nils-herrmann/clean_scirepeval huggingface dataset:
Intrusion Detection in the RPL-connected 6LoWPAN Networks

The interconnectivity of 6LoWPAN networks with the Internet raises serious security concerns, as constrained 6LoWPAN devices are accessible anywhere from the untrusted global Internet. Also, 6LoWPAN devices are mostly deployed in unattended environments, hence easy to capture and clone. Despite that state of the art crypto solutions provide information security, IPv6 enabled smart objects are vulnerable to attacks from outside and inside 6LoWPAN networks that are aimed to disrupt networks. This paper attempts to identify intrusions aimed to disrupt the Routing Protocol for Low-Power and Lossy Networks (RPL).In order to improve the security within 6LoWPAN networks, we extend SVELTE, an intrusion detection system for the Internet of Things, with an intrusion detection module that uses the ETX (Expected Transmissions) metric. In RPL, ETX is a link reliability metric and monitoring the ETX value can prevent an intruder from actively engaging 6LoWPAN nodes in malicious activities. We also propose geographic hints to identify malicious nodes that conduct attacks against ETX-based networks. We implement these extensions in the Contiki OS and evaluate them using the Cooja simulator.
Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm

Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.
Vulnerabilities in hub architecture IoT devices

This paper introduces new methods for gaining sensitive information from Internet of Things (IoT) devices. Generally, manufacturers tend to forget about security when putting a new product on the market, this notion does not exclude IoT. This has been proven by current research using simple techniques and devices to obtain unencrypted information. Using similar attacks, the IoT hubs themselves was chosen as a target instead of the devices connected to them. Two different attacks will be used. First, using various methods for sniffing Hub traffic, we will attempt to gain credentials on the uplink side. Secondly, port scans will be used to gain any exploited services in order to obtain root access. After our attempts, it was found that IoT hubs can be very susceptible to tech savvy attackers using simple Man in the Middle attacks. If network access was achieved, the security of ones home is greatly compromised allowing intruders an 8–10 minute window to enter a house undetected. Using modest methodologies, consumers and companies can prevent future exploits.
MusicMixer: computer-aided DJ system based on an automatic song mixing

In this paper, we present MusicMixer, a computer-aided DJ system that helps DJs, specifically with song mixing. MusicMixer continuously mixes and plays songs using an automatic music mixing method that employs audio similarity calculations. By calculating similarities between song sections that can be naturally mixed, MusicMixer enables seamless song transitions. Though song mixing is the most fundamental and important factor in DJ performance, it is difficult for untrained people to seamlessly connect songs. MusicMixer realizes automatic song mixing using an audio signal processing approach; therefore, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system, enabling effective DJ song mixing and lowering entry barriers for the inexperienced. We also propose personalization for song suggestions using a preference memorization function of MusicMixer.
Detecting Webshell Based on Random Forest with FastText

Web-based remote access Trojan (or webshell) is a kind of tool for network intrusion, which can be uploaded to a website to access web service management authority. Once attacker injected successfully, it can cause great damage so that it is crucial to detect webshell effectively. Webshells are flexible and changeable by using of obfuscation techniques, which compounds the difficulties of detecting. A PHP webshell detection model is proposed in this paper, which based on a combination of fastText and random forest algorithm and called FRF-WD. The PHP opcode sequences as an important feature applied for webshell detection. The experimental results show that the model can provide high detection rate and low false alarm rate, which proved the feasibility and validity of the model.
Development of an Omni-Directional Mobile Robot with 3 DOF Decoupling Drive Mechanism

A ne'ui driving mech,anism for holonomic ornwidirectional mobi le robots i s designzed, ,which enu.bles 3 DOF m o t i o n control by three correspondent actuators in, (I decozipled ma'nner ,with n o redundan,cy. A prototype of the omni-drrectional mobile robot w i th th,e dr iu ing mechan i sm is developed including a parallel link suspemion m,echanism. T h e k inemat ics of th.e omnidirectionsal mobile robot i s also analyzed, and simulat i o n fo,r velocity control of the robot is perform.ed by od f o r ,oelocity modula t ion wi th in,terpolation. to uchieve the given target pos i t ion and velocity.
Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach

This paper addresses the problem of detecting coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition. We first introduce a coarse-to-fine thermal-diffusionbased approach. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. Finally, these semantic regions are used to recognize activities in crowded scenes. Experiments on various videos demonstrate the effectiveness of our approach.
Robust Nonlinear Fusion of Inertial and Visual Data for position, velocity and attitude estimation of UAV

This paper presents a coupled observer that uses accelerometer, gyrometer and vision sensors to provide estimates of pose and linear velocity for an aerial robotic vehicle. The observer is based on a non-linear complimentary filter framework and incorporates adaptive estimates of measurement bias in gyrometers and accelerometers commonly encountered in low-cost inertial measurement systems. Asymptotic stability of the observer estimates is proved as well as bounded energy of the observer error signals. Experimental data is provided for the proposed filter run on data obtained from an experiment involving a remotely controlled helicopter.
An integrated fuzzy MCDM approach for supplier evaluation and selection

A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.
GPU-based cone beam computed tomography

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s.
Transition-based Dependency DAG Parsing Using Dynamic Oracles

In most of the dependency parsing studies, dependency relations within a sentence are often presented as a tree structure. Whilst the tree structure is sufficient to represent the surface relations, deep dependencies which may result to multi-headed relations require more general dependency structures, namely Directed Acyclic Graphs (DAGs). This study proposes a new dependency DAG parsing approach which uses a dynamic oracle within a shift-reduce transitionbased parsing framework. Although there is still room for improvement on performance with more feature engineering, we already obtain competitive performances compared to static oracles as a result of our initial experiments conducted on the ITU-METU-Sabancı Turkish Treebank (IMST).
An adaptive threshold deep learning method for fire and smoke detection

This paper proposes a novel method for fire and smoke detection using video images. The ViBe method is used to extract a background from the whole video and to update the exact motion areas using frame-by-frame differences. Dynamic and static features extraction are combined to recognize the fire and smoke areas. For static features, we use deep learning to detect most of fire and smoke areas based on a Caffemodel. Another static feature is the degree of irregularity of fire and smoke. An adaptive weighted direction algorithm is further introduced to this paper. To further reduce the false alarm rate and locate the original fire position, every frame image of video is divided into 16×16 grids and the times of smoke and fire occurrences of each part is recorded. All clues are combined to reach a final detection result. Experimental results show that the proposed method in this paper can efficiently detect fire and smoke and reduce the loss and false detection rates.
Wireless Power transmission for solar input

Wireless Power transmission is the transmission of electrical power from the source to load without the use of wires. Wireless transfer of electrical energy eliminates the use of copper cables and conducting wires. Previously wireless transfer of power has been achieved for AC source or for charged batteries. In this project we have used output from the solar panel as the output. Since the output from the solar panel is very small it has been stepped up to appropriate values using the DC-DC step up converter. The stepped up DC is then converted into an oscillating signal. This signal is then transferred wirelessly using a transformer. The electrical energy is transferred using the magnetic coupling that exists between the transformer setup. The oscillating signal is the passed through a rectifier circuit and converted into DC before it is fed into the load. The proposed wireless transmission is verified using MATLAB/SIMULINK.
Pore-scale flow simulation in anisotropic porous material via fluid-structure coupling

This paper describes a novel hybrid method for fluid simulation of saturating anisotropic porous material via fluid-structure coupling. Our framework employs particle finite element method (PFEM) that not only adopts Lagrangian scheme to model the motion of freely-moving particles, but also produces the extended Delaunay Tessellation to furnish the governing equations with FEM discretization. We first employ adaptive smoothed particle hydrodynamics (SPH) to simulate porous flow respecting the anisotropic permeability with little cost. Second, the extended Delaunay Tessellation is obtained to solve differential equations for skeletal deformation. Third, a hybrid particle system is adopted to track the surface and topological changes. At the physical level, we introduce dynamic permeability considering skeletal deformation via fluid-structure coupling. At the geometric level, PFEM reduces the computational cost and effectively tracks topological changes. Moreover, our implementation on CUDA improves the performance in high-quality physics-based graphics applications. Consequently, the proposed method realistically reproduces interactions between pore-scale flow and anisotropic porous material.
Clustering millions of tandem mass spectra.

