nils-herrmann/clean_scirepeval huggingface dataset:
Anticipative Interaction Primitives for Human-Robot Collaboration

This paper introduces our initial investigation on the problem of providing a semi-autonomous robot collaborator with anticipative capabilities to predict human actions. Anticipative robot behavior is a desired characteristic of robot collaborators that lead to fluid, proactive interactions. We are particularly interested in improving reactive methods that rely on human action recognition to activate the corresponding robot action. Action recognition invariably causes delay in the robot’s response, and the goal of our method is to eliminate this delay by predicting the next human action. Prediction is achieved by using a lookup table containing variations of assembly sequences, previously demonstrated by different users. The method uses the nearest neighbor sequence in the table that matches the actual sequence of human actions. At the movement level, our method uses a probabilistic representation of interaction primitives to generate robot trajectories. The method is demonstrated using a 7 degree-offreedom lightweight arm equipped with a 5-finger hand on an assembly task consisting of 17 steps.
Personalized entity recommendation: a heterogeneous information network approach

Among different hybrid recommendation techniques, network-based entity recommendation methods, which utilize user or item relationship information, are beginning to attract increasing attention recently. Most of the previous studies in this category only consider a single relationship type, such as friendships in a social network. In many scenarios, the entity recommendation problem exists in a heterogeneous information network environment. Different types of relationships can be potentially used to improve the recommendation quality. In this paper, we study the entity recommendation problem in heterogeneous information networks. Specifically, we propose to combine heterogeneous relationship information for each user differently and aim to provide high-quality personalized recommendation results using user implicit feedback data and personalized recommendation models.
 In order to take full advantage of the relationship heterogeneity in information networks, we first introduce meta-path-based latent features to represent the connectivity between users and items along different types of paths. We then define recommendation models at both global and personalized levels and use Bayesian ranking optimization techniques to estimate the proposed models. Empirical studies show that our approaches outperform several widely employed or the state-of-the-art entity recommendation techniques.
Extraction of high confidence minutiae points from fingerprint images

Fingerprint is one of the well accepted biometric traits for building automatic human recognition system. These systems mainly involves matching of two fingerprints based on their minutiae points. Therefore, extraction of reliable or true minutiae from the fingerprint image is critically important. This paper proposed an novel algorithm for automatic extraction of highly reliable minutiae points from a fingerprint image. It utilizes frequency domain image enhancement and heuristics. Experiments have been conducted on two databases. FVC2002 which is a publicly available fingerprint database containing 800 fingerprint images of 100 persons and IITK-Sel500FP which is an in-house database containing 1000 fingerprint images of 500 persons. Minutiae points extracted by the proposed algorithm are benchmarked against the manually marked ones and the comparison is done with an open source minutiae extractor mindtct of NIST. Experimental results have shown that the proposed algorithm has significantly higher confidence of extracted minutiae points being true.
System design of a low noise, low offset instrumentation amplifier with chopper stabilization

A low noise, low offset instrumentation amplifier with chopper stabilization is presented. The equivalent input noise and offset is reduced using the technique of chopper stabilization with a chopping frequency of 100 kHz. The channel charge injection is cancelled by the symmetrical circuit topology. With the wide swing bias circuit design, the amplifier achieves a very high power-supply-rejection ratio (PSRR), and it is operated at a single supply voltage of 5 V. The instrumentation amplifier is simulated using TSMC 0.35-mum CMOS technology. It achieves an input-referred noise voltage of 345 nV / radicHz @ about 1 kHz, which is not very good for muV level signal processing because of the large thermal noise parameter of the process model.
Tree-Based Batch Mode Reinforcement Learning

Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.
Structural analysis of cooking preparation steps in Japanese

We propose a method to create process flow graphs automatically from textbooks for cooking programs. This is realized by understanding context by narrowing down the domain to cooking, and making use of domain specific constraints and knowledge. Since it is relatively easy to extract significant keywords from cooking procedures, we create a domain specific dictionary by statistical methods, and propose a structural analysis method using the dictionary. In order to evaluate the ability of the proposed method, we applied the method to actual procedures as an experiment, which showed effective results. The same experiment was also performed on a different program, which showed lower accuracy but also showed realistic results.
The Diffusion of Innovations in Social Networks ∗

