might be used when searching for the most likely word sequence. One way to include these knowledge sources in the search process is to use them simultaneously to constrain a single search. Since many of the natural language knowledge sources contain "long-distance" effects, the search can become quite complex. Furthermore, the common left-to-right search strategy requires that also all knowledge sources are formulated in a predictive, left-to-right manner, which restricts the type of knowledge that can be used. One way to solve these problems is to apply sources not simultaneously but sequentially so that the search for the most likely hypothesis is constrained progressively. Thus the advantages provided by a knowledge source can be traded-off against the costs of applying it. First, the most powerful and cheapest knowledge sources are applied to generate a list of the top
‘Smartphine': Supporting students' well-being according to their calendar and mood

Smartphones have become devices of choice for conducting studies on health and well-being, especially among young people. When accessing higher education, students frequently experience significant challenges, ranging from adapting to new situations and new social relations to heavier workload than in previous levels of education, short deadlines, teamwork assignments and so on. In this paper we present the results of three studies examining students' well-being and propose a mobile application that acts as a complement to a successful tutoring project deployed at our University over the last four years. The app allows the student to keep their schedules and deadlines in one place while integrating a virtual tutor features. By using both, the events from the student's calendar and his or her mood indicators, the app sends notifications accordingly. These notifications range from motivational phrases to time management guidelines and relaxation tips.
Information-centric Multilayer Network: ICN/WDM

Information-centric networking (ICN) facilitates content identification in networks and offers parametric representation of content semantics. This work, proposes an ICN/WDM network architecture that uses these features to offer superior network utilization, in terms of performance and power consumption. The architecture introduces an ICN publish/subscribe communication approach to the wavelength layer; whereby, content is aggregated according to its popularity rank into wavelength-size groups that can be published and "subscribed to" by multiple nodes. Consequently, routing and wavelength assignment (RWA) algorithms benefit from anycast to identify multiple sources of aggregate content and allow optimization of the source selection of light-paths. A power-aware algorithm, Maximum Degree of connectivity (MaxDeg), has been developed with the objective of exploiting this flexibility to address the trade-off between power consumption and network performance. The algorithm is also applicable to IP architectures, albeit with less flexibility. Evaluation results indicate the superiority of the proposed ICN architecture, even when utilizing conventional routing methods, compared to its IP counterpart. The results further highlight the performance improvement achieved by the proposed algorithm, compared to conventional RWA methods such as Shortest-path First Fit (SFF).
The osteoporotic male: Overlooked and undermanaged?

Age-related bone loss in men is a poorly understood phenomenon, although increasing data on the pathophysiology of bone in men is becoming available. Most of what we know on bone pathophysiology derives from studies on women. The well-known association between menopause and osteoporosis is far from been disproven. However, male osteoporosis is a relatively new phenomenon. Its novelty is in part compensated for by the number of studies on female osteoporosis and bone pathophysiology. On the other hand, the deeper understanding of female osteoporosis could lead to an underestimation of this condition in the male counterpart. The longer life-span exposes a number of men to the risk of mild-to-severe hypogonadism which in turn we know to be one of the pathogenetic steps toward the loss of bone mineral content in men and in women. Hypogonadism might therefore be one among many corrigible risk factors such as cigarette smoking and alcohol abuse against which clinicians should act in order to prevent osteoporosis and its complications. Treatments with calcium plus vitamin D and bisphophonates are widely used in men, when osteoporosis is documented and hypogonadism has been excluded. The poor knowledge on male osteoporosis accounts for the lack of well shared protocols for the clinical management of the disease. This review focuses on the clinical approach and treatment strategy for osteoporosis in men with particular attention to its relationship with male hypogonadism.
Applying 'Second Life' to a CAVE#8482;-like system for the elaboration of interaction methods with programmable interfaces

With its multiple types of interaction scenarios both between people and between people and objects in virtual space, ‘Second Life’ can be used as a platform for both social and technological research. Set up in a stereoscopic environment, the Open Source Viewer is implemented in a technological framework for iteratively creating, evaluating and optimizing appropriate input/output patterns and interface metaphors for immersive environments. Focus of R&D are the real-time interaction methods rendered possible by a programmable Interface. Thus, the Ars Electronica Futurelab has developed a technological framework based on own R&D results. The framework consists of an interaction wrapper (Palmist), a distribution engine (ARSBOX) [Berger et al. 2004] and a render unit (VRizer) [Berger et al. 2005].
How Much Does Investor Sentiment Really Matter for Equity Issuance Activity?

