nils-herrmann/clean_scirepeval huggingface dataset:
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE).  IDS, DDoS, MLP, Naïve Bayes, Random Forest
Relation Extraction : A Survey

With the advent of the Internet, large amount of digital text is generated everyday in the form of news articles, research publications, blogs, question answering forums and social media. It is important to develop techniques for extracting information automatically from these documents, as lot of important information is hidden within them. This extracted information can be used to improve access and management of knowledge hidden in large text corpora. Several applications such as Question Answering, Information Retrieval would benefit from this information. Entities like persons and organizations, form the most basic unit of the information. Occurrences of entities in a sentence are often linked through well-defined relations; e.g., occurrences of person and organization in a sentence may be linked through relations such as employed at. The task of Relation Extraction (RE) is to identify such relations automatically. In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques. We also cover the paradigms of Open Information Extraction (OIE) and Distant Supervision. Finally, we describe some of the recent trends in the RE techniques and possible future research directions. This survey would be useful for three kinds of readers i) Newcomers in the field who want to quickly learn about RE; ii) Researchers who want to know how the various RE techniques evolved over time and what are possible future research directions and iii) Practitioners who just need to know which RE technique works best in various settings.
Real-time performance evaluation of a genetic-algorithm-based fuzzy logic controller for IPM motor drives

This work presents a novel speed control scheme using a genetic-based fuzzy logic controller (GFLC) for an interior permanent-magnet synchronous motor (IPMSM) drive. The proposed GFLC is developed to have less computational burden, which makes it suitable for real-time implementation. The parameters for the GFLC are tuned by genetic algorithm (GA). The complete drive incorporating the GFLC is successfully implemented in real-time using a digital signal processor board DS 1102 for a laboratory 1-hp interior permanent magnet motor. The efficacy of the proposed GFLC-based IPMSM drive is verified by simulation as well as experimental results at various operating conditions. A performance comparison with a conventional proportional-integral controller is also provided to show the superiority of the proposed controller. The proposed GFLC is found to be robust for high-performance industrial drive applications.
Double-Boost DC to DC Converter

In this paper a new boost topology is proposed. The circuit is similar with two parallel boost DC-to-DC converters, but the two inductors are charged in parallel and release energy in series, thus enhancing the voltage boost ratio. After a short analysis of the circuit, a comparative study with other classic boost converter (single boost and two-cascade) is presented. The simulation results show a net improvement of the boost ratio for the new proposed topology.
A new 6-DOF parallel robot with simple kinematic model

This paper presents a novel six-legged parallel robot, the kinematic model of which is simpler than that of the simplest six-axis serial robot. The new robot is the 6-DOF extension of the Cartesian parallel robot. It consists of three pairs of base-mounted prismatic actuators, the directions in each pair parallel to one of the axes of a Cartesian coordinate system. In each of the six legs, there are also two passive revolute joints, the axes of which are parallel to the direction of the prismatic joint. Finally, each leg is attached to the mobile platform via a spherical joint. The direct kinematics of the novel parallel robot can be solved easily by partitioning the orientation and the position of the mobile platform. There are eight distinct solutions, which can be found directly by solving a linear system and alternating the signs of three radicals. This parallel robot has a large workspace and is suitable for machining or rapid prototyping, as detailed in this paper.
High Efficiency AC – AC Power Electronic Converter Applied To Domestic Induction Heating

Induction heating is a well-known technique to produce very high temperature for applications. A large number of topologies have been developed in this area such as voltage and current source inverter. This project presents the analysis and design of a new ac–ac resonant converter applied to domestic induction heating. The proposed topology, based on the halfbridge series resonant inverter, uses only two diodes to rectify the mains voltage.The proposed converter can operate with zerovoltage switching during both switches-on and switch-off transitions. This topology doubles the output voltage, and therefore, the current in the load is reduced for the same output power. As a consequence, the converter efficiency is significantly improved.The output power is controlled using the PWM technique. This results in an increase of the net efficiency of the induction heating system. The circuit is simulated using MATLAB. The circuit is implemented using PIC controller. Both simulation and hardware results are compared.
An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization

This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.
The design of early childhood makerspaces to support positive technological development: Two case studies

With the advent of the maker movement, there has been a new push to explore how spaces of learning ought to be designed. The purpose of this paper is to integrate three approaches for thinking about the role of design of the learning environment: the makerspace movement, Reggio Emilia’s Third Teacher approach, and the positive technological development (PTD) framework.  This paper describes two case studies that involved the design of two different early childhood makerspaces (ECMSs) through a co-participatory design experience: the Kindergarten Creator Space at the International School of Billund in Denmark; and the ECMS at (removed for blind review), a resource library in Medford, MA.  Based on the foundational education framework of PTD, and ideas from the field of interior design, this paper describes the design principles of several successful makerspaces, and case examples of children who use them.  By grounding the theoretical discussion in three approaches, the authors aim to suggest design elements of physical spaces in schools and libraries that can promote young children’s learning through making. Recommendations are discussed for practitioners and researchers interested in ECMSs.
A concept for automated construction progress monitoring using BIM-based geometric constraints and photogrammetric point clouds

