nils-herrmann/clean_scirepeval huggingface dataset:
Bit-Width Based Resource Partitioning for CNN Acceleration on FPGA

Convolutional neural networks (CNNs) haveachieved great success in many applications. Recently, variousFPGA-based accelerators have been proposed to improve theperformance of CNNs. However, current most FPGA-basedmethods use single bit-width selection for all CNN layers, which lead to very low resource utilization efficiency anddifficulty in further performance improvement. In this paper, we propose a new approach utilizing bit-width partitioning ofFPGA DSP resources to improve the performance andresource utilization efficiency of CNN accelerator. Moreover, we use optimization approach to find the optimal allocationplan for DSP resources. On a Xilinx Virtex-7 FPGA, ourdesign approach achieves performance over the state-of-the-artFPGA-based CNN accelerators from 5.48x to 7.25x and by6.21x on average, when we evaluate the popular CNNs.
Full-mouth adhesive rehabilitation of a severely eroded dentition: the three-step technique. Part 2.

Traditionally, a full-mouth rehabilitation based on full-crown coverage has been recommended treatment for patients affected by severe dental erosion. Nowadays, thanks to improved adhesive techniques, the indications for crowns have decreased and a more conservative approach may be proposed. Even though adhesive treatments simplify both the clinical and laboratory procedures, restoring such patients still remains a challenge due to the great amount of tooth destruction. To facilitate the clinician's task during the planning and execution of a full-mouth adhesive rehabilitation, an innovative concept has been developed: the three-step technique. Three laboratory steps are alternated with three clinical steps, allowing the clinician and the laboratory technician to constantly interact to achieve the most predictable esthetic and functional outcome. During the first step, an esthetic evaluation is performed to establish the position of the plane of occlusion. In the second step, the patient's posterior quadrants are restored at an increased vertical dimension. Finally, the third step reestablishes the anterior guidance. Using the three-step technique, the clinician can transform a full-mouth rehabilitation into a rehabilitation for individual quadrants. The present article focuses on the second step, explaining all the laboratory and clinical steps necessary to restore the posterior quadrants with a defined occlusal scheme at an increased vertical dimension. A brief summary of the first step is also included.
Radiation Enhanced Vivaldi Antenna With Double-Antipodal Structure

This letter presents a novel Vivaldi antenna that can be used to the microwave measurement because of the character of near-field linear polarization plane-like waves. Compared to previous double-slot Vivaldi antenna with the same dimension, the proposed antenna's gain, standing-wave ratio, and radiation characteristics are both improved greatly above 12 GHz by the inclusion of the double-antipodal structure, corrugated edges, and a feature called “director.” Furthermore, the double-antipodal structure introduces a coupled E-field; this reduces total cross-polarization level of the antenna. The proposed antenna is measured, and results show that it has a 4.7- to 20-GHz impedance bandwidth and maintains 20-dB copolarization-to-cross-polarization ratio below 10 GHz and 15 dB below 16 GHz, and its gain increases from 10.3 to 14.8 dBi without a significant decline at high frequencies. Symmetrical feed network also results in better symmetry of the beam, especially in H-plane.
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks. By building upon ideas from network quantization and pruning, we learn binary masks that “piggyback” on an existing network, or are applied to unmodified weights of that network to provide good performance on a new task. These masks are learned in an end-toend differentiable fashion, and incur a low overhead of 1 bit per network parameter, per task. Even though the underlying network is fixed, the ability to mask individual weights allows for the learning of a large number of filters. We show performance comparable to dedicated fine-tuned networks for a variety of classification tasks, including those with large domain shifts from the initial task (ImageNet), and a variety of network architectures. Our performance is agnostic to task ordering and we do not suffer from catastrophic forgetting or competition between tasks.
Big Data Model of Security Sharing Based on Blockchain

The rise of big data age in the Internet has led to the explosive growth of data size. However, trust issue has become the biggest problem of big data, leading to the difficulty in data safe circulation and industry development. The blockchain technology provides a new solution to this problem by combining non-tampering, traceable features with smart contracts that automatically execute default instructions. In this paper, we present a credible big data sharing model based on blockchain technology and smart contract to ensure the safe circulation of data resources.
Entity recognition from clinical texts via recurrent neural network

Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. In recent years, recurrent neural network (RNN), one of deep learning methods that has shown great potential on many problems including named entity recognition, also has been gradually used for entity recognition from clinical texts.



In this paper, we comprehensively investigate the performance of LSTM (long-short term memory), a representative variant of RNN, on clinical entity recognition and protected health information recognition. The LSTM model consists of three layers: input layer - generates representation of each word of a sentence; LSTM layer - outputs another word representation sequence that captures the context information of each word in this sentence; Inference layer - makes tagging decisions according to the output of LSTM layer, that is, outputting a label sequence.