We study the extent to which investor sentiment matters for aggregate equity issuance activity. We focus on firms that are susceptible to investor sentiment and for which accurate measures of economic fundamentals are available. While sentiment on its own matters for equity issuance, it matters relatively little once we control for accurately measured fundamentals. Collectively, proxies for sentiment explain roughly 10 percentage points of the time-series variation of equity issuance beyond the roughly 40% explained by fundamentals. We conclude that investor sentiment does not seem to matter very much for aggregate equity issuance activity.
Keeping the vehicle on the road: A survey on on-road lane detection systems

The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.
Automatically building probabilistic databases from the web

A relevant number of web sites publish structured data about recognizable concepts (such as stock quotes, movies, restau- rants, etc.). There is a great chance to create applications that rely on a huge amount of data taken from the Web. We present an automatic and domain independent system that performs all the steps required to benefit from these data: it discovers data intensive web sites containing information about an entity of interest, extracts and integrate the published data, and finally performs a probabilistic analysis to characterize the impreciseness of the data and the accuracy of the sources. The results of the processing can be used to populate a probabilistic database.
Dynamic and Adaptive Organization of Data-Collection Infrastructures in Sustainable Wireless Sensor Networks

National institute of information and communications technology, Japan Sensor networks are associated with the natural complex networks (such as biological networks and social networks) in the aspect of cooperative organizations of small sensor nodes into various functional patterns of network structures so as to perform ubiquitous sensing and control. A specific feature of communication in sensor networks is the many to one model of data collections, which is subject to the problem of energy overuse at some particular nodes near the sink. To enable balanced energy consumption among sensor nodes, the desirable infrastructures should organize sensor nodes to fairly share the role of data collection. In this paper, we study the construction of both dynamic and adaptive network infrastructures for data collections in a static wireless sensor network. The organization of sensor nodes in data collections is adaptive to the data aggregation capacity at sensor nodes, leading to both energy saving and balanced energy consumption.
Liberalization and Growth: An Econometric Study of Bangladesh

Bangladesh adopted a liberal economic regime, particularly in the areas of trade, finance, and capital account, since mid-1980s. This study seeks to evaluate the impact of liberalization on the country’s economic growth by analyzing the 1974-2002 data with the help of cointegration and error correction methods. The empirical results suggest that long-run economic growth in Bangladesh is largely explained by physical capital and real interest rate and growth remains unaffected by short-term changes in labor force and secondary enrollment ratio. While financial liberalization has had significant negative impacts on economic growth implying that financial reforms failed to attract new investment due to adverse investment climate, the effects of trade and capital account liberalizations were rather insignificant, possibly due to weak supply response and lack of credibility of such reform programs. Bangladesh will not reap the full benefits of any comprehensive liberalization measures unless it can improve infrastructure and quality of governance, the paper argues.
TUB @ MediaEval 2012 Tagging Task: Feature Selection Methods for Bag-of-(visual)-Words Approaches

This paper describes our participation in the Genre Tagging Task of MediaEval 2012, which aims to predict the videos’ category label. In last year’s participation, we performed experiments with bag-of-words (BoW) approaches in which dierent constellations in respect of modalities, features, and methods were investigated. This year, we focus on feature selection methods to improve the classication performance in terms of mean average precision (mAP) and classication accuracy (CA). We investigated the eectiveness of selection methods based on scores calculated using mutual information (MI) or term frequency (TF) and the eectiveness of transformation methods like the principle component analysis (PCA).
Classification of vocal and non-vocal regions from audio songs using spectral features and pitch variations

