Miracle-dz/UniFAR-FTUs huggingface dataset:
Shallow Semantic Parsing of Chinese

In this paper we address the question of assigning semantic roles to sentences in Chinese. We show that good semantic parsing results for Chinese can be achieved with a small 1100-sentence training set. In order to extract features from Chinese, we describe porting the Collins parser to Chinese, resulting in the best performance currently reported on Chinese syntactic parsing; we include our headrules in the appendix. Finally, we compare English and Chinese semantic-parsing performance. While slight differences in argument labeling make a perfect comparison impossible, our results nonetheless suggest significantly better performance for Chinese. We show that much of this difference is due to grammatical differences between English and Chinese, such as the prevalence of passive in English, and the strict word order constraints on adjuncts in Chinese.
Chaos suppression of generalized Lorenz system: Adaptive fuzzy sliding mode control approach

This study presents the chaos suppression for generalized Lorenz system via an adaptive fuzzy sliding mode controller (AFSMC). The proposed scheme can asymptotically drive the state trajectory of the generalized Lorenz system with unknown parameter to a desired switching surface and ensure the stability of the closed-loop system. In addition, the control signal is chattering free by the AFSMC reaching law that is inferred a set of fuzzy logic rules with output of sliding mode controller (SMC). A numerical simulation is included to illustrate the feasibility and effectiveness of the proposed scheme.
Isolated speech recognition using Fuzzy C Means technique

Automatic speech recognition is one of the challenging area in the field of speech signal processing. Automatic speech recognition technology converts speech signal into text. This paper presents the implementation of isolated kannada word recognizer using Vector Quantization (VQ) and Fuzzy-C Means (FCM) techniques. The paper compares and contrasts the recognition accuracies of FCM and k-means techniques. It also highlights the importance of the fuzziness parameter `m' in the FCM technique for a range of `m' (fuzzifier) values between 1.5 to 2. The simulation analysis shows the performance of the FCM is dependent on Fuzzy parameter (`m'). It is observed that the recognition accuracies are better for FCM than VQ for clean and noisy speech signals. The results are tested and evaluated for both speaker dependent and independent speech signals. It is clear from the simulation that the recognition accuracies are better for the values of `m' between 1.8 to 2.0. It also highlights the mathematical reason for the better accuracy of speech recognition process. Recognition accuracy obtained is 90 to 95% for clean and speaker dependent signals. 20 to 25% of recognition accuracy is obtained for noisy and speaker independent signals.
An Examination of Non-Muslim College Students’ Attitudes Toward Muslims

ABSTRACT The current study enhances the understanding of campus climate for religious and worldview diversity by examining how non-Muslim college students perceive Muslims and Islam and what predispositions, environmental factors, and experiences predict their attitudes toward Muslims. Results indicate that informal engagement with diverse peers, interfaith engagement, and space for spiritual expression on campus are positively related to appreciative attitudes toward Muslims. With respect to specific worldviews, Unitarian Universalist and agnostic students tended to have more positive attitudes toward Muslims (relative to peers of other religions), and Eastern Orthodox and evangelical Christian students tended to have less appreciative attitudes.
Burst Imaging—Can It Ever Be Useful in the Clinic?

Burst is an ultra-rapid imaging sequence that works by excitation of mag- netization using a series of regularly spaced, low flip-angle pulses, applied at the same time as a gradient in the read direction. In the original concept, this gradient is constant, and multiple echoes are refocused under a constant-read gradient. This article brings together the previous literature on the subject, placing the various developments within a common framework. The potential of the technique is discussed, with the aid of a comparison of the signal-to-noise ratio for the various Burst sequences previously proposed and a detailed case study of one of the more promising variants. © 2005 Wiley Periodicals, Inc. Concepts Magn Reson Part A 26A: 11-34, 2005
Residual Enhanced Visual Vectors for on-device image matching

Most mobile visual search (MVS) systems query a large database stored on a server. This paper presents a new architecture for searching a large database directly on a mobile device, which has numerous benefits for network-independent, low-latency, and privacy-protected image retrieval. A key challenge for on-device MVS is storing a memory-intensive database in the limited RAM of the mobile device. We design and implement a new compact global image signature called the Residual Enhanced Visual Vector (REVV) that is optimized for the local features typically used in MVS. REVV outperforms existing compact database representations in the MVS setting and attains similar retrieval accuracy in large-scale retrieval tests as a Vocabulary Tree that uses 26× more memory. The compactness of REVV consequently enables many database images to be queried on a mobile device.
Grasp motion learning with Gaussian Process Dynamic Models