Experiments conducted on corpora of the 2010, 2012 and 2014 i2b2 NLP challenges show that LSTM achieves highest micro-average F1-scores of 85.81% on the 2010 i2b2 medical concept extraction, 92.29% on the 2012 i2b2 clinical event detection, and 94.37% on the 2014 i2b2 de-identification, which is considerably competitive with other state-of-the-art systems.



LSTM that requires no hand-crafted feature has great potential on entity recognition from clinical texts. It outperforms traditional machine learning methods that suffer from fussy feature engineering. A possible future direction is how to integrate knowledge bases widely existing in the clinical domain into LSTM, which is a case of our future work. Moreover, how to use LSTM to recognize entities in specific formats is also another possible future direction.
A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach.
Group sparse representation based classification for multi-feature multimodal biometrics

Multimodal biometrics technology consolidates various information obtained from multiple sources at sensor level, feature level, match score level, and decision level. It is used to increase robustness and provide broader population coverage for inclusion. Due to the inherent challenges involved with featurelevel fusion, combining multiple evidences is attempted at score, rank, or decision level where only a minimal amount of information is preserved. In this paper, we propose the Group Sparse Representation based Classifier (GSRC) which removes the requirement for a separate feature-level fusion mechanism and integrates multi-feature representation seamlessly into classification. The performance of the proposed algorithm is evaluated on two multimodal biometric datasets. Experimental results indicate that the proposed classifier succeeds in efficiently utilizing a multi-feature representation of input data to perform accurate biometric recognition.
Uncovering Four Strategies to Approach Master Data Management

Just recently much Information Systems (IS) research focuses on master data management (MDM) which promises to increase an organization's overall core data quality. Above any doubt, however, MDM initiatives confront organizations with multi-faceted and complex challenges that call for a more strategic approach to MDM. In this paper we introduce a framework for approaching MDM projects that has been developed in the course of a design science research study. The framework distinguishes four major strategies of MDM project initiations all featuring their specific assets and drawbacks. The usefulness of our artifact is illustrated in a short case narrative.
A sparse signal reconstruction perspective for source localization with sensor arrays

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the /spl lscr//sub 1/-norm. A number of recent theoretical results on sparsifying properties of /spl lscr//sub 1/ penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Crame/spl acute/r-Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.
Collaborative networks: a new scientific discipline

Collaborative networks manifest in a large variety of forms, including virtual organizations, virtual enterprises, dynamic supply chains, professional virtual communities, collaborative virtual laboratories, etc. A large body of empiric knowledge related to collaborative networks is already available, but there is an urgent need to consolidate this knowledge and build the foundations for a more sustainable development of this area. The establishment of a scientific discipline for collaborative networks is a strong instrument in achieving this purpose. In this article the main characteristics of a “discipline” are analyzed in the context of collaborative networks, showing that the pre-conditions necessary for building this new discipline are available.
Task oriented optimal grasping by multifingered robot hands

The problem of optimal grasping of an object by a multifingered robot hand is discussed. Using screw theory and elementary differential geometry, the concept of a grasp is axiomated and its stability characterized. Three quality measures for evaluating a grasp are then proposed. The last quality measure is task oriented and needs the development of a procedure for modeling tasks as ellipsoids in the wrench space of the object. Numerical computations of these quality measures and the selection of an optimal grasp are addressed in detail. Several examples are given using these quality measures to show that they are consistent with measurements yielded by our experiments on grasping.
Measuring the Influence of Perceived Cybercrime Risk on Online Service Avoidance

Cybercrime is a pervasive threat for today's Internet-dependent society. While the real extent and economic impact is hard to quantify, scientists and officials agree that cybercrime is a huge and still growing problem. A substantial fraction of cybercrime's overall costs to society can be traced to indirect opportunity costs, resulting from unused online services. This paper presents a parsimonious model that builds on technology acceptance research and insights from criminology to identify factors that reduce Internet users' intention to use online services. We hypothesize that avoidance of online banking, online shopping and online social networking is increased by cybercrime victimization and media reports. The effects are mediated by the perceived risk of cybercrime and moderated by the user's confidence online. We test our hypotheses using a structural equation modeling analysis of a representative pan-European sample. Our empirical results confirm the negative impact of perceived risk of cybercrime on the use of all three online service categories and support the role of cybercrime experience as an antecedent of perceived risk of cybercrime. We further show that more confident Internet users perceive less cybercriminal risk and are more likely to use online banking and online shopping, which highlights the importance of consumer education.
Auto-Detect: Data-Driven Error Detection in Tables