This paper determines how different network structures influence the diffusion of innovations. We develop a model of diffusion where: 1. an individual’s decision to adopt a new technology is influenced by his contacts; and 2. contacts can discuss, coordinate, and make adoption decisions together. A measure of connectedness, ‘cohesion’, determines diffusion. A cohesive community is defined as a group in which all members have a high proportion of their contacts within the group. We show a key trade-off: on one hand, a cohesive community can hinder diffusion by blocking the spread of a technology into the group; on the other hand, cohesive communities can be particularly effective at acting collectively to adopt an innovation. We find that for technologies with low externalities (that require few people to adopt before others are willing to adopt), social structures with loose ties, where people are not part of cohesive groups, enable greater diffusion. However, as externalities increase (technologies require more people to adopt before others are willing to adopt), social structures with increasingly cohesive groups enable greater diffusion. Given that societal structure is known to differ systematically along this dimension, our findings point to specialization in technological progress exhibiting these patterns. ∗Bryony Reich, Faculty of Economics, University College London.  b.reich@ucl.ac.uk. I would like to thank Alberto Alesina, Antonio Cabrales, Sanjeev Goyal, and Jorgen Weibull for their invaluable guidance and support. I benefited greatly from conversations with and comments of Marco Bassetto, Lars Nesheim and Imran Rasul. I am grateful to Jonathan Newton for numerous interactions at all stages of this project. For helpful comments I would also like to thank Lucie Gadenne, Terri Kneeland, and Sueyhun Kwon, as well as seminar participants at Alicante, Cambridge, INET Contagion Conference, Oxford, PET Luxembourg, and UCL. I gratefully acknowledge financial support from the UK Economic and Social Research Council (grant number ES/K001396/1).
The Meteor metric for automatic evaluation of machine translation

The Meteor Automatic Metric for Machine Translation evaluation, originally developed and released in 2004, was designed with the explicit goal of producing sentence-level scores which correlate well with human judgments of translation quality. Several key design decisions were incorporated into Meteor in support of this goal. In contrast with IBM’s Bleu, which uses only precision-based features, Meteor uses and emphasizes recall in addition to precision, a property that has been confirmed by several metrics as being critical for high correlation with human judgments. Meteor also addresses the problem of reference translation variability by utilizing flexible word matching, allowing for morphological variants and synonyms to be taken into account as legitimate correspondences. Furthermore, the feature ingredients within Meteor are parameterized, allowing for the tuning of the metric’s free parameters in search of values that result in optimal correlation with human judgments. Optimal parameters can be separately tuned for different types of human judgments and for different languages. We discuss the initial design of the Meteor metric, subsequent improvements, and performance in several independent evaluations in recent years.
Distributed Energy Resources Topology Identification via Graphical Modeling

Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution grid. But the exact distribution system topology is difficult to obtain due to frequent distribution grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution grid topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity Analysis on Medical Datasets

Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of logistic regression and neural network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The classification accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens

In this paper we report on the approach we have developed and the lessons we have learned in an implementation of the monitoring and control layer for continuous monitoring of business process controls (CMBPC) in the US internal IT audit department of Siemens Corporation. The architecture developed by us implements a completely independent CMBPC system running on top of Siemens’ own enterprise information system which has read-only interaction with the application tier of the enterprise system. Among our key conclusions is that “formalizability” of audit procedures and audit judgment is grossly underestimated. Additionally, while cost savings and expedience force the implementation to closely follow the existing and approved internal audit program, a certain level of reengineering of audit processes is inevitable due to the necessity to separate formalizable and non-formalizable parts of the program. Our study identifies the management of audit alarms and the prevention of the alarm floods as critical tasks in the CMBPC implementation process. We develop an approach to solving these problems utilizing the hierarchical structure of alarms and the role-based approach to assigning alarm destinations. We also discuss the content of the audit trail of CMBPC.
Understanding the Value of Features for Coreference Resolution

In recent years there has been substantial work on the important problem of coreference resolution, most of which has concentrated on the development of new models and algorithmic techniques. These works often show that complex models improve over a weak pairwise baseline. However, less attention has been given to the importance of selecting strong features to support learning a coreference model. This paper describes a rather simple pairwise classification model for coreference resolution, developed with a well-designed set of features. We show that this produces a state-of-the-art system that outperforms systems built with complex models. We suggest that our system can be used as a baseline for the development of more complex models – which may have less impact when a more robust set of features is used. The paper also presents an ablation study and discusses the relative contributions of various features.
Mobile phone applications in Parkinson's disease: A systematic review.

Parkinson's disease (PD) is the second most common neurodegenerative disease. However, diagnosing, assessing, and treating these patients is a complex process requiring continuous monitoring. In this context, smartphones may be useful in the management of patients with PD.



The purpose of this study is to perform a systematic review of the literature addressing the use of mobile phone applications (apps) in PD.



We conducted a literature search of articles published in English or Spanish between 2011 and 2016 analysing or validating apps specifically designed for or useful in PD. In addition, we searched for apps potentially useful for PD patients in the leading app stores.



