nils-herrmann/clean_scirepeval huggingface dataset:
On Integrating Data Mining into Business Processes

Integrating data mining into business processes becomes crucial for business today. Modern business process management frameworks provide great support for flexible design, deployment and management of business processes. However, integrating complex data mining services into such frameworks is not trivial due to unclear definitions of user roles and missing flexible data mining services as well as missing standards and methods for the deployment of data mining solutions. This work contributes an integrated view on the definition of user roles for business, IT and data mining and discusses the integration of data mining in business processes and its evaluation in the context of BPR.
Robust Camera Calibration and Player Tracking in Broadcast Basketball Video

With the growth of fandom population, a considerable amount of broadcast sports videos have been recorded, and a lot of research has focused on automatically detecting semantic events in the recorded video to develop an efficient video browsing tool for a general viewer. However, a professional sportsman or coach wonders about high level semantics in a different perspective, such as the offensive or defensive strategy performed by the players. Analyzing tactics is much more challenging in a broadcast basketball video than in other kinds of sports videos due to its complicated scenes and varied camera movements. In this paper, by developing a quadrangle candidate generation algorithm and refining the model fitting score, we ameliorate the court-based camera calibration technique to be applicable to broadcast basketball videos. Player trajectories are extracted from the video by a CamShift-based tracking method and mapped to the real-world court coordinates according to the calibrated results. The player position/trajectory information in the court coordinates can be further analyzed for professional-oriented applications such as detecting wide open event, retrieving target video clips based on trajectories, and inferring implicit/explicit tactics. Experimental results show the robustness of the proposed calibration and tracking algorithms, and three practicable applications are introduced to address the applicability of our system.
Verification of Computer Switching Networks: An Overview

Formal verification has seen much success in several domains of hardware and software design. For example, in hardware verification there has been much work in the verification of microprocessors (e.g. ) and memory systems (e.g. ). Similarly, software verification has seen success in device-drivers (e.g. ) and concurrent software (e.g. ). The area of network verification, which consists of both hardware and software components, has received relatively less attention. Traditionally, the focus in this domain has been on performance and security, with less emphasis on functional correctness. However, increasing complexity is resulting in increasing functional failures and thus prompting interest in verification of key correctness properties. This paper reviews the formal verification techniques that have been used here thus far, with the goal of understanding the characteristics of the problem domain that are helpful for each of the techniques, as well as those that pose specific challenges. Finally, it highlights some interesting research challenges that need to be addressed in this important emerging domain.
Edge Analytics in the Internet of Things

High-data-rate sensors, such as video cameras, are becoming ubiquitous in the Internet of Things. This article describes GigaSight, an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls. The GigaSight architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet, thus reducing the demand for ingress bandwidth into the cloud. Denaturing, which is an owner-specific reduction in fidelity of video content to preserve privacy, is one form of analytics on cloudlets. Content-based indexing for search is another form of cloudlet-based analytics. This article is part of a special issue on smart spaces.
Internet Addiction among Greek Adolescent Students

This research aimed to assess the prevalence of Internet addiction among Greek adolescent students, ages 12 to 18. The sample of 2,200 students was recruited from 120 classes among 85 schools in Thessaly, Greece. The sample included 10% of all classes in schools of Thessaly. The method of randomized stratified selection in every school was used for its constitution. Participants were asked to complete the Diagnostic Questionnaire for Internet Addiction (YDQ), based on eight criteria, as well as an inventory that included sociodemographic factors and questions about the use of Internet, their social life, and their habits. In Greece, 70.8% of adolescents had access to the Internet. The consistency of the YDQ was tested with Cronbach's alpha (0.719), with standardized item alpha (0.728). Proportions are also calculated only on the frequent Internet users, which results in 11% fulfilling five YDQ criteria. The most frequent type of Internet use is online games, representing 50.9% of Internet users, and information services, representing 46.8%. The prevalence of Internet addiction among Internet users of Central Greece is 8.2%, and it concerns mainly the male students who play online games and visit Internet cafés.
Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.
Modeling Context Information in Pervasive Computing Systems

As computing becomes more pervasive, the nature of applications must change accordingly. In particular, applications must become more flexible in order to respond to highly dynamic computing environments, and more autonomous, to reflect the growing ratio of applications to users and the corresponding decline in the attention a user can devote to each. That is, applications must become more context-aware. To facilitate the programming of such applications, infrastructure is required to gather, manage, and disseminate context information to applications. This paper is concerned with the development of appropriate context modeling concepts for pervasive computing, which can form the basis for such a context management infrastructure. This model overcomes problems associated with previous context models, including their lack of formality and generality, and also tackles issues such as wide variations in information quality, the existence of complex relationships amongst context information and temporal aspects of context.
Fashion image classification on mobile phones using layered deep convolutional neural networks

