Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategies for wound image understanding

This paper presents a Java framework for analyzing, processing and understanding wound images, to be used in teaching, learning and research activities. We intend to promote e-learning technologies in medical, pharmaceutical and health care domains. Using Java and XML technologies, we build models for various categories of wounds, due to various etiologies. Based on color and texture analysis, we identify the main barriers to wound healing, such as tissue non-viable, infection, inflammation, moisture imbalance, or edge non-advancing. This framework provides the infrastructure for preparing e-learning scenarios based on practice and real world experiences. We make experiments for wound healing simulation using various treatments and compare the results with experimental observations. Our experiments are supported by XML based databases containing knowledge extracted from previous wound healing experiences and from medical experts' knowledge. Also, we rely on new paradigms of the Artificial Intelligence for creating e-learning scenarios to be used in a context of active learning, for wound image understanding. To implement the e-learning tools, we use Java technologies for dynamic processes and XML technologies for dynamic content. Our approach to e-learning is so called blended learning, which combines traditional face-to-face and Web-based on-line learning, with focus on principles of active learning.
NPI-Based Adaptive Software Rejuvenation Schedule under Random Censoring

In this paper, we consider a nonparametric adaptive software rejuvenation schedule under a random censored data. For u failure time data and v random censored data, we formulate upper and lower bounds of the predictive system availability based on a nonparametric predictive inference (NPI). Then, we derive adaptive rejuvenation policies which maximizes the upper or lower bound. In simulation experiments, we show that estimates of the software rejuvenation schedule are updated by acquisition of new failure data, and converge to the theoretical optimal solution.
DIRS: Distributed image retrieval system based on MapReduce

With information technology developing rapidly, variety and quantity of image data is increasing fast. How to retrieve desired images among massive images storage is getting to be an urgent problem. In this paper, we established a Distributed Image Retrieval System (DIRS), in which images are retrieved in a content-based way, and the retrieval among massive image data storage is speeded up by utilizing MapReduce distributed computing model. Moreover, fault tolerance, ability to run in a heterogeneous environment and scalability are supported in our system. Experiments are carried out to verify the improvement of performance when MapReduce model is utilized. Results have shown that image storage and image retrieval based on MapReduce outperform that in centralized way greatly when total number of images is large.
Mesh traversal and sorting for efficient memory usage in scientific codes

Applications that operate on meshes are very popular in High Performance Computing (HPC) environments. In the past, many techniques have been developed in order to optimize the memory accesses for these datasets. Different loop transformations and domain decompositions are commonly used for structured meshes. However, unstructured grids are more challenging. The memory accesses, based on the mesh connectivity, do not map well to the usual linear memory model. This work presents a method to improve the memory performance which is suitable for HPC codes that operate on meshes. We develop a method to adjust the sequence in which the data are used inside the algorithm, by means of traversing and sorting the mesh. This sorted mesh can be transferred sequentially to the lower memory levels and allows for minimum data transfer requirements. The method also reduces the lower memory requirements dramatically: up to 63% of the L1 cache misses are removed in a traditional cache system. We have obtained speedups of up to 2.58 on memory operations as measured in a general-purpose CPU. An improvement is also observed with sequential access memories, where we have observed reductions of up to 99% in the required low-level memory size.
Higher Lower Bounds from the 3SUM Conjecture

