nils-herrmann/clean_scirepeval huggingface dataset:
An expert system for detecting automobile insurance fraud using social network analysis

The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many other approaches, the system uses networks for representation of data. Networks are the most natural representation of such a relational domain, allowing formulation and analysis of complex relations between entities. Fraudulent entities are found by employing a novel assessment algorithm, Iterative Assessment Algorithm (IAA), also presented in the article. Besides intrinsic attributes of entities, the algorithm explores also the relations between entities. The prototype was evaluated and rigorously analyzed on real world data. Results show that automobile insurance fraud can be efficiently detected with the proposed system and that appropriate data representation is vital.
Idempotent work stealing

Load balancing is a technique which allows efficient parallelization of irregular workloads, and a key component of many applications and parallelizing runtimes. Work-stealing is a popular technique for implementing load balancing, where each parallel thread maintains its own work set of items and occasionally steals items from the sets of other threads.
 The conventional semantics of work stealing guarantee that each inserted task is eventually extracted exactly once. However, correctness of a wide class of applications allows for relaxed semantics, because either: i) the application already explicitly checks that no work is repeated or ii) the application can tolerate repeated work.
 In this paper, we introduce idempotent work tealing, and present several new algorithms that exploit the relaxed semantics to deliver better performance. The semantics of the new algorithms guarantee that each inserted task is eventually extracted at least once-instead of exactly once.
 On mainstream processors, algorithms for conventional work stealing require special atomic instructions or store-load memory ordering fence instructions in the owner's critical path operations. In general, these instructions are substantially slower than regular memory access instructions. By exploiting the relaxed semantics, our algorithms avoid these instructions in the owner's operations.
 We evaluated our algorithms using common graph problems and micro-benchmarks and compared them to well-known conventional work stealing algorithms, the THE Cilk and Chase-Lev algorithms. We found that our best algorithm (with LIFO extraction) outperforms existing algorithms in nearly all cases, and often by significant margins.
Deep beamforming networks for multi-channel speech recognition

Despite the significant progress in speech recognition enabled by deep neural networks, poor performance persists in some scenarios. In this work, we focus on far-field speech recognition which remains challenging due to high levels of noise and reverberation in the captured speech signals. We propose to represent the stages of acoustic processing including beamforming, feature extraction, and acoustic modeling, as three components of a single unified computational network. The parameters of a frequency-domain beam-former are first estimated by a network based on features derived from the microphone channels. These filter coefficients are then applied to the array signals to form an enhanced signal. Conventional features are then extracted from this signal and passed to a second network that performs acoustic modeling for classification. The parameters of both the beamforming and acoustic modeling networks are trained jointly using back-propagation with a common cross-entropy objective function. In experiments on the AMI meeting corpus, we observed improvements by pre-training each sub-network with a network-specific objective function before joint training of both networks. The proposed method obtained a 3.2% absolute word error rate reduction compared to a conventional pipeline of independent processing stages.
Opportunistic sensing: Security challenges for the new paradigm

We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm.
Privacy Protection for Wireless Medical Sensor Data

In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients' privacy.
Iterative Crowd Counting

In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two branch CNN architecture for generating high resolution density maps, where the first branch generates a low resolution density map, and the second branch incorporates the low resolution prediction and feature maps from the first branch to generate a high resolution density map. We also propose a multi-stage extension of our approach where each stage in the pipeline utilizes the predictions from all the previous stages. Empirical comparison with the previous state-of-the-art crowd counting methods shows that our method achieves the lowest mean absolute error on three challenging crowd counting benchmarks: Shanghaitech, WorldExpo’10, and UCF datasets.
Object Oriented Software Modeling using NLP Based Knowledge Extraction

This paper presents a natural language processing based automated system for NL text to OO modeling the user requirements and generating code in multi-languages. A new rule-based model is presented for analyzing the natural languages (NL) and extracting the relative and required information from the given software requirement notes by the user. User writes the requirements in simple English in a few paragraphs and the designed system incorporates NLP methods to analyze the given script. First the NL text is semantically analyzed to extract classes, objects and their respective, attributes, methods and associations. Then UML diagrams are generated on the bases of previously extracted information. The designed system also provides with the respective code automatically of the already generated diagrams. The designed system provides a quick and reliable way to generate UML diagrams to save the time and budget of both the user and system analyst.
DBSeer: Resource and Performance Prediction for Building a Next Generation Database Cloud

