Miracle-dz/UniFAR-FTUs huggingface dataset:
An ontology based-approach for semantic search in portals

Portals have become very popular on the Internet. They allow users to access many online transactions and customized information. More recently, semantic portals emerged as a natural extension of traditional portals and they have been attracting researchers and enterprise attention as a new way to provide information. This paper covers these issues and shows how ontologies can improve results relevance in one of the most important component of a portal: the search mechanism. It describes a search tool developed in the context of a semantic portal named PASS (portal with access to semantic search). this tool uses components of domain ontologies stored in the portal to expand terms during the search process.
Modelo para la evaluación de ontologías. Aplicación en Onto-Satcol

This paper analyzes the conceptual and theoretical framework for the evaluation of ontologies, in order to understand the procedures used in the evaluation of these systems and to establish new guidelines for evaluating the system employed by the ontological program, SATCOL, that specializes in Port and Coastal Engineering. This paper describes the characteristics of the Onto-SATCOL ontology and evaluates it by using several indicators (lexical, information retrieval, and syntactic structure). Through an experiment conducted by six experts aided by the tool, Protex, semantic and structural inconsistencies are identified, as are errors in the ontology’s organization of knowledge.
Modeling and Technique to Improve PSRR and PS-IMD in Analog PWM Class-D Amplifiers

Although power-supply noise, qualified by power- supply rejection ratio (PSRR), has been recognized as a potential drawback of Class-D amplifiers (CDAs) compared to linear amplifiers, the mechanisms of PSRR for CDAs are not well established. It is also not well recognized that the power-supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD), and that the PS-IMD can be significantly larger than the output distortion component at supply noise frequency. Furthermore, techniques to improve PSRR and PS-IMD are largely unreported in literature. In this brief, by means of a linear model, the PSRR and PS-IMD of single-feedback and double-feedback CDAs are analyzed and analytical expressions derived. A simple method is proposed to improve PSRR and PS-IMD with very low hardware overheads, and the improvement is ~ 26 dB. Analytical expressions for PSRR and PS-IMD of the improved design are derived and the pertinent parameters thereof are investigated. The model and analyses provide practical insight to the mechanisms of PSRR and PS-IMD, and how various parameters may be varied to meet a given specification.
Congestion-Aware Embedding of Heterogeneous Bandwidth Virtual Data Centers With Hose Model Abstraction

Predictable network performance is critical for cloud applications and can be achieved by providing tenants a dedicated virtual data center (VDC) with bandwidth guarantee. Recently, the extended Hose model was applied to the VDC abstraction to characterize the tradeoff between cost and network performance. The acceptability determination problem of a VDC with heterogeneous bandwidth demand was proved to be NP-complete, even in the simple tree topology. In this paper, we investigate the embedding problem for heterogeneous bandwidth VDC in substrate networks of general topology. The embedding problem involves two coupled sub-problems: virtual machine (VM) placement and multipath route assignment. First, we formulate the route assignment problem with linear programming to minimize the maximum link utilization, and provide $K$ -widest path load-balanced routing with controllable splitting paths. Next, we propose a polynomial-time heuristic algorithm, referred to as the perturbation algorithm, for the VM placement. The perturbation algorithm is congestion-aware as it detects the bandwidth bottlenecks in the placement process and then selectively relocates some assigned VMs to eliminate congestion. Simulation results show that our algorithm performs better in comparison with the existing well-known algorithms: first-fit, next-fit, and greedy, and very close to the exponential-time complexity backtracking algorithm in typical data center network architectures. For the tree substrate network, the perturbation algorithm performs better than the allocation-range algorithm. For the homogeneous bandwidth VDC requests, the perturbation algorithm produces a higher success rate than the recently proposed HVC-ACE algorithm. Therefore, it provides a compromised solution between time complexity and network performance.
Electron Tomography of Cryofixed, Isometrically Contracting Insect Flight Muscle Reveals Novel Actin-Myosin Interactions