The 3SUM conjecture has proven to be a valuable tool for proving conditional lower bounds on dynamic data structures and graph problems. This line of work was initiated by Pătrascu (STOC 2010) who reduced 3SUM to an offline SetDisjointness problem. However, the reduction introduced by Pătrascu suffers from several inefficiencies, making it difficult to obtain tight conditional lower bounds from the 3SUM conjecture. In this paper we address many of the deficiencies of Pătrascu's framework. We give new and efficient reductions from 3SUM to offline SetDisjointness and offline SetIntersection (the reporting version of SetDisjointness) which leads to polynomially higher lower bounds on several problems. Using our reductions, we are able to show the essential optimality of several algorithms, assuming the 3SUM conjecture. • Chiba and Nishizeki's O(mα)-time algorithm (SICOMP 1985) for enumerating all triangles in a graph with arboricity/degeneracy α is essentially optimal, for any α. • Bjorklund, Pagh, Williams, and Zwick's algorithm (ICALP 2014) for listing t triangles is essentially optimal (assuming the matrix multiplication exponent is ω = 2). • Any static data structure for SetDisjointness that answers queries in constant time must spend Ω(N2--o(1)) time in preprocessing, where N is the size of the set system. These statements were unattainable via Pătrascu's reductions. We also introduce several new reductions from 3SUM to pattern matching problems and dynamic graph problems. Of particular interest are new conditional lower bounds for dynamic versions of Maximum Cardinality Matching, which introduce a new technique for obtaining amortized lower bounds.
Modeling and identification of a variable stiffness joint based on twisted string actuators

In this paper, the implementation of a variable stiffness joint actuated by a couple of twisted string actuators in antagonistic configuration is presented. The twisted string actuation system is particularly suitable for very compact and light-weight robotic devices, like artificial limbs and exoskeletons, since it renders a very low apparent inertia at the load side, allowing the implementation of powerful tendon-based driving systems, using small-size DC motors characterized by high speed, low torque and very limited inertia. After the presentation of the basic properties of the twisted string actuation system, the way how they are used for the implementation of a variable stiffness joint is discussed. A simple PID-based motor-side algorithm for controlling simultaneously both the joint stiffness and position is discussed, then the identification of the system parameters is performed on an experimental setup for verifying the proposed model and control approach.
A Model for Incremental Construction of Command Trees

Application histories have been used for a variety of purposes including error recovery, browsing past activities, macro recording and demonstrational interfaces. However, in most systems the history is kept as a simple list of primitive commands, which poorly reflects the user task structure. In this paper we first present Command Trees, a richer representation of command histories that offers better support for undo/redo mechanisms and programming by demonstration. We then introduce a new model to support incremental construction of command trees and an objectoriented application framework that implements this model. An important property of this model is that it is independent of the interaction modality, thus extending its purpose.
Non-Coherent Cooperative Communications Dispensing with Channel Estimation Relying on Erasure Insertion Aided Reed-Solomon Coded SFH M-ary FSK Subjected to Partial-Band Interference and Rayleigh Fading

The rationale of our design is that although much of the literature of cooperative systems assumes perfect coherent detection, the assumption of having any channel estimates at the relays imposes an unreasonable burden on the relay station. Hence, non-coherently detected Reed-Solomon (ReS) coded Slow Frequency Hopping (SFH) assisted M-ary Frequency Shift Keying (FSK) is proposed for cooperative wireless networks, subjected to both partial-band interference and Rayleigh fading. Erasure insertion (EI) assisted ReS decoding based on the joint maximum output-ratio threshold test (MO-RTT) is investigated in order to evaluate the attainable system performance. Compared to the conventional error-correction-only decoder, the EI scheme may achieve an E_b/N_0 gain of approximately 3 dB at the Codeword Error Probability, P_w, of 10^{-4}, when employing the ReS(31,20) code combined with 32-FSK modulation. Additionally, we evaluated the system's performance, when either equal gain combining (EGC) or selection combining (SC) techniques are employed at the destination's receiver. The results demonstrated that in the presence of one and two assisting relays, the EGC scheme achieves gains of 1.5 dB and 1.0 dB at the P_w of 10^{-6}, respectively, compared to the SC arrangement. Furthermore, we demonstrated that for the same coding rate and packet size, the ReS(31,20) code using EI decoding is capable of outperforming convolutional coding, when 32-FSK modulation is considered, whilst LDPC coding had an edge over the above two schemes.
Metrical Structure and Jaw Opening

