nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers. .
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.
High-speed switched reluctance machine design with toroidal-windings

With the advancement of power electronics, new materials and novel bearing technologies, there has been an active development of high speed machines in recent years. The simple rotor structure makes switched reluctance machines (SRM) candidates for high speed operation. This paper has presents the design of a low power, 50,000 RPM 6/4 SRM having a toroidally wound stator. Finite element analysis (FEA) shows an equivalence to conventionally wound SRMs in terms of torque capability. With the conventional asymmetric converter and classic angular control, this toroidal-winding SRM (TSRM) is able to produce 233.20 W mechanical power with an efficiency of 75% at the FEA stage. Considering the enhanced cooling capability as the winding is directly exposed to air, the toroidal-winding is a good option for high-speed SRM.
Adaptive ADMM with Spectral Penalty Parameter Selection

The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions. Unfortunately, its performance is highly sensitive to a penalty parameter, which makes ADMM often unreliable and hard to automate for a non-expert user. We tackle this weakness of ADMM by proposing a method to adaptively tune the penalty parameters to achieve fast convergence. The resulting adaptive ADMM (AADMM) algorithm, inspired by the successful Barzilai-Borwein spectral method for gradient descent, yields fast convergence and relative insensitivity to the initial stepsize and problem scaling.
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.
Stabilization of Lateral Motion in Passive Dynamic Walking

Passive dynamic walking refers to a class of bipedal machines that are able to walk down a gentle slope with no external control or energy input. The legs swing naturally as pendula, and conservation of angular momentum governs the contact of the swing foot with the ground. Previous machines have been limited to planar motions. We extend the planar motions to allow for tilting side to side (roll motion). Passive walking cycles exist, but the roll motion is unstable, resembling that of an inverted pendulum. The instability is due to mismatching of roll velocity with the ground contact conditions. Several strategies are presented for stabilizing this motion, of which the quasi-static control of step width is determined to be both simple
A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition

Deep convolutional neural networks (DCNN) based algorithm methods have swept face-recognition. DCNNbased algorithms have shown significant improvements in accuracy on the Labeled Faces in the Wild (LFW) and the YouTube1 Video face-recognition benchmarks. These two benchmarks consist of images and videos of celebrities downloaded from the World Wide Web. Since 2004, the National Institute of Standards and Technology (NIST) has established a series of face-recognition benchmarks that span a range of scenarios and difficulties. The scenarios range from comparing frontal faces taken in studio lighting to comparing faces acquired with cell phone cameras taken outdoors. The VGG-face algorithm [7] was ran on eight NIST face-recognition benchmarks. The Vision Geometry Group (VGG)-face algorithm excelled on the most difficult benchmarks; existing algorithms excelled the benchmarks with higher quality images. This finding is consistent with the design of the algorithms. The VGG-face algorithm was designed to recognize faces in variable illumination; the existing algorithms were designed to operate on face-images taken in controlled illuminations. To accurately characterize the performance of face recognition algorithms, we recommend that performance is reported on multiple benchmarks.
Distributed Privacy-Preserving Access Control in Sensor Networks

The owner and users of a sensor network may be different, which necessitates privacy-preserving access control. On the one hand, the network owner need enforce strict access control so that the sensed data are only accessible to users willing to pay. On the other hand, users wish to protect their respective data access patterns whose disclosure may be used against their interests. This paper presents DP2AC, a Distributed Privacy-Preserving Access Control scheme for sensor networks, which is the first work of its kind. Users in DP2AC purchase tokens from the network owner whereby to query data from sensor nodes which will reply only after validating the tokens. The use of blind signatures in token generation ensures that tokens are publicly verifiable yet unlinkable to user identities, so privacy-preserving access control is achieved. A central component in DP2AC is to prevent malicious users from reusing tokens, for which we propose a suite of distributed token reuse detection (DTRD) schemes without involving the base station. These schemes share the essential idea that a sensor node checks with some other nodes (called witnesses) whether a token has been used, but they differ in how the witnesses are chosen. We thoroughly compare their performance with regard to TRD capability, communication overhead, storage overhead, and attack resilience. The efficacy and efficiency of DP2AC are confirmed by detailed performance evaluations.
Characterizing privacy in online social networks