Cloud computing is characterized by shared infrastructure and a decoupling between its operators and tenants. These two characteristics impose new challenges to databases applications hosted in the cloud, namely: (i) how to price database services, (ii) how to isolate database tenants, and (iii) how to optimize database performance on this shared infrastructure. We argue that today’s solutions, based on virtual-machines, do not properly address these challenges. We hint at new research directions to tackle these problems and argue that these three challenges share a common need for accurate predictive models of performance and resource utilization. We present initial predictive models for the important class of OLTP/Web workloads and show how they can be used to address these challenges.
Towards a SCADA Forensics Architecture

With the increasing threat of sophisticated attacks on critical infrastructures, it is vital that forensic investigations take place immediately following a security incident. This paper presents an existing SCADA forensic process model and proposes a structured SCADA forensic process model to carry out a forensic investigations. A discussion on the limitations of using traditional forensic investigative processes and the challenges facing forensic investigators. Furthermore, flaws of existing research into providing forensic capability for SCADA systems are examined in detail. The study concludes with an experimentation of a proposed SCADA forensic capability architecture on the Siemens S7 PLC. Modifications to the memory addresses are monitored and recorded for forensic evidence. The collected forensic evidence will be used to aid the reconstruction of a timeline of events, in addition to other collected forensic evidence such as network packet captures.
Microstrip high-pass filter with attenuation poles using cross-coupling

A microstrip HPF with sharp attenuation by using cross-coupling is proposed in this paper. The HPF consists of parallel plate- and gap type- capacitors and inductor lines. The one block of the HPF has two sections of a constant K filter in the bridge T configuration. Thus the one block HPF is first coarsely designed and the performance is optimized by circuit simulator. With the gap capacitor adjusted the proposed HPF illustrates the sharp attenuation characteristics near the cut-off frequency made by cross-coupling between the inductor lines. In order to improve the stopband performance, the cascaded two block HPF is examined. Its measured results show the good agreement with the simulated ones giving the sharper attenuation slope.
The spread of true and false news online

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish inand out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers since they only focus on improving inference procedures. In this paper, we develop a novel training method for classifiers so that such inference algorithms can work better. In particular, we suggest two additional terms added to the original loss (e.g., cross entropy). The first one forces samples from out-of-distribution less confident by the classifier and the second one is for (implicitly) generating most effective training samples for the first one. In essence, our method jointly trains both classification and generative neural networks for out-of-distribution. We demonstrate its effectiveness using deep convolutional neural networks on various popular image datasets.
The blood supply to the femoral head after posterior fracture/dislocation of the hip, assessed by CT angiography.

The femoral head receives blood supply mainly from the deep branch of the medial femoral circumflex artery (MFCA). In previous studies we have performed anatomical dissections of 16 specimens and subsequently visualised the arteries supplying the femoral head in 55 healthy individuals. In this further radiological study we compared the arterial supply of the femoral head in 35 patients (34 men and one woman, mean age 37.1 years (16 to 64)) with a fracture/dislocation of the hip with a historical control group of 55 hips. Using CT angiography, we identified the three main arteries supplying the femoral head: the deep branch and the postero-inferior nutrient artery both arising from the MFCA, and the piriformis branch of the inferior gluteal artery. It was possible to visualise changes in blood flow after fracture/dislocation. Our results suggest that blood flow is present after reduction of the dislocated hip. The deep branch of the MFCA was patent and contrast-enhanced in 32 patients, and the diameter of this branch was significantly larger in the fracture/dislocation group than in the control group (p = 0.022). In a subgroup of ten patients with avascular necrosis (AVN) of the femoral head, we found a contrast-enhanced deep branch of the MFCA in eight hips. Two patients with no blood flow in any of the three main arteries supplying the femoral head developed AVN.
Current status and future potential of computer-aided diagnosis in medical imaging.

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.
Your Voice Assistant is Mine: How to Abuse Speakers to Steal Information and Control Your Phone

Previous research about sensor based attacks on Android platform focused mainly on accessing or controlling over sensitive components, such as camera, microphone and GPS. These approaches obtain data from sensors directly and need corresponding sensor invoking permissions.
 This paper presents a novel approach (GVS-Attack) to launch permission bypassing attacks from a zero-permission Android application (VoicEmployer) through the phone speaker. The idea of GVS-Attack is to utilize an Android system built-in voice assistant module -- Google Voice Search. With Android Intent mechanism, VoicEmployer can bring Google Voice Search to foreground, and then plays prepared audio files (like "call number 1234 5678") in the background. Google Voice Search can recognize this voice command and perform corresponding operations. With ingenious design, our GVS-Attack can forge SMS/Email, access privacy information, transmit sensitive data and achieve remote control without any permission. Moreover, we found a vulnerability of status checking in Google Search app, which can be utilized by GVS-Attack to dial arbitrary numbers even when the phone is securely locked with password.
 A prototype of VoicEmployer has been implemented to demonstrate the feasibility of GVS-Attack. In theory, nearly all Android (4.1+) devices equipped with Google Services Framework can be affected by GVS-Attack. This study may inspire application developers and researchers to rethink that zero permission doesn't mean safety and the speaker can be treated as a new attack surface.
Exponentiated Gradient Versus Gradient Descent for Linear Predictors

