nils-herrmann/clean_scirepeval huggingface dataset:
A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases

DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results.
Learning Word Vectors for 157 Languages

Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.
Enabling Technologies for Green Internet of Things

Recent technological advances have led to an increase in the carbon footprint. Energy efficiency in the Internet of Things (IoT) has been attracting a lot of attention from researchers and designers over the last couple of years, paving the way for an emerging area called green IoT. There are various aspects (such as key enablers, communications, services, and applications) of IoT, where efficient utilization of energy is needed to enable a green IoT environment. We explore and discuss how the various enabling technologies (such as the Internet, smart objects, sensors, etc.) can be efficiently deployed to achieve a green IoT. Furthermore, we also review various IoT applications, projects and standardization efforts that are currently under way. Finally, we identify some of the emerging challenges that need to be addressed in the future to enable a green IoT.
Resonant snubbers with auxiliary switches

A resonant snubber is described for voltage-source inverters, current-source inverters, and self-commutated frequency changers. The main self-turn-off devices have shunt capacitors directly across them. The lossless resonant snubber described avoids trapping energy in a converter circuit where high dynamic stresses at both turn-on and turn-off are normally encountered. This is achieved by providing a temporary parallel path through a small ordinary thyristor (or other device operating in a similar node) to take over the high-stress turn-on duty from the main gate turn-off (GTO) or power transistor, in a manner that leaves no energy trapped after switching.<<ETX>>
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON). Our method is dedicated to automatically selecting relevant image regions from weak annotations, e.g. global image labels, and encompasses the following contributions. Firstly, WELDON leverages recent improvements on the Multiple Instance Learning paradigm, i.e. negative evidence scoring and top instance selection. Secondly, the deep CNN is trained to optimize Average Precision, and fine-tuned on the target dataset with efficient computations due to convolutional feature sharing. A thorough experimental validation shows that WELDON outperforms state-of-the-art results on six different datasets.
Energy-efficient signal processing via algorithmic noise-tolerance

In this paper, we propose a framework for low-energy digital signal processing (DSP) where the supply voltage is scaled beyond the critical voltage required to match the critical path delay to the throughput. This deliberate introduction of input-dependent errors leads to degradation in the algorithmic performance, which is compensated for via algorithmic noise-tolerance (ANT) schemes. The resulting setup that comprises of the DSP architecture operating at sub-critical voltage and the error control scheme is referred to as soft DSP. It is shown that technology scaling renders the proposed scheme more effective as the delay penalty suffered due to voltage scaling reduces due to short channel effects. The effectiveness of the proposed scheme is also enhanced when arithmetic units with a higher “delay-imbalance” are employed. A prediction based error-control scheme is proposed to enhance the performance of the filtering algorithm in presence of errors due to soft computations. For a frequency selective filter, it is shown that the proposed scheme provides 60% 81% reduction in energy dissipation for filter bandwidths up to 0.5~ (where 27r corresponds to the sampling frequency fs) over that achieved via conventional voltage scaling, with a maximum of 0.5dB degradation in the output signal-to-noise ratio (SN%). It is also shown that the proposed algorithmic noise-tolerance schemes can be used to improve the performance of DSP algorithms in presence of bit-error rates of upto 10-s due to deep submicron (DSM) noise.
A generic approach to the theory of superregenerative reception

The superregenerative receiver has been widely used for many decades in short-range wireless communications because of its relative simplicity, reduced cost, and low power consumption. However, the theory that describes the behavior of this type of receiver, which was mainly developed prior to 1950, is of limited scope, since it applies to particular implementations, usually operating under continuous-wave signal or narrow-band modulation. As a novelty, we present the theory of superregenerative reception from a generic point of view. We develop an analytic study based on a generic block diagram of the receiver and consider not only narrow-band, but a wider variety of input signals. The study allows general results and conclusions that can be easily particularized to specific implementations to be obtained. Starting from the proposed model, the differential equation that describes the operation of the receiver in the linear mode is deducted and solved. Normalized parameters and functions characterizing the performance of the receiver are presented, as well as the requirements for proper operation. Several characteristic phenomena, such as hangover and multiple resonance, are described. The nonlinear behavior of the active device is also modeled to obtain a solution of the differential equation in the logarithmic mode of operation. The study is completed with a practical example operating at 2.4 GHz and illustrating the typical performance of a superregenerative receiver.
Neural network-based adaptive controller design of robotic manipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies.
Longitudinal Data Analysis

A complete list of the titles in this series appears at the end of this volume. No part of this publication may he reproduced, stored in a retrieval system, or transmitted in any form or by any means Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic format. For information about Wiley products, visit our web site at www.wiley.com.
Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics.



