nils-herrmann/clean_scirepeval huggingface dataset:
FEEL: Featured Event Embedding Learning

Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.
Self-assembly of neural networks viewed as swarm intelligence

While self-assembly is a fairly active area of research in swarm intelligence, relatively little attention has been paid to the issues surrounding the construction of network structures. In this paper we extend methods developed previously for controlling collective movements of agent teams to serve as the basis for self-assembly or “growth” of networks, using neural networks as a concrete application to evaluate our approach. Our central innovation is having network connections arise as persistent “trails” left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. We demonstrate our model’s effectiveness by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be adopted to facilitate network self-assembly.
Studying depression using imaging and machine learning methods

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.
Hierarchical Amharic Base Phrase Chunking Using HMM with Error Pruning

Segmentation of a text into non-overlapping syntactic units (chunks) has become an essential component of many applications of natural language processing. This paper presents Amharic base phrase chunker that groups syntactically correlated words at different levels using HMM. Rules are used to correct chunk phrases incorrectly chunked by the HMM. For the identification of the boundary of the phrases IOB2 chunk specification is selected and used in this work. To test the performance of the system, corpus was collected from Amharic news outlets and books. The training and testing datasets were prepared using the 10-fold cross validation technique. Test results on the corpus showed an average accuracy of 85.31% before applying the rule for error correction and an average accuracy of 93.75% after applying rules.
Dark vs. Dim Silicon and Near-Threshold Computing

Due to limited scaling of supply voltage, power density is expected to grow in future technology nodes. This increasing power density potentially limits the number of transistors switching at full speed in the future. Near-threshold operation can increase the number of simultaneously active cores, at the expense of much lower operating frequency (“dim silicon”). Although promising to increase overall throughput, dim cores suffer from diminishing returns as the number of cores increases. At this point, hardware accelerators become more efficient alternatives. To explore such a broad design space, we have developed a framework called Lumos to analytically quantify the performance limits of many-core, heterogeneous systems operating at near-threshold voltage. Lumos augments Amdahl’s Law with detailed scaling of frequency and power, calibrated by circuit-level simulations using a modified Predictive Technology Model (PTM) and factors in effects of process variations. While our results show that dim cores do indeed boost throughput, even in the presence of process variations, significant benefits are only achieved in applications with very high parallelism or with novel architectures to mitigate variation. A more beneficial 1 and scalable approach is to use accelerators. However, reconfigurable logic that supports a variety of accelerators is more beneficial than a dedicated, fixed-logic accelerator, unless 1) the dedicated kernel has overwhelming coverage across applications (e.g. twice as large as the total of all others), or 2) the speedup of the dedicated accelerator over the reconfigurable equivalent is significant (e.g. 10x-50x).
Multiple-Instance Active Learning

We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instead of individual instances, that are labeled for training. MI learners assume that every instance in a bag labeled negative is actually negative, whereas at least one instance in a bag labeled positive is actually positive. We consider the particular case in which an MI learner is allowed to selectively query unlabeled instances from positive bags. This approach is well motivated in domains in which it is inexpensive to acquire bag labels and possible, but expensive, to acquire instance labels. We describe a method for learning from labels at mixed levels of granularity, and introduce two active query selection strategies motivated by the MI setting. Our experiments show that learning from instance labels can significantly improve performance of a basic MI learning algorithm in two multiple-instance domains: content-based image retrieval and text classification.
Opportunities and advances in ultra-wideband electronically scanned arrays

This paper discusses bandwidth and polarization challenges in modern dual-polarized ultra-wideband (UWB) electronically scanned array (ESA) aperture design and two recent solution advances aiming to remedy such issues: a 6:1 Planar Ultrawideband Modular Antenna (PUMA) array and a 10:1 Sliced Notch Antenna (SNA) array. The proposed arrays enable wide bandwidth, low cross-polarization, and high efficiency from both low-profile and high-profile array research directions to appeal to a wider variety of applications and design embodiments. The proposed UWB-ESAs are analyzed alongside a conventional 10:1 all-metal Vivaldi array to shed light upon modern design trends and implications.
The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta.

