nils-herrmann/clean_scirepeval huggingface dataset:
S-HOT: Scalable High-Order Tucker Decomposition

Multi-aspect data appear frequently in many web-related applications. For example, product reviews are quadruplets of (user, product, keyword, timestamp). How can we analyze such web-scale multi-aspect data? Can we analyze them on an off-the-shelf workstation with limited amount of memory?
 Tucker decomposition has been widely used for discovering patterns in relationships among entities in multi-aspect data, naturally expressed as high-order tensors. However, existing algorithms for Tucker decomposition have limited scalability, and especially, fail to decompose high-order tensors since they explicitly materialize intermediate data, whose size rapidly grows as the order increases (≥ 4). We call this problem M-Bottleneck ("Materialization Bottleneck").
 To avoid M-Bottleneck, we propose S-HOT, a scalable high-order tucker decomposition method that employs the on-the-fly computation to minimize the materialized intermediate data. Moreover, S-HOT is designed for handling disk-resident tensors, too large to fit in memory, without loading them all in memory at once. We provide theoretical analysis on the amount of memory space and the number of scans of data required by S-HOT. In our experiments, S-HOT showed better scalability not only with the order but also with the dimensionality and the rank than baseline methods. In particular, S-HOT decomposed tensors 1000× larger than baseline methods in terms dimensionality. S- HOT also successfully analyzed real-world tensors that are both large-scale and high-order on an off-the-shelf workstation with limited amount of memory, while baseline methods failed. The source code of S-HOT is publicly available at http://dm.postech.ac.kr/shot to encourage reproducibility.
Lens distortion correction using ideal image coordinates

This paper proposes a fast and simple mapping method for lens distortion correction. Typical correction methods use a distortion model defined on distorted coordinates. They need inverse mapping for distortion correction. Inverse mapping of distortion equations is not trivial; approximation must be taken for real time applications. We propose a distortion model defined on ideal undistorted coordinates, so that we can reduce computation time and maintain the high accuracy. We verify accuracy and efficiency of the proposed method from experiments.
Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks

Access to electronic health records (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data could mitigate risk. In this paper, we propose a new approach, medical Generative Adversarial Network (medGAN), to generate realistic synthetic EHRs. Based on an input EHR dataset, medGAN can generate high-dimensional discrete variables (e.g., binary and count features) via a combination of an autoencoder and generative adversarial networks. We also propose minibatch averaging to efficiently avoid mode collapse, and increase the learning efficiency with batch normalization and shortcut connections. To demonstrate feasibility, we showed that medGAN generates synthetic EHR datasets that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and medical expert review.
A Review on Substrate Integrated Waveguide and its Microstrip Interconnect

Substrate Integrated Waveguides (SIW) are used for transmission of Electromagnetic waves. They are planar structures belonging to the family of Substrate Integrated Circuits. Because of their planar nature, they can be fabricated on planar circuits like Printed Circuit Boards (PCB) and can be integrated with other planar transmission lines like microstrips. They retain the low loss property of their conventional metallic waveguides and are widely used as interconnection in high speed circuits, filters, directional couplers, antennas. This paper is a comprehensive review of Substrate Integrated Waveguide and its integration with Microstrip line. In this paper, design techniques for SIW and its microstrip interconnect are presented. HFSS is used for simulation results. The objective of this paper is to provide broad perspective of SIW Technology.
Image-Text Multi-Modal Representation Learning by Adversarial Backpropagation

We present novel method for image-text multi-modal representation learning. In our knowledge, this work is the first approach of applying adversarial learning concept to multi-modal learning and not exploiting image-text pair information to learn multi-modal feature. We only use category information in contrast with most previous methods using image-text pair information for multi-modal embedding. In this paper, we show that multi-modal feature can be achieved without image-text pair information and our method makes more similar distribution with image and text in multi-modal feature space than other methods which use image-text pair information. And we show our multi-modal feature has universal semantic information, even though it was trained for category prediction. Our model is end-to-end backpropagation, intuitive and easily extended to other multimodal learning work.
Approximating meta-heuristics with homotopic recurrent neural networks