We propose an online method for grasp motion learning using the Gaussian Process Dynamic Model (GPDM). Given human grasp motion data (in the form of position and orientation trajectories of the fingertips and palm), from approach to final grasp pose, a GPDM is trained with this data, and then used to generate new grasping motions even when the path to the object is partially blocked by obstacles. Variance tubes are applied to ensure that collision avoidance and other physical constraints are satisfied. Case studies reporting on the efficiency and naturalness of our grasp motions are presented.
A Privacy-Aware Protocol for Sociometric Questionnaires

In the paper we design a protocol for sociometric questionnaires, which serves the privacy of responders. We propose a representation of a sociogram by a weighted digraph and interpret individual and collective phenomena of sociometry in terms of graph theory. We discuss security requirements for a privacy-aware protocol for sociometric questionnaires. In the scheme we use additively homomorphic public key cryptosystem, which allows single multiplication. We present the threshold version of the public key system and define individual phases of the scheme. The proposed protocol ensures desired security requirements and can compute sociometric indices without revealing private information about choices of responders.
Construction method of dynamic microgrid by using optimized grouping method

Dynamic microgrid was proposed in our laboratory in order to utilize distributed power sources effectively. It means that structure of microgrid is varying according to demand fluctuation. To realize the dynamic microgrid, it is indispensable to obtain both energy demand and supply and keeps balance them by using ICT based sensor network. KNIVES (Keio University Network oriented Intelligent and Versatile Energy saving System) is an ICT based supply and demand side management system that supports distributed and cooperative power control. In this study, KNIVES is introduced to actual consumers such as care facility, school and store as a platform of dynamic microgrid. Topology of the microgrid is changeable dynamically, and it is determined to conduct optimized calculation by referring energy consumption data and targeted value set measured from individual groups. This paper describes construction method of the dynamic microgrid and evaluation of feasibility and availability from a standpoint of calculation cost.
A New Automatic Recognition System of Gender, Age and Ethnicity

In this paper a novel approach for recognizing the gender, ethnicity and age with facial images is proposed. The approach is a novel combination of Gabor filter, Adaboost learning and SVM classifier. Gabor filter banks and Adaboost learning are combined to extract key facial features of each pattern. Then use the Gabor+Adaboost features based SVM classifier to recognize the face image of each pattern. The experiment results in the system based on this approach are reported to show a good performance
How to Specify and Verify the Long-Run Average Behavior of Probabilistic Systems

Long-run average properties of probabilistic systems refer to the average behaviour of the system, measured over a period of time whose length diverges to infinity. These properties include many relevant performance and reliability indices, such as system throughput, average response time, and mean time between failures. In this paper, we argue that current formal specification methods cannot be used to specify long-run average properties of probabilistic systems. To enable the specification of these properties, we propose an approach based on the concept of experiments. Experiments are labeled graphs that can be used to describe behaviour patterns of interest, such as the request for a resource followed by either a grant or a rejection. Experiments are meant to be performed infinitely often. and it is possible to specify their long-run average outcome or duration. We propose simple extensions of temporal logics based on experiments, and we present model-checking algorithms for the verification of properties of finite-state timed probabilistic systems in which both probabilistic and nondeterministic choice are present. The consideration, of system models that include nondeterminism enables the performance and reliability analysis of partially specified systems, such as systems in their early design stages.
Spatial sensitivity of macaque inferior temporal neurons

Recent findings in dorsal visual stream areas and computational work raise the question whether neurons at the end station of the ventral visual stream can code for stimulus position. The authors provide the first detailed, quantitative data on the spatial sensitivity of neurons in the anterior part of the inferior temporal cortex (area TE) in awake, fixating monkeys. They observed a large variation in receptive field (RF) size (ranging from 2.8° to 26°). TE neurons differed in their optimal position, with a bias toward the foveal position. Moreover, the RF profiles of most TE neurons could be fitted well with a two‐dimensional Gaussian function. Most neurons had only one region of high sensitivity and showed a smooth decline in sensitivity toward more distal positions. In addition, the authors investigated some of the possible determinants of such spatial sensitivity. First, testing with low‐pass filtered versions of the stimuli revealed that the general preference for the foveal position and the size of the RFs was not due simply to TE neurons receiving input with a lower spatial resolution at more eccentric positions. The foveal position was still preferred after intense low‐pass filtering. Second, although an increase in stimulus size consistently broadened spatial sensitivity profiles, it did not change the qualitative features of these profiles. Moreover, size selectivity of TE neurons was generally position invariant. Overall, the results suggest that TE neurons can code for the position of stimuli in the central region of the visual field. J. Comp. Neurol. 426:505–518, 2000. © 2000 Wiley‐Liss, Inc.
The Effect of Test Suite Type on Regression Test Selection