Background Isometric muscle contraction, where force is generated without muscle shortening, is a molecular traffic jam in which the number of actin-attached motors is maximized and all states of motor action are trapped with consequently high heterogeneity. This heterogeneity is a major limitation to deciphering myosin conformational changes in situ. Methodology We used multivariate data analysis to group repeat segments in electron tomograms of isometrically contracting insect flight muscle, mechanically monitored, rapidly frozen, freeze substituted, and thin sectioned. Improved resolution reveals the helical arrangement of F-actin subunits in the thin filament enabling an atomic model to be built into the thin filament density independent of the myosin. Actin-myosin attachments can now be assigned as weak or strong by their motor domain orientation relative to actin. Myosin attachments were quantified everywhere along the thin filament including troponin. Strong binding myosin attachments are found on only four F-actin subunits, the “target zone”, situated exactly midway between successive troponin complexes. They show an axial lever arm range of 77°/12.9 nm. The lever arm azimuthal range of strong binding attachments has a highly skewed, 127° range compared with X-ray crystallographic structures. Two types of weak actin attachments are described. One type, found exclusively in the target zone, appears to represent pre-working-stroke intermediates. The other, which contacts tropomyosin rather than actin, is positioned M-ward of the target zone, i.e. the position toward which thin filaments slide during shortening. Conclusion We present a model for the weak to strong transition in the myosin ATPase cycle that incorporates azimuthal movements of the motor domain on actin. Stress/strain in the S2 domain may explain azimuthal lever arm changes in the strong binding attachments. The results support previous conclusions that the weak attachments preceding force generation are very different from strong binding attachments.
Towards an Interaction Language for Force-enabled Touchpads in Cars

Force-based touch input is a novel input method for automotive interfaces. It constitutes an efficient and effective way of in-car interaction with an added degree of freedom. Research on force-based in-car interfaces is still scarce. Recommendations such as gesture sets or interaction languages that could guide future implementations have not yet been proposed. As a first step towards closing this gap, we contribute an interaction language for force-enabled input on touchpads in the center console. The language was elicited in a controlled experiment and maps core interactions to common in-car commands. Results from the experiment also shed light onto mental models of in-car force-enabled touchpad input. The elicited interaction language provides guidelines for future force-based touchpad interfaces in the central console.
Video Character Recognition through Hierarchical Classification

We present a new video character recognition method based on hierarchical classification. In the first step, we propose a method for character segmentation of the text line detected by the text detection method. The segmentation algorithm uses dynamic programming to find least-cost paths in the gray domain to identify the spaces between characters. For the segmented characters, we get a Canny edge image as input for the character recognition step. We introduce hierarchical classification based on voting criteria with structural features to classify 62 character classes into different smaller classes. We divide the perimeter of a character into 8 segments according to 8 directions at the centroid. Then the shape of each segment is studied to recognize the characters based on distances between the centroid and end points, and distances between the midpoint and end points. Our experiments on 1462 characters of upper case, lower case and numerals shows that 10% samples per class for training is enough to obtain 94.5% recognition accuracy. The dataset is chosen from TRECVID database of 2005 and 2006.
The importance of maintaining behavioural link between parents and offspring

In a study of evolutionary artificial neural networks (Yao and Liu, 1996), it has been argued that a partial training process after an architectural mutation plays an important role in maintaining the behavioural link between parents and their offspring and thus is beneficial to the simulated evolution. This paper investigates the issue further through a number of experiments. The experimental results show that a closer behavioural link between parents and their offspring due to the partial training process does lead to better performance, i.e., evolved ANNs generalise better. The results also illustrate that given a fixed amount of time there is an optimal balance of time between evolution and training (learning).
Dynamic time-domain duplexing for self-backhauled millimeter wave cellular networks

Millimeter wave (mmW) bands between 30 and 300 GHz have attracted considerable attention for nextgeneration cellular networks due to vast quantities of availavery high-dimensional antenna arraysble spectrum and the possibility of very high-dimensional antenna arrays. However, a key issue in these systems is range: mmW signals are extremely vulnerable to shadowing and poor high-frequency propagation. Multi-hop relaying is therefore a natural technology for such systems to improve cell range and cell edge rates without the addition of wired access points. This paper studies the problem of scheduling for a simple infrastructure cellular relay system where communication between wired base stations and User Equipment follow a hierarchical tree structure through fixed relay nodes. Such a systems builds naturally on existing cellular mmW backhaul by adding mmW in the access links. A key feature of the proposed system is that TDD duplexing selections can be made on a link-by-link basis due to directional isolation from other links. We devise an efficient, greedy algorithm for centralized scheduling that maximizes network utility by jointly optimizing the duplexing schedule and resources allocation for dense, relay-enhanced OFDMA/TDD mmW networks. The proposed algorithm can dynamically adapt to loading, channel conditions and traffic demands. Significant throughput gains and improved resource utilization offered by our algorithm over the static, globally-synchronized TDD patterns are demonstrated through simulations based on empirically-derived channel models at 28 GHz.
An application study of manifold learning-ranking techniques in face recognition