Acoustic and articulatory measurements were made of utterances containing seven syllables using the vowel /aI/ with varying degrees of prominence, including contrastive emphasis/focus. A trained phonetician judged the amount of prominence for each of the seven syllables. The findings show a significant correlation between amount of jaw opening for each of the target syllables and perceived prominence. There is also similarity between the patterns of perceived prominence for each sentence and the pattern of jaw opening for each utterance; however, more striking is the finding of alternating amounts of jaw opening for each syllable, suggesting that jaw opening may play an important articulatory role in implementing the foot-structure of an utterance.
Self Learning Live Translation System

Overcoming all language ambiguities and achieving machine translation accuracy that is on par with human translation becomes more feasible as more linguistic data is available to developers. This paper outlines such a translation system that is designed to take advantage of massive corpora (prepared by Google and Kaleido K Translation) and is designed on three key fundamentals. Simplicity - to be simple in design to be portable across different languages pairs, Adaptability - to compensate for the evolution of language, and Multiplicity - to determine a final translation from as many candidate translations as possible. Lastly, the translation direction of Korean to English will be used to provide examples and explanations.
N C ] 2 1 N ov 2 01 2 A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced threecompartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential that contributes to the local field potential of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the “open-field” configuration of the dendritic field potential around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach
How do community health committees contribute to capacity building for maternal and child health? A realist evaluation protocol

Introduction The proposed research is part of ongoing operations research within World Vision's Access: Infant and Maternal Health Programme. This study aims to identify key context features and underlying mechanisms through which community health committees build community capacity within the field of maternal and child health. This may help to improve programme implementation by providing contextually informed and explanatory findings for how community health committees work, what works best and for whom do they work for best for. Though frequently used within health programmes, little research is carried out on such committees’ contribution to capacity building—a frequent goal or proposed outcome of these groups. Methods and analysis The scarce information that does exist often fails to explain ‘how, why, and for whom’ these committees work best. Since such groups typically operate within or as components of complex health interventions, they require a systems thinking approach and design, and thus so too does their evaluation. Using a mixed methods realist evaluation with intraprogramme case studies, this protocol details a proposed study on community health committees in rural Tanzania and Uganda to better understand underlying mechanisms through which these groups work (or do not) to build community capacity for maternal and child health. This research protocol follows the realist evaluation methodology of eliciting initial programme theories, to inform the field study design, which are detailed within. Thus far, the methodology of a realist evaluation has been well suited to the study of community health committees within these contexts. Implications for its use within these contexts are discussed within. Ethics and dissemination Institutional Review Boards and the appropriate research clearance bodies within Ireland, Uganda and Tanzania have approved this study. Planned dissemination activities include via academic and programme channels, as well as feedback to the communities in which this work occurs.
Generalized Gaussian Random Fields using hidden selections

We study non-Gaussian random fields constructed by the selection normal distribution, and we term them selection Gaussian random fields. The selection Gaussian random field can capture skewness, multi-modality, and to some extend heavy tails in the marginal distribution. We present a Metropolis-Hastings algorithm for efficient simulation of realizations from the random field, and a numerical algorithm for estimating model parameters by maximum likelihood. The algorithms are demonstrated and evaluated on synthetic cases and on a real seismic data set from the North Sea. In the North Sea data set we are able to reduce the mean square prediction error by 20-40% compared to a Gaussian model, and we obtain more reliable prediction intervals.
Willingness to Pay for Water in Kuwait

Kuwait lies in an arid region with limited options for sustainable water supply. Annual water share is less than 500 cubic meters and it depends exclusively on water desalinization. The consumption rate is among the world’s highest (550-600 liters per day), while the water tariff is the world’s lowest ($0.50/m3) with a 92% subsidy. In response to increasing demand of 3.5 million people, growing at a rate of 2.3% per year, the government adopted a supply-side strategy of expanding production rather than ratcheting up low water rates. Kuwait could very well cut water use and meet its future demand with existing production capacity by improving water use efficiency, by using, for example, a progressive rate structure, instead of a very low flat rate. A 17-question survey was designed to assess willingness to pay (WTP) among Kuwaiti households under different pricing and subsidy schemes, as well as with or without specific information that Kuwait potentially faces serious water shortages. Using a sample of 446, analysis indicates that Kuwaitis are somewhat sensitive to water pricing. They are also sensitive to the presence of a water subsidy for basic household use, and to information about water vulnerability and scarcity. The effect sizes are mostly too small to show significance using standard statistical techniques. However, the non-parametric binomial test shows that the set of correct predictions regarding WTP at different combinations of price, subsidy, and information cannot be random. These findings could be instrumental in devising water conservation policy options to foster sustainable water use in Kuwait.
Vehicle Detection and Scale Adaptive Tracking Using Tail Light Segmentation

