nils-herrmann/clean_scirepeval huggingface dataset:
Virus Analysis on IDT Hooks of Rootkits Trojan

Rootkits Trojan virus, which can control attacked computers, delete import files and even steal password, are much popular now. Interrupt Descriptor Table (IDT) hook is rootkit technology in kernel level of Trojan. The paper makes deeply analysis on the IDT hooks handle procedure of rootkit Trojan according to previous other researchers methods. We compare its IDT structure and programs to find how Trojan interrupt handler code can respond the interrupt vector request in both real address mode and protected address mode. Finally, we analyze the IDT hook detection methods of rootkits Trojan by Windbg or other professional tools.
The mediating effect of ethical climate on religious orientation and ethical behavior.

Nurses' behavior in Educational-Medical centers is very important for improving the condition of patients. Ethical climate represents the ethical values and behavioral expectations. Attitude of people toward religion is both intrinsic and extrinsic. Different ethical climates and attitude toward religion could be associated with nurses' behavior.



To study the mediating effect of ethical climate on religious orientation and ethical behaviors of nurses.



In an exploratory analysis study, the path analysis method was used to identify the effective variables on ethical behavior.  The participants consisted of 259 Iranian nurses from Hamadan University of Medical Sciences.  This project with an ethical code and a unique ID IR.UMSHA.REC.1395.67 was approved in the Research Council of Hamadan University of Medical Sciences.



The beta coefficients obtained by regression analysis of perception of ethical climate of individual egoism (B = -0.202, p < 0.001), individual ethical principles (B = -0.184, p = 0.001), local egoism (B = -0.136, p = 0.003), and extrinsic religious orientation (B = -0.266, p = 0.007) were significant that they could act as predictors of ethical behavior. The summary of regression model indicated that 0.27% of ethical behaviors of nurses are justified by two variables: ethical climate and religious orientation.



Intrinsic religious orientation has the most direct impact and then, respectively, the variables of ethical climate of perceptions in the dimensions of individual egoism, individual ethical principles, local egoism, global ethical principle, and ethical behavior and extrinsic religious orientation follow. All the above, except global ethical principles and intrinsic orientation of religion have a negative effect on ethical behavior and can be predictors of ethical behavior. Therefore, applying strategies to promote theories of intrinsic religious orientation and global ethical principles in different situations of nursing is recommended.
A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery

Label propagation is an effective and efficient technique to utilize local and global features in a network for semi-supervised learning. In the literature, one challenge is how to propagate information in heterogeneous networks comprising several subnetworks, each of which has its own cluster structures that need to be explored independently. In this paper, we introduce an intutitive algorithm MINProp (Mutual Interaction-based Network Propagation) and a simple regularization framework for propagating information between subnetworks in a heterogeneous network. MINProp sequentially performs label propagation on each individual subnetwork with the current label information derived from the other subnetworks and repeats this step until convergence to the global optimal solution to the convex objective function of the regularization framework. The independent label propagation on each subnetwork explores the cluster structure in the subnetwork. The label information from the other subnetworks is used to capture mutual interactions (bicluster structures) between the vertices in each pair of the subnetworks. MINProp algorithm is applied to disease gene discovery from a heterogeneus network of disease phenotypes and genes. In the experiments, MINProp significantly output-performed the original label propagation algorithm on a single network and the state-of-the-art methods for discovering disease genes. The results also suggest that MINProp is more effective in utilizing the modular structures in a heterogenous network. Finally, MINProp discovered new disease-gene associations that are only reported recently.
A fast pruned-extreme learning machine for classification problem

Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification. One key strength of ELM is the significantly low computational time required for training new classifiers since the weights of the hidden and output nodes are randomly chosen and analytically determined, respectively. In this paper, we address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to underfitting/overfitting issues in pattern classification. In particular, we describe the proposed pruned-ELM (P-ELM) algorithm as a systematic and automated approach for designing ELM classifier network. P-ELM uses statistical methods to measure the relevance of hidden nodes. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned by considering their relevance to the class labels. As a result, the architectural design of ELM network classifier can be automated. Empirical study of P-ELM on several commonly used classification benchmark problems and with diverse forms of hidden node functions show that the proposed approach leads to compact network classifiers that generate fast response and robust prediction accuracy on unseen data, comparing with traditional ELM and other popular machine learning approaches.
Patch Antenna with Low Pass Filter Design to Supress Higher Order Harmonics in Efficient Rectenna Design