Locally linear embedding (LLE), isometric mapping (Isomap) are two relatively new nonlinear dimensionality reduction algorithms also used in face recognition applications. Their main aim is to create a low-dimensional embeddings of the original high-dimensional data, laying the face data points on a 'face manifold'. In this work in order to test their performance we applied LLE and Isomap in two face databases together with principal component analysis (PCA), their linear counterpart, varying as parameters the (i) number embedding dimensions and (ii) the number of neighbours. Furthermore, at the final stage we used a data ranking algorithm, which ranks the data with respect to the intrinsic manifold structure and its geometric properties. Experimental results indicate the superiority of the data ranking algorithm on face manifolds against the classical Euclidean distance measure.
Secure and Private Management of Healthcare Databases for Data Mining

There has been a tremendous growth in health data collection since the development of Electronic Medical Record (EMR) systems. Such collected data is further shared and analyzed for diverse purposes. Despite many benefits, data collection and sharing have become a big concern as it threatens individual privacy. In this paper, we propose a secure and private data management framework that addresses both the security and privacy issues in the management of medical data in outsourced databases. The proposed framework ensures the security of data by using semantically-secure encryption schemes to keep data encrypted in outsourced databases. The framework also provides a differentially-private query interface that can support a number of SQL queries and complex data mining tasks. We experimentally evaluate the performance of the proposed framework, and the results show that the proposed framework is practical and has low overhead.
Mycobacterium tuberculosis Chaperonin 10 Stimulates Bone Resorption: A Potential Contributory Factor in Pott's Disease

Pott's disease (spinal tuberculosis), a condition characterized by massive resorption of the spinal vertebrae, is one of the most striking pathologies resulting from local infection with Mycobacterium tuberculosis (Mt; Boachie-Adjei, O., and R.G. Squillante. 1996. Orthop. Clin. North Am. 27:95–103). The pathogenesis of Pott's disease is not established. Here we report for the first time that a protein, identified by a monoclonal antibody to be the Mt heat shock protein (Baird, P.N., L.M. Hall, and A.R.M. Coates. 1989. J. Gen. Microbiol. 135:931–939) chaperonin (cpn) 10, is responsible for the osteolytic activity of this bacterium. Recombinant Mt cpn10 is a potent stimulator of bone resorption in bone explant cultures and induces osteoclast recruitment, while inhibiting the proliferation of an osteoblast bone–forming cell line. Furthermore, we have found that synthetic peptides corresponding to sequences within the flexible loop and sequence 65–70 of Mt cpn10 may comprise a single conformational unit which encompasses its potent bone-resorbing activity. Our findings suggest that Mt cpn10 may be a valuable pharmacological target for the clinical therapy of vertebral tuberculosis and possibly other bone diseases.
Digital Economy and Rural Household Resilience: Evidence from China

Strengthening the ability of peasant households to resist the impact of risks and shaping their "small but strong" resilience is of great practical significance for maintaining and consolidating the achievements of poverty alleviation, narrowing the gap between urban and rural areas, and promoting the realization of common prosperity. Taking "how digital economy changes the rural household resilience " and the micro data of China Labor-force Dynamics Survey (CLDS), this paper systematically measures the rural household resilience index for the first time, analyzes the impact of digital economy on the resilience of rural household, and dissects the group differences and mechanism of action. The study found that: (1) from 2012 to 2018, the Chinese rural household resilience index had significant differences in time and space, and village market, gentry assistance, economic organization, income from collective operation were the most important indicators affecting the rural household resilience index. (2) The improvement of the digital economy index has to some extent suppressed the improvement of the rural household resilience index, and this conclusion is still true after a series of robustness tests. (3) Heterogeneity analysis found that depending on family size and housing property rights, the impact of the digital economy on the resilience of rural households will be divided. (4) Mechanism analysis showed that the digital economy further affects the resilience of rural households through the employment comprehensive effect, income structure effect and member security effect. This paper indicates that the rational use of digital economy, improving the level of agricultural digitalization, enhancing the resilience of rural households, and vigilance against the weakening of resilience by the externalities of digital economy are the realistic ways of forging "resilient small farmers" and realizing common prosperity.
An XVCL-based Approach to Software Product Line Development

