nils-herrmann/clean_scirepeval huggingface dataset:
Substrate Modeling and Lumped Substrate Resistance Extraction for CMOS ESD/Latchup Circuit Simulation

Due to interactions through the common silicon substrate, the layout and placement of devices and substrate contacts can have signi cant impacts on a circuit's ESD (Electrostatic Discharge) and latchup behavior in CMOS technologies. Proper substrate modeling is thus required for circuit-level simulation to predict the circuit's ESD performance and latchup immunity. In this work we propose a new substrate resistance network model, and develop a novel substrate resistance extraction method that accurately calculates the distribution of injection current into the substrate during ESD or latchup events. With the proposed substrate model and resistance extraction, we can capture the three-dimensional layout parasitics in the circuit as well as the vertical substrate doping pro le, and simulate these e ects on circuit behavior at the circuit-level accurately. The usefulness of this work for layout optimization is demonstrated with an industrial circuit example.
The staircase to terrorism: a psychological exploration.

To foster a more in-depth understanding of the psychological processes leading to terrorism, the author conceptualizes the terrorist act as the final step on a narrowing staircase. Although the vast majority of people, even when feeling deprived and unfairly treated, remain on the ground floor, some individuals climb up and are eventually recruited into terrorist organizations. These individuals believe they have no effective voice in society, are encouraged by leaders to displace aggression onto out-groups, and become socialized to see terrorist organizations as legitimate and out-group members as evil. The current policy of focusing on individuals already at the top of the staircase brings only short-term gains. The best long-term policy against terrorism is prevention, which is made possible by nourishing contextualized democracy on the ground floor.
2009-02 February 2009 Logical Analysis of Data : Classification with Justification 1 by

Learning from examples is a frequently arising challenge, with a large number of algorithms proposed in the classification and data mining literature. The evaluation of the quality of such algorithms is usually carried out ex post, on an experimental basis: their performance is measured either by cross validation on benchmark data sets, or by clinical trials. None of these approaches evaluates directly the learning process ex ante, on its own merits. In this paper, we discuss a property of rule-based classifiers which we call “justifiability”, and which focuses on the type of information extracted from the given training set in order to classify new observations. We investigate some interesting mathematical properties of justifiable classifiers. In particular, we establish the existence of justifiable classifiers, and we show that several well-known learning approaches, such as decision trees or nearest neighbor based methods, automatically provide justifiable classifiers. We also identify maximal subsets of observations which must be classified in the same way by every justifiable classifiers. Finally, we illustrate by a numerical example that using classifiers based on “most justifiable” rules does not seem to lead to overfitting, even though it involves an element of optimization.
Strategic Human Resources Management : Where Do We Go From Here ? †

The authors identify the key challenges facing strategic human resource management (SHRM) going forward and discuss several new directions in both the scholarship and practice of SHRM. They focus on a clearer articulation of the “black box” between HR and firm performance, emphasizing the integration of strategy implementation as the central mediating variable in this relationship. There are direct implications for the nature of fit and contingencies in SHRM. They also highlight the significance of a differentiated HR architecture not just across firms but also within firms.
Privacy-preserving distributed clustering

Clustering is a very important tool in data mining and is widely used in on-line services for medical, financial and social environments. The main goal in clustering is to create sets of similar objects in a data set. The data set to be used for clustering can be owned by a single entity, or in some cases, information from different databases is pooled to enrich the data so that the merged database can improve the clustering effort. However, in either case, the content of the database may be privacy sensitive and/or commercially valuable such that the owners may not want to share their data with any other entity, including the service provider. Such privacy concerns lead to trust issues between entities, which clearly damages the functioning of the service and even blocks cooperation between entities with similar data sets. To enable joint efforts with private data, we propose a protocol for distributed clustering that limits information leakage to the untrusted service provider that performs the clustering. To achieve this goal, we rely on cryptographic techniques, in particular homomorphic encryption, and further improve the state of the art of processing encrypted data in terms of efficiency by taking the distributed structure of the system into account and improving the efficiency in terms of computation and communication by data packing. While our construction can be easily adjusted to a centralized or a distributed computing model, we rely on a set of particular users that help the service provider with computations. Experimental results clearly indicate that the work we present is an efficient way of deploying a privacy-preserving clustering algorithm in a distributed manner.
LSVT LOUD and LSVT BIG: Behavioral Treatment Programs for Speech and Body Movement in Parkinson Disease

