nils-herrmann/clean_scirepeval huggingface dataset:
An Improved Binary Whale Optimization Algorithm for Feature Selection of Network Intrusion Detection

As for network intrusion detection, the time and space consumption of the detection algorithm increases with the feature dimension. The irrelevant or redundant features may cause the detection accuracy to decrease. In order to improve the timeliness and reliability of intrusion detection, feature selection is one of the most effective methods proposed currently. Considering that Whale Optimization Algorithm (WOA) has good classification performance and dimensionality reduction in reducing feature redundancy, it can be applied to feature selection of network intrusion detection. The WOA converges slowly in the search process and easily falls into a local optimum in the updating mechanism, which restricts the classification performance and dimensionality reduction of the algorithm. Therefore, this paper adopts the nonlinear convergence factor strategy in the search process and introduces the Particle Swarm Optimization (PSO) strategy in the updating mechanism. An Improved Binary Whale Optimization Algorithm (IBWOA) is then proposed in order to achieve a better dimension reduction on the premise of ensuring accuracy for feature selection of intrusion detection. In the experimental stage, multiple data sets in the UCI database for machine learning were used for verification by comparing with other feature selection methods. Finally, the KDD CUP 99 data set was used for verification of feature selection in intrusion detection. The experimental results show that, compared with Genetic Algorithm (GA), PSO and WOA, the IBWOA improves the classification accuracy and dimensionality reduction in feature selection of intrusion detection.
Comparative Evaluation of Direct Torque Control Strategies for Permanent Magnet Synchronous Machines

This paper presents a comprehensive evaluation of several direct torque control (DTC) strategies for permanent magnet synchronous machines (PMSMs), namely DTC, model predictive DTC, and duty ratio modulated DTC. Moreover, field-oriented control is also included in this study. The aforementioned control strategies are reviewed and their control performances are analyzed and compared. The comparison is carried out through simulation, finite-element analysis, and experimental results of a PMSM fed by a two-level voltage source inverter. With the intent to fully reveal the advantages and disadvantages of each control strategy, critical evaluation has been conducted on the basis of several criteria: Torque ripple, stator flux ripple, switching frequency of inverter, steady-state control performance, dynamic response, machine losses, parameter sensitivity, algorithm complexity, and stator current total harmonic distortion.
Flying ad hoc networks (FANET) test bed implementation

One of the most important design problems for the multi unmanned aerial vehicles systems is communication between UAVs. In a multi-UAV system, the communication between UAVs is provided with all UAVs connecting directly to the ground station via satellite or infrastructure. However, infrastructure or satellite-based communication architectures restrict the capabilities of the multi-UAV systems. Infrastructure or satellite-based communication problems of multi-UAV systems can be solved with ad hoc networks among UAVs. This special ad hoc network structure is called as FANET. In this paper, a FANET test bed implementation study is presented.
Energy-efficient cluster-based security mechanism for intra-WBAN and inter-WBAN communications for healthcare applications

Wireless body area networks (WBANs) are formed by using tiny health monitoring sensors on the human body in order to collect and communicate the human personal data. WBANs serve as a solution to facilitate the tasks performed in the medical sector, and minimize the chances of errors during the process of medical diagnosis. Due to the unreliable wireless media, the communication in a WBAN is exposed to a variety of attacks. These attacks pose major threats to WBAN security. In order to overcome these threats, several cryptographic techniques have been proposed in the recent past. Effectiveness of these cryptographic techniques largely depends on a good key management scheme. However, using an expensive key management scheme is not feasible in highly resource-constrained WBANs. Therefore, we propose and evaluate an energy-efficient key management scheme for WBANs that takes into account available resources of a node during the whole life cycle of key management. Our proposed scheme is a cluster-based hybrid security framework that supports both intra-WBAN and inter-WBAN communications. By using multiple clusters, energy-efficiency can be ensured. The cluster formation process itself is secured by using electrocardiogram (EKG)-based key agreement scheme. The proposed technique is hybrid because we use both preloading of keys and physiological value-based generated keys. We use highly dynamic and random EKG values of the human body for pairwise key generation and refreshment. The performance comparison of our proposed cluster-based key management scheme and low-energy adaptive clustering hierarchy (LEACH)-based key agreement scheme shows that the proposed scheme is secure, more energy-efficient, and provides better network lifetime.
A Feature Learning Based Approach for Automated Fruit Yield Estimation

