nils-herrmann/clean_scirepeval huggingface dataset:
PsyBoG: A scalable botnet detection method for large-scale DNS traffic

Domain Name System (DNS) traffic has become a rich source of information from a security perspective. However, the volume of DNS traffic has been skyrocketing, such that security analyzers experience difficulties in collecting, retrieving, and analyzing the DNS traffic in response to modern Internet threats. More precisely, much of the research relating to DNS has been negatively affected by the dramatic increase in the number of queries and domains. This phenomenon has necessitated a scalable approach, which is not dependent on the volume of DNS traffic. In this paper, we introduce a fast and scalable approach, called PsyBoG, for detecting malicious behavior within large volumes of DNS traffic. PsyBoG leverages a signal processing technique, power spectral density (PSD) analysis, to discover the major frequencies resulting from the periodic DNS queries of botnets. The PSD analysis allows us to detect sophisticated botnets regardless of their evasive techniques, sporadic behavior, and even normal users’ traffic. Furthermore, our method allows us to deal with large-scale DNS data by only utilizing the timing information of query generation regardless of the number of queries and domains. Finally, PsyBoG discovers groups of hosts which show similar patterns of malicious behavior. PsyBoG was evaluated by conducting experiments with two different data sets, namely DNS traces generated by real malware in controlled environments and a large number of real-world DNS traces collected from a recursive DNS server, an authoritative DNS server, and Top-Level Domain (TLD) servers. We utilized the malware traces as the ground truth, and, as a result, PsyBoG performed with a detection accuracy of 95%. By using a large number of DNS traces, we were able to demonstrate the scalability and effectiveness of PsyBoG in terms of practical usage. Finally, PsyBoG detected 23 unknown and 26 known botnet groups with 0.1% false positives.
Deep Residual Learning and PDEs on Manifold

In this paper, we formulate the deep residual network (ResNet) as a control problem of transport equation. In ResNet, the transport equation is solved along the characteristics. Based on this observation, deep neural network is closely related to the control problem of PDEs on manifold. We propose several models based on transport equation, Hamilton-Jacobi equation and Fokker-Planck equation. The discretization of these PDEs on point cloud is also discussed.  Deep residual network; control problem; manifold learning; point cloud; transport equation; Hamilton-Jacobi equation 1 Deep Residual Network Deep convolution neural networks have achieved great successes in image classification. Recently, an approach of deep residual learning is proposed to tackle the degradation in the classical deep neural network [7, 8]. The deep residual network can be realized by adding shortcut connections in the classical CNN. A building block is shown in Fig. 1. Formally, a building block is defined as: y = F (x, {Wi}) + x. Here x and y are the input and output vectors of the layers. The function F (x, {Wi}) represents the residual mapping to be learned. ) in which σ = ReLU ◦ BN denotes composition of ReLU and Batch-Normalization. ∗Department of Mathematics, Hong Kong University of Science & Technology, Hong Kong.  mazli@ust.hk. †Yau Mathematical Sciences Center, Tsinghua University, Beijing, China, 100084.  zqshi@tsinghua.edu.cn. 1 ar X iv :1 70 8. 05 11 5v 3 [ cs .I T ] 2 5 Ja n 20 18
Psychological resilience, positive emotions, and successful adaptation to stress in later life.

In 3 studies, the authors investigated the functional role of psychological resilience and positive emotions in the stress process. Studies 1a and 1b explored naturally occurring daily stressors. Study 2 examined data from a sample of recently bereaved widows. Across studies, multilevel random coefficient modeling analyses revealed that the occurrence of daily positive emotions serves to moderate stress reactivity and mediate stress recovery. Findings also indicated that differences in psychological resilience accounted for meaningful variation in daily emotional responses to stress. Higher levels of trait resilience predicted a weaker association between positive and negative emotions, particularly on days characterized by heightened stress. Finally, findings indicated that over time, the experience of positive emotions functions to assist high-resilient individuals in their ability to recover effectively from daily stress. Implications for research into protective factors that serve to inhibit the scope, severity, and diffusion of daily stressors in later adulthood are discussed.
Integration of agent-based and Cloud Computing for the smart objects-oriented IoT

