Miracle-dz/UniFAR-FTUs huggingface dataset:
SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies

Motivated by evolving business requirements and novel enterprise applications, we propose and implement the Semantic Master Data Management (SMDM), a semantics-level enhancement to the existing MDM solutions. The SMDM system publishes relational-based master data as virtual RDF store, and injects instantaneous reasoning capabilities into semantic queries. Two kinds of ontologies are introduced to the system, the core MDM ontology and the external imported domain ontology. SMDM enables data linking among multi-domains, implicit relationship discovery, and declarative definition and extension of business policies and entities. Based on these functions, modern companies can customize their applications and services on demand within the MDM hub. In the demonstration, we build the system environment based on IBM's MDM solution, and run the use cases on the master data of an insurance company.
Effect of garlic powder on performance and lipid profile of broilers

This experiment was conducted to investigate the effect of feeding garlic powder (GP) on the performance, digestibility, digestive organs, carcass cuts and lipid profile of broilers. A total of 270 day-old Cobb-500 chicks were used in the experiment. Birds were partitioned into three experimental groups of 90 birds in each. Each treatment was composed of 6 replicates with 15 birds in each. The control group was fed with a commercial starter and finisher diet. The second and third groups were supplemented with GP at the rate of 0.2% and 0.4% respectively. In the last week of experiment, 18 birds from each experimental group (3 birds per replicate) were used in a metabolic trial. At the time of termination of the experiment, the same number of birds were killed for digestive organs and carcass components measurements. Blood samples from 3 birds per replicate were collected on 3 rd , 5 th and 6 th week post feeding from wing vein for blood lipid profile determination. Total cholesterol (COL), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL) levels were determined. Results of this study showed that GP had no significant effects on broilers weight gain, feed intake (FI), feed conversion ratio (FCR), carcass cuts, visceral organs. However, GP decreased (P < 0.05) COL, TG, LDL and increased HDL levels compared to control birds. The dry matter (DM), crude protein (CP) and ether extract (EE) digestibility were improved by feeding GP. It can be concluded that GP could provide positive advantages in broilers performance.
Formalizing Opacity and Transparency in Belief

One of the most interesting puzzles in formalizing belief contexts is the fact that many belief reports can be given both an opaque and a transparent reading. A traditional explanation is that the two readings are related to the failure and success of the principle of substitutivity respectively, and this in turn is explained with the de re/de dicto distinction. We propose an alternative analysis, based on the idea that another agent's beliefs can just be quoted (preserving opaqueness) or translated into the reporter's language (allowing transparency). We show that MultiContext systems allow the formalization of these two phenomena at the same time, thanks to their multi-language feature. 1 Beliefs and substitutivity A very important capability is reasoning about our own and other people's beliefs. We have beliefs about our beliefs; we learn from other people's beliefs; we accept (don't accept) other people's beliefs among our beliefs; we share our beliefs with other people; we report other people's beliefs; we infer new information by combining diierent people's beliefs; we make decisions based on conjectures about other people's beliefs; many communication conventions are based on what we believe other people believe; and so on. Despite the pervasiveness of the notion of belief and the apparent easiness for humans to deal with it, representing beliefs and formalizing reasoning with and about beliefs has raised very diicult problems. From a logical point of view, a well-known problem is that a very general and intuitive logical principle, the principle of substitutivity, fails in its unrestricted version 1. Intuitively, the unrestricted principle of substitutivity (UPS) states that whenever two terms (two sentences) have the same meaning 2 , they can be substituted one for the other in any sentence salva veritate. More formally, let be a sentence and ; 0 two terms (sentences). If = 0 (0), then == 0 ], where == 0 ] is the result of replacing some occurrences of with 0. But UPS is not sound (validity preserving) when applied in belief contexts (namely within the scope of an operator of the form X-believes-that : : :], where : : :] stands for any sentence). Consider the following instance of UPS: Mr. A believes that the president of the local football team is a corruptor The president of the local football team is Mr. C Mr. A believes that Mr. C is a corruptor It is easy to see that the …
Heuristic Models of Task Assignment Scheduling in Distributed Systems