First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project.



We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.
Building intelligent Web applications using lightweight wrappers

The Web so far has been incredibly successful at delivering information to human users. So successful actually, that there is now an urgent need to go beyond a browsing human. Unfortunately, the Web is not yet a well organized repository of nicely structured documents but rather a conglomerate of volatile HTML pages. To address this problem, we present the World Wide Web Wrapper Factory (W4F), a toolkit for the generation of wrappers for Web sources, that offers: (1) an expressive language to specify the extraction of complex structures from HTML pages; (2) a declarative mapping to various data formats like XML; (3) some visual tools to make the engineering of wrappers faster and easier.
Conditional Restricted Boltzmann Machines for Structured Output Prediction

Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, classification, and modeling motion capture data. While much progress has been made in training non-conditional RBMs, these algorithms are not applicable to conditional models and there has been almost no work on training and generating predictions from conditional RBMs for structured output problems. We first argue that standard Contrastive Divergence-based learning may not be suitable for training CRBMs. We then identify two distinct types of structured output prediction problems and propose an improved learning algorithm for each. The first problem type is one where the output space has arbitrary structure but the set of likely output configurations is relatively small, such as in multi-label classification. The second problem is one where the output space is arbitrarily structured but where the output space variability is much greater, such as in image denoising or pixel labeling. We show that the new learning algorithms can work much better than Contrastive Divergence on both types of problems.
A varactor-tuned dual-band slot antenna

A new technique for designing dual-band reconfigurable slot antennas is introduced. The technique is based on loading a slot antenna with a lumped capacitor (or varactor) at a certain location along the slot. Given a fixed capacitor location along the slot, decreasing the capacitance results in increasing the first and second resonant frequencies of the slot antenna. However, the changes in the resonant frequencies are significantly different for the first and second resonances and, hence, a dual-band antenna with considerable frequency ratio tuning range can be obtained. Based on this technique, an electronically tunable dual-band antenna with a frequency ratio in the range of 1.2-1.65 is designed and fabricated using a single varactor with a capacitance range of 0.5-2.2 pF. The antenna has similar radiation patterns with low cross-polarization levels at both bands and across the entire tunable frequency range.
Natural Resources , Conflict , and Conflict Resolution : Uncovering the Mechanisms

The interpretation of the resource-conflict link that has become most publicized—the rebel greed hypothesis—depends on just one of many plausible mechanisms that could underlie a relationship between resource dependence and violence. The author catalogues a large range of rival possible mechanisms, highlights a set of techniques that may be used to identify these mechanisms, and begins to employ these techniques to distinguish between rival accounts of the resource-conflict linkages. The author uses finer natural resource data than has been used in the past, gathering and presenting new data on oil and diamonds production and on oil stocks. The author finds evidence that (1) conflict onset is more responsive to the impacts of past natural resource production than to the potential for future production, supporting a weak states mechanism rather than a rebel greed mechanism; (2) the impact of natural resources on conflict cannot easily be attributed entirely to the weak states mechanism, and in particular, the impact of natural resources is independent of state strength; (3) the link between primary commodities and conflict is driven in part by agricultural dependence rather than by natural resources more narrowly defined, a finding consistent with a “sparse networks” mechanism; (4) natural resources are associated with shorter wars, and natural resource wars are more likely to end with military victory for one side than other wars. This is consistent with evidence that external actors have incentives to work to bring wars to a close when natural resource supplies are threatened. The author finds no evidence that resources are associated with particular difficulties in negotiating ends to conflicts, contrary to arguments that loot-seeking rebels aim to prolong wars.
Interpretable semantic textual similarity: Finding and explaining differences between sentences

User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning, which requires adding an interpretability layer that facilitates users to understand their behavior. This paper focuses on adding an interpretable layer on top of Semantic Textual Similarity (STS), which measures the degree of semantic equivalence between two sentences. The interpretability layer is formalized as the alignment between pairs of segments across the two sentences, where the relation between the segments is labeled with a relation type and a similarity score. We present a publicly available dataset of sentence pairs annotated following the formalization. We then develop a system trained on this dataset which, given a sentence pair, explains what is similar and different, in the form of graded and typed segment alignments. When evaluated on the dataset, the system performs better than an informed baseline, showing that the dataset and task are well-defined and feasible. Most importantly, two user studies show how the system output can be used to automatically produce explanations in natural language. Users performed better when having access to the explanations, providing preliminary evidence that our dataset and method to automatically produce explanations is useful in real applications.
Risk assessment in internal auditing: a neural network approach

