nils-herrmann/clean_scirepeval huggingface dataset:
Node Embedding via Word Embedding for Network Community Discovery

Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate that the proposed approach consistently improves over the current state-of-the-art. Specifically, our approach empirically attains the information-theoretic limits for community recovery under the benchmark stochastic block models for graph generation and exhibits better stability and accuracy over both spectral clustering and acyclic belief propagation in the community recovery limits.
Interruptions in manufacturing from a distributed cognition perspective

This thesis aims at portraying interruptions in the socio-technical domain of manufacturing industry, from a distributed cognition perspective. The research problem addressed is the lack of naturalistic inquiry in prior interruption research. Further, manufacturing is a complex socio-technical domain where interruptions have not previously been studied. In this thesis, a workplace study is applied with distributed cognition as its theoretical framework. The results of the study identify two new types of interruptions, as well as one new dimension of interruptions. This result shows that interruptions are a multifaceted phenomenon that frequently occurs within manufacturing. An integration of the theoretical background and the empirical work resulted in five recommendations concerning how to reduce the amount of interruptions and how to minimize their disruptive effects. This study complements prior interruption research, emphasises the importance of studying interruptions in natural settings, and provides several insights regarding future interruption research.
Optimizing orthodontic treatment in patients taking bisphosphonates for osteoporosis.

Bisphosphonates have unique pharmacological characteristics unlike those of any other drug group. Millions of adults take oral bisphosphonates for long-term treatment of osteoporosis and osteopenia; some of these people will most likely also seek orthodontic treatment. Adverse dental effects from bisphosphonates have been reported, including decreased tooth movement, impaired bone healing, and osteonecrosis in the mandible and the maxilla. Osteonecrosis has been rarely observed after bisphosphonate use for osteoporosis. However, adverse drug effects might occur more frequently in orthodontic patients, and they would probably be noted before the end-stage pathology of osteonecrosis. Adverse effects during orthodontic treatment, including decreased tooth movement, could last for years after the drug therapy is stopped. Successful orthodontic treatment requires optimal bone healing to prevent excessive tooth mobility. Bisphosphonates appear to have 2 bone elimination rates--a fast elimination of weeks from the bone surface and a slow elimination of years after incorporation into the bone structure. This article presents methods to clinically and radiographically monitor orthodontic patients who are taking oral bisphosphonates. Efforts to minimize adverse effects and optimize orthodontic procedures with physician-approved drug holidays are discussed. The orthodontic treatment results of 3 patients who received bisphosphonate therapy are reported.
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Lévy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.
Automated Assessment of Surgical Skills Using Frequency Analysis

We present an automated framework for visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis techniques for extracting motion quality via frequency coefficients are introduced. The framework is tested on videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques.
BER analysis of optical eU-OFDM transmission over AWGN

Enhanced unipolar orthogonal frequency-division multiplexing (eU-OFDM) was recently introduced to improve the power-spectral efficiency trade-off of intensity-modulated direct-detection (IM/DD) optical OFDM transmission schemes. This is accomplished by superimposing unipolar signals over several layers. Bit-error-rate (BER) analysis for each layer however, is eluded by error-propagation from previous layers in the eU-OFDM receiver. This paper presents a general mathematical model for eU-OFDM transmission. From the model, we introduce successive interference cancellation using soft-symbol estimates, which leads to slight performance improvement compared to the original hard-decision counterpart. More importantly, by using soft-symbol estimates we derive analytically tractable expressions that accurately predict the system's performance.
Applying an optimized switching strategy to a high gain boost converter for input current ripple cancellation