When confronted with the need to utilize a remote computer facility or a data base that does not exist in a local computer system, a user looks to distributed processing. Distributed processing not only solves the above problems, it can also increase processing speed by providing facilities for parallel execution. Furthermore, its interconnected set of mini-or microprocessors is flexible, efficient, reliable, modular, and (comparatively) inexpensive. Distributed processing applications can be found in large networks covering a number of computing centers and also in small signal-processing systems. When referring to any of these systems in this article, I call the processing elements simply "processors" and use the term "task" for programs or other kinds of code units submitted to a system. Although there are related areas of research in distributed processing environments, including file allocation and processor scheduling, this article concentrates on the problem of task allocation management. The purpose of task allocation scheduling in a set of interconnected processors is to reduce job turnaround time. This is done by maximizing the utilization of resources while mimimizing any communication between processors. No access conflicts arise from a shared memory (as in a multiple processing system) because every processor is assumed to have its own working area. The benefits of task allocation scheduling make distributed processing desirable, but several problems must be solved before they can be realized. For example, when the number of processors in a system increases beyond a certain level, throughput decreases.1 This "saturation ef-fect" can be simply explained: If there are n processors in the system and if each has a processing speed of k, then one would expect the speed of the system to be n x k. In reality, however, a lower processing speed results, caused by such factors as control overheads, communication between processors, unbalanced loading, queueing delays, and the precedence order of the parts of a task assigned to separate processors. In order to eliminate or minimize saturation, these inhibiting factors must also be eliminated or minimized. First, a fast, dynamic assignment algorithm can be used to attack the control overhead problem. Additionally, load distribution must be provided for a system as a means of balancing and minimizing both interprocessor communications (IPC) and queueing delays. Since, in reality, any system's resources are limited, the effect of limited memory size in each processor on saturation must also be considered, as must response time. Response time …
Improved Accuracy in Severe Storm Forecasting by the Severe Local Storms Unit during the Last 25 Years: Then versus Now

Abstract The purpose of this paper is to review the large strides made in tornado and severe thunderstorm forecasting by the Severe Local Storms Unit (SELS) of the National Severe Storms Forecast Center during the last 25 years or so of its existence. The author compares and illustrates the tools available to the SELS forecasters in the early 1970s versus those of the 1990s. Also discussed is the transition over the years from a largely empirical forecast approach to an approach based strongly on physical reasoning. The evolution of the computer systems employed at SELS and their impact on the forecast operation are traced. With the advent of interactive computer processing capability, SELS forecasters were able to assess the potential for severe convection with much greater precision than ever before. Noteworthy was the improvement brought about by the automation of largely clerical tasks, allowing the forecasters more time to focus on the forecast problem at hand. In addition, the forecast staff was abl.
Does Financial Development 'Lead' Economic Growth? The Case of China

Using a Vector Autoregression (VAR) approach, we examine the impact of financial development on economic growth in China. Innovation accounting (variance decomposition and impulse response function) analysis is applied to examine interrelationships between variables in the VAR system and, therefore, diers from the more usual approach. We find that financial development comes as the second force (after the contribution from labor input) in leading economic growth in China. This study has supported the view in the literature that financial development and economic growth exhibit a two-way causality and hence is against the so-called “finance-led growth” hypothesis. The study of this kind in the case of China is limited; it therefore provides an interesting advance in the literature on the finance-growth nexus. c 2006 Peking University Press
VAR Models in Macroeconomics - New Developments and Applications: Essays in Honor of Christopher A. Sims

Vector autoregressive (VAR) models are among the most widely used econometric tools in the fields of macroeconomics and financial economics. Much of what we know about the response of the economy to macroeconomic shocks and about how various shocks have contributed to the evolution of macroeconomic and financial aggregates is based on VAR models. VAR models also have been used successfully for economic and business forecasting, for modelling risk and volatility, and for the construction of forecast scenarios. Since the introduction of VAR models by C.A. Sims in 1980, the VAR methodology has continuously evolved. Even today important extensions and reinterpretations of the VAR framework are being developed. Examples include VAR models for mixed-frequency data, VAR models as approximations to DSGE models, factor-augmented VAR models, new tools for the identification of structural shocks in VAR models, panel VAR approaches, and time-varying parameter VAR models. This volume collects contributions from some of the leading VAR experts in the world on VAR methods and applications.Each chapter highlights and synthesizes a new development in this literature in a way that is accessible to practitioners, to graduate students, and to readers in other fields.
An Anomaly Detection System Based on Chi-Square Method with Dynamic BIN Algorithm

The statistic researches have been proposed to detect anomaly attacks using chi-square. In these researches, features such as the IP address and the port number are used as the probabilistic variables. The method based on multiple variables has not been proposed to aim to improve the accuracy of anomaly detection. If the number of packets increase, these packets are classified into BINs before the calculation of chi-square method. The classification method depends on the calculation parameters such as the window width and the number of BIN, and the packet distribution of night and day time. In addition, the classification method should be changed based on these parameters. In this paper, we propose the dynamic BIN method to classify the incoming packets automatically. We also propose the CSDM (Chi-square-based Space Division Method) to detect anomaly attacks using the dynamic BIN methods with multiple probabilistic variables. As the results of experiments using the source IP address, the destination port number, and the interval time deviation of arriving packets as the probabilistic variables, the proposed dynamic BIN realized the equal classification, which does not depends on the features of packets and the number of BIN. In addition, the dynamic BIN mechanism and CSDM method using two probabilistic variables could improve F-measure compared to the conventional method.
Personal Income Tax Progressivity and Output Volatility: Evidence from OECD Countries