In this work, an effort has been made to identify vocal and non-vocal regions from a given song using signal processing techniques and machine learning algorithm. Initially spectral features like mel-frequency cepstral coefficients (MFCCs) are used to develop the baseline system. Statistical values of pitch, jitter and shimmer are considered to improve performance of the system. Artificial neural networks (ANNs) are used to capture the characteristics of vocal and non-vocal segments of the songs. The experiment is conducted on 60 vocal and 60 non-vocal clips extracted from Telugu albums. 11-point moving window is used to ensure the continuity of vocal and non-vocal segments, thus improving the accuracy of system. With this approach system achieves 85.59% accuracy for vocal and 88.52% for non-vocal segment classification.
Subcarrier Block Power Control for Adaptive Downlink OFDM with Frequency Spreading and Equalization

In this paper, we propose the transmit power controlled adaptive downlink frequency symbol spreading OFDM (TPC-AMS/FSS-OFDM) system. In the TPC-AMS/FSS-OFDM, each serial to parallel (S/P) transformed signal is spread by orthogonal spreading codes and combined in the transmitter, so the detected signals obtain the same SINR for each frequency symbol spreading block in the receiver. In this case, we can assign the same modulation level and transmit power for each frequency symbol spreading block for next transmission. Thus, the proposed system not only increases throughput performance but also reduces the total transmit power, FBI and MLI.
OPTIMAL DESIGN OF NONLINEAR MAGNETIC SYSTEMS USING FINITE ELEMENTS

An inverse finite element method was developed to find optimal geometric parameters of a magnetic device to approximate a desired magnetic flux density distribution at certain test points and directions selected in the device. The augmented Lagrange multipliers method was utilized to transform the constrained problem consisting of a least-square objective function and a set of constraint equations to the unconstrained problem. A second-order approach based on the Lagrange-Newton method was used to minimize the unconstrained problem to improve the design iteratively. Numerical calculation of derivatives in the second-order design sensitivity analysis becomes a difficult task if saturation in material properties is accounted. A novel approach is developed to minimize the computational effort by directly combining the optimization process with the nonlinear finite element equations. The best capabilities to parametrize the device geometry and to model the nonlinear material characteristics were incorporated into the optimization program for rapid sensitivity analysis. Demonstration of various test cases arising from optimally designing electrical machinery verified the validity of the overall theory and developments.
Housing Variables and Monetary Policy - A Study of House Prices and Residential Investment and Their Importance for Monetary Policy

Housing constitutes a large amount of all economic activity and plays a signi cant role for the business cycle, and is of great importance for monetary policy. How central banks should approach di erent housing variables is contended. This thesis considers two variables, house prices and residential investment, and how they are of interest for monetary policy makers. House prices are di cult to include in in ation indices, and their developments are accordingly hard to respond to when conducting in ation targeting monetary policy. Furtermore, they are commonly associated with nancial imbalances, but the exact role they play in this respect is a contended issue. House prices are found to be dealt with in varying ways among central banks, as there is a lack of consensus on the way to approach them. Residential investment is shown to play a signi cant role for the U.S. business cycle by Leamer (2007). Following his analysis, I consider its importance for recessions of 17 other OECD countries. I nd that, while its role is less prominent than for the U.S., residential investment contains useful information about economic turmoil, and could potentially be used as a target variable for central banks in their pursuit to stabilize the business cycle.
Making Through the Lens of Culture and Power: Toward Transformative Visions for Educational Equity

In this essay, Shirin Vossoughi, Paula Hooper, and Meg Escude advance a critique of branded, culturally normative definitions of making and caution against their uncritical adoption into the educational sphere. The authors argue that the ways making and equity are conceptualized can either restrict or expand the possibility that the growing maker movement will contribute to intellectually generative and liberatory educational experiences for working-class students and students of color. After reviewing various perspectives on making as educative practice, they present a framework that treats the following principles as starting points for equity-oriented research and design: critical analyses of educational injustice; historicized approaches to making as cross-cultural activity; explicit attention to pedagogical philosophies and practices; and ongoing inquiry into the sociopolitical values and purposes of making. These principles are grounded in their own research and teaching in the Tinkering Afterschool.