The purpose of this paper is to design a microstrip patch antenna with low pass filter for efficient rectenna design this structure having the property of rejecting higher harmonics than 2GHz. As the design frequency is 2GHz.in first step we design a patch antenna in second step we design patch antenna with low pass filter and combine these two. The IE3D software is used for the simulation of this structure.
Computer Bridge - A Big Win for AI Planning

niques is now the world champion computer program in the game of Contract Bridge. As reported in The New York Times and The Washington Post, this program—a new version of Great Game Products’ BRIDGE BARON program—won the Baron Barclay World Bridge Computer Challenge, an international competition hosted in July 1997 by the American Contract Bridge League. It is well known that the game tree search techniques used in computer programs for games such as Chess and Checkers work differently from how humans think about such games. In contrast, our new version of the BRIDGE BARON emulates the way in which a human might plan declarer play in Bridge by using an adaptation of hierarchical task network planning. This article gives an overview of the planning techniques that we have incorporated into the BRIDGE BARON and discusses what the program’s victory signifies for research on AI planning and game playing.
Hybrid DWT-DCT algorithm for image and video compression applications

I agree that the Library, University of Saskatchewan, may make this thesis freely available for inspection. I further agree that permission for copying of this thesis for scholarly purposes may be granted to the professors who supervised the thesis work recorded herein or, in their absence, by the Head of the Department of Electrical and Computer Engineering or the Dean of the College of Engineering. It is understood that due recognition will be given to me and to the University of Saskatchewan in any use of the material in this thesis. Copying or publication or any other use of this thesis for financial gain without approval by the University of Saskatchewan and my written permission is prohibited.  Digital image and video in their raw form require an enormous amount of storage capacity. Considering the important role played by digital imaging and video, it is necessary to develop a system that produces high degree of compression while preserving critical image/video information. There are various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multiresolution transformation. In this work, we propose a hybrid DWT-DCT algorithm for image compression and reconstruction taking benefit from the advantages of both algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the Discrete Wavelet Transform (DWT) coefficients. Simulations have been conducted on several natural, benchmark, medical and endoscopic images. Several QCIF, high definition, and endoscopic videos have also been used to demonstrate the advantage of the proposed scheme. The simulation results show that the proposed hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT, and WHT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The new scheme reduces " false contouring " and " blocking artifacts " significantly. The rate distortion analysis shows that for a fixed level of distortion, the number of bits required to transmit the hybrid coefficients would be less than those required for other schemes Furthermore, the proposed algorithm is also compared with the some existing hybrid algorithms. The comparison results show that, the proposed hybrid algorithm has better performance and reconstruction quality. The proposed scheme is intended to be used as the image/video compressor engine in imaging …
Being with virtual others: Neural correlates of social interaction

To characterize the neural correlates of being personally involved in social interaction as opposed to being a passive observer of social interaction between others we performed an fMRI study in which participants were gazed at by virtual characters (ME) or observed them looking at someone else (OTHER). In dynamic animations virtual characters then showed socially relevant facial expressions as they would appear in greeting and approach situations (SOC) or arbitrary facial movements (ARB). Differential neural activity associated with ME>OTHER was located in anterior medial prefrontal cortex in contrast to the precuneus for OTHER>ME. Perception of socially relevant facial expressions (SOC>ARB) led to differentially increased neural activity in ventral medial prefrontal cortex. Perception of arbitrary facial movements (ARB>SOC) differentially activated the middle temporal gyrus. The results, thus, show that activation of medial prefrontal cortex underlies both the perception of social communication indicated by facial expressions and the feeling of personal involvement indicated by eye gaze. Our data also demonstrate that distinct regions of medial prefrontal cortex contribute differentially to social cognition: whereas the ventral medial prefrontal cortex is recruited during the analysis of social content as accessible in interactionally relevant mimic gestures, differential activation of a more dorsal part of medial prefrontal cortex subserves the detection of self-relevance and may thus establish an intersubjective context in which communicative signals are evaluated.
Network access authentication infrastructure using EAP-TTLS on diameter EAP application

In our universal AAA (Authentication, Authorization, and Accounting) infrastructure project, we have already developed the implementations of Diameter Base Protocol and Diameter EAP Application. As part of this project, we developed the first open-source of an EAP-TTLS server on Diameter EAP Application for network access control. EAP-TTLS is one of the authentication methods in EAP. EAP-TTLS has two phases. In phase 1, the user authenticates the EAP-TTLS server by the certificate of the EAP-TTLS server. In phase 2, the EAP-TTLS server authenticates the user by user's password transmitted through the secure tunnel established in phase 1. Our implementation supports several authentication methods in phase 2 such as PAP, CHAP, MS-CHAP, and MS-CHAPv2. It was made sure that the EAP-TTLS server worked correctly for several types of user terminals such as Windows, Linux, iPad, and Android. The evaluation results show that the processing time of the EAP-TTLS server is short enough for practical use.
Low-Power High-Voltage Power Modulator for Motor Insulation Testing