This paper discusses applying various switching strategies such as conventional complementary and proportional methods on a specific converter. This converter was recently presented and it can provide high voltage gain and has current ripple cancelation ability at a preselected duty-cycle. The input current ripple is zero when the converter works at a special duty-cycle. But load disturbance leads to output voltage changes that maybe cause the duty-cycle to deviate from its preselected value. In this situation, the input current ripple cannot be completely canceled. The proposed proportional strategy is an optimized method for minimizing input current ripple at other operating duty-cycles and also it provides a voltage gain lower than conventional complementary strategy. Here, the converter's performance is analyzed under two switching strategies and the effect of various strategies has been investigated on converter parameters including output voltage and input current ripple. These considerations are verified by implementing a 100-W prototype of the proposed converter in laboratory.
Neural Ordinary Differential Equations

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.
Enhancing Cross-Device Interaction Scripting with Interactive Illustrations

Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process.
Enhancing recurrent neural network-based language models by word tokenization

Statistical language models estimate the probability for a given sequence of words. Given a sentence s with n words such as s = (w1,w2 . . .wn), the language model assigns P(s). Statistical language models assess good word sequence estimations based on the sequence probability estimation. Building robust, fast and accurate language models is one of the main factors in the success of building systems such as machine translation systems and automatic speech recognition systems. Statistical language models can be estimated based on various approaches. Classical language models are estimated based on n-gram word sequences such as P(s) = P(w1,w2 . . .wn) = ∏n i=1 P(wi|wi−1) and can be approximated based on the Markov concept for shorter contexts (for example, bigram if n = 2 or trigram if n = 3 and so on). Recent researchers have applied neural networks different architectures to build and estimate language models. The classical feed-forward neural network-based language models have been continuously reporting good results among the traditional n-gram language modeling techniques .
A Flexible Low-Power 130 nm CMOS Read-Out Circuit With Tunable Sensitivity for Commercial Robotic Resistive Pressure Sensors

This paper presents a flexible and low-power ReadOut Circuit (ROC) with tunable sensitivity, designed to interface a wide range of commercial resistive pressure sensors for robotic applications. The ROC provides contact detection, monitoring small capacitance variations at low pressure (<;100 mbar), and encodes pressure measurement on 8 bit, evaluating resistive variations. Two all-digital circuits implement the conversion of the input resistance and capacitance-to-frequency, exploiting an on-chip ring oscillator as timing reference. A 130 nm RFCMOS prototype (with an active area of 428 × 159 μm2) has a power consumption of 27.2 μW, for VDD 1 V. Using digital control inputs, the ROC allows a wide range tuning of measurement sensitivity (6.7-46.4 mbar/LSB) and adjustable acquisition time (226.6-461.7 μs and 648-890 μs, for contact detection and pressure evaluation, respectively). The read-out time of ~1 ms is compatible with human response after touch.
Fast Text Classification Using Randomized Explicit Semantic Analysis

Document classification or document categorization is one of the most studied areas in computer science due to its importance. The problem is to assign a document using its text to one or more classes or categories from a predefined set. We propose a new approach for fast text classification using randomized explicit semantic analysis (RS-ESA). It is based on a state of the art approach for word sense disambiguation based on Wikipedia, the largest encyclopedia in existence. Our method reduces Wikipedia repository using a random sample approach resulting in a throughput, which is an order of magnitude faster than the original explicit semantic analysis. RS-ESA approach has been implemented as part of the LITMUS project due to a need in classifying data from Social Media into relevant and irrelevant items with respect to landslide as a natural disaster. We demonstrate that our approach achieves 96% precision when classifying Social Media landslide data collected in December 2014. We also demonstrate the genericity of the proposed approach by using it for separating factual texts from fictional based on Wikipedia articles and fan fiction stories, where we achieve 97% in precision.
A Compact Fractal Loop Rectenna for RF Energy Harvesting

This letter presents a compact fractal loop rectenna for RF energy harvesting at GSM1800 bands. First, a fractal loop antenna with novel in-loop ground-plane impedance matching is proposed for the rectenna design. Also, a high-efficiency rectifier is designed in the loop antenna to form a compact rectenna. Measured results show that an efficiency of 61% and an output dc voltage of 1.8 V have been achieved over 12-kΩ resistor for 10 μW/cm2 power density at 1.8 GHz. The rectenna is able to power up a battery-less LCD watch at a distance of 10 m from the cell tower. The proposed rectenna is compact, easy to fabricate, and useful for various energy harvesting applications.
Bayesian Estimation of Beta Mixture Models with Variational Inference