Much combinatorial optimisation problems constitute a nonpolynomial (NP) hard optimisation problem, i.e., they can not be solved in polynomial time. One such problem is finding the shortest route between two nodes on a graph. Meta-heuristic algorithms such as A∗ along with mixed-integer programming (MIP) methods are often employed for these problems. Our work demonstrates that it is possible to approximate solutions generated by a meta-heuristic algorithm using a deep recurrent neural network. We compare different methodologies based on reinforcement learning (RL) and recurrent neural networks (RNN) to gauge their respective quality of approximation. We show the viability of recurrent neural network solutions on a graph that has over 300 nodes and argue that a sequence-to-sequence network rather than other recurrent networks has improved approximation quality. Additionally, we argue that homotopy continuation – that increases chances of hitting an extremum – further improves the estimate generated by a vanilla RNN.
Discovering Rules from Disk Events for Predicting Hard Drive Failures

Detecting impending failure of hard disks is an important prediction task which might help computer systems to prevent loss of data and performance degradation. Currently most of the hard drive vendors support self-monitoring, analysis and reporting technology (SMART) which are often considered unreliable for such tasks. The problem of finding alternatives to SMART for predicting disk failure is an area of active research. In this paper, we consider events recorded from live disks and show that it is possible to construct decision support systems which can detect such failures. It is desired that any such prediction methodology should have high accuracy and ease of interpretability. Black box models can deliver highly accurate solutions but do not provide an understanding of events which explains the decision given by it. To this end we explore rule based classifiers for predicting hard disk failures from various disk events. We show that it is possible to learn easy to understand rules, from disk events, which have extremely low false alarm rates on real world data.
Programming full-body movements for humanoid robots by observation

The formulation and optimization of joint trajectories for humanoid robots is quite different from this same task for standard robots because of the complexity of humanoid robots’ kinematics and dynamics. In this paper we exploit the similarity between human motion and humanoid robot motion to generate joint trajectories for humanoids. In particular, we show how to transform human motion information captured by an optical tracking device into a high dimensional trajectory for a humanoid robot. We propose an automatic approach to relate humanoid robot kinematic parameters to the kinematic parameters of a human performer. Based on this relationship we infer the desired trajectories in robot joint space. B-spline wavelets are utilized to efficiently represent the trajectories. The density of the basis functions on the time axis is selected automatically. Large-scale optimization techniques are employed to solve the underlying computational problems efficiently. We applied our method to the task of teaching a humanoid robot how to make various naturally looking movements.
Open linked data and mobile devices as e-tourism tools. A practical approach to collaborative e-learning

Collaborative e-learning is based on a model where knowledge is created through interaction between members that interact by sharing experiences and knowledge. Linked Open Data (LOD) allow to link data with other related data in which people are interested. As a practical approach we introduce a system where is built cultural heritage knowledge from collaboration between open data published by a regional government, enriched with data from other sources of open linked data cloud like DBPedia. Mobile technologies in e-learning allow learning in any place and moment. We try to take advantage of mobile and semantic technologies, like LOD, in a collaborative e-learning environment. Knowledge is built by enriching open data that are shared by institutions to knowledge shared on Semantic Web, by using LOD techniques, leading a scenario of collaborative and human learning by linking data sources and people who share these data. The collaboration between web of data and open data from a public institution has resulted in practice, in a mobile tourism application that works as a tourist guide for citizens. Application also allows the collaboration of tour guides to add new knowledge about the cultural heritage resources. In brief, collaboration between different profiles generates knowledge and collaborative learning.
Introduction to machine learning for brain imaging

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences.
Intrusion Detection System Using Data Mining Technique : Support Vector Machine

Security and privacy of a system is compromised, when an intrusion happens. Intrusion Detection System (IDS) plays vital role in network security as it detects various types of attacks in network. So here, we are going to propose Intrusion Detection System using data mining technique: SVM (Support Vector Machine). Here, Classification will be done by using SVM and verification regarding the effectiveness of the proposed system will be done by conducting some experiments using NSL-KDD Cup’99 dataset which is improved version of KDD Cup’99 data set. The SVM is one of the most prominent classification algorithms in the data mining area, but its drawback is its extensive training time. In this proposed system, we have carried out some experiments using NSLKDD Cup’99 data set. The experimental results show that we can reduce extensive time required to build SVM model by performing proper data set pre-processing. Also when we do proper selection of SVM kernel function such as Gaussian Radial Basis Function, attack detection rate of SVM is increased and False Positive Rate (FPR) is decrease.  Classification, Intrusion Detection System (IDS), Kernel Function, NSLKDD, Pre-processing, Support Vector Machine (SVM)
Will Media Influence Learning : Refraining the Debate