Toward implementation of fashion recommendation system based on photos taken with mobile phones, we propose a framework to recognize hierarchical categories of a fashion item. To classify an arbitrary photo of clothes robustly, (1) we collected two kind of dataset: (I) 120K datasets of clothes images on EC sites to train the classifier and (II) the clothing image set composed of photos taken by participants with their mobile phones. (2) we proposed Layered Deep Convolutional Neural Networks (LDCNNs) which is specialized in classifying images into hierarchical categories: hoodie is a lower in hierarchy in tops category. Experimental result shows proposed LDCNNs obtained mean accuracy of 92.7% for datasets from EC sites and 96.9% for those from mobile phones. This results are better than those (84.9%, 90.6 % respectively) for MLR+CNN in classification accuracy.
Affective content detection using HMMs

This paper discusses a new technique for detecting affective events using Hidden Markov Models(HMM). To map low level features of video data to high level emotional events, we perform empirical study on the relationship between emotional events and low-level features. After that, we compute simple low-level features that represent emotional characteristics and construct a token or observation vector by combining low level features. The observation vector sequence is tested to detect emotional events through HMMs. We create two HMM topologies and test both topologies. The affective events are detected from our proposed models with good accuracy.
Medical Image Segmentation Using Genetic Algorithms

Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.
Performance comparison between buck and boost CRM PFC converter

This paper presents a detailed performance comparison (including efficiency, EMC performance and component electrical stress) between boost and buck type PFC under critical conduction mode (CRM). In universal input (90–265Vac) applications, the CRM buck PFC has around 1% higher efficiency compared to its counterpart at low-line (90Vac) condition. Due to the low voltage swing of switch, buck PFC has a better CM EMI performance than boost PFC. It seems that the buck PFC is more attractive in low power applications which only need to meet the IEC61000-3-2 Class D standard based on the comparison. The experimental results from two 100-W prototypes are also presented for side by side comparison.
DIFUZE: Interface Aware Fuzzing for Kernel Drivers

Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.
Hardware architecture design and implementation for FMCW radar signal processing algorithm

Chirp-sequence-based Frequency Modulation Continuous Wave (FMCW) radar is effective at detecting range and velocity of a target. However, the target detection algorithm is based on two-dimensional Fast Fourier Transform, which uses a great deal of data over several PRIs (Pulse Repetition Intervals). In particular, if the multiple-receive channel is employed to estimate the angle position of a target; even more computational complexity is required. In this paper, we report on how a newly developed signal processing module is implemented in the FPGA, and on its performance measured under test conditions. Moreover, we have presented results from analysis of the use of hardware resources and processing times.
De-obfuscation and Detection of Malicious PDF Files with High Accuracy

Due to its high popularity and rich functionalities, the Portable Document Format (PDF) has become a major vector for malware propagation. To detect malicious PDF files, the first step is to extract and de-obfuscate Java Script codes from the document, for which an effective technique is yet to be created. However, existing static methods cannot de-obfuscate Java Script codes, existing dynamic methods bring high overhead, and existing hybrid methods introduce high false negatives. Therefore, in this paper, we present MPScan, a scanner that combines dynamic Java Script de-obfuscation and static malware detection. By hooking the Adobe Reader's native Java Script engine, Java Script source code and op-code can be extracted on the fly after the source code is parsed and then executed. We also perform a multilevel analysis on the resulting Java Script strings and op-code to detect malware. Our evaluation shows that regardless of obfuscation techniques, MPScan can effectively de-obfuscate and detect 98% malicious PDF samples.
Body joints regression using deep convolutional neural networks

Human pose estimation is a well-known computer vision problem that receives intensive research interest. The reason for such interest is the wide range of applications that the successful estimation of human pose offers. Articulated pose estimation includes real time acquisition, analysis, processing and understanding of high dimensional visual information. Ensemble learning methods operating on hand-engineered features have been commonly used for addressing this task. Deep learning exploits representation learning methods to learn multiple levels of representations from raw input data, alleviating the need to hand-crafted features. Deep convolutional neural networks are achieving the state-of-the-art in visual object recognition, localization, detection. In this paper, the pose estimation task is formulated as an offset joint regression problem. The 3D joints positions are accurately detected from a single raw depth image using a deep convolutional neural networks model. The presented method relies on the utilization of the state-of-the-art data generation pipeline to generate large, realistic, and highly varied synthetic set of training images. Analysis and experimental results demonstrate the generalization performance and the real time successful application of the proposed method.
Job recommender systems: A survey

