nils-herrmann/clean_scirepeval huggingface dataset:
A Fully Integrated 900-MHz Passive RFID Transponder Front End With Novel Zero-Threshold RF–DC Rectifier

This paper presents a fully integrated CMOS analog front end for a passive 900-MHz radio-frequency identification (RFID) transponder. The power supply in this front end is generated from the received RF electromagnetic energy by using an RF-dc voltage rectifier. In order to improve the compatibility with standard CMOS technology, Schottky diodes in conventional RF-dc rectifiers are replaced by diode-connected MOS transistors with zero threshold. Meanwhile, theoretical analyses for the proposed rectifier are provided and verified by both simulation and measurement results. The design considerations of the pulsewidth-modulation (PWM) demodulator and the backscatter modulator in the front end are also discussed for low-power applications. The proposed front end is implemented in a 0.35-mum 2P4M CMOS technology. The whole chip occupies a die area of 490 times 780 mum2 and consumes only 2.1 muW in reading mode under a self-generated 1.5-V supply voltage. The measurement results show that the proposed rectifier can properly operate with a -14.7-dBm input RF power at a power conversion efficiency of 13.0%. In the proposed RFID applications, this sensitivity corresponds to 10.88-m communication distance at 4-W equivalent isotropically radiated power from a reader base station.
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.
Exploring What not to Clean in Urban Data: A Study Using New York City Taxi Trips

Traditionally, data cleaning has been performed as a pre-processing task: after all data are selected for a study (or application), they are cleaned and loaded into a database or data warehouse. In this paper, we argue that data cleaning should be an integral part of data exploration. Especially for complex, spatio-temporal data, it is only by exploring a dataset that one can discover which constraints should be checked. In addition, in many instances, seemingly erroneous data may actually reflect interesting features. Distinguishing a feature from a data quality issue requires detailed analyses which often includes bringing in new datasets. We present a series of case studies using the NYC taxi data that illustrate data cleaning challenges that arise for spatial-temporal urban data and suggest methodologies to address these challenges.
Alert Detection in System Logs

We present Nodeinfo, an unsupervised algorithm for anomaly detection in system logs. We demonstrate Nodeinfo's effectiveness on data from four of the world's most powerful supercomputers: using logs representing over 746 million processor-hours, in which anomalous events called alerts were manually tagged for scoring, we aim to automatically identify the regions of the log containing those alerts. We formalize the alert detection task in these terms, describe how Nodeinfo uses the information entropy of message terms to identify alerts, and present an online version of this algorithm, which is now in production use. This is the first work to investigate alert detection on (several) publicly-available supercomputer system logs, thereby providing a reproducible performance baseline.
Perseverance of Social Theories : The Role of Explanation in the Persistence of Discredited Information

The perseverance of social theories was examined in two experiments within a debriefing paradigm. Subjects were initially given two case studies suggestive of either a positive or a negative relationship between risk taking and success as a firefighter. Some subjects were asked to provide a written explanation of the relationship; others were not. In addition, experimental subjects were thoroughly debriefed concerning the fictitious nature of the initial case studies. Subsequent assessments of subjects' personal beliefs about the relationship indicated that even when initially based on weak data, social theories can survive the total discrediting of that initial evidential base. Both correlational and experimental results suggested that such unwarranted theory perseverance may be mediated, in part, by the cognitive process of formulating causal scenarios or explanations. Normative issues and the cognitive processes underlying perseverance were examined in detail, and possible techniques for overcoming unwarranted theory perseverance were discussed.
Machine learning, medical diagnosis, and biomedical engineering research - commentary

A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique.
Applying Data Mining Techniques in Property ~ Casualty Insurance

This paper addresses the issues and techniques for Property/Casualty actuaries using data mining techniques. Data mining means the efficient discovery of previously unknown patterns in large databases. It is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the information discovery techniques and introduces some important data mining techniques for application to insurance including cluster discovery methods and decision tree analysis.
Dual-Band Circularly-Polarized Unidirectional Patch Antenna for RFID Reader Applications

A dual-band 0.92/2.45 GHz circularly-polarized (CP) unidirectional antenna using the wideband dual-feed network, two orthogonally positioned asymmetric H-shape slots, and two stacked concentric annular-ring patches is proposed for RF identification (RFID) applications. The measurement result shows that the antenna achieves the impedance bandwidths of 15.4% and 41.9%, the 3-dB axial-ratio (AR) bandwidths of 4.3% and 21.5%, and peak gains of 7.2 dBic and 8.2 dBic at 0.92 and 2.45 GHz bands, respectively. Moreover, the antenna provides stable symmetrical radiation patterns and wide-angle 3-dB AR beamwidths in both lower and higher bands for unidirectional wide-coverage RFID reader applications. Above all, the dual-band CP unidirectional patch antenna presented is beneficial to dual-band RFID system on configuration, implementation, as well as cost reduction.
Dynamic Steerable Blocks in Deep Residual Networks

Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve performance on Cifar-10+ consistently, while having additional pleasant properties like steerability. By exploiting these transformation properties explicitly, we arrive at dynamic steerable blocks. They are an extension of residual blocks, that are able to seamlessly transform filters under pre-defined transformations, conditioned on the input at training and inference time. Dynamic steerable blocks learn the degree of invariance from data and locally adapt filters, allowing them to apply a different geometrical variant of the same filter to each location of the feature map. When evaluated on the Berkeley Segmentation contour detection dataset, our approach outperforms all competing approaches that do not utilize pre-training. Our results highlight the benefits of image-based regularization to deep networks.
Automatic 3D liver location and segmentation via convolutional neural network and graph cut

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.
Invisible loading: access-driven data transfer from raw files into database systems

Commercial analytical database systems suffer from a high "time-to-first-analysis": before data can be processed, it must be modeled and schematized (a human effort), transferred into the database's storage layer, and optionally clustered and indexed (a computational effort). For many types of structured data, this upfront effort is unjustifiable, so the data are processed directly over the file system using the Hadoop framework, despite the cumulative performance benefits of processing this data in an analytical database system. In this paper we describe a system that achieves the immediate gratification of running MapReduce jobs directly over a file system, while still making progress towards the long-term performance benefits of database systems. The basic idea is to piggyback on MapReduce jobs, leverage their parsing and tuple extraction operations to incrementally load and organize tuples into a database system, while simultaneously processing the file system data. We call this scheme Invisible Loading, as we load fractions of data at a time at almost no marginal cost in query latency, but still allow future queries to run much faster.
A disposable power source in resource-limited environments: A paper-based biobattery generating electricity from wastewater.

We report a novel paper-based biobattery which generates power from microorganism-containing liquid derived from renewable and sustainable wastewater which is readily accessible in the local environment. The device fuses the art of origami and the technology of microbial fuel cells (MFCs) and has the potential to shift the paradigm for flexible and stackable paper-based batteries by enabling exceptional electrical characteristics and functionalities. 3D, modular, and retractable battery stack is created from (i) 2D paper sheets through high degrees of folding and (ii) multifunctional layers sandwiched for MFC device configuration. The stack is based on ninja star-shaped origami design formed by eight MFC modular blades, which is retractable from sharp shuriken (closed) to round frisbee (opened). The microorganism-containing wastewater is added into an inlet of the closed battery stack and it is transported into each MFC module through patterned fluidic pathways in the paper layers. During operation, the battery stack is transformed into the round frisbee to connect eight MFC modules in series for improving the power output and simultaneously expose all air-cathodes to the air for their cathodic reactions. The device generates desired values of electrical current and potential for powering an LED for more than 20min.
Graph-Based Fraud Detection in the Face of Camouflage

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look “normal.” Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic.
 Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower--followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4, 000 detected accounts, of which a majority had tweets showing that they used follower-buying services.
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy

The use of Convolutional Neural Networks (CNN) in natural im age classification systems has produced very impressive results. Combined wit h the inherent nature of medical images that make them ideal for deep-learning, fu rther application of such systems to medical image classification holds much prom ise. However, the usefulness and potential impact of such a system can be compl etely negated if it does not reach a target accuracy. In this paper, we present a s tudy on determining the optimum size of the training data set necessary to achiev e igh classification accuracy with low variance in medical image classification s ystems. The CNN was applied to classify axial Computed Tomography (CT) imag es into six anatomical classes. We trained the CNN using six different sizes of training data set ( 5, 10, 20, 50, 100, and200) and then tested the resulting system with a total of 6000 CT images. All images were acquired from the Massachusetts G eneral Hospital (MGH) Picture Archiving and Communication System (PACS). U sing this data, we employ the learning curve approach to predict classificat ion ccuracy at a given training sample size. Our research will present a general me thodology for determining the training data set size necessary to achieve a cert in target classification accuracy that can be easily applied to other problems within such systems.
The rubber hand illusion revisited: visuotactile integration and self-attribution.