We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.
The influence of shallow trench isolation angle on hot carrier effect of STI-based LDMOS transistors

Hot carrier reliability imposes challenges in the design of STI-based laterally diffused metal-oxide-semiconductor (LDMOS) devices as the device feature is miniaturized. Efforts to quantify the degradation are crucial in countering the device reliability risk. This paper investigates the effect of shallow trench isolation (STI) angle on hot carrier effect (HCI) of STI-based LDMOS devices. The effect on critical device parameters specifically the saturation drain current (Idsat), on-resistance (Ron) as well as the rate of impact ionization of the device had been studied and discussed in detail. From the result obtained, it is found that the drain current for device with 100° STI angle is reduced by 58.78% compared to device with 45° STI angle. Larger STI angle shows higher HCI degradation and the physical mechanism behind the results is analyzed from the Sentaurus 2D techplot.
Intelligent parking lot application using wireless sensor networks

Wireless sensor networks offer an attractive choice for low- cost and easy-to-deploy solutions for intelligent traffic guidance systems and parking lot applications. In this paper, we propose the use of a combination of magnetic and ultrasonic sensors for accurate and reliable detection of vehicles in a parking lot. We describe a modified version of the min-max algorithm for detection of vehicles using magnetometers, and also an algorithm for ultrasonic sensors. Through extensive real world experiments conducted in a multi-storied university parking space we compare the pros and cons of using different sensing modalities, and show that ultrasonic sensors along with magnetometers is an excellent choice for accurate vehicle detections. We demonstrate the efficacy of our proposed approach by conducting an elaborate car counting experiment lasting over a day, and show promising results using these two sensing modalities.
Adaptive Courseware: A Literature Review

This paper gives a detailed description of how adaptivity is realized in e-learning systems, with a focus on adaptivity of courseware. The review aims to answer three basic questions on adaptive courseware: what has been adapted, how and why. Researchers have tried to adapt according to these different elements: student knowledge, behaviour, interests, preferences, learning style, cognitive style, goals, courseware presentation, navigation, annotation, sequencing and knowledge testing. An historical background of adaptivity is described: from word definition, through adaptive instructions and adaptive systems, all the way to adaptive courseware. The biggest challenge was to find the most prominent representatives of adaptive systems in courseware generation that were selected after an exhaustive search through relevant scientific paper databases. Each prevailing system is briefly described and the student features, to which the system adapts, are highlighted, together with the level of adaptation. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from elearning systems in general to intelligent tutoring systems in particular. Such an analysis has not been done so far, especially in a way that adaptive systems are described uniformly. Finally, a comparative analysis of those systems and conclusions and demands about the ultimate adaptive system is given. This paper can be used as a guide for making decisions about what to adapt, how and why, while designing an adaptive e-learning system.
Business Models : A Discovery Driven Approach

The business model concept offers strategists a fresh way to consider their options in uncertain, fast-moving and unpredictable environments. In contrast to conventional assumptions, recognizing that more new business models are both feasible and actionable than ever before is creating unprecedented opportunities for today’s organizations. However, unlike conventional strategies that emphasize analysis, strategies that aim to discover and exploit new models must engage in significant experimentation and learning e a ‘discovery driven,’ rather than analytical approach.
Principles of GNSS, inertial, and multisensor integrated navigation systems, 2nd edition [Book review]

This second edition of Dr. Grove's book (the original was published in 2008) could arguably be considered a new work. At just under 1,000 pages (including the 11 appendices on the DVD), the second edition is 80% longer than the original. Frankly, the word "book" hardly seems adequate, considering the wide range of topics covered. "Mini-encyclopedia" seems more appropriate. The hardcover portion of the book comprises 18 chapters, and the DVD includes the aforementioned appendices plus 20 fully worked examples, 125 problems or exercises (with answers), and MATLAB routines for the simulation of many of the algorithms discussed in the main text. Here is a brief overview of the contents: ▸ Chapters 1–3: an overview of the diversity of positioning techniques and navigation systems; fundamentals of coordinate frames, kinematics and earth models; introduction to Kaiman filtering ▸ Chapters 4–6: inertial sensors, inertial navigation, and lower-cost dead reckoning systems ▸ Chapters 7–12: principles of radio positioning, short-, medium-, and long-range radio navigation, as well as extensive coverage of global navigation satellite systems (GNSS) ▸ Chapter 13: environmental feature matching. ▸ Chapters 14–16: various integration topics, including inertial navigation system (INS)/GNSS integration, alignment, zero-velocity updates, and multisensor integration ▸ Chapter 17: fault detection. ▸ Chapter 18: applications and trends. In summary, this book is an excellent reference (with numerous nuggets of wisdom) that should be readily handy on the shelf of every practicing navigation engineer. In the hands of an experienced instructor, the book will also serve students as a great textbook. However, the lack of examples integrated in the main text makes it difficult for the book to serve as a self-study guide for those that are new to the field.
Crossing analytics systems: A case for integrated provenance in data lakes

