Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidimensional knowledge representation of text analytics results in knowledge bases

In the age of digitization, intelligent systems have to cope with an ever-growing amount of data. Therefore, knowledge representation plays a key-role for applications to handle continuously created data and to enable an access on flexible and extensively well-described data structures. This paper introduces a knowledge base design which has the capability of dimensional structuring of semantically-related data and explains how text analytic results can be integrated into a knowledge base. The paper discusses the main advantages of this design and shows how the data can be arranged in the knowledge base. The multidimensional structure of the knowledge base helps to resolve one of the main challenges of knowledge discovery which is the extraction of meaningful information from data in a context.
Monte Carlo Sampling for Regret Minimization in Extensive Games

Sequential decision-making with multiple agents and imperfect information is commonly modeled as an extensive game. One efficient method for computing Nash equilibria in large, zero-sum, imperfect information games is counterfactual regret minimization (CFR). In the domain of poker, CFR has proven effective, particularly when using a domain-specific augmentation involving chance outcome sampling. In this paper, we describe a general family of domain-independent CFR sample-based algorithms called Monte Carlo counterfactual regret minimization (MCCFR) of which the original and poker-specific versions are special cases. We start by showing that MCCFR performs the same regret updates as CFR on expectation. Then, we introduce two sampling schemes: outcome sampling and external sampling, showing that both have bounded overall regret with high probability. Thus, they can compute an approximate equilibrium using self-play. Finally, we prove a new tighter bound on the regret for the original CFR algorithm and relate this new bound to MCCFR's bounds. We show empirically that, although the sample-based algorithms require more iterations, their lower cost per iteration can lead to dramatically faster convergence in various games.
Prostate Cancer Staging and Grading at Radical Prostatectomy Over Time

Prostate-specific antigen (PSA) testing has been associated with a sharp increase in prostate cancer (PCA) detection after its introduction in the late 1980s. Since its launch and its implementation as diagnostic test in 1994, temporal patterns in patients' age and serum PSA level at presentation have changed, with younger patients being diagnosed at lower PSA cutoff levels. Many studies suggest that PSA screening has resulted in a profound downward migration in clinical and pathologic stage of newly diagnosed PCA, although the effect has slowed in the most recent years. The impact on tumor grading is less clear. Histologic grading of PCA, based on the Gleason system, is predictive of the biological behavior and prognosis of the tumor. If tumor progresses from low grade to high grade, the early detection would lead to a higher percentage of low-grade disease diagnosed over time. However, published data suggest that tumor grade shifts have occurred over time and are unlikely to be attributable to changes in tumor biology, but rather to changes in practice with respect to Gleason grading. This review will address PCA staging and grading trends from the pre-PSA era to the present time with emphasis on the potential role played by changes in clinical and pathologic practice.
Users' Behavioral Analysis on Weblogs

This study investigates Weblog users’ behavioral data on a hosting service in Japan. The study analyzes users’ behaviors, such as browsing and bookmarking, in addition to posting comments and sending trackbacks. Results of this study reveal the causes of a user’s visiting and regularly browsing behaviors, especially from a social network point of view. The correlation between the strength of two users on the social network and their visiting behavior is analyzed.
Quality, cleanroom and formal methods

We have proposed a new approach to software quality combining cleanroom methodologies and formal methods. Cleanroom emphasizes defect prevention rather than defect removal. Formal methods use mathematical and logical formalizations to find defects early in the software development lifecycle. These two methods have been used separately to improve software quality since the 1980's. The combination of the two methods may provide further quality improvements through reduced software defects. This result, in turn, may reduce development costs, improve time to market, and increase overall product excellence.Defects in computer software are costly. Their detection is usually postponed to the test phase, and their removal is also a very time consuming and expensive task. Cleanroom software engineering is a methodology which relies on preventing the defects, rather than removing them. It is based on incremental development and it emphasizes the development phase. An enhancement to this methodology is presented in this paper, which combines formal methods and cleanroom. The efficiency of the new model rests on an appropriate logical representation, to write the specification of the intended system. In the new model, design plans are formally verified before any implementation is done. The advantages of finding defects in the early stages are decreased cost and increased quality. Results show that, by using formal methods, a higher quality will be achieved and the software project can also benefit from the existing mechanized tools of these two techniques.
A novel intelligent mobile robot navigation technique for avoiding obstacles using RBF neural network