Regression test selection (RTS) techniques reduce the cost of regression testing by running only test cases related to code modifications. RTS techniques have been extensively researched, and the effects of several context factors on techniques have been empirically studied, but no prior work has explored the effects that might arise due to differences in types of test suites. We believe such differences may matter, and thus, we designed an empirical study to investigate them. Specifically, we consider two types of test suites obtained with automated test case generation techniques-feedback-directed random techniques and search-based techniques-along with manually written test suites. We assess the effects of these test suite types on two RTS techniques: a "fine-grained" technique that selects test cases based on dependencies tracked at the method level and a "coarse-grained" technique that selects test cases based on dependencies tracked at the file level. We performed our study on eight open-source projects across 800 commits. Our results show that on average, fine-grained RTS was more effective for test suites created by search-based test case generation techniques whereas coarse-grained RTS was more effective for test suites created by feedback-directed random techniques, and that commits affect RTS techniques differently for different types of test suites.
On Pseudo Random Bit Generators via Two-Dimensional Hybrid Cellular Automata

All cryptography schemes are based on producing keys such that they are not predictable. Hence, the key that is random is used in a cryptosystem and producing such keys is one of the main problems in cryptography [1]. Thus, several methods have been proposed in order to accomplish this problem that is to generate pseudo random numbers (PRN) or bits. Generating PRN by using cellular automata (CAs for brevity) is one of these methods. In spite of very good randomness properties of nonlinear CAs [2] ([3]), linear CAs provide a huge convenience due to its algebraic structure. However, it is a well-known fact that linear CA is not good at generating PRNG. Indeed, evolving a nonlinear cellular automaton (singular, CA) requires nding the value of a function with multiple variables for each cell, however with a linear CA since it is a linear transformation, all cells can be evolved at a reasonable time owing to the transition matrix. Without giving up of linearity structure together with its weakness, it is reasonable to use linear hybrid CAs and while applying it instead of choosing blocks we choose a particular cell for this purpose. By this means, we avoid expensiveness of time consuming comparing to nonlinear CA. The key that is generated through this method or any other in general to be in random it has to pass some basic statistical tests: the frequency test, the serial test, the poker test, the run test, and the autocorrelation test [4]. In [5], by using two-dimensional linear hybrid cellular automata (2D) LHCA, generating pseudo random bit sequences has been attempted and the generated bit sequences have been tested whether they are pseudo random or not. Although some very speci c examples were given, any classi cation of the rules was not proposed. Here in this work, we study with respect to another boundary condition which is so-called adiabatic. Moreover, we intend to classify the rules in order to predict which ones or how many of them are suitable to generate pseudo random bit sequences.
Dynamic real-time scheduler for large-scale MPSoCs

Large-scale MPSoCs requires a scalable and dynamic real-time (RT) task scheduler, able to handle non-deterministic computational behaviors. Current proposals for MPSoCs have limitations, as lack of scalability, complex static steps, validation with abstract models, or are not flexible to enable changes at runtime of the RT constraints. This work proposes a hierarchical task scheduler with monitoring features. The scheduler is dynamic, supporting changes in RT constraints at runtime. An API enables these features allowing to the application developer to reconfigure the tasks' period, deadline, and execution time by annotating the task code. At runtime, according to the task execution, the scheduler handles the API calls and adjust itself to ensure RT guarantees according to the new constraints. Scalability is ensured by dividing the scheduler into two hierarchical levels: LS (Local Schedulers), and CS (Cluster Schedulers). The LS runs at the processor level, using the LST (Least Slack-Time) algorithm. The CS runs at the cluster level, i.e., a group of processors controlled by a manager processor. The CS receives messages from the LSs, informing the processor slack-time, deadline violations, and RT changes. The CS implements an RT adaptation heuristic, triggering task migrations according to RT reconfiguration or deadline misses. Results show a negligible overhead in the applications' execution time and the fulfillment of the applications' RT constraints even with a high degree of resources sharing, in both processors and NoC.
The Biological Response following Autogenous Bone Grafting for Large-Volume Defects of the Knee

Objective: This report focuses on the biological events occurring at various intervals following autogenous bone grafting of large-volume defects of the knee joint’s femoral condyle secondary to osteochondritis dissecans (OCD) or osteonecrosis (ON). It was hypothesized that the autogenous bone graft would integrate and the portion exposed to the articular surface would form fibrocartilage, which would endure for years. Methods: Between September 29, 1987 and August 8, 1994, there were 51 patients treated with autogenous bone grafting for large-volume osteochondral defects. Twenty-five of the 51 patients were available for long-term follow-up up to 21 years. Patient follow-up was accomplished by clinical opportunity and intentional research. Videotapes were available on all index surgeries for review and comparison. All had preoperative and postoperative plain film radiographs. Long-term follow-up included MRI up to 21 years. Second-look arthroscopy and biopsy were obtained on 14 patients between 8 weeks and 20 years. Results: Radiological assessment showed the autogenous bone grafts integrated with the host bone. The grafts retained the physical geometry of the original placement. MRI showed soft tissue covering the grafts in all cases at long-term follow-up. Interval biopsy showed the surface covered with fibrous tissue at 8 weeks and subsequently converted to fibrocartilage with hyaline cartilage at 20 years. Conclusion: Autogenous bone grafting provides a matrix for large osteochondral defects that integrates with the host bone and results in a surface repair of fibrocartilage and hyaline cartilage that can endure for up to 20 years.
A Model for Detection of Application Layer Denial of Service Attacks using Group Testing Theory