This paper demonstrates a generalised multi-scale feature learning approach to multi-class segmentation, applied to the estimation of fruit yield on treecrops. The learning approach makes the algorithm flexible and adaptable to different classification problems, and hence applicable to a wide variety of tree-crop applications. Extensive experiments were performed on a dataset consisting of 8000 colour images collected in an apple orchard. This paper shows that the algorithm was able to segment apples with different sizes and colours in an outdoor environment with natural lighting conditions, with a single model obtained from images captured using a monocular colour camera. The segmentation results are applied to the problem of fruit counting and the results are compared against manual counting. The results show a squared correlation coefficient of R2 = 0.81.
StarsRace: A mobile collaborative seriuos game for obesity

Many researchers in the literature participated in solving the obesity epidemic that has spread worryingly nowadays. One of these solutions is the mobile serious games. In this paper, we designed, developed and deployed a mobile serious game entitled StarsRace. It is a collaborative game, which two players involve in one match. This competition depends on rapid arrival to pre-defined geographical points (representing by stars images) on the map. Players collect stars form these locations and gain points. The therapist with respect to the players' profiles distributed those stars. To make the game more fun, we have added an aid toolbox that can increase the competition. Lastly, we evaluate our game using an evaluating model proposed by Nokia Research Center. We find that our game cover nearly all the model criteria.
A cognitive-affective system theory of personality: reconceptualizing situations, dispositions, dynamics, and invariance in personality structure.

A theory was proposed to reconcile paradoxical findings on the invariance of personality and the variability of behavior across situations. For this purpose, individuals were assumed to differ in (a) the accessibility of cognitive-affective mediating units (such as encodings, expectancies and beliefs, affects, and goals) and (b) the organization of relationships through which these units interact with each other and with psychological features of situations. The theory accounts for individual differences in predictable patterns of variability across situations (e.g., if A then she X, but if B then she Y), as well as for overall average levels of behavior, as essential expressions or behavioral signatures of the same underlying personality system. Situations, personality dispositions, dynamics, and structure were reconceptualized from this perspective.
A new method for heart rate monitoring during physical exercise using photoplethysmographic signals

Accurate and reliable estimation of the heart rate using wearable devices, especially during physical exercise, must deal with noisy signals that contain motion artifacts. We present an approach that is based on photoplethysmographic (PPG) signals which are measured with two wrist-type pulse oximeters. The heart rate is related to intensity changes of the reflected light. Our proposed method suppresses the motion artifacts by adaptively estimating the transfer functions of each of the three-axis acceleration signals that produce the artifacts in the PPG signals. We combined the output of the six adaptive filters into a single enhanced time-frequency domain signal based on which we track the heart rate with a high accuracy. Our approach is real-time capable, computationally efficient and real data results for a benchmark data set illustrate the superior performance compared to a recently proposed approach.
Mixed Methods Research Designs in Counseling Psychology

With the increased popularity of qualitative research, researchers in counseling psychology are expanding their methodologies to include mixed methods designs. These designs involve the collection, analysis, and integration of quantitative and qualitative data in a single or multiphase study. This article presents an overview of mixed methods research designs. It defines mixed methods research, discusses its origins and philosophical basis, advances steps and procedures used in these designs, and identifies 6 different types of designs. Important design features are illustrated using studies published in the counseling literature. Finally, the article ends with recommendations for designing, implementing, and reporting mixed methods studies in the literature and for discussing their viability and continued usefulness in the field of counseling psychology.
Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set

In this age of computer science each and every thing becomes intelligent and perform task as human. For that purpose there are various tools, techniques and methods are proposed. Support vector machine is a model for statistics and computer science, to perform supervised learning, methods that are used to make analysis of data and recognize patterns. SVM is mostly used for classification and regression analysis. And in the same way k-nearest neighbor algorithm is a classification algorithm used to classify data using training examples. In this paper we use SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target. Here we use medical patients nominal data to classify and discover the data pattern to predict future disease, Uses data mining which is use to classify text analysis in future.
Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity.