In recent years, software product line approach has emerged as a promising way to improve software productivity and quality. How to effectively handle variants (including functional variants, variant design decisions, implementation-level variants) has been a major challenge in product line development. We apply XVCL (XMLbased Variant Configuration Language), a variability mechanism based on frame technology [1], to product line development. In XVCL, x-frames represent domain knowledge in the forms of product line assets. Specific systems, members of a product line, can be constructed by reusing the x-frames. In this paper, we describe an XVCLbased approach to software product line development, using examples from our product line project on Computer Aided Dispatch domain.
Generating Disambiguating Paraphrases for Structurally Ambiguous Sentences

We present a method that, for the ﬁrst time in a broad coverage setting, uses nat-ural language generation to automatically construct disambiguating paraphrases for structurally ambiguous sentences. By simply asking naive annotators to clarify which paraphrase is closer in meaning to the original sentence, the resulting paraphrases can potentially enable meaning judgments for parser training and domain adaptation to be crowd-sourced on a massive scale. To validate the method, we demonstrate that meaning judgments crowd-sourced in this way via Amazon Mechanical Turk have reasonably high accuracy—e.g. 80%, given a strong majority choice between two paraphrases—with accuracy increasing as the level of agreement among annotators increases. We also show that even with just the limited validation data gathered to date, the crowd-sourced judgments make it possible to retrain a parser to achieve signiﬁcantly higher accuracy in a novel domain. We conclude with lessons learned for gathering such judgments on a much larger scale.
Presynaptic Kainate Receptor Activation Is a Novel Mechanism for Target Cell-Specific Short-Term Facilitation at Schaffer Collateral Synapses

Target cell-specific differences in short-term plasticity have been attributed to differences in the initial release probability of synapses. Using GIN (GFP-expressing inhibitory neurons) transgenic mice that express enhanced green fluorescent protein (EGFP) in a subset of interneurons containing somatostatin, we show that Schaffer collateral synapses onto the EGFP-expressing somatostatin interneurons in CA1 have very large short-term facilitation, even larger facilitation than onto pyramidal cells, in contrast to the majority of interneurons that have little or no facilitation. Using a combination of electrophysiological recordings and mathematical modeling, we show that the large short-term facilitation is caused both by a very low initial release probability and by synaptic activation of presynaptic kainate receptors that increase release probability on subsequent stimuli. Thus, we have discovered a novel mechanism for target cell-specific short-term plasticity at Schaffer collateral synapses in which the activation of presynaptic kainate receptors by synaptically released glutamate contributes to large short-term facilitation, enabling selective enhancement of the inputs to a subset of interneurons.
Improving physical layer security in underlay D2D communication via Stackelberg game based power control

Device-to-Device (D2D) communication has advantages of improving the spectral resource efficiency. But unfortunately, it brings about interference and potential secrecy hazard. In this paper, a Stackelberg game based power control algorithm is provided to enhance the physical layer security underlaying D2D communication by utilizing the interference. Then, a mathematical model based on Stackelberg game is set up, in which we regard the secrecy energy efficiency as the utility function. After that, the existence and uniqueness of the Stackelberg Equilibrium is proved. In addition, a power control algorithm is offered, which can iteratively converge to the Stackelberg Equilibrium. Finally the simulation results show that by applying the provided algorithm, both the cellular user and the D2D pair have the secrecy energy efficiency improved.
Frame alignment processes, micromobilization, and movement participation.

This paper attempts to further theoretical and empirical understanding of adherent and constituent mobilization by proposing and analyzing frame alignment as a conceptual bridge linking social psychological and resource mobilization views on movement participation. Extension of Goffinan's (1974) frame analytic perspective provides the conceptualltheoretical framework; field research on two religious movements, the peace movement, and several neighborhood movements provide the primary empirical base. Four frame alignment processes are identified and elaborated: frame bridging, frame amplification, frame extension, and frame transformation. The basic underlying premise is that frame alignment, of one variety or another, is a necessary condition for participation, whatever its nature or intensity, and that it is typically an interactional and ongoing accomplishment. The paper concludes with an elaboration of several sets of theoretical and research implications.
Multiscale Asymmetric Orthogonal Wavelet Kernel for Linear Programming Support Vector Learning and Nonlinear Dynamic Systems Identification

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.
Communication Patterns that Define the Role of the University-Level Tutor

This study considers how communication patterns shape the university tutor role. A combination of ethnographic observations and interviews were used to determine the communication behaviors of university tutors and tutees, which help to define what is, otherwise, an ambiguous role. Specifically, the communicative behaviors engaged in by tutors are relational questioning, content questioning, content explaining, and relational disclosure. Drawing on a relational perspective of communication, the uniqueness of the tutoring role, in comparison to other personal and educational roles, is discussed.