Recent advances in neuroscience have suggested that exercise-based behavioral treatments may improve function and possibly slow progression of motor symptoms in individuals with Parkinson disease (PD). The LSVT (Lee Silverman Voice Treatment) Programs for individuals with PD have been developed and researched over the past 20 years beginning with a focus on the speech motor system (LSVT LOUD) and more recently have been extended to address limb motor systems (LSVT BIG). The unique aspects of the LSVT Programs include the combination of (a) an exclusive target on increasing amplitude (loudness in the speech motor system; bigger movements in the limb motor system), (b) a focus on sensory recalibration to help patients recognize that movements with increased amplitude are within normal limits, even if they feel "too loud" or "too big," and (c) training self-cueing and attention to action to facilitate long-term maintenance of treatment outcomes. In addition, the intensive mode of delivery is consistent with principles that drive activity-dependent neuroplasticity and motor learning. The purpose of this paper is to provide an integrative discussion of the LSVT Programs including the rationale for their fundamentals, a summary of efficacy data, and a discussion of limitations and future directions for research.
GaN HEMT based Doherty amplifier for 3.5-GHz WiMAX Applications

We have implemented a Doherty amplifier for 3.5-GHz World Interoperability for Microwave Access (WiMAX) applications using Eudyna 90-W (P3 dB) Gallium-Nitride (GaN) High Electron Mobility Transistor (HEMT) because of the poor efficiency of a standard class AB amplifier when the linearity performance is good. The load modulation performance of the GaN HEMT device for the Doherty operation is rather moderate but workable. The linearity is improved using the in-band error cancellation technique of the Doherty amplifier. The implemented Doherty amplifier has been designed at an average output power of 43 dBm, backed-off about 8 dB from the 51 dBm (P3 dB). For WiMAX signal with 28 MHz signal bandwidth, the measured drain efficiency of the amplifier is 27.8%, and the measured Relative Constellation Error (RCE) is -33.17 dB, while those of the comparable class AB amplifier are 19.42% and -24.26 dB, respectively, at the same average output power level.
A High-Speed Mesh of Tactile Sensors Fitting Arbitrary Surfaces

A tactile sensor is developed with the aim of covering a robot's entire structure, while reducing wiring requirement and ensuring high-speed response. The sensor detects the center point of load distribution on 2-D surfaces as well as the overall load. There are only four signal wires from the sensor. The sensor response time is nearly constant (within 1 ms) regardless of the number of detection elements, their placements or sensor areas. In this paper, the principles behind the operation of this sensor and the results of experiments using the sensor are described.
Mining Frequent Patterns in Data Streams at Multiple Time Granularities

Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time. In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. We extended the framework to mine time-sensitive patterns with approximate support guarantee. We incrementally maintain tilted-time windows for each pattern at multiple time granularities.
SURF: Speeded Up Robust Features

We demonstrate the performance of our interest point detector/descriptor scheme SURF – Speeded Up Robust Features – in an application that finds correspondences to a reference image in realtime. The user takes a reference image with a handheld video camera and then moves the camera around the object. The system identifies interest points in every newly acquired image and matches them with the ones in the reference image. The matches are then displayed on a computer screen. This is repeated subsequently for every frame. This application shows the speed and accuracy of SURF. We don’t exploit tracking. For every new image an independent set of interest points is computed and matched with the ones in the reference image, using standard wide baseline matching techniques.
A complete system for vehicle plate localization, segmentation and recognition in real life scene.

There are many applications of plate identification in pattern recognition and machine vision. These applications range from complex security systems to commons areas and from parking admission to urban traffic control. Car license plate recognition (CLPR) has complex characteristics due to diverse effects as fog, rain, shadows, irregular illumination conditions, partial occlusion, variable distances, cars' velocity, scene's angle on frame, plate rotation and conservation, number of vehicles in the scene and other. These factors make plate recognition much more complex and difficult than the traditional pattern or optical character recognition (OCR) systems. The main objective of this work is to show a system that solves the practical problem of car identification for real scenes. All steps of the process, from image scene acquisition to optical character recognition are considered to achieve an automatic identification of plates. It can be used with all type of country rules or plates design and adapted to each situation. The system is computationally very efficient and it is suitable for others related image recognition applications.
A Novel Connectionist System for Unconstrained Handwriting Recognition

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.
Semi-Supervised Learning for Imbalanced Sentiment Classification

Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, most existing studies assume the balance between negative and positive samples in both the labeled and unlabeled data, which may not be true in reality. In this paper, we investigate a more common case of semi-supervised learning for imbalanced sentiment classification. In particular, various random subspaces are dynamically generated to deal with the imbalanced class distribution problem. Evaluation across four domains shows the effectiveness of our approach.
Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks

This paper introduces a new semi-automated methodology combining a level set method with a top-down segmentation produced by a deep belief network for the problem of left ventricle segmentation from cardiac magnetic resonance images (MRI). Our approach combines the level set advantages that uses several a priori facts about the object to be segmented (e.g., smooth contour, strong edges, etc.) with the knowledge automatically learned from a manually annotated database (e.g., shape and appearance of the object to be segmented). The use of deep belief networks is justified because of its ability to learn robust models with few annotated images and its flexibility that allowed us to adapt it to a top-down segmentation problem. We demonstrate that our method produces competitive results using the database of the MICCAI grand challenge on left ventricle segmentation from cardiac MRI images, where our methodology produces results on par with the best in the field in each one of the measures used in that challenge (perpendicular distance, Dice metric, and percentage of good detections). Therefore, we conclude that our proposed methodology is one of the most competitive approaches in the field.
Distributed Practice and Procedural Memory Consolidation in Musicians ’ Skill Learning