Online social networks (OSNs) with half a billion users have dramatically raised concerns on privacy leakage. Users, often willingly, share personal identifying information about themselves, but do not have a clear idea of who accesses their private information or what portion of it really needs to be accessed. In this study we examine popular OSNs from a viewpoint of characterizing potential privacy leakage. Our study identifies what bits of information are currently being shared, how widely, and what users can do to prevent such sharing. We also examine the role of third-party sites that track OSN users and compare with privacy leakage on popular traditional Web sites. Our long term goal is to identify the narrow set of private information that users really need to share to accomplish specific interactions on OSNs.
Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification

We propose a method of face verification that takes advantage of a reference set of faces, disjoint by identity from the test faces, labeled with identity and face part locations. The reference set is used in two ways. First, we use it to perform an “identity-preserving” alignment, warping the faces in a way that reduces differences due to pose and expression but preserves differences that indicate identity. Second, using the aligned faces, we learn a large set of identity classifiers, each trained on images of just two people. We call these “Tom-vs-Pete” classifiers to stress their binary nature. We assemble a collection of these classifiers able to discriminate among a wide variety of subjects and use their outputs as features in a same-or-different classifier on face pairs. We evaluate our method on the Labeled Faces in the Wild benchmark, achieving an accuracy of 93.10%, significantly improving on the published state of the art.
Strategic Management Joumal , Vol . 18 : 7 , 509-533 ( 1997 ) DYNAMIC CAPABILITIES AND STRATEGIC MANAGEMENT

The dynamic capabilities framework analyzes the sources and methods of wealth creation and capture by private enterprise firms operating in environments of rapid technological change. The competitive advantage of firms is seen as resting on distinctive processes (ways of coordinating and combining), shaped by the firm's (specific) asset positions (such as the firm's portfolio of difftcult-to-trade knowledge assets and complementary assets), and the evolution path(s) it has aflopted or inherited. The importance of path dependencies is amplified where conditions of increasing retums exist. Whether and how a firm's competitive advantage is eroded depends on the stability of market demand, and the ease of replicability (expanding intemally) and imitatability (replication by competitors). If correct, the framework suggests that private wealth creation in regimes of rapid technological change depends in large measure on honing intemal technological, organizational, and managerial processes inside the firm. In short, identifying new opportunities and organizing effectively and efficiently to embrace them are generally more fundamental to private wealth creation than is strategizing, if by strategizing one means engaging in business conduct that keeps competitors off balance, raises rival's costs, and excludes new entrants.
Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

In this paper, we present a new approach for fingerprint class ification based on Discrete Fourier Transform (DFT) and nonlinear discrimina nt nalysis. Utilizing the Discrete Fourier Transform and directional filters, a relia ble and efficient directional image is constructed from each fingerprint image, and then no nlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The pr oposed method explores the capability of DFT and directional filtering in dealing with l ow quality images and the effectiveness of nonlinear feature extraction method in fin gerprint classification. Experimental results demonstrates competitive performance compared with other published results.
Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure

It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%.
Understanding Neural Networks through Representation Erasure

While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing the relative difference in evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model’s decision. In a comprehensive analysis of multiple NLP tasks, including linguistic feature classification, sentence-level sentiment analysis, and document level sentiment aspect prediction, we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models.
Social Media Data Mining: A Social Network Analysis Of Tweets During The 2010-2011 Australian Floods