In the future Internet of Things (IoT), smart objects will be the fundamental building blocks for the creation of cyber-physical smart pervasive systems in a great variety of application domains ranging from health-care to transportation, from logistics to smart grid and cities. The implementation of a smart objects-oriented IoT is a complex challenge as distributed, autonomous, and heterogeneous IoT components at different levels of abstractions and granularity need to cooperate among themselves, with conventional networked IT infrastructures, and also with human users. In this paper, we propose the integration of two complementary mainstream paradigms for large-scale distributed computing: Agents and Cloud. Agent-based computing can support the development of decentralized, dynamic, cooperating and open IoT systems in terms of multi-agent systems. Cloud computing can enhance the IoT objects with high performance computing capabilities and huge storage resources. In particular, we introduce a cloud-assisted and agent-oriented IoT architecture that will be realized through ACOSO, an agent-oriented middleware for cooperating smart objects, and BodyCloud, a sensor-cloud infrastructure for large-scale sensor-based systems.
End-fire phased array 5G antenna design using leaf-shaped bow-tie elements for 28/38 GHz MIMO applications

In this paper, a new design of mm-Wave phased array 5G antenna for multiple-input multiple-output (MIMO) applications has been introduced. Two identical linear phased arrays with eight leaf-shaped bow-tie antenna elements have been used at different sides of the mobile-phone PCB. An Arlon AR 350 dielectric with properties of h=0.5 mm, ε=3.5, and δ=0.0026 has been used as a substrate of the proposed design. The antenna is working in the frequency range of 25 to 40 GHz (more than 45% FBW) and can be easily fit into current handheld devices. The proposed MIMO antenna has good radiation performances at 28 and 38 GHz which both are powerful candidates to be the carrier frequency of the future 5G cellular networks.
Aligning Cyber-Physical System Safety and Security

Safety and security are two key properties of Cyber-Physical Systems (CPS). Safety is aimed at protecting the systems from accidental failures in order to avoid hazards, while security is focused on protecting the systems from intentional attacks. They share identical goals – protecting CPS from failing. When aligned within a CPS, safety and security work well together in providing a solid foundation of an invincible CPS, while weak alignment may produce inefficient development and partially-protected systems. The need of such alignment has been recognized by the research community, the industry, as well as the International Society of Automation (ISA), which identified a need of alignment between safety and security standards ISA84 (IEC 61511) and ISA99 (IEC 62443). We propose an approach for aligning CPS safety and security at early development phases by synchronizing safety and security lifecycles based on ISA84 and ISA99 standards. The alignment is achieved by merging safety and security lifecycle phases, and developing an unified model – Failure-Attack-CounTermeasure (FACT) Graph. The FACT graph incorporates safety artefacts (fault trees and safety countermeasures) and security artefacts (attack trees and security countermeasures), and can be used during safety and security alignment analysis, as well as in later CPS development and operation phases, such as verification, validation, monitoring, and periodic safety and security assessment.
Process Mining for the multi-faceted analysis of business processes - A case study in a financial services organization

Most organizations have some kind of process-oriented information system that keeps track of business events. Process Mining starts from event logs extracted from these systems in order to discover, analyze, diagnose and improve processes, organizational, social and data structures. Notwithstanding the large number of contributions to the process mining literature over the last decade, the number of studies actually demonstrating the applicability and value of these techniques in practice has been limited. As a consequence, there is a need for real-life case studies suggesting methodologies to conduct process mining analysis and to show the benefits of its application in real-life environments. In this paper we present a methodological framework for a multi-faceted analysis of real-life event logs based on Process Mining. As such, we demonstrate the usefulness and flexibility of process mining techniques to expose organizational inefficiencies in a real-life case study that is centered on the back office process of a large Belgian insurance company. Our analysis shows that process mining techniques constitute an ideal means to tackle organizational challenges by suggesting process improvements and creating a companywide process awareness.
Analysis and Design of an Optimal Energy Management and Control System for Hybrid Electric Vehicles