This paper proposes a path planning and intelligent control of mobile robot in unknown environment with static/dynamic obstacles and fixed target. A radial basis function (RBF) network approach is proposed in this work for obtaining optimized path to reach the goal without collision. The competency of the proposed approach is verified by means of simulation results using MATLAB where robot moves in a variety of environments with obstacles of different shapes and sizes.
High blood sugar levels significantly impact the prognosis of colorectal cancer patients through down-regulation of microRNA-16 by targeting Myb and VEGFR2

The high prevalence of type 2 diabetes mellitus in colorectal cancer patients is a crucial public health issue worldwide. The deregulation of microRNAs has been shown to be associated with the progression of CRC; however, the effects of high blood sugar levels on miR deregulation and, in turn, CRC remain unexplored. In this study, 520 CRC patients were classified into two groups according to their blood sugar levels (≧110 or <110 mg/dL). Clinicopathologic features, clinical outcomes, and serum miR-16 levels of the two groups were then analyzed, while cell cycles, cell proliferation, migration, and cellular miR-16 expression were investigated via D-(+)-glucose administration. Additionally, the target genes of miR-16 were identified. Through multivariate analysis, both the disease-free survival and overall survival of the CRC patients were found to be associated with the UICC stage, perineural invasion, and blood glucose levels (P < 0.05). Serum miR-16 levels were significantly lower in the high blood glucose patients than in the normal blood glucose patients (P = 0.0329). With D-(+)-glucose administration, the proliferation and migration of CRC cells in vitro increased remarkably (P < 0.05), while their accumulation in the G1 phase decreased significantly. Cellular miR-16 expression was suppressed by D-(+)-glucose administration. The expression levels of two target genes, Myb and VEGFR2, were affected significantly by miR-16, while glucose administration inhibited miR-16 expression and enhanced tumor cell proliferation and migration. Hyperglycemia can impact the clinical outcomes of CRC patients, likely by inhibiting miR-16 expression and the expression of its downstream genes Myb and VEGFR2.
Distributed neuro-fuzzy feature forecasting approach for condition monitoring

The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.
A binaural room impulse response database for the evaluation of dereverberation algorithms

This paper describes a new database of binaural room impulse responses (BRIR), referred to as the Aachen Impulse Response (AIR) database. The main field of application of this database is the evaluation of speech enhancement algorithms dealing with room reverberation. The measurements with a dummy head took place in a low-reverberant studio booth, an office room, a meeting room and a lecture room. Due to the different dimensions and acoustic properties, it covers a wide range of situations where digital hearing aids or other hands-free devices can be used. Besides the description of the database, a motivation for using binaural instead of monaural measurements is given. Furthermore an example using a coherence-based dereverberation technique is provided to show the advantage of this database for algorithm evaluation. The AIR database is being made available online.
Modular Tracking Framework: A Unified Approach to Registration based Tracking

This paper presents a modular, extensible and highly efficient open source framework for registration based tracking called Modular Tracking Framework (MTF). Targeted at robotics applications, it is implemented entirely in C++ and designed from the ground up to easily integrate with systems that support any of several major vision and robotics libraries including OpenCV, ROS, ViSP and Eigen. It implements more methods, is faster, and more precise than other existing systems. Further, the theoretical basis for its design is a new way to conceptualize registration based trackers that decomposes them into three constituent sub modules - Search Method (SM), Appearance Model (AM) and State Space Model (SSM). In the process, we integrate many important advances published after Baker \& Matthews' landmark work in 2004. In addition to being a practical solution for fast and high precision tracking, MTF can also serve as a useful research tool by allowing existing and new methods for any of the sub modules to be studied better. When a new method is introduced for one of these, the breakdown can help to experimentally find the combination of methods for the others that is optimum for it. By extensive use of generic programming, MTF makes it easy to plug in a new method for any of the sub modules so that it can not only be tested comprehensively with existing methods but also become immediately available for deployment in any project that uses the framework. With 16 AMs, 11 SMs and 13 SSMs implemented already, MTF provides over 2000 distinct single layer trackers. It also allows two or more of these to be combined together in several ways to create a practically unlimited variety of novel multi layer trackers.
The BT-tree: A Branched and Temporal Access Method