We consider two algorithms for on-line prediction based on a linear model. The algorithms are the well-known gradient descent (GD) algorithm and a new algorithm, which we call EG. They both maintain a weight vector using simple updates. For the GD algorithm, the update is based on subtracting the gradient of the squared error made on a prediction. The EG algorithm uses the components of the gradient in the exponents of factors that are used in updating the weight vector multiplicatively. We present worst-case loss bounds for EG and compare them to previously known bounds for the GD algorithm. The bounds suggest that the losses of the algorithms are in general incomparable, but EG has a much smaller loss if only few components of the input are relevant for the predictions. We have performed experiments which show that our worst-case upper bounds are quite tight already on simple artificial data.
Automated Crop Yield Estimation for Apple Orchards

Crop yield estimation is an important task in apple orchard management. The current manual sampling-based yield estimation is time-consuming, labor-intensive and inaccurate. To deal with this challenge, we develop and deploy a computer vision system for automated, rapid and accurate yield estimation. The system uses a two-camera stereo rig for image acquisition. It works at nighttime with controlled artificial lighting to reduce the variance of natural illumination. An autonomous orchard vehicle is used as the support platform for automated data collection. The system scans the both sides of each tree row in orchards. A computer vision algorithm is developed to detect and register apples from acquired sequential images, and then generate apple counts as crop yield estimation. We deployed the yield estimation system in Washington state in September, 2011. The results show that the developed system works well with both red and green apples in the tall-spindle planting system. The errors of crop yield estimation are -3.2% for a red apple block with about 480 trees, and 1.2% for a green apple block with about 670 trees.
Multi-agent reinforcement learning as a rehearsal for decentralized planning

Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of during learning and policy execution are identical. In some practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy execution. The key is for the agents to learn policies that do not explicitly rely on these rehearsal features. We also establish a weak convergence result for our algorithm, RLaR, demonstrating that RLaR converges in probability when certain conditions are met. We show experimentally that incorporating rehearsal features can enhance the learning rate compared to non-rehearsalbased learners, and demonstrate fast, (near) optimal performance on many existing benchmark DecPOMDP problems. We also compare RLaR against an existing approximate Dec-POMDP solver which, like RLaR, does not assume a priori knowledge of the model. While RLaR's policy representation is not as scalable, we show that RLaR produces higher quality policies for most problems and horizons studied. & 2016 Elsevier B.V. All rights reserved.
A Coordinated Approach for Practical OS-Level Cache Management in Multi-core Real-Time Systems

Many modern multi-core processors sport a large shared cache with the primary goal of enhancing the statistic performance of computing workloads. However, due to resulting cache interference among tasks, the uncontrolled use of such a shared cache can significantly hamper the predictability and analyzability of multi-core real-time systems. Software cache partitioning has been considered as an attractive approach to address this issue because it does not require any hardware support beyond that available on many modern processors. However, the state-of-the-art software cache partitioning techniques face two challenges: (1) the memory co-partitioning problem, which results in page swapping or waste of memory, and (2) the availability of a limited number of cache partitions, which causes degraded performance. These are major impediments to the practical adoption of software cache partitioning. In this paper, we propose a practical OS-level cache management scheme for multi-core real-time systems. Our scheme provides predictable cache performance, addresses the aforementioned problems of existing software cache partitioning, and efficiently allocates cache partitions to schedule a given task set. We have implemented and evaluated our scheme in Linux/RK running on the Intel Core i7 quad-core processor. Experimental results indicate that, compared to the traditional approaches, our scheme is up to 39% more memory space efficient and consumes up to 25% less cache partitions while maintaining cache predictability. Our scheme also yields a significant utilization benefit that increases with the number of tasks.
Coresets for $k$-Means and $k$-Median Clustering and their Applications

In this paper, we show the existence of small coresets for the problems of computing k-median and kmeans clustering for points in low dimension. In other words, we show that given a point set P in <, one can compute a weighted set S ⊆ P , of size O(kε−d log n), such that one can compute the k-median/means clustering on S instead of on P , and get an (1 + ε)-approximation. As a result, we improve the fastest known algorithms for (1+ε)-approximate k-means and k-median. Our algorithms have linear running time for a fixed k and ε. In addition, we can maintain the (1+ε)-approximate k-median or k-means clustering of a stream when points are being only inserted, using polylogarithmic space and update time.