nils-herrmann/clean_scirepeval huggingface dataset:
Control of Two-wheels Inverted Pendulum Mobile Robot Using Full Order Sliding Mode Control

The research on two-wheel inverted pendulum or commonly call balancing robot has gained momentum over the last decade in a number of robotic laboratories around the world This paper deals with the modeling of 2-wheels Inverted Pendulum and the design of Full Order Sliding Mode Control (FOSMC) for the system. The mathematical model of 2-wheels inverted pendulum system that is highly nonlinear is derived. The final model is then represented in state-space form and the system suffers from mismatched condition. A robust controller based on Sliding Mode Control is proposed to perform the robust stabilization and disturbance rejection of the system. A computer simulation study is carried out to access the performance of the proposed control law.
An approach for detection and family classification of malware based on behavioral analysis

Malware, i.e., malicious software, represents one of the main cyber security threats today. Over the last decade malware has been evolving in terms of the complexity of malicious software and the diversity of attack vectors. As a result modern malware is characterized by sophisticated obfuscation techniques, which hinder the classical static analysis approach. Furthermore, the increased amount of malware that emerges every day, renders a manual approach inefficient. This study tackles the problem of analyzing, detecting and classifying the vast amount of malware in a scalable, efficient and accurate manner. We propose a novel approach for detecting malware and classifying it to either known or novel, i.e., previously unseen malware family. The approach relies on Random Forests classifier for performing both malware detection and family classification. Furthermore, the proposed approach employs novel feature representations for malware classification, that significantly reduces the feature space, while achieving encouraging predictive performance. The approach was evaluated using behavioral traces of over 270,000 malware samples and 837 samples of benign software. The behavioral traces were obtained using a modified version of Cuckoo sandbox, that was able to harvest behavioral traces of the analyzed samples in a time-efficient manner. The proposed system achieves high malware detection rate and promising predictive performance in the family classification, opening the possibility of coping with the use of obfuscation and the growing number of malware.
Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment

As Cloud computing is reforming the infrastructure of IT industries, it has become one of the critical security concerns of the defensive mechanisms applied to secure Cloud environment. Even if there are tremendous advancements in defense systems regarding the confidentiality, authentication and access control, there is still a challenge to provide security against availability of associated resources. Denial-of-service (DoS) attack and distributed denial-of-service (DDoS) attack can primarily compromise availability of the system services and can be easily started by using various tools, leading to financial damage or affecting the reputation. These attacks are very difficult to detect and filter, since packets that cause the attack are very much similar to legitimate traffic. DoS attack is considered as the biggest threat to IT industry, and intensity, size and frequency of the attack are observed to be increasing every year. Therefore, there is a need for stronger and universal method to impede these attacks. In this paper, we present an overview of DoS attack and distributed DoS attack that can be carried out in Cloud environment and possible defensive mechanisms, tools and devices. In addition, we discuss many open issues and challenges in defending Cloud environment against DoS attack. This provides better understanding of the DDoS attack problem in Cloud computing environment, current solution space, and future research scope to deal with such attacks efficiently.
3D Pose Regression Using Convolutional Neural Networks

3D pose estimation is a key component of many important computer vision tasks like autonomous navigation and robot manipulation. Current state-of-the-art approaches for 3D object pose estimation, like Viewpoints & Keypoints and Render for CNN, solve this problem by discretizing the pose space into bins and solving a pose-classification task. We argue that 3D pose is continuous and can be solved in a regression framework if done with the right representation, data augmentation and loss function. We modify a standard VGG network for the task of 3D pose regression and show competitive performance compared to state-of-the-art.
An improved and very efficient MPPT controller for PV systems subjected to rapidly varying atmospheric conditions and partial shading

Maximum power point tracking (MPPT) is a very important necessity in a system of energy conversion from a renewable energy source. Many research papers have been produced with various schemes over past decades for the MPPT in photovoltaic (PV) system. This research paper inspires its motivation from the fact that the keen study of these existing techniques reveals that there is still quite a need for an absolutely generic and yet very simple MPPT controller which should have all the following traits: total independence from system's parameters, ability to reach the global maxima in minimal possible steps, the correct sense of tracking direction despite the abrupt atmospheric or parametrical changes, and finally having a very cost-effective and energy efficient hardware with the complexity no more than that of a minimal MPPT algorithm like Perturb and Observe (P&O). The MPPT controller presented in this paper is a successful attempt to fulfil all these requirements. It extends the MPPT techniques found in the recent research papers with some innovations in the control algorithm and a simplistic hardware. The simulation results confirm that the proposed MPPT controller is very fast, very efficient, very simple and low cost as compared to the contemporary ones.
Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils

The advances in computing and information storage have provided vast amounts of data. The challenge has been to extract knowledge from this raw data that has lead to new methods and techniques such as data mining that can bridge the knowledge gap. This research aimed to assess these new data mining techniques and apply them to a soil science database to establish if meaningful relationships can be found. A large data set of Soil database is extracted from the Department of Soil Sciences and Agricultural Chemistry, S V Agricultural College, Tirupati, The database contains measurements of soil profile data from various locations of Chandragiri Mandal, Chittoor District. The research establishes whether Soils are Classified Using various data mining techniques. In addition, comparison was made between Naive bayes classification and analyse the most effective technique. The outcome of the research may have many benefits, t o agriculture, soil management and environmental.
Direct digital synthesis: a tool for periodic wave generation (part 2)

Direct digital synthesis (DDS) is a useful tool for generating periodic waveforms. In this two-part article, the basic idea of this synthesis technique is presented and then focused on the quality of the sinewave a DDS can create, introducing the SFDR quality parameter. Next effective methods to increase the SFDR are presented through sinewave approximations, hardware schemes such as dithering and noise shaping, and an extensive list of reference. When the desired output is a digital signal, the signal's characteristics can be accurately predicted using the formulas given in this article. When the desired output is an analog signal, the reader should keep in mind that the performance of the DDS is eventually limited by the performance of the digital-to-analog converter and the follow-on analog filter. Hoping that this article would incite engineers to use DDS either in integrated circuits DDS or software-implemented DDS. From the author's experience, this technique has proven valuable when frequency resolution is the challenge, particularly when using low-cost microcontrollers.
Accent: A Communication Oriented Network Operating System Kernel

Accent is a communication oriented operating system kernel being built at Carnegie-Mellon University to support the distributed personal computing project, Spice, and the development of a fault-tolerant distributed sensor network (DSN). Accent is built around a single, powerful abstraction of communication between processes, with all kernel functions, such as device access and virtual memory management accessible through messages and distributable throughout a network. In this paper, specific attention is given to system supplied facilities which support transparent network access and fault-tolerant behavior. Many of these facilities are already being provided under a modified version of VAX/UNIX. The Accent system itself is currently being implemented on the Three Rivers Corp. PERQ.
Voronoi diagram and fast marching applied to path planning

This paper presents a new path planning method which operates in two steps. In the first step the safest areas in the environment are extracted by means of a Voronoi diagram. In the second step fast marching method is applied to the Voronoi extracted areas in order to obtain the shortest path. In this way the trajectory obtained is the shortest between the safe possible ones. This two step method combines speed and reliability, because the map dimensions is reduced to a unidimensional map and this map represents the safest areas in the environment for moving the robot
Neighbor Discovery for IP Version 6 (IPv6)

Neighbor Discovery for IP Version 6 (IPv6) | <draft-ietf-ipngwg-discovery-v2-01.txt> | Status of this Memo This document is an Internet-Draft. Internet-Drafts are working * documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as ''work in progress.'' To learn the current status of any Internet-Draft, please check the ''1id-abstracts.txt'' listing contained in the Internet-Drafts Shadow Directories on ds.internic.net (US East Coast), nic.nordu.net Abstract This document specifies the Neighbor Discovery protocol for IP * Version 6. IPv6 nodes on the same link use Neighbor Discovery to discover each other's presence, to determine each other's link-layer addresses, to find routers and to maintain reachability information about the paths to active neighbors.  draft-ietf-ipngwg-discovery-v2-01.txt [
Building a Recommender Agent for e-Learning Systems

A recommender systemin ane-learningcontext is a software agent that tries to ”intellig ently” recommendactions to a learnerbasedon theactionsof previouslearners. This recommendationcould be an on-line activity such as doing anexercise, readingpostedmessagesona conferencing system,or runningan on-linesimulation,or couldbesimply a webresource. Theserecommendationsystemshave beentried in e-commer ceto enticepurchasingof goods,but haven’ t beentried in e-learning. This papersuggeststhe useof webmining techniquesto build such an agent that couldrecommendon-linelearningactivitiesor shortcutsin a coursewebsite basedon learners’ accesshistory to improve coursematerial navigationas well as assistthe online learningprocess.Thesetechniquesare consider ed integratedwebminingasopposedto off-line webminingused byexpertusers to discoveron-lineaccesspatterns.
Root mean square error (RMSE) or mean absolute error (MAE)

Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura(2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura(2005) andWillmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereasWillmott et al. (2009) indicated that the sums-ofsquares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
Normal-Force Control for an In-Pipe Robot According to the Inclination of Pipelines