Temporal databases assume a single line of time evolution. In other words, they support timeevolving data. However there are applications which require the support of temporal data with branched time evolution. With new branches created as time proceeds, branched and temporal data tends to increase in size rapidly, making the need for e cient indexing crucial. We propose a new (paginated) access method for branched and temporal data: the BT-tree. The BT-tree is both storage e cient and access e cient. We have implemented the BT-tree and performance results con rm these properties.
Ultrasonic Pulse Velocity Evaluation of the Pull-Off Adhesion between Epoxy Resin and Concrete Substrate

Adhesion assessment between epoxy resin and concrete substrate is described in the article. Two-layered cylindrical elements were obtained from the borehole in the object created for the purpose of the test. Adhesion analysis has been performed for two groups of elements of different composition. The ultrasonic pulse velocity (UPV) method with two-sided access was used to assess the adhesion. Pull-off adhesion fb compared with the ultrasonic wave velocity V has been determined. Relationships between the ultrasonic wave velocity V and pull-off adhesion fb, obtained with the semi-destructive pull-off method, were determined for both groups of elements. It was proved that together with an increase on the elements of the ultrasonic velocity wave V, the pull-off adhesion value fb between the epoxy resin and the concrete substrate also increases.
Simulation of Throughput in UMTS Networks with Different Spreading Factors

We design and implement a local session admission control (SAC) algorithm for third-generation wireless networks which allows for the simulation of network throughput for different spreading factors and various mobility scenarios. The design of the SAC algorithm uses global information; it incorporates the session arrival rates and the user mobilities across the network and guarantees the users' quality of service as well as pre-specified blocking probabilities. On the other hand, its implementation in each cell uses local information; it only requires the number of sessions currently active in that cell. A global SAC algorithm is also implemented and used as a benchmark since it is inherently optimized and uses global information in making every session admission decision; it yields the best possible performance but has an intensive computational complexity. Using simulation, we determine the network throughput, and show that our optimized local SAC algorithm achieves almost the same performance as our global SAC algorithm at a fraction of the computational cost for pre-specified blocking probabilities and quality of service requirements and spreading factor values of 256, 64, 16, and 4.
Pseudoaneurysm and aorto-bronchial fistula following balloon dilation of recoarctation

Abstract This case report documents the successful surgical repair of an aorto-bronchial fistula and a giant aortic pseudoaneurysm at the proximal anastomosis of a dacron interposition tube graft that was balloon dilated for recurrent coarctation. Balloon dilation for recoarctation of a dacron interposition tube graft may lead to serious complications.
Centralized Adaptive Source-Routing for Networks-on-Chip as HW/SW-Solution with Cluster-based Workload Isolation

The growing number of applications and processing units in modern MPSoCs comes along with dynamic and diverse workload characteristics at runtime. Thus, the communication infrastructure, e.g., Networks-on-Chip (NoC), operation on time dependent dynamic traffic loads makes adaptive congestion and load management indispensable. This paper introduces a centralized adaptive path management for oblivious source routing. Thereby, a cluster-based, runtimeconfigurable software solution continuously monitors the global traffic situation and calculates the needed routing adaptations for each active source-destination pair of the current workload inside a cluster. Contrary to other published solutions, a HW/SW-Co-Design for configurable clustering, traffic monitoring and path calculation is applied. Furthermore, the isolation of workload fractions by the spatial clustering allows application specific configurations. Keywords-Network-on-Chip, MPSoC, Adaptive Routing, Traffic Monitoring, Clustering, HW/SW-Co-Design.
A fast classification scheme and its application to face recognition