This paper investigates empirically the effect of personal income tax progressivity on output volatility using macro data from a sample of OECD countries over the period 19822009. Our measure of progressivity is based on the difference between the marginal and the average personal income tax rate for the average production worker. We find supportive empirical evidence for the hypothesis that higher personal income tax progressivity leads to lower output volatility. This effect comes in addition to the stabilizing impact of government size and it is equally important in economic terms. All other factors constant, countries with more progressive personal income tax systems seem to benefit from stronger automatic stabilizers.
Provisioning Authentication , Authorization , and Administration Services in Cloud and Enterprise Environments

Authentication, authorization, and administration are critical components in secure internet computing. This paper presents a unified architecture for provisioning these services in cloud and enterprise environments. The architecture aims at providing robustness, performance, scalability, high availability, and low total cost of ownership (TCO) for deployments, while preserving the compatibility with existing IT infrastructure. The design scales vertically and horizontally by distributing workload among multiple servers. The flexible cache configuration minimizes network access traffic, delivering the high performance demanded by peak business workloads. The architecture offers high availability via replication services. The design is the result of the cumulative efforts of the open source community in the past many years, involving technologies such as Name Service Switch (NSS), Pluggable Authentication Modules (PAM), Lightweight Directory Access Protocol (LDAP), LDAP Content Synchronization, Secure Socket Layer/Transport Layer Security (SSL/TLS), OpenLDAP, Proxy Cache, nss-pam-ldapd, and so on.
A 2.28 mW 80.8 GHz CMOS divide-by-4 DILFD with 18.24% locking range using tunable LC source-degeneration

A 80.8-GHz CMOS divide-by-4 direct injection-locked frequency divider (DILFD4) using tunable LC source-degeneration (TLCSD) for operation frequency and locking-range enhancement is demonstrated. The TLCSD is made by adding two tunable LC tanks, where varactors are used as the required capacitors, to the source terminals of the cross-coupled transistor pair of the DILFD4. Compared with the traditional cross-coupled transistor pair, the proposed one significantly decreases the equivalent parallel capacitance (Ceq). This in turn results in the increase of both the operation frequency and locking-range of the DILFD4. The DILFD4 dissipated 2.28 mW from a 1.2 V power supply, and achieved a total locking range of 18.24% (i.e. 13.5 GHz, from 67.28 GHz to 80.78 GHz). Furthermore, the DILFD4 achieved excellent output phase-noise of −137.14. dBc/Hz at 1 MHz offset when the input signal was locked at 68.116 GHz. The chip area was only 1.025×0.765 mm excluding the test pads.
Cartesian impedance control for dexterous manipulation

In this work, a cartesian impedance controller purposely designed for dexterous manipulation is described. Based on the main features of the DLR Hand II, concerning kinematic structure and sensory equipment of fingers, this control strategy allows to overcome the main problems encountered in fine manipulation, namely: effects of the friction (and unmodeled dynamics) on robot performances and occurrence of singularity conditions. The achieved control scheme bas been experimentally validated by testing it on a finger of the DLR Hand.
Call admission control using differentiated outage probabilities in multimedia DS-CDMA networks with imperfect power control

A key problem under imperfect power control in multimedia DS-CDMA networks is how to guarantee the differentiated outage probabilities of different traffic classes resulted by the uncertainty of received powers. In addition, in order to utilize the scarce wireless resource efficiently, as many users as possible should be admitted into the network while providing guaranteed quality-of-service support for them. In this work, a call admission control scheme, differentiated outage probabilities CAC or DOP-CAC, is proposed to achieve the above goals for imperfectly power controlled multimedia CDMA networks. Two important features of CDMA system are considered in our scheme: one is the power multiplexing among bursty traffic and the other is the power allocation scheme employed at the physical layer. The validation and efficiency of DOP-CAC are verified by numerical examples.
Ro 20-1724 Ameliorates Learning Deficit and Long-Term Memory Impairment Secondary to Repeated Ketamine Anesthesia in Young Rats