This research was designed to determine whether musicians’ learning is affected by the time intervals interposed between practice sessions. Twenty-nine non-pianist musicians learned a 9-note sequence on a piano keyboard in three practice sessions that were separated by 5 min, 6 hr, or 24 hr. Significant improvements in performance accuracy were observed in Session 2 only in the group whose sessions were separated by 24 hr. There were significant increases in performance speed in Session 2 in all three practice conditions, results which likely were attributable to the inclusion of all Session 1 data in the analysis. Additional significant speed increases were observed in Session 3 only in the groups whose sessions were separated by 6 and 24 hr. These results suggest that sleep-based procedural memory consolidation may enhance performance accuracy in music skill learning, whereas enhancements in performance speed may be attributable to both wakeand sleep-based consolidation processes.
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.
Review on Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning

Big Data Analytics and Deep Learning are two high-center of information science. Big Data has become important because of companies i.e. both public and private have been gathering huge measures of domain-specific data, which can contain helpful data about issues, for example, national intelligence, cyber security, fraud detection, marketing, and medical informatics. Deep Learning algorithms extract high-level, complex reflections as information representations through a progressive learning process. A advantage of Deep Learning is the study and learning of huge size of unsupervised information, making it a significant instrument for Big Data Analytics where crude information is to a great extent unlabeled and un-sorted. The present survey gives an idea of the previous work done by several researchers on the Big Data Analytics and Deep Learning applications.
Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks

Financial market dynamics forecasting has long been a focus of economic research. A stochastic time effective function neural network (STNN) with principal component analysis (PCA) developed for financial time series prediction is presented in the present work. In the training modeling, we first use the approach of PCA to extract the principal components from the input data, then integrate the STNN model to perform the financial price series prediction. By taking the proposed model compared with the traditional backpropagation neural network (BPNN), PCA-BPNN and STNN, the empirical analysis shows that the forecasting results of the proposed neural network display a better performance in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, HS300, S&P500 and DJIA in the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. & 2015 Elsevier B.V. All rights reserved.
Finding Rough Set Reducts with Ant Colony Optimization

Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset preprocessor with much success, but current methods are inadequate at finding minimal reductions. This paper proposes a new feature selection mechanism based on Ant Colony Optimization to combat this difficulty. It also presents a new entropybased modification of the original rough set-based approach. These are applied to the problem of finding minimal rough set reducts, and evaluated experimentally.
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss

Monocular depth estimation benefits greatly from learning based techniques. By studying the training data, we observe that the per-pixel depth values in existing datasets typically exhibit a long-tailed distribution. However, most previous approaches treat all the regions in the training data equally regardless of the imbalanced depth distribution, which restricts the model performance particularly on distant depth regions. In this paper, we investigate the long tail property and delve deeper into the distant depth regions (i.e. the tail part) to propose an attentiondriven loss for the network supervision. In addition, to better leverage the semantic information for monocular depth estimation, we propose a synergy network to automatically learn the information sharing strategies between the two tasks. With the proposed attention-driven loss and synergy network, the depth estimation and semantic labeling tasks can be mutually improved. Experiments on the challenging indoor dataset show that the proposed approach achieves state-of-the-art performance on both monocular depth estimation and semantic labeling tasks.
AT WHAT RATE DO LEARNERS LEARN AND RETAIN NEW VOCABULARY FROM READING A GRADED READER

This study examined the rate at which vocabulary was learned from reading the 400 headword graded reader A Little Princess . To ascertain whether words of different frequency of occurrence rates were more likely to be learned and retained or forgotten, 25 words within five bands of differing frequency of occurrence (15 to 18 times to those appearing only once) were selected. The spelling of each word was changed to ensure that each test item was unknown to the 15 intermediate level (or above) female Japanese subjects. Three tests (word-form recognition, prompted meaning recognition and unprompted meaning recognition) were administered immediately after reading, after one week and after a three month delay. The results show that words can be learned incidentally but that most of the words were not learned. More frequent words were more likely to be learned and were more resistant to decay. The data suggest that, on average, the meaning of only one of the 25 items will be remembered after three months, and the meaning of none of the items that were met fewer than eight times will be remembered three months later. The data thus suggest that very little new vocabulary is retained from reading one graded reader, and that a massive amount of graded reading is needed to build new vocabulary. It is suggested that the benefits of reading a graded reader should not only be assessed by researching vocabulary gains and retention, but by looking at how graded readers help develop and enrich already known vocabulary.