This article addresses the position taken by Clark (1983) that media do not influence learning under any conditions. The article reframes the questions raised by Clark to explore the conditions under which media will influence learning. Specifically, it posits the need to consider the capabilities of media, and the methods that employ them, as they interact with the cognitive and social processes by which knowledge is constructed. This approach is examined within the context of two major media-based projects, one which uses computers and the other,video. The article discusses the implications of this approach for media theory, research and practice.
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input-output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.
Process and temperature compensation in a 7-MHz CMOS clock oscillator

This paper reports on the design and characterization of a process, temperature and supply compensation technique for a 7-MHz clock oscillator in a 0.25-/spl mu/m, two-poly five-metal (2P5M) CMOS process. Measurements made across a temperature range of -40/spl deg/C to 125/spl deg/C and 94 samples collected over four fabrication runs indicate a worst case combined variation of /spl plusmn/2.6% (with process, temperature and supply). No trimming was performed on any of these samples. The oscillation frequencies of 95% of the samples were found to fall within /spl plusmn/0.5% of the mean frequency and the standard deviation was 9.3 kHz. The variation of frequency with power supply was /spl plusmn/0.31% for a supply voltage range of 2.4-2.75 V. The clock generator is based on a three-stage differential ring oscillator. The variation of the frequency of the oscillator with temperature and process has been discussed and an adaptive biasing scheme incorporating a unique combination of a process corner sensing scheme and a temperature compensating network is developed. The biasing circuit changes the control voltage of the differential ring oscillator to maintain a constant frequency. A comparator included at the output stage ensures rail-to-rail swing. The oscillator is intended to serve as a start-up clock for micro-controller applications.
High-Frequency Transformer Isolated Bidirectional DC–DC Converter Modules With High Efficiency Over Wide Load Range for 20 kVA Solid-State Transformer

This paper presents the design of new high-frequency transformer isolated bidirectional dc-dc converter modules connected in input-series-output-parallel (ISOP) for 20-kVA-solid-state transformer. The ISOP modular structure enables the use of low-voltage MOSFETs, featuring low on-state resistance and resulted conduction losses, to address medium-voltage input. A phase-shift dual-half-bridge (DHB) converter is employed to achieve high-frequency galvanic isolation, bidirectional power flow, and zero voltage switching (ZVS) of all switching devices, which leads to low switching losses even with high-frequency operation. Furthermore, an adaptive inductor is proposed as the main energy transfer element of a phase-shift DHB converter so that the circulating energy can be optimized to maintain ZVS at light load and minimize the conduction losses at heavy load as well. As a result, high efficiency over wide load range and high power density can be achieved. In addition, current stress of switching devices can be reduced. A planar transformer adopting printed-circuit-board windings arranged in an interleaved structure is designed to obtain low core and winding loss, solid isolation, and identical parameters in multiple modules. Moreover, the modular structure along with a distributed control provides plug-and-play capability and possible high-level fault tolerance. The experimental results on 1 kW DHB converter modules switching at 50 kHz are presented to validate the theoretical analysis.
Thermoelectric Converters of Human Warmth for Self-Powered Wireless Sensor Nodes

Solar cells are the most commonly used devices in customer products to achieve power autonomy. This paper discusses a complementary approach to provide power autonomy to devices on a human body, i.e., thermoelectric conversion of human heat. In indoor applications, thermoelectric converters on the skin can provide more power per square centimeter than solar cells, particularly in adverse illumination conditions. Moreover, they work day and night. The first sensor nodes powered by human heat have been demonstrated and tested on people in 2004-2005. They used the state-of-the-art 100-muW watch-size thermoelectric wrist generators fabricated at IMEC and based on custom-design small-size BiTe thermopiles. The sensor node is completed with a power conditioning module, a microcontroller, and a wireless transceiver mounted on a watchstrap
Robot Web Tools: Efficient messaging for cloud robotics