Bayesian estimation of the parameters in beta mixture models (BMM) is analytically intractable. The numerical solutions to simulate the posterior distribution are available, but incur high computational cost. In this paper, we introduce an approximation to the prior/posterior distribution of the parameters in the beta distribution and propose an analytically tractable (closed form) Bayesian approach to the parameter estimation. The approach is based on the variational inference (VI) framework. Following the principles of the VI framework and utilizing the relative convexity bound, the extended factorized approximation method is applied to approximate the distribution of the parameters in BMM. In a fully Bayesian model where all of the parameters of the BMM are considered as variables and assigned proper distributions, our approach can asymptotically find the optimal estimate of the parameters posterior distribution. Also, the model complexity can be determined based on the data. The closed-form solution is proposed so that no iterative numerical calculation is required. Meanwhile, our approach avoids the drawback of overfitting in the conventional expectation maximization algorithm. The good performance of this approach is verified by experiments with both synthetic and real data.
Isolation and characterization of microsatellite loci in the black pepper, Piper nigrum L. (piperaceae)

The black pepper, Piper nigrum L., which originated in Índia, is the World’s most important commercial spice. Brazil has a germplasm collection of this species preserved at the Brazilian Agricultural Research Corporation (Embrapa—Eastern Amazonia) where efforts are being made to generation information on the patterns of genetic variation and develop strategies for conservation and management of black pepper. Molecular markers of the SSR type are powerful tools for the description of material preserved in genetic resources banks, due to characteristics such as high levels of polymorphism, codominance and Mendelian segregation. Given this, we developed nine microsatellite markers from an enriched library of Piper nigrum L. Twenty varieties clonal from the Brazilian germplasm collection were analyzed, and observed and expected heterozygosity values ranged over 0.11–1.00 and 0.47–0.87, respectively. The nine microsatellite loci characterized here will contribute to studies of genetic diversity and conservation of Piper nigrum L.
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents

Most current chatbot engines are designed to reply to user utterances based on existing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chatbot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances. A learning to rank model with features designed at different levels of granularity is proposed to measure the relevance between utterances and responses directly. We evaluate our proposed approach in both English and Chinese: (i) For English, we evaluate DocChat on WikiQA and QASent, two answer sentence selection tasks, and compare it with state-of-the-art methods. Reasonable improvements and good adaptability are observed. (ii) For Chinese, we compare DocChat with XiaoIce2, a famous chitchat engine in China, and side-by-side evaluation shows that DocChat is a perfect complement for chatbot engines using Q-R pairs as main source of responses.
Survey of Event Correlation Techniques for Attack Detection in Early Warning Systems

In the context of early warning systems for detecting Internet worms and other attacks, event correlation techniques are needed for two reasons. First, network attack detection is usually based on distributed sensors, e.g. intrusion detection systems. During attacks but even in normal operation, the generated amount of events is hard to handle in order to evaluate the current attack situation for a larger network. Thus, the concept of event or alert correlation has been introduced. This survey was motivated by recent work on early warning systems. We summarize and clarify the typical terminology used in this context and present a requirement analysis from an early warning system’s point of view. In the main part of this survey, we summarize and classify event correlation techniques as described in the literature.
Diffusion Variational Autoencoders

A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstructions, we introduce Diffusion Variational Autoencoders with arbitrary manifolds as a latent space. A Diffusion Variational Autoencoder uses transition kernels of Brownian motion on the manifold. In particular, it uses properties of the Brownian motion to implement the reparametrization trick and fast approximations to the KL divergence. We show that the Diffusion Variational Autoencoder is capable of capturing topological properties of synthetic datasets. Additionally, we train MNIST on spheres, tori, projective spaces, SO(3), and a torus embedded in R. Although a natural dataset like MNIST does not have latent variables with a clear-cut topological structure, training it on a manifold can still highlight topological and geometrical properties.
Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning

We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporallylayered task given as a signal temporal logic formula. We represent the system as a Markov decision process in which the states are built from a partition of the statespace and the transition probabilities are unknown. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given formula and to maximize the average expected robustness, i.e., a measure of how strongly the formula is satisfied. We demonstrate via a pair of robot navigation simulation case studies that reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness.
Mining Recurring Concept Drifts with Limited Labeled Streaming Data

Tracking recurring concept drifts is a significant issue for machine learning and data mining that frequently appears in real-world stream classification problems. It is a challenge for many streaming classification algorithms to learn recurring concepts in a data stream environment with unlabeled data, and this challenge has received little attention from the research community. Motivated by this challenge, this article focuses on the problem of recurring contexts in streaming environments with limited labeled data. We propose a semi-supervised classification algorithm for data streams with REcurring concept Drifts and Limited LAbeled data, called REDLLA, in which a decision tree is adopted as the classification model. When growing a tree, a clustering algorithm based on k-means is installed to produce concept clusters and unlabeled data are labeled in the method of majority-class at leaves. In view of deviations between history and new concept clusters, potential concept drifts are distinguished and recurring concepts are maintained. Extensive studies on both synthetic and real-world data confirm the advantages of our REDLLA algorithm over three state-of-the-art online classification algorithms of CVFDT, DWCDS, and CDRDT and several known online semi-supervised algorithms, even in the case with more than 90% unlabeled data.
A Hybrid Algorithm for Stemming of Nepali Text

In this paper, a new context free stemmer is proposed which consists of the combination of traditional rule based system with string similarity approach. This algorithm can be called as hybrid algorithm. It is language dependent algorithm. Context free stemmer means that stemmer which stems the word that is not based on the context i.e., for every context such rule is applied. After stripping the words using traditional context free rule based approach, it may over stem or under stem the inflected words which are overcome by applying string similarity function of dynamic programming. For measuring the string similarity function, edit distance is used. The stripped inflected word is compared with the words stored in a text database available. That word having minimum distance is taken as the substitution of the stripped inflected word which leads to the stem of it. The concept of traditional rule based system and corpus based approach is heavily used in this approach. This algorithm is tested for Nepali Language which is based on Devanagari Script. The approach has given better result in comparison to traditional rule based system particularly for Nepali Language only. The total accuracy of this hybrid algorithm is 70.10% whereas the total accuracy of traditional rule based system is 68.43%.
Chatty Tenants and the Cloud Network Sharing Problem

The emerging ecosystem of cloud applications leads to significant inter-tenant communication across a datacenter’s internal network. This poses new challenges for cloud network sharing. Richer inter-tenant traffic patterns make it hard to offer minimum bandwidth guarantees to tenants. Further, for communication between economically distinct entities, it is not clear whose payment should dictate the network allocation. Motivated by this, we study how a cloud network that carries both intraand inter-tenant traffic should be shared. We argue for network allocations to be dictated by the least-paying of communication partners. This, when combined with careful VM placement, achieves the complementary goals of providing tenants with minimum bandwidth guarantees while bounding their maximum network impact. Through a prototype deployment and large-scale simulations, we show that minimum bandwidth guarantees, apart from helping tenants achieve predictable performance, also improve overall datacenter throughput. Further, bounding a tenant’s maximum impact mitigates malicious behavior.
A Design Of Omni-Directional Mobile Robot Based On Mecanum Wheels

As one of the important branch of mobile robotics, wheel mobile robot has long been paid atten tion to by the research people at home and abroad for its high load ability, positioning accuracy, high efficiency, simple control, etc. Mobile robot has close relation to many technologies suc-h as control theory, computer tech nology, sensor technology, etc. Therefore, research on the mobile robot has important significance.  Mobile robot; Four wheel drive and steering; Mecanum wheels