Variable-speed pulsewidth-modulated (PWM) drives allow for precise speed control of induction motors, as well as a high power factor and fast response characteristics, compared with nonelectronic speed controllers. However, due to the high switching frequencies and the high dV/dt, there are increased dielectric stresses in the insulation system of the motor, leading to premature failure, in high power and medium- and high-voltage motors. Studying the degradation mechanism of these insulation systems on an actual motor is both extremely costly and impractical. In addition, to replicate the aging process, the same waveform that the motor is subjected to should be applied to the test samples. As a result, a low-power two-level high-voltage PWM inverter has been built to replicate the voltage waveforms for aging processes. This generator allows for testing the insulation systems considering a real PWM waveform in which both the fast pulses and the fundamental low frequency are included. The results show that the effects of PWM waveforms cannot be entirely replicated by a unipolar pulse generator.
iPACKMAN: high-quality, low-complexity texture compression for mobile phones

We present a novel texture compression scheme, called iPACKMAN, targeted for hardware implementation. In terms of image quality, it outperforms the previous de facto standard texture compression algorithms in the majority of all cases that we have tested. Our new algorithm is an extension of the PACKMAN texture compression system, and while it is a bit more complex than PACKMAN, it is still very low in terms of hardware complexity.
S4D-Cache: Smart Selective SSD Cache for Parallel I/O Systems

Parallel file systems (PFS) are widely-used in modern computing systems to mask the ever-increasing performance gap between computing and data access. PFSs favor large requests, and do not work well for small requests, especially small random requests. Newer Solid State Drives (SSD) have excellent performance on small random data accesses, but also incur a high monetary cost. In this study, we propose a hybrid architecture named the Smart Selective SSD Cache (S4D-Cache), which employs a small set of SSD-based file servers as a selective cache of conventional HDD-based file servers. A novel scheme is introduced to identify performance-critical data, and conduct selective cache admission to fully utilize the hybrid architecture in terms of data-access parallelism and randomness. We have implemented an S4D-Cache under the MPI-IO and PVFS2 parallel file system. Our experiments show that S4D-Cache can significantly improve I/O throughput, and is a promising approach for parallel applications.
Jointly Parse and Fragment Ungrammatical Sentences

This paper is about detecting incorrect arcs in a dependency parse for sentences that contain grammar mistakes. Pruning these arcs results in well-formed parse fragments that can still be useful for downstream applications. We propose two automatic methods that jointly parse the ungrammatical sentence and prune the incorrect arcs: a parser retrained on a parallel corpus of ungrammatical sentences with their corrections, and a sequence-to-sequence method. Experimental results show that the proposed strategies are promising for detecting incorrect syntactic dependencies as well as incorrect semantic dependencies.
Planar graphs, negative weight edges, shortest paths, and near linear time

In this paper, we present anO(n log3 n) time algorithm for finding shortest paths in a planar graph with real weights. This can be compared to the best previous strongly polynomial time algorithm developed by Lipton, Rose, and Tarjan in 1978 which ran inO(n3=2) time, and the best polynomial algorithm developed by Henzinger, Klein, Subramanian, and Rao in 1994 which ran ine O(n4=3) time. We also present significantly improved algorithms for query and dynamic versions of the shortest path problems.
Pain assessment for people with dementia: a systematic review of systematic reviews of pain assessment tools

There is evidence of under-detection and poor management of pain in patients with dementia, in both long-term and acute care. Accurate assessment of pain in people with dementia is challenging and pain assessment tools have received considerable attention over the years, with an increasing number of tools made available. Systematic reviews on the evidence of their validity and utility mostly compare different sets of tools. This review of systematic reviews analyses and summarises evidence concerning the psychometric properties and clinical utility of pain assessment tools in adults with dementia or cognitive impairment.



We searched for systematic reviews of pain assessment tools providing evidence of reliability, validity and clinical utility. Two reviewers independently assessed each review and extracted data from them, with a third reviewer mediating when consensus was not reached. Analysis of the data was carried out collaboratively. The reviews were synthesised using a narrative synthesis approach.