Since its official introduction in 2012, the Robot Web Tools project has grown tremendously as an open-source community, enabling new levels of interoperability and portability across heterogeneous robot systems, devices, and front-end user interfaces. At the heart of Robot Web Tools is the rosbridge protocol as a general means for messaging ROS topics in a client-server paradigm suitable for wide area networks, and human-robot interaction at a global scale through modern web browsers. Building from rosbridge, this paper describes our efforts with Robot Web Tools to advance: 1) human-robot interaction through usable client and visualization libraries for more efficient development of front-end human-robot interfaces, and 2) cloud robotics through more efficient methods of transporting high-bandwidth topics (e.g., kinematic transforms, image streams, and point clouds). We further discuss the significant impact of Robot Web Tools through a diverse set of use cases that showcase the importance of a generic messaging protocol and front-end development systems for human-robot interaction.
Open data for competitive advantage: insights from open data use by companies

Politicians have high expectations for commercial open data use. Yet, companies appear to challenge the assumption that open data can be used to create competitive advantage, since any company can access open data and since open data use requires scarce resources. In this paper we examine commercial open data use for creating competitive advantage from the perspective of Resource Based Theory (RBT) and Resource Dependency Theory (RDT). Based on insights from a scenario, interviews and a survey and from RBT and RDT as a reference theory, we derive seven propositions. Our study suggests that the generation of competitive advantage with open data requires a company to have in-house capabilities and resources for open data use. The actual creation of competitive advantage might not be simple. The propositions also draw attention to the accomplishment of unique benefits for a company through the combination of internal and external resources. Recommendations for further research include testing the propositions.
Antimicrobial effects of tea-tree oil and its major components on Staphylococcus aureus, Staph. epidermidis and Propionibacterium acnes.

Major components of two tea-tree oil samples were identified using thin layer and gas-liquid chromatography (TLC and GLC). Using a TLC-bioautographic technique, the tea-tree oils, terpinen-4-ol, alpha-terpineol and alpha-pinene were found to be active against Staphylococcus aureus, Staph. epidermidis and Propionibacterium acnes whereas cineole was inactive against these organisms. The MIC values of the three active compounds increased in the order alpha-terpineol < terpinen-4-ol < alpha-pinene for all three micro-organisms. MIC values of the tea-tree oils and terpinen-4-ol were lower for P. acnes than for the two staphylococci. This study supports the use of tea-tree oil in the treatment of acne, and demonstrates that terpinen-4-ol is not the sole active constituent of the oil.
Mapping Fishing Effort through AIS Data

Several research initiatives have been undertaken to map fishing effort at high spatial resolution using the Vessel Monitoring System (VMS). An alternative to the VMS is represented by the Automatic Identification System (AIS), which in the EU became compulsory in May 2014 for all fishing vessels of length above 15 meters. The aim of this paper is to assess the uptake of the AIS in the EU fishing fleet and the feasibility of producing a map of fishing effort with high spatial and temporal resolution at European scale. After analysing a large AIS dataset for the period January-August 2014 and covering most of the EU waters, we show that AIS was adopted by around 75% of EU fishing vessels above 15 meters of length. Using the Swedish fleet as a case study, we developed a method to identify fishing activity based on the analysis of individual vessels' speed profiles and produce a high resolution map of fishing effort based on AIS data. The method was validated using detailed logbook data and proved to be sufficiently accurate and computationally efficient to identify fishing grounds and effort in the case of trawlers, which represent the largest portion of the EU fishing fleet above 15 meters of length. Issues still to be addressed before extending the exercise to the entire EU fleet are the assessment of coverage levels of the AIS data for all EU waters and the identification of fishing activity in the case of vessels other than trawlers.
Image Feature Learning for Cold Start Problem in Display Advertising

In online display advertising, state-of-the-art Click Through Rate(CTR) prediction algorithms rely heavily on historical information, and they work poorly on growing number of new ads without any historical information. This is known as the the cold start problem. For image ads, current stateof-the-art systems use handcrafted image features such as multimedia features and SIFT features to capture the attractiveness of ads. However, these handcrafted features are task dependent, inflexible and heuristic. In order to tackle the cold start problem in image display ads, we propose a new feature learning architecture to learn the most discriminative image features directly from raw pixels and user feedback in the target task. The proposed method is flexible and does not depend on human heuristic. Extensive experiments on a real world dataset with 47 billion records show that our feature learning method outperforms existing handcrafted features significantly, and it can extract discriminative and meaningful features.