On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. The research presented here, describes a concept for an automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. The actual state of the construction site is detected by photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model (BIM). For matching the point cloud and the BIM, the distances between individual points of the cloud and a component’s surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as basis for the existence decision concerning the respective component. Additionally, processand dependency-relations are included to further enhance the detection process. Experimental results from a real-world case study are presented and discussed.
Rigor mortis in an unusual position: Forensic considerations

We report a case in which the dead body was found with rigor mortis in an unusual position. The dead body was lying on its back with limbs raised, defying gravity. Direction of the salivary stains on the face was also defying the gravity. We opined that the scene of occurrence of crime is unlikely to be the final place where the dead body was found. The clues were revealing a homicidal offence and an attempt to destroy the evidence. The forensic use of 'rigor mortis in an unusual position' is in furthering the investigations, and the scientific confirmation of two facts - the scene of death (occurrence) is different from the scene of disposal of dead body, and time gap between the two places.
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights

A two-layer neural network can be used to approximate any nonlinear function. T h e behavior of the hidden nodes tha t allows the network to do this is described. Networks w i th one input are analyzed first, and the analysis is then extended to networks w i t h mult iple inputs. T h e result of th is analysis is used to formulate a method for ini t ial izat ion o f the weights o f neural networks to reduce t ra in ing t ime. Training examples are given and the learning curve for these examples are shown to illustrate the decrease in necessary training t ime.  Two-layer feed forward neural networks have been proven capable of approximating any arbitrary functions [l], given that they have sufficient numbers of nodes in their hidden layers. We offer a description of how this works, along with a method of speeding up the training process by choosing the networks’ initial weights. The relationship between the inputs and the output of a two-layer neural network may be described by Equation (1) H l y = wi . sigmoid(LqX + W b i ) (1) i=O where y is the network’s output, X is the input vector, H is the number of hidden nodes, Wi is the weight vector of the i th node of the hidden layer, Wbi is the bias weight of the ith hidden node, w i is the weight of the output layer which connects the i th hidden unit to the output. The behavior of hidden nodes in two-layer networks with one input To illustrate the behavior of the hidden nodes, a two-layer network with one input is trained to approximate a function of one variable d(z) . That is, the network is trained to produce d(z) given z as input using the back-propagation algorithm . The output of the network is given as
Your Cart tells You: Inferring Demographic Attributes from Purchase Data

Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users' demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N-2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often suffer from under specification. To address these problems, we propose a novel Structured Neural Embedding (SNE) model to automatically learn the representations from users' purchase data for predicting multiple demographic attributes simultaneously. Experiments are conducted on a real-world retail dataset where five attributes (gender, marital status, income, age, and education level) are to be predicted. The empirical results show that our SNE model can improve the performance significantly compared with state-of-the-art baselines.
Portable Exoskeleton Glove With Soft Structure for Hand Assistance in Activities of Daily Living

This paper presents a new portable exoskeleton glove developed for assistance in activities of daily living (ADL). The proposed glove is soft, adjustable, allows unconstrained motion of the wrist, and leaves the palm free for object grasping and manipulation. In the proposed design, both flexion and extension motions of the thumb and three fingers (excluding the little finger) are supported by respective bidirectional actuators. The kinematics of the device were derived and are presented along with an experimental evaluation. The developed glove system is compact and light, with the weights of the wearable part and the whole system being 250 and 340 g, respectively. The maximum pinch force provided by the device was experimentally measured to be 16 N, which gives the proposed device one of the highest power-to-weight ratio among existing portable devices for hand ADL assistance. The usability of the developed device was verified in a series of indoor and outdoor tests. The glove allowed test subjects to hold and manipulate versatile objects with diameters up to 90 mm and weighing 300 g. When powered by a conventional 3000-mAh battery, the glove could operate continuously performing grasping task for 4 h.
Detailed slip dynamics for nonholonomic mobile robotic system

These It is well known in the literature that wheeled mobile robot undergoes slip while moving through a flat surface, if certain conditions are violated. Slip is a very common phenomena present in all kind of wheeled mobile robotic system and particularly become prominent while taking a sharp bend on a flat ground. The degree of slip mainly depends on the wheel ground interaction properties at the contact point. Slip is absolutely undesirable as it consumes part of the system energy, erodes the wheel surface and also hampers the system performance and stability. While a system undergoes slip eventually it is very difficult to control the system and to get a proper estimate of system's position. It is very much essential to detect the slip when it is taking place and also to estimate the slip parameters with a set of specific sensors. Once the system undergoes slip it needs to go back to no-slip fulfilling certain conditions. The aim of this paper lies in formulating a detailed slip dynamics & its' solution for a differentially driven non-holonomic mobile robot. Certain switching conditions have been proposed for the transition from no-slip to slip & vice versa. The holonomic and non-holonomic constraints of slip and no-slip are taken into account in the model and when there is no slip then the constraints converge to no-slip constraints. A traction model for each wheel has been proposed which is a function of slip velocities. For motion simulation of WMR the traction model is used in conjunction with system dynamic equations. The simulation has been carried out with the data of a real robot and the simulation results illustrate its performance. Simulation results clearly show that the system dynamics is able to switch from slip to no-slip and vice versa.
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search

Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinforced learning methods (RL) have been dedicated to solve neural architecture search problems. However, these combinations usually concentrate on a single objective such as the error rate of image classification. They also fail to harness the very benefits from both sides. In this paper, we present a new multi-objective oriented algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by leveraging good virtues from both EA and RL. In particular, we incorporate a variant of multi-objective genetic algorithm NSGA-II, in which the search space is composed of various cells so that crossovers and mutations can be performed at the cell level. Moreover, reinforced control is mixed with a natural mutating process to regulate arbitrary mutation, maintaining a delicate balance between exploration and exploitation. Therefore, not only does our method prevent the searched models from degrading during the evolution process, but it also makes better use of learned knowledge. Our experiments conducted in Super-resolution domain (SR) deliver rivalling models compared to some state-of-the-art methods with fewer FLOPS 1.
You Cannot Sense My PINs: A Side-Channel Attack Deterrent Solution Based on Haptic Feedback on Touch-Enabled Devices

In this paper, we introduce a novel and secure solution to mitigate side-channel attacks to capture the PINs like touchID and other credentials of touch-enabled devices. Our approach can protect haptic feedback enabled devices from potential direct observation techniques such as cameras and motion sense techniques including such as accelerometers in smart-watch. Both attacks use the concept of shoulder surfing in social engineering and were published recently (CCS'14 and CCS'15). Hand-held devices universally employ small vibration motors as an inexpensive way to provide haptic feedback. The strength of the haptic feedback depends on the brand and the device manufacturer. They are usually strong enough to produce sliding movement and make audible noises if the device is resting on the top of a desk when the vibration motor turns. However, when the device is held in the hand the vibration can only be sensed by the holder; it is usually impossible or uncertain for an observer to know when the vibration motor turns. Our proposed solution uses the haptic feedback to inform the internal state of the keypad to the user and takes advantage of the fact that the effect of haptic feedback can be easily cloaked in such a way that direct observation techniques and indirect sensing techniques will fail. We develop an application on Android cell phones to demonstrate it and invite users to test the code. Moreover, we use real smart-watch to sense the vibration of Android cell phones. Our experimental results show that our approach can mitigate the probability of sensing a 4-digit or 6-digit PINs using smart-watch to below practical value. Our approach also can mitigate the probability of recognizing a 4-digit or 6-digit PINs using a camera within 1 meter to below practical value because the user does not need to move his or her hand during the internal states to input different PINs.
A DC-4-GHz true logarithmic amplifier: theory and implementation

A 40-dB dynamic range, dc–4-GHz parallel-summation logarithmic amplifier is presented in this paper. The amplifier realizes a piecewise approximation to an exact logarithmic response. A design procedure that yields breakpoints on the exact response is described, along with delay-matching networks for parallel-summation logarithmic amplifiers. The amplifier was constructed in a 35-GHz silicon bipolar process, has 5 dB logarithmic conformity over a dc–4-GHz bandwidth and has rise and fall times of 100 ps. The integrated circuit has dimensions of 2 2 mm and consumes 750 mW from a 5-V supply.
A survey on security control and attack detection for industrial cyber-physical systems

Cyber-physical systems (CPSs), which are an integration of computation, networking, and physical processes, play an increasingly important role in critical infrastructure, government and everyday life. Due to physical constraints, embedded computers and networks may give rise to some additional security vulnerabilities, which results in losses of enormous economy benefits or disorder of social life. As a result, it is of significant to properly investigate the security issue of CPSs to ensure that such systems are operating in a safe manner. This paper, from a control theory perspective, presents an overview of recent advances on security control and attack detection of industrial CPSs. First, the typical system modeling on CPSs is summarized to cater for the requirement of the performance analysis. Then three typical types of cyber-attacks, i.e. denial-of-service attacks, replay attacks, and deception attacks, are disclosed from an engineering perspective. Moreover, robustness, security and resilience as well as stability are discussed to govern the capability of weakening various attacks. The development on attack detection for industrial CPSs is reviewed according to the categories on detection approaches. Furthermore, the security control and state estimation are discussed in detail. Finally, some challenge issues are raised for the future
Beamforming for MIMO-OFDM Wireless Systems

The smart antennas are widely used for wireless com munication, because it has a ability to increase th coverage and capacity of a communication system. Smart anten na performs two main functions such as direction of arrival estimation (DOA) and beam forming. Using beam formi ng algorithm smart antenna is able to form main bea m towards desired user and null in the direction of i nterfering signals. In this project Direction of ar rival (DOA) is estimated by using MUSIC algorithm.  Beam fo r ing is performed by using LMS and LLMS algorithm .In this Paper, in order to perform secure transmission of signal over wireless communication we have used chaotic sequences. This paper evaluates the performance of B am forming with and without LMS and LLMS algorith m for MIMO-OFDM wireless system. The simulations are carr ied out using MATLAB.