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.
Trajectory learning from human demonstrations via manifold mapping

This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire a skill, and reproduce it in real-time. The diversity of robots active in non-laboratory environments is growing constantly, and to this end we present an approach for users to be able to easily teach a skill to a robot with any body configuration. Our proposed method requires a motion trajectory obtained from human demonstrations via a Kinect sensor, which is then projected onto a corresponding human skeleton model. The kinematics mapping between the robot and the human model is learned by employing Local Procrustes Analysis, which enables the transfer of the demonstrated trajectory from the human model to the robot. Finally, the transferred trajectory is modeled using Dynamic Movement Primitives, allowing it to be reproduced in real time. Experiments in simulation on a 4 degree of freedom robot show that our method is able to correctly imitate various skills demonstrated by a human.
Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning

Facebook "post popularity" analysis is fundamental for differentiating between relevant posts and posts with low user engagement and consequently their characteristics. This research study aims at health and care organizations to improve information dissemination on social media platforms by reducing clutter and noise. At the same time, it will help users navigate through vast amount of information in direction of the relevant health and care content. Furthermore, study explores prediction of popularity of healthcare posts on the largest social media platform Facebook. Methodology is presented in this paper to predict user engagement based on eleven characteristics of the post: Post Type, Hour Span, Facebook Wall Category, Level, Country, isHoliday, Season, Created Year, Month, Day of the Week, Time of the Day. Finally, post performance prediction is conducted using Artificial Neural Networks (ANN) and Deep Neural Networks (DNN). Different network topology measures are used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.
DESIGN GUIDELINES FOR AUDIO PRESENTATION OF GRAPHS AND TABLES

2.1. General Line Graph Sonification Guidelines Audio can be used to make visualisations accessible to blind and visually impaired people. The MultiVis Project has carried out research into suitable methods for presenting graphs and tables to blind people through the use of both speech and nonspeech audio. This paper presents guidelines extracted from this research. These guidelines will enable designers to implement visualisation systems for blind and visually impaired users, and will provide a framework for researchers wishing to investigate the audio presentation of more complex visualisations. The first set of guidelines are general guidelines for the sonification of line graphs, and are applicable regardless of the number of data series present in the graph. 2.1.1. Map the y-axis of graphs to the pitch of musical notes Research has shown that the pitch of musical notes can be used to represent data by mapping values to corresponding musical notes. Mansur [6] devised a method for line graph sonification, called Sound-Graphs, in which the y-axis of a graph is mapped to pitch, and the x-axis is mapped to time such that moving along the x-axis causes a musical note, representing the corresponding y-value, to be played. The higher the y-value, the higher the pitch . This sonification method is used by MultiVis for line graphs and tables.
ERP Implementation Challenges & Critical Organizational Success Factors

The empirical research describes six key issues that affect the successful implementation of Enterprise Resource Planning (ERP). These issues include unclear objectives, missions and vision, lack of management commitment, lack of end user training, incompatible organization culture and inadequate management of knowledge. The other two important issues include change management and business process reengineering. The involvement and commitment of the executive forms the foundation of the success of the project. The control ensures objectives and goals of the project are formulated and communicated effectively to all stakeholders. The top management plays a vital role in reducing resistance and improving the adoption of technology. Failure to manage change is a major contributor of ERP failure. ERP implementation should be considered as an organization-wide project that advantages from the contribution of all stakeholders. The entire ERP lifecycle involve knowledge creation, storage, and sharing. Therefore, lack of an effective knowledge management can devastate the implementation of ERP. Change management needs to understand organization culture as the basis of behaviors and values among employees. The implementing team needs to create a learning organization that is open to ideas and innovation. In addition, the entire ERP project should be an organization-wide project that benefits from the positive contributions of all stakeholders.
A High-Performance Cable-Drive Module for the Development of Wearable Devices