Using tweets extracted from Twitter during the Australian 2010-2011 floods, social network analysis techniques were used to generate and analyse the online networks that emerged at that time. The aim was to develop an understanding of the online communities for the Queensland, New South Wales and Victorian floods in order to identify active players and their effectiveness in disseminating critical information. A secondary goal was to identify important online resources disseminated by these communities. Important and effective players during the Queensland floods were found to be: local authorities (mainly the Queensland Police Services), political personalities (Queensland Premier, Prime Minister, Opposition Leader, Member of Parliament), social media volunteers, traditional media reporters, and people from not-for-profit, humanitarian, and community associations. A range of important resources were identified during the Queensland flood; however, they appeared to be of a more general information nature rather than vital information and updates on the disaster. Unlike Queensland, there was no evidence of Twitter activity from the part of local authorities and the government in the New South Wales and Victorian floods. Furthermore, the level of Twitter activity during the NSW floods was almost nil. Most of the active players during the NSW and Victorian floods were volunteers who were active during the Queensland floods. Given the positive results obtained by the active involvement of the local authorities and government officials in Queensland, and the increasing adoption of Twitter in other parts of the world for emergency situations, it seems reasonable to push for greater adoption of Twitter from local and federal authorities Australia-wide during periods of mass emergencies.
Markov Logic: An Interface Layer for Artificial Intelligence

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Cloud-based malware detection for evolving data streams

Data stream classification for intrusion detection poses at least three major challenges. First, these data streams are typically infinite-length, making traditional multipass learning algorithms inapplicable. Second, they exhibit significant concept-drift as attackers react and adapt to defenses. Third, for data streams that do not have any fixed feature set, such as text streams, an additional feature extraction and selection task must be performed. If the number of candidate features is too large, then traditional feature extraction techniques fail.
 In order to address the first two challenges, this article proposes a multipartition, multichunk ensemble classifier in which a collection of v classifiers is trained from r consecutive data chunks using v-fold partitioning of the data, yielding an ensemble of such classifiers. This multipartition, multichunk ensemble technique significantly reduces classification error compared to existing single-partition, single-chunk ensemble approaches, wherein a single data chunk is used to train each classifier. To address the third challenge, a feature extraction and selection technique is proposed for data streams that do not have any fixed feature set. The technique's scalability is demonstrated through an implementation for the Hadoop MapReduce cloud computing architecture. Both theoretical and empirical evidence demonstrate its effectiveness over other state-of-the-art stream classification techniques on synthetic data, real botnet traffic, and malicious executables.
Unsupervised topic discovery in micro-blogging networks

Unsupervised automatic topic discovery in micro-blogging social networks is a very challenging task, as it involves the analysis of very short, noisy, ungrammatical and uncontextual messages. Most of the current approaches to this problem are basically syntactic, as they focus either on the use of statistical techniques or on the analysis of the co-occurrences between the terms. This paper presents a novel topic discovery methodology, based on the mapping of hashtags to WordNet terms and their posterior clustering, in which semantics plays a centre role. The paper also presents a detailed case study in the field of Oncology, in which the discovered topics are thoroughly compared to a golden standard, showing promising results.
Name disambiguation from link data in a collaboration graph using temporal and topological features

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error lead to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from timestamped link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.
An EV SRM Drive Powered by Battery/Supercapacitor With G2V and V2H/V2G Capabilities

This paper develops an electric vehicle switched-reluctance motor (SRM) drive powered by a battery/supercapacitor having grid-to-vehicle (G2V) and vehicle-to-home (V2H)/vehicle-to-grid (V2G) functions. The power circuit of the motor drive is formed by a bidirectional two-quadrant front-end dc/dc converter and an SRM asymmetric bridge converter. Through proper control and setting of key parameters, good acceleration/deceleration, reversible driving, and braking characteristics are obtained. In idle condition, the proposed motor drive schematic can be rearranged to construct the integrated power converter to perform the following functions: 1) G2V charging mode: a single-phase two-stage switch-mode rectifier based charger is formed with power factor correction capability; 2) autonomous V2H discharging mode: the 60-Hz 220-V/110-V ac sources are generated by the developed single-phase three-wire inverter to power home appliances. Through the developed differential mode and common mode control schemes, well-regulated output voltages are achieved; 3) grid-connected V2G discharging mode: the programmed real power can be sent back to the utility grid.