Watching a rubber hand being stroked, while one's own unseen hand is synchronously stroked, may cause the rubber hand to be attributed to one's own body, to "feel like it's my hand." A behavioral measure of the rubber hand illusion (RHI) is a drift of the perceived position of one's own hand toward the rubber hand. The authors investigated (a) the influence of general body scheme representations on the RHI in Experiments 1 and 2 and (b) the necessary conditions of visuotactile stimulation underlying the RHI in Experiments 3 and 4. Overall, the results suggest that at the level of the process underlying the build up of the RHI, bottom-up processes of visuotactile correlation drive the illusion as a necessary, but not sufficient, condition. Conversely, at the level of the phenomenological content, the illusion is modulated by top-down influences originating from the representation of one's own body.
Scalable Fog Computing with Service Offloading in Bus Networks

With the rapid increase of mobile devices, the computing load of roadside cloudlets is fast growing. When the computation tasks of the roadside cloudlet reach the limit, the overload may generate heat radiation problem and unacceptable delay to mobile users. In this paper, we leverage the characteristics of buses and propose a scalable fog computing paradigm with servicing offloading in bus networks. The bus fog servers not only provide fog computing services for the mobile users on bus, but also are motivated to accomplish the computation tasks offloaded by roadside cloudlets. By this way, the computing capability of roadside cloudlets is significantly extended. We consider an allocation strategy using genetic algorithm (GA). With this strategy, the roadside cloudlets spend the least cost to offload their computation tasks. Meanwhile, the user experience of mobile users are maintained. The simulations validate the advantage of the propose scheme.
Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks

Recent advances have allowed Deep Spiking Neural Networks (SNNs) to perform at the same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a unique property of SNNs: whereas in ANNs, every neuron needs to update once before an output can be created, the computational effort in an SNN depends on the number of spikes created in the network. While higher spike rates and longer computing times typically improve classification performance, very good results can already be achieved earlier. Here we investigate how Deep SNNs can be optimized to reach desired high accuracy levels as quickly as possible. Different approaches are compared which either minimize the number of spikes created, or aim at rapid classification by enforcing the learning of feature detectors that respond to few input spikes. A variety of networks with different optimization approaches are trained on the MNIST benchmark to perform at an accuracy level of at least 98%, while monitoring the average number of input spikes and spikes created within the network to reach this level of accuracy. The majority of SNNs required significantly fewer computations than frame-based ANN approaches. The most efficient SNN achieves an answer in less than 42% of the computational steps necessary for the ANN, and the fastest SNN requires only 25% of the original number of input spikes to achieve equal classification accuracy. Our results suggest that SNNs can be optimized to dramatically decrease the latency as well as the computation requirements for Deep Neural Networks, making them particularly attractive for applications like robotics, where real-time restrictions to produce outputs and low energy budgets are common.
Architectural Support for Software Transactional Memory

Transactional memory provides a concurrency control mechanism that avoids many of the pitfalls of lock-based synchronization. Researchers have proposed several different implementations of transactional memory, broadly classified into software transactional memory (STM) and hardware transactional memory (HTM). Both approaches have their pros and cons: STMs provide rich and flexible transactional semantics on stock processors but incur significant overheads. HTMs, on the other hand, provide high performance but implement restricted semantics or add significant hardware complexity. This paper is the first to propose architectural support for accelerating transactions executed entirely in software. We propose instruction set architecture (ISA) extensions and novel hardware mechanisms that improve STM performance. We adapt a high-performance STM algorithm supporting rich transactional semantics to our ISA extensions (called hardware accelerated software transactional memory or HASTM). HASTM accelerates fully virtualized nested transactions, supports language integration, and provides both object-based and cache-line based conflict detection. We have implemented HASTM in an accurate multi-core IA32 simulator. Our simulation results show that (1) HASTM single-thread performance is comparable to a conventional HTM implementation; (2) HASTM scaling is comparable to a STM implementation; and (3) HASTM is resilient to spurious aborts and can scale better than HTM in a multi-core setting. Thus, HASTM provides the flexibility and rich semantics of STM, while giving the performance of HTM.
A Privacy-Preserving Attribute-Based Authentication System for Mobile Health Networks

Electronic healthcare (eHealth) systems have replaced paper-based medical systems due to the attractive features such as universal accessibility, high accuracy, and low cost. As a major component of eHealth systems, mobile healthcare (mHealth) applies mobile devices, such as smartphones and tablets, to enable patient-to-physician and patient-to-patient communications for better healthcare and quality of life (QoL). Unfortunately, patients' concerns on potential leakage of personal health records (PHRs) is the biggest stumbling block. In current eHealth/mHealth networks, patients' medical records are usually associated with a set of attributes like existing symptoms and undergoing treatments based on the information collected from portable devices. To guarantee the authenticity of those attributes, PHRs should be verifiable. However, due to the linkability between identities and PHRs, existing mHealth systems fail to preserve patient identity privacy while providing medical services. To solve this problem, we propose a decentralized system that leverages users' verifiable attributes to authenticate each other while preserving attribute and identity privacy. Moreover, we design authentication strategies with progressive privacy requirements in different interactions among participating entities. Finally, we have thoroughly evaluated the security and computational overheads for our proposed schemes via extensive simulations and experiments.