The literature and app searches yielded a total of 125 apps, 56 of which were classified as potentially useful in PD and 69 as specifically designed for PD (23 information apps, 29 assessment apps, 13 treatment apps, and 4 assessment and treatment apps). Numerous mobile apps are potentially useful in or specifically designed for PD management. However, scientific evidence of their usefulness is scarce and of poor quality. Further studies are needed to validate these tools and regulate their use.
Effective Inference for Generative Neural Parsing

Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited to reranking the output of external parsers in which decoding is more tractable. We describe an alternative to the conventional action-level beam search used for discriminative neural models that enables us to decode directly in these generative models. We then show that by improving our basic candidate selection strategy and using a coarse pruning function, we can improve accuracy while exploring significantly less of the search space. Applied to the model of Choe and Charniak (2016), our inference procedure obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior state-of-the-art results for single-model systems.
Co-Design for Low Warpage and High Reliability in Advanced Package with TSV-Free Interposer (TFI)

TSV-Free Interposer (TFI) technology eliminates TSV fabrication and reduces manufacturing and material cost. Co-design modelling methodology is established for TFI technology with considering wafer process, package assembly and package/board level reliability and thermal performance to optimize structure design, wafer process, assembly process and material selection. Experimental results are used for validating warpage modelling results. Through wafer level modelling, suitable carrier wafer and EMC materials are recommended to control wafer warpage less than 2mm. Effects of package substrate coefficient of thermal expansion (CTE) and stiffener on assembly induced package warpage are simulated to reduce package warpage. The recommended materials and geometry design based on reliability are aligned with that from wafer and package warpage simulation results. The final test vehicle (TV) design and material selection are determined based on co-design modelling results for achieving successful TFI wafer process and package assembly process and long term package/board level reliability.
Expectation-maximization for sparse and non-negative PCA

We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of non-zero elements, and non-negativity forces the elements to have equal sign. This problem is known as sparse and non-negative principal component analysis (PCA), and has many applications including dimensionality reduction and feature selection. Based on expectation-maximization for probabilistic PCA, we present an algorithm for any combination of these constraints. Its complexity is at most quadratic in the number of dimensions of the data. We demonstrate significant improvements in performance and computational efficiency compared to other constrained PCA algorithms, on large data sets from biology and computer vision. Finally, we show the usefulness of non-negative sparse PCA for unsupervised feature selection in a gene clustering task.
Locking the Elbow: Improved Antibody Fab Fragments as Chaperones for Structure Determination.

Antibody Fab fragments have been exploited with significant success to facilitate the structure determination of challenging macromolecules as crystallization chaperones and as molecular fiducial marks for single particle cryo-electron microscopy approaches. However, the inherent flexibility of the "elbow" regions, which link the constant and variable domains of the Fab, can introduce disorder and thus diminish their effectiveness. We have developed a phage display engineering strategy to generate synthetic Fab variants that significantly reduces elbow flexibility, while maintaining their high affinity and stability. This strategy was validated using previously recalcitrant Fab-antigen complexes where introduction of an engineered elbow region enhanced crystallization and diffraction resolution. Furthermore, incorporation of the mutations appears to be generally portable to other synthetic antibodies and may serve as a universal strategy to enhance the success rates of Fabs as structure determination chaperones.
Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.
The effects of home interferential therapy on post-operative pain, edema, and range of motion of the knee.

We studied the effects of home interferential current therapy (IFC) on postoperative pain, range of motion, and edema in subjects undergoing anterior cruciate ligament (ACL) reconstruction, menisectomy, or knee chondroplasty.



Randomized, double-blind, placebo-controlled prospective study.



A tertiary care outpatient orthopaedic clinic/ambulatory surgery center.



Eighty-seven subjects were separated into three groups based on their type of knee surgery and within each group randomized into a treatment or placebo group.



All subjects received home IFC units. Subjects randomized to treatment group received a working IFC unit. Placebo subjects received units that were previously set to deliver no current.



Post-operative edema at 24, 48, and 72 hours, and weeks 1-8; range of motion at 1, 3, 6, and 9 weeks; pain immediately after surgery, at 24, 48, and 72 hours, and weeks 1-7; and amount of pain medication taken at days 1-10 were compared between treatment and placebo groups.



All IFC subjects reported significantly less pain and had significantly greater range of motion at all post-operative time points. ACL and menisectomy IFC subjects experienced significantly less edema at all time points, while chondroplasty subjects experienced significantly less edema until 4 weeks postoperatively.



These findings indicate that home IFC may help reduce pain, pain medication taken, and swelling while increasing range of motion in patients undergoing knee surgery. This could result in quicker return to activities of daily living and athletic activities.