We retrieved 441 potentially eligible reviews, 23 met the criteria for inclusion and 8 provided data for extraction. Each review evaluated between 8 and 13 tools, in aggregate providing evidence on a total of 28 tools. The quality of the reviews varied and the reporting often lacked sufficient methodological detail for quality assessment. The 28 tools appear to have been studied in a variety of settings and with varied types of patients. The reviews identified several methodological limitations across the original studies. The lack of a 'gold standard' significantly hinders the evaluation of tools' validity. Most importantly, the samples were small providing limited evidence for use of any of the tools across settings or populations.



There are a considerable number of pain assessment tools available for use with the elderly cognitive impaired population. However there is limited evidence about their reliability, validity and clinical utility. On the basis of this review no one tool can be recommended given the existing evidence.
Self-awareness deficits following loss of inner speech: Dr. Jill Bolte Taylor’s case study

In her 2006 book "My Stroke of Insight" Dr. Jill Bolte Taylor relates her experience of suffering from a left hemispheric stroke caused by a congenital arteriovenous malformation which led to a loss of inner speech. Her phenomenological account strongly suggests that this impairment produced a global self-awareness deficit as well as more specific dysfunctions related to corporeal awareness, sense of individuality, retrieval of autobiographical memories, and self-conscious emotions. These are examined in details and corroborated by numerous excerpts from Taylor's book.
RoboSeT: A Tool to Support Cataloging and Discovery of Services for Service-Oriented Robotic Systems

Robotics has played an increasingly important role in several sectors of the society. Nowadays, robots are not only used to support activities in factories, but also to assist house cleaning, border surveillance, and even surgeries. The variety of application domains and the rising complexity are challenging the design of robotic systems that control such robots. In this perspective, Service-Oriented Architecture (SOA) has been adopted as a promising architectural style to design large, complex robotic systems in a flexible and reusable manner. Several Service-Oriented Robotic Systems (SORS) have been developed in the recent years and a large number of services are available for reuse. Nevertheless, none of the environments dedicated to the development of SORS provide an efficient mechanism for publishing and discovering services. As a consequence, services for SORS have to be manually searched, reducing significantly the potential of reuse and productivity provided by SOA. This paper presents RoboSeT, a mechanism that supports cataloging and discovery of services for robotic systems. RoboSeT is based on semantic search and classifies the services using a taxonomy of the robotics domain. Results of our case study indicate that RoboSeT facilitates the development of robotic systems, since it presents the potential to widely promote reusability of services for SORS.
Big Data Security Issues and Quantum Cryptography for Cloud Computing

Enhancement of security in cloud computing is challengeable when big data such as medical and government confidential information involves in a cloud environment. The managing key for individual user participated in the mobile cloud will be the problem when big data is handled without efficient security key management. Especially, mobile cloud nodes where users handle their data can be anywhere else in the cloud environment. So, managing keys for random users and finding correct locations of users’ nodes and their identities are interesting areas for the research. To solve this problem related to key management, cloud services need efficient key management that employs quantum cryptography using Grover’s algorithm. Moreover, in this research, we introduce effective authentication technique in order to improve the level of security (with minimum complexity) In this paper, theoretical model, Grover’s algorithm and quantum cryptography with the PairHand protocol are considered as methodologies. As summarized results, security levels of users and services providers are considered to verify the services used in a cloud environment. In spite of same quantum properties characterize the light, data security issues are still not matured with the light technology. Hiding the big data in
Dynamic Self-modifying Code Detection Based on Backward Analysis

Self-modifying code (SMC) is widely used in obfuscated program for enhancing the difficulty in reverse engineering. The typical mode of self-modifying code is restore-execute-hide, it drives program to conceal real behaviors at most of the time, and only under actual running will the real code be restored and executed. In order to locate the SMC and further recover the original logic of code for guiding program analysis, dynamic self-modifying code detecting method based on backward analysis is proposed. Our method first extracts execution trace such as instructions and status through dynamic analysis. Then we maintain a memory set to store the memory address of execution instructions, the memory set will update dynamically while backward searching the trace, and simultaneously will we check the memory write address to match with current memory set in order to identify the mode "modify then execute". By means of validating self-modifying code which is identified via above procedures, we can easily deobfuscate the program which use self-modifying code and achieve its original logic. A prototype that can be applied in self-modifying code detection is designed and implemented. The evaluation results show our method can trace the execution of program effectively, and can reduce the consumption in time and space.