Computer network technology is a very important technology for most of the applications. So security for these applications is most important. As network security is a great requirement in the growing networks, there is a lack of security techniques that can be implemented easily. Everyday a number of attacks are launched. There are different categories of attacks. Some of them are used to gain personnel information and some are used to interfere with the intended function of the system. Some attacks are used to consume all the resources of the system uselessly. Among different categories of security attacks, Denial of Service Attack is considered as the most dangerous type of attack. Denial of Service Attacks are increasing exponentially in the today’s world of internet. These attacks are one of the critical issues in the network security. These are consuming the bandwidth and all the services of the target computer. In Denial of Service Attacks, Application Layer Denial of Service Attacks are more harmful as these attacks are harder to get detected. Many methods have been proposed so as to detect and mitigate Application Layer Denial of Service Attacks. In this Paper, we propose a security model for the detection of Application Layer Denial of Service Attacks. Here we have used the Group Testing for proposing the model for Detection of Application Layer Denial of Service Attacks. Keywords— Denial of Service Attack,Passive Attacks.Active Attacks, Masquerade, CAPTCHA ,Group testing.
Massive Image Editing on the Cloud

Processing massive imagery in a distributed environment currently requires the effort of a skilled team to efficiently handle communication, synchronization, faults, and data/process distribution. Moreover, these implementations are highly optimized for a specific system or cluster, therefore portability or improved performance due to system improvements is rarely considered. Much like early GPU computing, cluster computing for graphics is a highly-specialized field for few experts. In this work, we experiment using the cloud as a possible alternative to the status quo, abstracting away much of the complexity associated with current implementations. As gigapixel images increase in prevalence, the need for a higher level of abstraction for broadly accessible deployment is clear, much like the emergence of CUDA, OpenCL and DirectCompute for multicore and general purpose GPU computing. The increased availability of cloud resources as a commodity offers a unique opportunity to adopt this level of abstraction and extend the distribution and development of large image algorithms to a much wider community. This can potentially lead to a drastic decrease in deployment time for algorithms allowing for faster testing of new ideas. The abstraction of the cloud can allow simple, system oblivious implementations which are more portable, fault-tolerant, and likely to scale as hardware improves. In this paper, we detail how to reformulate graphics algorithms to perform well on the cloud and what considerations need to be made for an efficient implementation. Specifically, we show the use of gradient domain techniques to stitch large panoramas on Apache Foundation’s open source implementation of Google’s MapReduce framework called Hadoop. With the proper redesign of current algorithms, we show how one can balance processing and data movement to achieve implementations well suited for the cloud.
An Exploratory Study of Communication Practices Affecting Church Leadership Team Performance

ABSTRACT This study extends top management team (TMT) research by examining the “black box” of teams’ communication processes through survey data collected from church senior leadership teams. Seven hundred sixty-four members of leadership teams of 145 churches completed two survey instruments, the Team Diagnostic Survey and an exploratory Team Communication Questionnaire. Bootstrap forest analysis indicated that senior leadership team performance was most affected by the time the team spent on making critical decisions, the team’s utilization of careful decision-making processes, members taking a churchwide perspective versus a position-based perspective when making decisions, members’ perception of stress caused by dysfunctional communication among the team, and the degree to which the team sought God’s perspective on decisions.
A post-disaster mobility model for Delay Tolerant Networking

Delay Tolerant Networking (DTN) considers how to provide communication in contexts where it is unreasonable to assume end-to-end connectivity. Network devices exchange buffered messages when they come into communication range; messages may be buffered and carried physically several times before ultimately being received. Service characteristics of a DTN depend intimately on the underlying movement of devices through physical space; correspondingly, an assessment of DTN technology (e.g. routing protocols, message exchange policies, etc.) depends on that same movement. Existing mobility models provided in simulators lack characteristics one expects in post-disaster communication. We propose a mobility model that includes the impact of the disaster on the transportation network, and that models population and relief vehicle movement. We augment the “Opportunistic Network Environment” (ONE) simulator of DTNs with required extensions and show that characteristics of the DTN are very different using the new model than it is under models that ONE currently provides.