Given a single column of values, existing approaches typically employ regex-like rules to detect errors by finding anomalous values inconsistent with others. Such techniques make local decisions based only on values in the given input column, without considering a more global notion of compatibility that can be inferred from large corpora of clean tables. We propose \sj, a statistics-based technique that leverages co-occurrence statistics from large corpora for error detection, which is a significant departure from existing rule-based methods. Our approach can automatically detect incompatible values, by leveraging an ensemble of judiciously selected generalization languages, each of which uses different generalizations and is sensitive to different types of errors. Errors so detected are based on global statistics, which is robust and aligns well with human intuition of errors. We test \sj on a large set of public Wikipedia tables, as well as proprietary enterprise Excel files. While both of these test sets are supposed to be of high-quality, \sj makes surprising discoveries of over tens of thousands of errors in both cases, which are manually verified to be of high precision (over 0.98). Our labeled benchmark set on Wikipedia tables is released for future research.
Design and experiments of advanced leg module (HRP-2L) for humanoid robot (HRP-2) development

This paper presents an advanced leg module developed for, HRP-2, a new humanoid robotics platform, which has been developed in the phase two of the Humanoid Robotics Project(HRP), a five year program sponsored by the Ministry of Economy, Trade and Industry of Japan (METI) from 1998FY to 2002FY. The biped locomotion ability of HRP-1, the humanoid robotics platform developed in the phase 1 of HRP, is to be improved so that HRP-2 can cope with rough terrains and can prevent possible damage to the robot’s body caused by tipping over. In this paper, the mechanisms and specifications of the leg module, the electrical system, the simulation results utilized for deciding specifications, and some experiment results are presented.
Development of slider-crank based pole climbing robot

In this paper, a slider-crank based pole climbing robot will be discussed. The robot is designed to climb a pole of perimeter 35cm upward and downward, and its weight is limited to 3kg. It utilizes the combination of slider-crank mechanism for its climbing module and modular mechanism for its gripping module. In this work, the overall climbing motion can be completed in 6 steps. The benefits of this robot are low cost, simple mechanism, easy to control and fabricate. The robot has been built and successfully tested on a PVC tube of diameter 35cm. The average upward and downward climbing speed of the developed robot is 0.375cm/s and 0377cm/s respectively.
A Study of Face Recognition as People Age

In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.
WHICH COLORS BEST CATCH YOUR EYES : A SUBJECTIVE STUDY OF COLOR SALIENCY

To determine Regions of Interest (ROI) in a scene, perceptual saliency of regions has to be measured. When scenes are viewed with the same context and motivation, these ROIs are often highly correlated among different people. As a result, it is possible to develop a computational model of visual attention that can analyze a scene and accurately estimate the location of viewers’ ROIs. Color saliency is investigated in this paper. In particular, a subjective experiment has been carried out to estimate which hues attract more human attention. The performance of the visual attention model including color saliency are assessed in the context of a segmentation evaluation application.
Communication-constrained multi-AUV cooperative SLAM

Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.
A DNA barcode for land plants.

DNA barcoding involves sequencing a standard region of DNA as a tool for species identification. However, there has been no agreement on which region(s) should be used for barcoding land plants. To provide a community recommendation on a standard plant barcode, we have compared the performance of 7 leading candidate plastid DNA regions (atpF-atpH spacer, matK gene, rbcL gene, rpoB gene, rpoC1 gene, psbK-psbI spacer, and trnH-psbA spacer). Based on assessments of recoverability, sequence quality, and levels of species discrimination, we recommend the 2-locus combination of rbcL+matK as the plant barcode. This core 2-locus barcode will provide a universal framework for the routine use of DNA sequence data to identify specimens and contribute toward the discovery of overlooked species of land plants.
The Toronto Paper Matching System: An automated paper-reviewer assignment system

One of the most important tasks of conference organizers is the assignment of papers to reviewers. Reviewers’ assessments of papers is a crucial step in determining the conference program, and in a certain sense to shape the direction of a field. However this is not a simple task: large conferences typically have to assign hundreds of papers to hundreds of reviewers, and time constraints make the task impossible for one person to accomplish. Furthermore other constraints, such as reviewer load have to be taken into account, preventing the process from being completely distributed. We built the first version of a system to suggest reviewer assignments for the NIPS 2010 conference, followed, in 2012, by a release that better integrated our system with Microsoft’s popular Conference Management Toolkit (CMT). Since then our system has been widely adopted by the leading conferences in both the machine learning and computer vision communities. This paper provides an overview of the system, a summary of learning models and methods of evaluation that we have been using, as well as some of the recent progress and open issues.