To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called ‘reconstruction proportion’ is exploited to reflect the discriminant information. A novel space called the ‘reconstruction space’ is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.
Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Sparse unmixing is a promising approach that acts as a semi-supervised unmixing strategy by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, conventional sparse unmixing involves finding the optimal subset of signatures for the observed data in a very large standard spectral library, without considering the spatial information. In this paper, a new sparse unmixing algorithm based on non-local means, namely non-local sparse unmixing (NLSU), is proposed to perform the unmixing task for hyperspectral remote sensing imagery. In NLSU, the non-local means method, as a regularizer for sparse unmixing, is used to exploit the similar patterns and structures in the abundance image. The NLSU algorithm based on the sparse spectral unmixing model can improve the spectral unmixing accuracy by incorporating the non-local spatial information by means of a weighting average for all the pixels in the abundance image. Five experiments with three simulated and two real hyperspectral images were performed to evaluate the performance of the proposed algorithm in comparison to the previous sparse unmixing methods: sparse unmixing via variable splitting and augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV). The experimental results demonstrate that NLSU outperforms the other algorithms, with a better spectral unmixing accuracy, and is an effective spectral unmixing algorithm for hyperspectral remote sensing imagery.
Early Handwritten Music Recognition with Hidden Markov Models

This work presents a statistical method to tackle the Handwritten Music Recognition task for Early notation, which comprises more than 200 different symbols. Unlike previous approaches to deal with music notation, our strategy is to perform a holistic recognition without any previous segmentation or staff removal process. The input consists of a page of a music book, which is processed to extract and normalize the staves contained. Then, a feature extraction process is applied to define such sections as a sequence of numerical vectors. The recognition is based on the use of Hidden Markov Models for the optical processing and smoothed N-grams as language model. Experimentation results over a historical archive of Hispanic music reported an error around 40 %, which confirms our proposal as a good starting point taking into account the difficulty of the task.
Extended SWN solvers in GreatSPN

Well-formed nets and their stochastic timed extension, take advantage of symmetries to allow efficient solution techniques for both qualitative and quantitative properties. During the last decade, these techniques have been enriched with increasingly more powerful ones: this has been achieved by exploiting partial symmetries and decomposition. In this paper, we present a suite of new software tools based on the original SWN solver of the GreatSPN tool, which implements these new features.
Integration of microplasma and microfluidic technologies for localised microchannel surface modification

In this paper we describe the spatial surface chemical modification of bonded microchannels through the integration of microplasmas into a microfluidic chip (MMC). The composite MMC comprises an array of precisely aligned electrodes surrounding the gas/fluid microchannel. Pairs of electrodes are used to locally ignite microplasmas inside the microchannel. Microplasmas, comprising geometrically confined microscopic electrically-driven gas discharges, are used to spatially functionalise the walls of the microchannels with proteins and enzymes down to scale lengths of 300 μm inside 50 μm-wide microchannels. Microchannels in poly(dimethylsiloxane) (PDMS) or glass were used in this study. Protein specifically adsorbed on to the regions inside the PDMS microchannel that were directly exposed to the microplasma. Glass microchannels required pre-functionalisation to enable the spatial patterning of protein. Firstly, the microchannel wall was functionalised with a protein adhesion layer, 3-aminopropyl-triethoxysilane (APTES), and secondly, a protein blocking agent (bovine serum albumin, BSA) was adsorbed onto APTES. The functionalised microchannel wall was then treated with an array of spatially localised microplasmas that reduced the blocking capability of the BSA in the region that had been exposed to the plasma. This enabled the functionalisation of the microchannel with an array of spatially separated protein. As an alternative we demonstrated the feasibility of depositing functional thin films inside the MMC by spatially plasma depositing acrylic acid and 1,7-octadiene within the microchannel. This new MMC technology enables the surface chemistry of microchannels to be engineered with precision, which is expected to broaden the scope of lab-on-a-chip type applications.
Avian E2A Expression Level Variation Associated with Phenotypic Variation of Two Chinese Indigenous Duck Breeds, (Anas platyrhynchos)

Semi-quantitative polymerase chain reaction (RT-PCR) was applied to quantify and compare variations in avian E2A expression level between two Chinese indigenous duck breeds for different tissues. Six primer combinations in addition to $-actin as an internal control were used. The expression level of E2A was significantly (p<0.05) higher in kidney tissues than lung tissues for both Gao you and Kun shan indigenous duck breeds. The variability of gene expression between two breeds showed 34.35% and 60.70% higher expression in Gao you breed than Kun shan breed for kidney and lung tissues, respectively. Differences were found to be significant (p<0.05) only for expression level in lung tissues. No significant effect of sex upon E2A expression was detected. The association between E2A relative expression level and body weight was found to be positive and moderate for the genetically lean breed (Gao you), while it was negative and has low values for genetically heavy breed (Kun shan).