This paper presents a preliminary design and analysis of an optimal energy management and control system for a parallel hybrid electric vehicle using hybrid dynamic control system theory and design tools. The vehicle longitudinal dynamics is analyzed. The practical operation modes of the hybrid electric vehicle are introduced with regard to the given power train configuration. In order to synthesize the vehicle continuous dynamics and the discrete transition between the vehicle operation modes, the hybrid dynamical system theory is applied to reformulate such a complex dynamical system in which the interaction of discrete and continuous dynamics are involved. A dynamic programming-based method is developed to determine the optimal power split between both sources of energy. Computer simulation results are presented and demonstrate the effectiveness of the proposed design and applicability and practicality of the design in real-time implementation. Copyright 2002 EVS19
Big Data Analytics in Healthcare

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities

Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is attained. We describe our vision for a "human-in-the-loop" ML system that accelerates this process: by intelligently tracking changes and intermediate results over time, such a system can enable rapid iteration, quick responsive feedback, introspection and debugging, and background execution and automation. We finally describe Helix, our preliminary attempt at such a system that has already led to speedups of upto 10x on typical iterative workflows against competing systems.
Non-reference audio quality assessment for online live music recordings

Immensely popular video sharing websites such as YouTube have become the most important sources of music information for Internet users and the most prominent platform for sharing live music. The audio quality of this huge amount of live music recordings, however, varies significantly due to factors such as environmental noise, location, and recording device. However, most video search engines do not take audio quality into consideration when retrieving and ranking results. Given the fact that most users prefer live music videos with better audio quality, we propose the first automatic, non-reference audio quality assessment framework for live music video search online. We first construct two annotated datasets of live music recordings. The first dataset contains 500 human-annotated pieces, and the second contains 2,400 synthetic pieces systematically generated by adding noise effects to clean recordings. Then, we formulate the assessment task as a ranking problem and try to solve it using a learning-based scheme. To validate the effectiveness of our framework, we perform both objective and subjective evaluations. Results show that our framework significantly improves the ranking performance of live music recording retrieval and can prove useful for various real-world music applications.
A comparison of inclusive and restrictive strategies in modern missing data procedures.

Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.
Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification

Recently, very high resolution (VHR) panchromatic and multispectral (MS) remote-sensing images can be acquired easily. However, it is still a challenging task to fuse and classify these VHR images. Generally, there are two ways for the fusion and classification of panchromatic and MS images. One way is to use a panchromatic image to sharpen an MS image, and then classify a pan-sharpened MS image. Another way is to extract features from panchromatic and MS images, respectively, and then combine these features for classification. In this paper, we propose a superpixel-based multiple local convolution neural network (SML-CNN) model for panchromatic and MS images classification. In order to reduce the amount of input data for the CNN, we extend simple linear iterative clustering algorithm for segmenting MS images and generating superpixels. Superpixels are taken as the basic analysis unit instead of pixels. To make full advantage of the spatial-spectral and environment information of superpixels, a superpixel-based multiple local regions joint representation method is proposed. Then, an SML-CNN model is established to extract an efficient joint feature representation. A softmax layer is used to classify these features learned by multiple local CNN into different categories. Finally, in order to eliminate the adverse effects on the classification results within and between superpixels, we propose a multi-information modification strategy that combines the detailed information and semantic information to improve the classification performance. Experiments on the classification of Vancouver and Xi’an panchromatic and MS image data sets have demonstrated the effectiveness of the proposed approach.
Wideband mm-wave phase shifters based on constant-impedance tunable transmission lines

This paper presents an integrated inverted stripline-like tunable transmission line structure where the propagation velocity can be modified as the characteristic impedance remains constant. As one application of this structure, a mm-wave phase shifter for massive hybrid MIMO applications is implemented in a 45 nm CMOS SOI process. Measurement results at 45 GHz of this phase shifter demonstrate a 79° phase shift tuning range, worst-case insertion loss of 3.3 dB, and effective area of 0.072 mm2. Compared to an on-chip reference phase shifter implemented based on a previously-reported tunable transmission line structure, this work achieves 35% less area occupied and 1.0 dB less insertion loss, while maintaining approximately the same phase shift tuning range.
Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm

In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice versa. It is very easy to solve the problem of transforming geodetic coordinates into geocentric cartesian coordinates. On the other hand, it is rather difficult to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates as it is very hard to define a mathematical relationship between the geodetic latitude (j) and the geocentric cartesian coordinates (X, Y, Z). In this paper, a new algorithm, the Differential Search Algorithm (DS), is presented to solve the problem of transforming the geocentric cartesian coordinates into geodetic coordinates and its performance is compared with the performances of the classical methods (i.e., Borkowski, 1989; Bowring, 1976; Fukushima, 2006; Heikkinen, 1982;  2002; Zhang, 2005; Borkowski, 1987; Shu, 2010 and Lin, 1995) and ComputationalIntelligence algorithms (i.e., ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA–ES). The statistical tests realized for the comparison of performances indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper.
SQL Based Association Rule Mining Using Commercial RDBMS (IBM DB2 UBD EEE)

Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized implementation and expensive mining tools being on sale. In this paper we present an evaluation of SQL based data mining on commercial RDBMS (IBM DB2 UDB EEE). We examine some techniques to reduce I/O cost by using View and Subquery. Those queries can be more than 6 times faster than SETM SQL query reported previously. In addition, we have made performance evaluation on parallel database environment and compared the performance result with commercial data mining tool (IBM Intelligent Miner). We prove that SQL based data mining can achieve sufficient performance by the utilization of SQL query customization and database tuning.
Design and Analysis of the Droop Control Method for Parallel Inverters Considering the Impact of the Complex Impedance on the Power Sharing

This paper investigates the characteristics of the active and reactive power sharing in a parallel inverters system under different system impedance conditions. The analyses conclude that the conventional droop method cannot achieve efficient power sharing for the case of a system with complex impedance condition. To achieve the proper power balance and minimize the circulating current in the different impedance situations, a novel droop controller that considers the impact of complex impedance is proposed in this paper. This controller can simplify the coupled active and reactive power relationships, which are caused by the complex impedance in the parallel system. In addition, a virtual complex impedance loop is included in the proposed controller to minimize the fundamental and harmonic circulating current that flows in the parallel system. Compared to the other methods, the proposed controller can achieve accurate power sharing, offers efficient dynamic performance, and is more adaptive to different line impedance situations. Simulation and experimental results are presented to prove the validity and the improvements achieved by the proposed controller.
Health Promotion from the Perspective of Social Cognitive Theory

This chapter examines health promotion and disease prevention from the perspective of social cognitive theory. This theory posits a multifaceted causal structure in which self-efficacy beliefs operate in concert with cognized goals, outcome expectations, and perceived environmental impediments and facilitators in the regulation of human motivation, action, and well-being. Perceived self-efficacy is a key factor in the causal structure because it operates on motivation and action both directly and through its impact on the other determinants. The areas of overlap of sociocognitive determinants with some of the most widely applied psychosocial models of health are identified. Social cognitive theory addresses the sociostructural determinants of health as well as the personal determinants. A comprehensive approach to health promotion requires changing the practices of social systems that have widespread detrimental effects on health rather than solely changing the habits of individuals. Further progress in this field requires building new structures for health promotion, new systems for risk reduction and greater emphasis on health policy initiatives. People's beliefs in their collective efficacy to accomplish social change, therefore, play a key role in the policy and public health perspective to health promotion and disease prevention. Bandura, A. (1998). Health promotion from the perspective of social cognitive theory. Psychology and Health, 13, 623-649.
A comparison of machine learning techniques for phishing detection

There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting phishing. The present study compares the predictive accuracy of several machine learning methods including Logistic Regression (LR), Classification and Regression Trees (CART), Bayesian Additive Regression Trees (BART), Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNet) for predicting phishing emails. A data set of 2889 phishing and legitimate emails is used in the comparative study. In addition, 43 features are used to train and test the classifiers.