To move freely, in-pipe robots must be able to adapt to the various geometric changes of pipes. Previously, we described an in-pipe robot that can adapt to changes in diameter and curvature of center curves. This robot is able to estimate the forces exerted on the inner surface of the pipes and balance its posture inside the pipe using angular sensors attached to its rotational joints. In this paper, a method is proposed to estimate the relative attitude between the robot's main body and the pipe using the angular sensors attached to a pantograph mechanism. The use of angular sensors makes the structure of the robot simpler and more effective than the use of force or vision sensors because the normal forces and attitude can be estimated from measured angle information. This geometric estimation of attitude relative to the pipes enables the robot to recognize the inclination of the pipes. The PAROYS-II robot can control its normal force according to the variation in pipe inclination. Thus, the proposed method could reduce power consumption and stress on the robot's parts. The algorithm has been validated by multiple experiments.
A Comparison of Outlier Detection Algorithms for Machine Learning

In this paper a comparison of outlier detection algorithms is presented, we present an overview on outlier detection methods and experimental results of six implemented methods. We applied these methods for the prediction of stellar populations parameters as well as on machine learning benchmark data, inserting artificial noise and outliers. We used kernel principal component analysis in order to reduce the dimensionality of the spectral data. Experiments on noisy and noiseless data were performed.
Design of an Average-Current-Mode Noninverting Buck–Boost DC–DC Converter With Reduced Switching and Conduction Losses

With the rapid growth of battery-powered portable electronics, an efficient power management solution is necessary for extending battery life. Generally, basic switching regulators, such as buck and boost converters, may not be capable of using the entire battery output voltage range (e.g., 2.5-4.7 V for Li-ion batteries) to provide a fixed output voltage (e.g., 3.3 V). In this paper, an average-current-mode noninverting buck-boost dc-dc converter is proposed. It is not only able to use the full output voltage range of a Li-ion battery, but it also features high power efficiency and excellent noise immunity. The die area of this chip is 2.14 × 1.92 mm2, fabricated by using TSMC 0.35 μm 2P4M 3.3 V/5 V mixed-signal polycide process. The input voltage of the converter may range from 2.3 to 5 V with its output voltage set to 3.3 V, and its switching frequency is 500 kHz. Moreover, it can provide up to 400-mA load current, and the maximal measured efficiency is 92.01%.
End-to-End Deep Reinforcement Learning for Lane Keeping Assist

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind’s successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.
Multichannel Variable-Size Convolution for Sentence Classification

We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show that pretraining MVCNN is critical for good performance. MVCNN achieves state-of-the-art performance on four tasks: on small-scale binary, small-scale multi-class and largescale Twitter sentiment prediction and on subjectivity classification.
Differences in Critical Success Factors in ERP Systems Implementation in Australia and China: A Cultural Analysis

Enterprise Resource Planning (ERP) systems are integrated, enterprise-wide systems that provide automated support for standard business processes within organisations. They have been adopted by organisations throughout the world with varying degrees of success. Implementing ERP systems is a complex, lengthy and expensive process. In this paper we synthesise an ERP systems implementation process model and a set of critical success factors for ERP systems implementation. Two case studies of ERP systems implementation, one in Australia and one in China are reported. The case studies identify which critical success factors are important in which process model phases. Case study analysis then explains the differences between the Australian and Chinese cases using national cultural characteristics. Outcomes of the research are important for multinational organisations implementing ERP systems and for consulting companies assisting with ERP systems implementation in different countries.
Navigation system using ZigBee wireless sensor network for parking

In large parking area attached to shopping malls and so on, it is difficult to find a vacant parking space. In addition, searching for parking space during long time leads to drivers stress and wasteful energy loss. In order to solve these problems, the navigation system in parking area by using ZigBee networks is proposed in this paper. The ZigBee is expected to realize low power consumption wireless system with low cost. Moreover, the ZigBee can form ad-hoc network easily and more than 65000 nodes can connect at the same time. Therefore, it is suitable for usage in the large parking area. In proposed system, the shortest route to the vacant parking space is transmitted to the own vehicle by the ZigBee ad-hoc network. Thus, the efficient guide is provided to the drivers. To show the effectiveness of the proposed parking system, the average time for arrival in the parking area is evaluated, and the performance of the vehicles that equips the ZigBee terminals is compared with the ordinary vehicles that do not equip the ZigBee terminals.
DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jena’s production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.
Convolutional Network Features for Scene Recognition

Convolutional neural networks have recently been used to obtain record-breaking results in many vision benchmarks. In addition, the intermediate layer activations of a trained network when exposed to new data sources have been shown to perform very well as generic image features, even when there are substantial differences between the original training data of the network and the new domain. In this paper, we focus on scene recognition and show that convolutional networks trained on mostly object recognition data can successfully be used for feature extraction in this task as well. We train a total of four networks with different training data and architectures, and show that the proposed method combining multiple scales and multiple features obtains state-of-the-art performance on four standard scene datasets.