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.
Personality and job performance: the Big Five revisited.

Prior meta-analyses investigating the relation between the Big 5 personality dimensions and job performance have all contained a threat to construct validity, in that much of the data included within these analyses was not derived from actual Big 5 measures. In addition, these reviews did not address the relations between the Big 5 and contextual performance. Therefore, the present study sought to provide a meta-analytic estimate of the criterion-related validity of explicit Big 5 measures for predicting job performance and contextual performance. The results for job performance closely paralleled 2 of the previous meta-analyses, whereas analyses with contextual performance showed more complex relations among the Big 5 and performance. A more critical interpretation of the Big 5-performance relationship is presented, and suggestions for future research aimed at enhancing the validity of personality predictors are provided.
Issues and Perspectives in Meditation Research: In Search for a Definition

Despite the growing interest in the neurobiological correlates of meditation, most research has omitted to take into account the underlying philosophical aspects of meditation and its wider implications. This, in turn, is reflected in issues surrounding definition, study design, and outcomes. Here, I highlight the often ignored but important aspect of definition in the existing scholarship on neuroscience and meditation practice. For a satisfactory account of a neuroscience of meditation, we must aim to retrieve an operational definition that is inclusive of a traditional ontological description as well as the modern neurocognitive account of the phenomena. Moving beyond examining the effects of meditation practice, to take a potential step forward in the direction to establish how meditation works, it becomes crucial to appraise the philosophical positions that underlie the phenomenology of meditation in the originating traditions. This endeavor may challenge our intuitions and concepts in either directions, but issues pertaining to definition, design, and validity of response measures are extremely important for the evolution of the field and will provide a much-needed context and framework for meditation based interventions.
Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.
Counter-Forensics: Attacking Image Forensics

This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images. Research on counter-forensics is motivated by the need to assess and improve the reliability of forensic methods in situations where intelligent adversaries make efforts to induce a certain outcome of forensic analyses. Counter-forensics is first defined in a formal decision-theoretic framework. This framework is then interpreted and extended to encompass the requirements to forensic analyses in practice, including a discussion of the notion of authenticity in the presence of legitimate processing, and the role of image models with regard to the epistemic underpinning of the forensic decision problem. A terminology is developed that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks. This terminology is directly applied in a self-contained technical survey of counter-forensics against image forensics, notably techniques that suppress traces of image processing and techniques that synthesize traces of authenticity, including examples and brief evaluations. A discussion of relations to other domains of multimedia security and an overview of open research questions concludes the chapter. 1 Definition of Counter-Forensics This final chapter changes the perspective. It is devoted to digital image counterforensics, the art and science of impeding and misleading forensic analyses of digital images. Rainer Böhme Westfälische Wilhelms-Universität Münster, Dept. of Information Systems, Leonardo-Campus 3, 48149 Münster, Germany e-mail: rainer.boehme@wi.uni-muenster.de Matthias Kirchner International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704 e-mail: kirchner@icsi.berkeley.edu
Occlusion-Free Visual Servoing for the Shared Autonomy Teleoperation of Dual-Arm Robots

Visual servoing in telerobotics provides information to the operator about the remote location and assists in the task execution to reduce stress on the user. Occlusions in this scenario can, on the one hand, lead to the visual servoing failure, and, on the other hand, degrade the user experience and navigation performance due to the obstructed vision of elements of interest. In this letter, we consider a teleoperation system composed of two robot arms where one is remotely operated, while the other is autonomous and equipped with an eye-in-hand camera sensor. We propose a reactive unified convex optimization based controller that allows the execution of occlusion-free tasks by autonomously adjusting the camera robot, so as to keep the teleoperated one in the field of view. The occlusion avoidance is formulated inside the optimization as a constraint in the image space, it is formally derived from a collision avoidance analogy and made robust against noisy measurements and dynamic environment. A state machine is used to switch the control policy whenever an occlusion might occur. We validate our approach with experiments on a 14 d.o.f. dual-arm ABB YuMi robot equipped with an red, green, blue (RGB) camera and teleoperated by a 3 d.o.f. Novint Falcon device.