—This paper proposes a vehicle detection and tracking system based on forward looking CCD camera. Vehicle tail light location information is employed to generate vehicle candidate, and tail light pair distance is used to adjust vehicle tracking window size. In vehicle detection step, it uses a back propagation neural network (BPNN) which is trained by Gabor feature set. BPNN verifies vehicle candidates and ensures system robustness. In vehicle tracking step, it also uses mean shift tracking algorithm using color feature space. In the experiment, 104 images in total are tested for vehicle detection, 87 vehicle images are detected successfully; a 175 frame video is tested for real-time vehicle tracking. These results show that the proposed system is effective for vehicle detection and tracking in the day time.
MR image reconstruction of a regularly undersampled signal using quadratic phase scrambling

In this paper, we propose a new fast image reconstruction method in which a regularly undersampled signal is used instead of random sampling, as is used in compressed sensing (CS). To diffuse the aliasing artifact caused by under-sampling, we adopt phase-scrambling Fourier transform imaging. The proposed method has an advantage over CS in that the quality of the image does not depend on the selection of the sampling trajectory. Simulation studies and experiments show that the proposed method has almost the same peak signal-to-noise ratio as that of a compressed sensing reconstruction.
Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model

We investigated the notion of “popularity” for machine-generated sentences. We defined a popular sentence as one that contains words that are frequently used, appear in many documents, and contain frequent dependencies. We measured the popularity of sentences based on three components: content morpheme count, document frequency, anddependency relationships. To consider the characteristics of agglutinative language, we used content morpheme frequency instead of term frequency. The key component in our method is that we use the product of content morpheme count and document frequency to measure word popularity, and apply language models based on dependency relationships to consider popularity from the context of words. We verify that our method accurately reflects popularity by using Pearson correlations. Human evaluation shows that our method has a high correlation with human judgments.
Probability Matching as a Computational Strategy Used in Perception

The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer's behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location—“model selection. ” A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of observers in this perceptual task was most consistent with probability matching. While this appears to be a suboptimal strategy and hence a surprising choice for the perceptual system to adopt, we discuss potential advantages of such a strategy for perception.
Incompleteness Errors in Ontology

Ontology evaluation is one of the most important phases of ontology engineering. Researchers have identified different types of errors that should be catered in ontology evaluation process for fulfillment of the semantic Web vision and classified them in error's taxonomy. We have found that some important errors are missing in the error's taxonomy. We have identified and defined two new incompleteness errors i.e. functional property omission (FPO) for single valued property and inverse-functional property omission (IFPO) for a unique valued property. We have demonstrated the importance of such errors by giving different scenarios where appropriate. We have evaluated different ontologies and presented empirical results.
Mayors Climate Protection Agreement in California

Little quantitative research has been devoted to voluntary climate actions at the local level in comparison to those at federal and state levels. It is unclear why some cities act as leaders in the fight against climate change, some act as followers, while others remain laggards. This study empirically tests some hypotheses about local political will to mitigate climate change. Applying a survival analysis to California cities’ adoption of the U.S. Conference of Mayors Climate Protection Agreement, this study examines the association between cities’ adoption of the Mayors Agreement and a broad range of characteristics, such as: local demographics, jurisdiction size, government structure, political preference and environmentalism, local air quality and congestion level, and behavior of neighboring jurisdictions. Results support the importance of income level, political preference and environmentalism of the local communities, as well as a city’s administrative capacity and autonomy. Congestion relief seems to be an important cobenefit motivating cities to reduce greenhouse gas emissions.