To investigate effects and possible mechanism of Ro 20-1724, a PDE4 inhibitor, on long-time learning and memory ability following repeated ketamine exposure in immature rats. Methods: Sixty 21-day-old SD rats were randomly divided into five groups (n = 12): C: Normal control group, S: Saline control group, K: Ketamine, K + Ro: Ketamine + Ro 20-1724, K + E: Ketamine + ethanol vehicle. Ro 20-1724 (0.5 mg·kg −1 ) or its vehicle (ethanol) was administered intraperitoneally 30 minutes after ketamine anesthesia (70 mg·kg −1 ), daily for seven days. Nine weeks after birth, the Morris water maze was used to test the ability of learning and spatial localization memory on the rats. Following behavioral testing, animals’ hippocampi were removed for Western blot and electron microscopic examination. Results: In the Morris water maze test, compared with controls, the escape latency in groups exposed to ketamine or ketamine plus the ethanol vehicle were significantly prolonged (P 0.05). Electron microscopy demonstrated degenerative changes in hippocampal neurons of animals repetitively exposed to 70 mg·kg −1 Ketamine, which was ameliorated by Ro 20-1724 (0.5 mg·kg −1 ). Conclusion: The PDE-4 inhibitor Ro 20-1724 (0.5 −1 ) reverses cognitive deficits associated with repeated ketamine exposure in young rats, most likely via increased cAMP/CREB signaling in the hippocampus and preservation of hippocampal neuronal ultrastructure.
A hybrid Support Vector Machine and autoregressive model for detecting gait disorders in the elderly

The consequence of tripping and falling in the elderly population is serious because of the life threatening fractures which occur and the high medical costs incurred. Recently, the minimum toe clearance (MTC) has been employed in gait analysis as a sensitive gait variable for early detection of elderly people at risk of falling. In previous work, we successfully applied statistical and wavelet analysis methods with Support Vector Machines (SVM) to model the risk of tripping in the elderly. In this work, we propose to model the MTC time series as a wide based stationary random signal using the autoregressive (AR) process. Initially, it was found that a fourth order AR model constructed from 512 MTC samples per subject on 23 subjects completely modelled the balance impaired gait (pathological) from normal gait. However, when the number of MTC samples were reduced to 32, the two groups became inseparable. We then proposed a hybrid system consisting of a SVM classifier with AR model coefficients as input features to separate the two classes. It was found that SVMs with linear and Gaussian kernels produced 100% leave one out accuracies without the need for prior feature selection algorithms. In contrast, SVM models built previously from the best set of wavelet features produced only 86.95% leave one out accuracies. These results suggest that pathological gait is best modelled by the AR process if sufficient MTC data is available. In the case of shorter MTC data, the AR model still provides powerful and robust discriminative features which can be used by the SVM to detect elderly people at risk of falling.
Improved Curvature Estimation for Watershed Segmentation of 3-Dimensional Meshes

Segmentation of a 3-dimensional (3D) polygonal mesh is a method of breaking the mesh down into \meaningful" connected subsets of vertices called regions. The method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem. The watershed algorithm is based on a scalar value (curvature) assigned to every mesh vertex which purports to capture some essence of the local mesh shape. Accuracy of the curvature estimate at mesh vertices is critical to the quality of the resulting segmentation. The basis of this paper is one such watershed based 3D mesh segmentation scheme, described in [17], wherein two discrete curvature estimation schemes were used, i.e. the curvature was extracted directly from the mesh geometry. This paper considerably improves on the quality of segmentation in the original paper primarily by applying more accurate and robust curvature estimation techniques. The results are illustrated on data sets which are inherently noisy, such as the ones obtained from laser digitizers.
On Using SVM and Kolmogorov Complexity for Spam Filtering

As a side effect of e-marketing strategy the number of spam e-mails is rocketing, the time and cost needed to deal with spam as well. Spam filtering is one of the most difficult tasks among diverse kinds of text categorization, sad consequence of spammers dynamic efforts to escape filtering. In this paper, we investigate the use of Kolmogorov complexity theory as a backbone for spam filtering, avoiding the burden of text analysis, keywords and blacklists update. Exploiting the fact that we can estimate a message information content through compression techniques, we represent an e-mail as a multidimensional real vector and then we implement a support vector machine classifier to classify new incoming e-mails. The first results we get exhibit interesting accuracy rates and emphasize the relevance of our idea.
Design and Control of a supercapacitor storage system for traction applications

The storage system in this paper is made of supercapacitors. The main goal is to ensure an efficient energy management in a series hybrid vehicle, even if braking resistors are still needed. Design considerations are discussed. In particular the influence of the inductor resistance on the system stability is described. A maximum control structure is then deduced from the energetic macroscopic representation of the storage system. Comparisons between experimentation and simulation are presented in order to highlight the influence of the inductor resistor. Experiments are then carried out on a normal operating cycle.