nils-herrmann/clean_scirepeval huggingface dataset:
Clarification Questions with Feedback

In this paper, we investigate how people construct clarification questions. Our goal is to develop similar strategies for handling errors in automatic spoken dialogue systems in order to make error recovery strategies more efficient. Using a crowd-sourcing tool [7], we collect a dataset of user responses to clarification questions when presented with sentences in which some words are missing. We find that, in over 60% of cases, users choose to continue the conversation without asking a clarification question. However, when users do ask a question, our findings support earlier research showing that users are more likely to ask a targeted clarification question than a generic question. Using the dataset we have collected, we are exploring machine learning approaches for determining which system responses are most appropriate in different contexts and developing strategies for constructing clarification
Friction modeling in linear chemical-mechanical planarization

In this article, we develop an analytical model of the relationship between the wafer/pad friction and process configuration. We also provide experimental validation of this model for in situ process monitoring. CMP thus demonstrates that the knowledge and methodologies developed for friction modeling and control can be used to advance the understanding, monitoring, and control of semiconductor manufacturing processes. Meanwhile, relevant issues and challenges in real-time monitoring of CMP are presented as sources of future development.
Discovering Conditional Matching Rules

Matching dependencies (MDs) have recently been proposed to make data dependencies tolerant to various information representations, and found useful in data quality applications such as record matching. Instead of the strict equality function used in traditional dependency syntax (e.g., functional dependencies), MDs specify constraints based on similarity and identification. However, in practice, MDs may still be too strict and applicable only in a subset of tuples in a relation. Thereby, we study the conditional matching dependencies (CMDs), which bind matching dependencies only in a certain part of a table, i.e., MDs conditionally applicable in a subset of tuples. Compared to MDs, CMDs have more expressive power that enables them to satisfy wider application needs. In this article, we study several important theoretical and practical issues of CMDs, including irreducible CMDs with respect to the implication, discovery of CMDs from data, reliable CMDs agreed most by a relation, approximate CMDs almost satisfied in a relation, and finally applications of CMDs in record matching and missing value repairing. Through an extensive experimental evaluation in real data sets, we demonstrate the efficiency of proposed CMDs discovery algorithms and effectiveness of CMDs in real applications.
Chip-First Fan-Out Panel-Level Packaging for Heterogeneous Integration

The design, materials, process, fabrication, and reliability of a heterogeneous integration of four chips by a fan-out panel-level packaging (FOPLP) method are investigated in this paper. Emphasis is placed on the application of a special assembly process called uni-substrate-integrated package for fabricating the redistribution layers (RDLs) of the FOPLP. The Ajinomoto build-up film is used as the dielectric of the RDLs and is built up by the semiadditive process. The electroless Cu is used to make the seed layer, the laser direct imaging is used for opening the photoresist, and the printed circuit board Cu plating is used for making the conductor wiring of the RDLs. Reliability assessments such as the drop test and thermal cycling test are also performed.
Learning styles and performance in the introductory programming sequence

This paper reports on the implication of different preferred learning styles on students' performance in the introductory programming sequence and on work in progress on how to accommodate these different styles.Students were given a learning styles preference test and then their preferred learning styles were compared to their performance on the exam and the practical programming part of the introductory programming module. There were significant differences in performance between groups of students.This result could lead one to two possible conclusions. One might be that some students' learning styles are more suited to learning programming than others.An alternative explanation is that our current methods of teaching advantage students with certain learning preference styles. We are at present in the process of testing this second assumption by providing students with a wider range of learning materials. We will then see if student performance is improved by using our current results as a baseline for comparison
CS224N Project: Let Computers Do Reading Comprehension

In this work, we want to teach the computers to do reading comprehension. More specifically, the task is to let computers read a short passage and then answer some simple questions based on the passage.[1] proposes an end-to-end, RNN-like approach which, according to them, needs significantly less supervision but performs comparatively good to benchmarks for many applications. However, we found that their Q&A result is based on a small synthetic data set defined in . We could see that the dataset is quite small and simple with a vocabulary size of 177. Here is a sample from the dataset.
A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm's robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities' space generated by the classifiers at the stage 1. The proposed method was ranked 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.
OBFS: A File System for Object-Based Storage Devices