The personalized recommender system is proposed to solve the problem of information overload and widely applied in many domains. The job recommender systems for job recruiting domain have emerged and enjoyed explosive growth in the last decades. User profiles and recommendation technologies in the job recommender system have gained attention and investigated in academia and implemented for some application cases in industries. In this paper, we introduce some basic concepts of user profile and some common recommendation technologies based on the existing research. Finally, we survey some typical job recommender systems which have been achieved and have a general comprehension of job recommender systems.
Joint Trajectory and Communication Design for UAV-Enabled Multiple Access

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV-enabled wireless network where the UAV is employed as an aerial mobile base station (BS) to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users by jointly optimizing the multiuser communication scheduling and UAV trajectory over a finite horizon. The formulated problem is shown to be a mixed integer non-convex optimization problem that is difficult to solve in general. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques, which is guaranteed to converge. To achieve fast convergence and stable throughput, we further propose a low-complexity initialization scheme for the UAV trajectory design based on the simple circular trajectory. Extensive simulation results are provided which show significant throughput gains of the proposed design as compared to other benchmark schemes.
A prototype empirical evaluation of test driven development

Test driven development (TDD) is a relatively new software development process. On the strength of anecdotal evidence and a number of empirical evaluations, TDD is starting to gain momentum as the primary means of developing software in organizations worldwide. In traditional development, tests are for verification and validation purposes and are built after the target product feature exists. In test-driven development, tests are used for specification purposes in addition to verification and validation. An experiment was devised to investigate the distinction between test-driven development and traditional, test-last development from the perspective of developer productivity and software quality. The results of the experiment indicate that while there is little or no difference in developer productivity in the two processes, there are differences in the frequency of unplanned test failures. This may lead to less debugging and more time spent on forward progress within a development project. As with many new software development technologies however, this requires further study, in particular to determine if the positive results translate into lower total costs of ownership.
EEG Signal Analysis: A Survey

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.
A 16–18.8-GHz Sub-Integer-N Frequency Synthesizer for 60-GHz Transceivers

An 18-GHz range frequency synthesizer is implemented in 0.13-mum SiGe BiCMOS technology as part of a 60-GHz superheterodyne transceiver chipset. It provides for RF channels of 56.5-64 GHz in 500-MHz steps, and features a phase-rotating multi-modulus divider capable of sub-integer division. Output frequency range from the synthesizer is 16.0 to 18.8 GHz, while the enabled RF frequency range is 3.5 times this, or 55.8 to 65.8 GHz. The measured RMS phase noise of the synthesizer is 0.8deg (1 MHz to 1 GHz integration), while phase noise at 100-kHz and 10-MHz offsets are -90 and -124 dBc/Hz, respectively. Reference spurs are 69 dBc; sub-integer spurs are -65 dBc; and combined power consumption from 1.2 and 2.7 V is 144 mW.
Link prediction: the power of maximal entropy random walk

Link prediction is a fundamental problem in social network analysis. The key technique in unsupervised link prediction is to find an appropriate similarity measure between nodes of a network. A class of wildly used similarity measures are based on random walk on graph. The traditional random walk (TRW) considers the link structures by treating all nodes in a network equivalently, and ignores the centrality of nodes of a network. However, in many real networks, nodes of a network not only prefer to link to the similar node, but also prefer to link to the central nodes of the network. To address this issue, we use maximal entropy random walk (MERW) for link prediction, which incorporates the centrality of nodes of the network. First, we study certain important properties of MERW on graph $G$ by constructing an eigen-weighted graph G. We show that the transition matrix and stationary distribution of MERW on G are identical to the ones of TRW on G. Based on G, we further give the maximal entropy graph Laplacians, and show how to fast compute the hitting time and commute time of MERW. Second, we propose four new graph kernels and two similarity measures based on MERW for link prediction. Finally, to exhibit the power of MERW in link prediction, we compare 27 various link prediction methods over 3 synthetic and 8 real networks. The results show that our newly proposed MERW based methods outperform the state-of-the-art method on most datasets.
A Nonlinear Bayesian Filtering Framework for ECG Denoising

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.