Risk assessment is a systematic process for integrating professional judgments about relevant risk factors, their relative significance and probable adverse conditions and/or events leading to identification of auditable activities (IIA, 1995, SIAS No. 9). Internal auditors utilize risk measures to allocate critical audit resources to compliance, operational, or financial activities within the organization (Colbert, 1995). In information rich environments, risk assessment involves recognizing patterns in the data, such as complex data anomalies and discrepancies, that perhaps conceal one or more error or hazard conditions (e.g. Coakley and Brown, 1996; Bedard and Biggs, 1991; Libby, 1985). This research investigates whether neural networks can help enhance auditors’ risk assessments. Neural networks, an emerging artificial intelligence technology, are a powerful non-linear optimization and pattern recognition tool (Haykin, 1994; Bishop, 1995). Several successful, real-world business neural network application decision aids have already been built (Burger and Traver, 1996). Neural network modeling may prove invaluable in directing internal auditor attention to those aspects of financial, operating, and compliance data most informative of high-risk audit areas, thus enhancing audit efficiency and effectiveness. This paper defines risk in an internal auditing context, describes contemporary approaches to performing risk assessments, provides an overview of the backpropagation neural network architecture, outlines the methodology adopted for conducting this research project including a Delphi study and comparison with statistical approaches, and presents preliminary results, which indicate that internal auditors could benefit from using neural network technology for assessing risk.
The computational modeling of analogy-making

Our ability to see a particular object or situation in one context as being 'the same as' another object or situation in another context is the essence of analogy-making. It encompasses our ability to explain new concepts in terms of already-familiar ones, to emphasize particular aspects of situations, to generalize, to characterize, to explain or describe new phenomena, to serve as a basis for how to act in unfamiliar surroundings, and to understand many types of humor. Within this framework, the importance of analogy-making in modeling cognition becomes clear.
Can Cryptography Secure Next Generation Air Traffic Surveillance ?

The proposed next-generation air traffic control system depends crucially on a surveillance technology called ADS-B. By 2020, nearly all aircraft flying through U.S. airspace must carry ADS-B transponders to continuously transmit their precise real-time location and velocity to ground-based air traffic control and to other en route aircraft. Surprisingly, the ADS-B protocol has no built-in security mechanisms, which renders ADS-B systems vulnerable to a wide range of malicious attacks. Herein, we address the question “can cryptography secure ADS-B?”— in other words, is there a practical and effective cryptographic solution that can be retrofit to the existing ADS-B system and enhance the security of this critical aviation technology?
Base-Delta-Immediate Compression: A Practical Data Compression Mechanism for On-Chip Caches

Cache compression is a promising technique to increase cache capacity and to decrease on-chip and off-chip bandwidth usage. Unfortunately, directly applying well-known compression algorithms (usually implemented in software) leads to high hardware complexity and unacceptable decompression/compression latencies, which in turn can negatively affect performance. Hence, there is a need for a simple yet efficient compression technique that can effectively compress common in-cache data patterns, and has minimal effect on cache access latency. In this paper, we propose a new compression algorithm called Base-Delta-Immediate (B∆I) compression, a practical technique for compressing data in on-chip caches. The key idea of the algorithm is that, for many cache lines, the values within the cache line have a low dynamic range – i.e., the differences between values stored within the cache line are small. As a result, a cache line can be represented using a base value and an array of differences whose combined size is much smaller than the original cache line (we call this the base+delta encoding). Moreover, many cache lines intersperse such base+delta values with small values – our B∆I technique efficiently incorporates such immediate values into its encoding. Compared to prior cache compression approaches, our studies show that B∆I strikes a sweet-spot in the tradeoff between compression ratio, decompression/compression latencies, and hardware complexity. Our results show that B∆I compression improves performance for both single-core (8.1% improvement) and multi-core workloads (9.5% / 11.2% improvement for two/four cores). For many applications, B∆I provides the performance benefit of doubling the cache size of the baseline system, effectively increasing average cache capacity by 1.53X.
Distribution System Topology Detection Using Consumer Load and Line Flow Measurements

We present a topology detection method combining smart meter sensor information and sparse line measurements. The problem is formulated as a spanning tree identification problem over a graph given partial nodal and edge power flow information. In the deterministic case of known nodal power consumption and edge power flow we provide sensor placement criterion which guarantees correct identification of all spanning trees. We then present a detection method which is polynomial in complexity to the size of the graph. In the stochastic case where loads are given by forecasts derived from delayed smart meter data, we provide a combinatorial complexity MAP detector and a polynomial complexity approximate MAP detector which is shown to work near optimum in all numerical cases.