The object-based storage model, in which files are made up of one or more data objects stored on self-contained Object-Based Storage Devices (OSDs), is emerging as an architecture for distributed storage systems. The workload presented to the OSDs will be quite different from that of generalpurpose file systems, yet many distributed file systems employ general-purpose file systems as their underlying file system. We present OBFS, a small and highly efficient file system designed for use in OSDs. Our experiments show that our user-level implementation of OBFS outperforms Linux Ext2 and Ext3 by a factor of two or three, and while OBFS is 1/25 the size of XFS, it provides only slightly lower read performance and 10%–40% higher write performance.
Navigation of a Telepresence Robot via Covert Visuospatial Attention and Real-Time fMRI

Brain–computer interfaces (BCIs) allow people with severe neurological impairment and without ability to control their muscles to regain some control over their environment. The BCI user performs a mental task to regulate brain activity, which is measured and translated into commands controlling some external device. We here show that healthy participants are capable of navigating a robot by covertly shifting their visuospatial attention. Covert Visuospatial Attention (COVISA) constitutes a very intuitive brain function for spatial navigation and does not depend on presented stimuli or on eye movements. Our robot is equipped with motors and a camera that sends visual feedback to the user who can navigate it from a remote location. We used an ultrahigh field MRI scanner (7 Tesla) to obtain fMRI signals that were decoded in real time using a support vector machine. Four healthy subjects with virtually no training succeeded in navigating the robot to at least three of four target locations. Our results thus show that with COVISA BCI, realtime robot navigation can be achieved. Since the magnitude of the fMRI signal has been shown to correlate well with the magnitude of spectral power changes in the gamma frequency band in signals measured by intracranial electrodes, the COVISA concept may in future translate to intracranial application in severely paralyzed people.
The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections.

Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. Here we report the results of five relevant double-blind, randomized controlled experiments, using a total of 4,556 undecided voters representing diverse demographic characteristics of the voting populations of the United States and India. The fifth experiment is especially notable in that it was conducted with eligible voters throughout India in the midst of India's 2014 Lok Sabha elections just before the final votes were cast. The results of these experiments demonstrate that (i) biased search rankings can shift the voting preferences of undecided voters by 20% or more, (ii) the shift can be much higher in some demographic groups, and (iii) search ranking bias can be masked so that people show no awareness of the manipulation. We call this type of influence, which might be applicable to a variety of attitudes and beliefs, the search engine manipulation effect. Given that many elections are won by small margins, our results suggest that a search engine company has the power to influence the results of a substantial number of elections with impunity. The impact of such manipulations would be especially large in countries dominated by a single search engine company.
Conditions for acceptance and usage of mobile payment procedures

Mobile payment (MP) is crucial for, but not limited to mobile commerce. The key to mobile payment acceptance is in the hands of customers. In this paper we examine the conditions for acceptance and actual usage of MP procedures by the customer. We identify essential conditions which belong to the categories costs, security and convenience. Different preferences lead to an individual set of essential conditions for any single user. We propose a scheme for their representation and comparison and, based on these results, examine the relevance of the different criteria with empirical results. Additionally, we propose an approach to a commensurate condition for the usage of MP procedures based on the theory of informational added values. Finally, applications and constrictions of the results are shown and an outlook on the future of mobile payment is given.
Vehicle location finder using Global position system and Global System for Mobile

Each year, the number of stolen vehicle is on the rise. Usually, to prevent theft, a physical type countermeasure is used such as padlock, disk break lock and other more which is a preventive action but it is not enough safe. The objective of this study is to create a controllable system that can display the location of a vehicle using Global position system (GPS) to pin point the location and Global System for Mobile (GSM) as a mean for communicating with the vehicle for ease of finding after a theft attempt. The system is made to test the accuracy of the location that is send to the user when the vehicle is in motion and stationary in the city and suburb. The system is made by combining a micro controller with GPS and GSM, then comparing it with other similar device available in the market like Garmin and a reference website to find the radius of error. The study of proposed device begins by studying IEEE journal about alternative product and the vehicle itself. The hardware and program development is done by research and trial and error as the controller do not interact with both module at the same time, after successfully programming both module, it is combined into a single program with addition of interrupt program. The experiment is done in three set of tests so that the system accuracy can be determine when stationary and in motion on vehicle, output controlling is the test to determine if the controller can be made into anti-theft system. The result of the test concludes that the system can provide standard GPS coordinate when requested via Short Message Service (SMS). The system can also be used to control an actuator.
Modeling of electric vehicle loads for power flow analysis based on PSAT