The volumes of data in Big Data, their variety and unstructured nature, have had researchers looking beyond the data warehouse. The data warehouse, among other features, requires mapping data to a schema upon ingest, an approach seen as inflexible for the massive variety of Big Data. The Data Lake is emerging as an alternate solution for storing data of widely divergent types and scales. Designed for high flexibility, the Data Lake follows a schema-on-read philosophy and data transformations are assumed to be performed within the Data Lake. During its lifecycle in a Data Lake, a data product may undergo numerous transformations performed by any number of Big Data processing engines leading to questions of traceability. In this paper we argue that provenance contributes to easier data management and traceability within a Data Lake infrastructure. We discuss the challenges in provenance integration in a Data Lake and propose a reference architecture to overcome the challenges. We evaluate our architecture through a prototype implementation built using our distributed provenance collection tools.
SemLens: visual analysis of semantic data with scatter plots and semantic lenses

Querying the Semantic Web and analyzing the query results are often complex tasks that can be greatly facilitated by visual interfaces. A major challenge in the design of these interfaces is to provide intuitive and efficient interaction support without limiting too much the analytical degrees of freedom. This paper introduces SemLens, a visual tool that combines scatter plots and semantic lenses to overcome this challenge and to allow for a simple yet powerful analysis of RDF data. The scatter plots provide a global overview on an object collection and support the visual discovery of correlations and patterns in the data. The semantic lenses add dimensions for local analysis of subsets of the objects. A demo accessing DBpedia data is used for illustration.
A Novel High Efficiency High Power Interleaved Coupled-Inductor Boost DC-DC Converter for Hybrid and Fuel Cell Electric Vehicle

In this paper an efficient high power high-step up dc- dc converter is presented for interfacing distributed energy storage elements like, fuel cells, batteries, and ultracapacitors with the high voltage dc bus in Electrical Vehicles (EV) and Hybrid Electric Vehicles (HEV). In the proposed converter, coupled-inductor boost converters are interleaved and a boost converter is used to clamp the switch voltage of the interleaved converters at a lower voltage. Leakage energy of all the interleaved converters are gathered in one clamping capacitor and discharged to the output by the clamping boost converter. The interleaving and low voltage clamping of coupled-inductor boost converters enable to achieve high efficiency. Design and analysis of the proposed converter are presented. Finally simulation results are provided for verification of the proposed converter.
Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods.

The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.
Large-Scale QA-SRL Parsing

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QASRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.
' s personal copy You are what you tweet : Personality expression and perception on Twitter

Microblogging services such as Twitter have become increasingly popular in recent years. However, little is known about how personality is manifested and perceived in microblogs. In this study, we measured the Big Five personality traits of 142 participants and collected their tweets over a 1-month period. Extraversion, agreeableness, openness, and neuroticism were associated with specific linguistic markers, suggesting that personality manifests in microblogs. Meanwhile, eight observers rated the participants’ personality on the basis of their tweets. Results showed that observers relied on specific linguistic cues when making judgments, and could only judge agreeableness and neuroticism accurately. This study provides new empirical evidence of personality expression in naturalistic settings, and points to the potential of utilizing social media for personality research. ! 2012 Elsevier Inc. All rights reserved.
Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods

The Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods constitutes " Supporting material " of the Intergovernmental Panel on Climate Change (as defined in the Procedures for the Preparation, Review, Acceptance, Adoption, Approval, and Publication of IPCC Reports). The Guidelines were prepared for consideration by the IPCC at the request of its Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA). This supporting material has not been subject to the formal intergovernmental IPCC review processes. Eduardo Zorito provided additional materials and feedback that were incorporated by June 2003. Revisions were made in the light of comments received from Elaine Barrow and John Mitchell in November 2003 – most notably the inclusion of a worked case study. Further comments were received from Penny Whetton and Steven Charles in February 2004.
Odor Recognition in Multiple E-Nose Systems With Cross-Domain Discriminative Subspace Learning

In this paper, we propose an odor recognition framework for multiple electronic noses (E-noses), machine olfaction odor perception systems. Straight to the point, the proposed transferring odor recognition model is called cross-domain discriminative subspace learning (CDSL). General odor recognition problems with E-nose are single domain oriented, that is, recognition algorithms are often modeled and tested on the same one domain data set (i.e., from only one E-nose system). Different from that, we focus on a more realistic scenario: the recognition model is trained on a prepared source domain data set from a master E-nose system <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>, but tested on another target domain data set from a slave system <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> with the same type of the master system <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>. The internal device parameter variance between master and slave systems often results in data distribution discrepancy between source domain and target domain, such that single-domain-based odor recognition model may not be adapted to another domain. Therefore, we propose domain-adaptation-based odor recognition for addressing the realistic recognition scenario across systems. Specifically, the proposed CDSL method consists of three merits: 1) an intraclass scatter minimization- and an interclass scatter maximization-based discriminative subspace learning is solved on source domain; 2) a data fidelity and preservation constraint of the subspace is imposed on target domain without distortion; and 3) a minipatch feature weighted domain distance is minimized for closely connecting the source and target domains. Experiments and comparisons on odor recognition tasks in multiple E-noses demonstrate the efficiency of the proposed method.