Bowden cables are frequently used as remote transmissions on wearable devices because of their light weight, simplicity, and flexibility of design. However, a Bowden cable suffers from inefficiency, nonlinear backlash, and cable tension variation due to the bending of the cable housing and friction losses, leading to deteriorating force control performance, and a wearers comfort level and augmentation effectiveness. In this paper, to enhance wearable device performance and to overcome the above drawbacks, we propose a novel high-performance, cable-drive module with rolling cable transmissions (RoCT) that can efficiently transmit motion and mechanical power from an input to an output via an inner cable/rope. The RoCT is compact, modular, lightweight, extremely stiff, highly backdrivable, and is freely rotatable and translatable during manipulation. It maintains the cable tension regardless of the transmission angle between the input and output. The configuration of the proposed tethered cable-drive module, the working principle of the rolling cable transmission, and its properties, are introduced. Mechanical designs based on the working principle are also provided. Finally, as one of the applications of the proposed module, a tethered ankle exoskeleton system and its experimental results demonstrating the properties of the proposed transmission are presented.
Automated Attendance System in College Using Face Recognition and NFC

An almost every institution and organization, attendance monitoring is a very important process. The current method involves the use of sheets of paper, books in taking student attendance. This method could easily allow for proxy and the attendance sheet could be lost or damaged. Taking attendance is thus time consuming using this traditional approach and hence there is a need of an reliable and a automated system. The Attendance monitoring system (AMS) will provide the needed solution. This system uses NFC along with Face Recognition to realize the functionalities. This system uses Face Recognition for the attendance of student at the college gate. The NFC Tag is used to tap on the NFC Reader while entering Classrooms, Labs, etc. The attendance taken at gate through face recognition is accessible to the Principal, HOD and class teacher. Attendance at gate through face recognition Mapped with attendance which is generated through NFC. This mapping is used to avoid fake attendance and to increase accuracy. The system consists of APK file, which contain two logins one for student and one for parent which is installed on their android devices. The AMS will be used to mark the attendance of the student and will also be used to generate weekly, monthly attendance report of student and thus will enable the teachers to keep track of students records. Rather than signing on the attendance sheets or taking attendance manually the student will mark the attendance by just a single click on his device. Also, the teachers has the facility to generate reports, and provide details of student like attendance, marks etc. to parent and also provide details to student like timetable, notes etc. on android application. Student can give feedback to the teacher using same android application  Image Processing, NFC (Near Field Communication) Card, NFC Reader, Face Recognition, AMS (Attendance monitoring system), APK (Android Application Package) file.
Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems

The technique of collaborative filtering is especially successful in generating personalized recommendations. More than a decade of research has resulted in numerous algorithms, although no comparison of the different strategies has been made. In fact, a universally accepted way of evaluating a collaborative filtering algorithm does not exist yet. In this work, we compare different techniques found in the literature, and we study the characteristics of each one, highlighting their principal strengths and weaknesses. Several experiments have been performed, using the most popular metrics and algorithms. Moreover, two new metrics designed to measure the precision on good items have been proposed.
 The results have revealed the weaknesses of many algorithms in extracting information from user profiles especially under sparsity conditions. We have also confirmed the good results of SVD-based techniques already reported by other authors. As an alternative, we present a new approach based on the interpretation of the tendencies or differences between users and items. Despite its extraordinary simplicity, in our experiments, it obtained noticeably better results than more complex algorithms. In fact, in the cases analyzed, its results are at least equivalent to those of the best approaches studied. Under sparsity conditions, there is more than a 20% improvement in accuracy over the traditional user-based algorithms, while maintaining over 90% coverage. Moreover, it is much more efficient computationally than any other algorithm, making it especially adequate for large amounts of data.
Linking antisocial behavior, substance use, and personality: an integrative quantitative model of the adult externalizing spectrum.

Antisocial behavior, substance use, and impulsive and aggressive personality traits often co-occur, forming a coherent spectrum of personality and psychopathology. In the current research, the authors developed a novel quantitative model of this spectrum. Over 3 waves of iterative data collection, 1,787 adult participants selected to represent a range across the externalizing spectrum provided extensive data about specific externalizing behaviors. Statistical methods such as item response theory and semiparametric factor analysis were used to model these data. The model and assessment instrument that emerged from the research shows how externalizing phenomena are organized hierarchically and cover a wide range of individual differences. The authors discuss the utility of this model for framing research on the correlates and the etiology of externalizing phenomena.