This paper proposes the modeling of electric vehicle(EV) loads for power flow analysis based on User Define Models (UDMs) in power system analysis toolbox (PSAT) programming, According to the development of EV technology especially motor controller and battery, the EV has totally designed and made for using in city area as small city cars. To study the performance of EV, this aims to present the model of EV loads for using to solve power flow analysis in the real power system. EV load is modeled as the function box equivalent on PSAT that can connect to the single line diagram of power system model. The IEEE 14 bus system is selected for simulating the performance of EV and charging station which compared between PQ load base case and EV load. The simulation result showed that the EV load was installed at bus No.14. The rated power of EV load was increased from 5.0 p.u. to 79 p.u. This proposed EV load model can be used to solve the power flow analysis with continuation power flow (CPF) method. The EV load model is directly effected to voltage stability margin when the EV load increased. This study can be verified that proposed EV load can use to study the EV load in the future works.
Stronger, Smarter, Softer: Next-Generation Wearable Robots

Exosuits show much promise as a method for augmenting the body with lightweight, portable, and compliant wearable systems. We envision that such systems can be further refined so that they can be sufficiently low profile to fit under a wearer's existing clothing. Our focus is on creating an assistive device that provides a fraction of the nominal biological torques and does not provide external load transfer. In early work, we showed that the system can substantially maintain normal biomechanics and positively affect a wearer's metabolic rate. Many basic fundamental research and development challenges remain in actuator development, textile innovation, soft sensor development, human-machine interface (control), biomechanics, and physiology, which provides fertile ground for academic research in many disciplines. While we have focused on gait assistance thus far, numerous other applications are possible, including rehabilitation, upper body support, and assistance for other motions. We look forward to a future where wearable robots provide benefits for people across many areas of our society.
Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.
ISO / IEC 9126 in practice : what do we need to know ?

ISO/IEC 9126 is currently one of the most widespread quality standards. In its actual form it embraces both quality models and metrics. Due to its generic nature, some of the concepts presented therein need to be refined before using the standard in a real project. In our paper, we aim at exploring which are the concepts that require being more elaborated before putting the standard to work. Specifically, with respect to the part 1 of the standard we focus on the hierarchical form of the quality entities (i.e., characteristics, subcharacteristics and attributes) that appear in quality models; we propose some criteria to distinguish among subcharacteristics and attributes; we distinguish among derived attributes and basic attributes; and we propose an extension for covering not only technical factors but also managerial and political ones. Concerning the other 3 parts of the standard (one of them currently pending of approval), we distinguish different categorization criteria of metrics (scale, type, objectivity, qualitative/quantitative) and we make explicit the rationale behind their definition with the Goal-Question-Metric approach. As a final contribution, we show the use of UML class diagrams for representing all the concepts of the standard and their relationships, and we highlight the need for having tool support for quality model development and metrics definition.
When Constant-Time Source Yields Variable-Time Binary: Exploiting Curve25519-donna Built with MSVC 2015

The elliptic curve Curve25519 has been presented as protected against state-of-the-art timing-attacks . This paper shows that a timing attack is still achievable against a particular X25519 implementation which follows the RFC 4 7748 requirements . The attack allows the retrieval of the complete private key used in the ECDH protocol. This is achieved due to timing leakage during Montgomery ladder execution and relies on a conditional branch in the Windows runtime library 2015. The attack can be applied remotely.
Overview of the ImageCLEF 2016 Medical Task

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results.
Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection

In this paper, we propose a differential evolution (DE) algorithm variant with a combination of multiple mutation strategies based on roulette wheel selection, which we call MMRDE. We first propose a new, reflection-based mutation operation inspired by the reflection operations in the Nelder–Mead method. We design an experiment to compare its performance with seven mutation strategies, and we prove its effectiveness at balancing exploration and exploitation of DE. Although our reflection-based mutation strategy can balance exploration and exploitation of DE, it is still prone to premature convergence or evolutionary stagnation when solving complex multimodal optimization problems. Therefore, we add two basic strategies to help maintain population diversity and increase the robustness. We use roulette wheel selection to arrange mutation strategies based on their success rates for each individual. MMRDE is tested with some improved DE variants based on 28 benchmark functions for real-parameter optimization that have been recommended by the Institute of Electrical and Electronics Engineers CEC2013 special session. Experimental results indicate that the proposed algorithm shows its effectiveness at cooperative work with multiple strategies. It can obtain a good balance between exploration and exploitation. The proposed algorithm can guide the search for a global optimal solution with quick convergence compared with other improved DE variants.
Innocent by association: early recognition of legitimate users

This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.
Multi-Scale Context Aggregation by Dilated Convolutions

State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multiscale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.