nils-herrmann/clean_scirepeval huggingface dataset:
Automatic image retargeting with fisheye-view warping

Image retargeting is the problem of adapting images for display on devices different than originally intended. This paper presents a method for adapting large images, such as those taken with a digital camera, for a small display, such as a cellular telephone. The method uses a non-linear fisheye-view warp that emphasizes parts of an image while shrinking others. Like previous methods, fisheye-view warping uses image information, such as low-level salience and high-level object recognition to find important regions of the source image. However, unlike prior approaches, a non-linear image warping function emphasizes the important aspects of the image while retaining the surrounding context. The method has advantages in preserving information content, alerting the viewer to missing information and providing robustness.
MDig : Multi-digit Recognition using Convolutional Nerual Network on Mobile

Multi-character recognition in arbitrary photographs on mobile platform is difficult, in terms of both accuracy and real-time performance. In this paper, we focus on the domain of hand-written multi-digit recognition. Convolutional neural network (CNN) is the state-of-the-art solution for object recognition, and presents a workload that is both compute and data intensive. To reduce the workload, we train a shallow CNN offline, achieving 99.07% top-1 accuracy. And we utilize preprocessing and segmentation to reduce input image size fed into CNN. For CNN implementation on the mobile platform, we adopt and modify DeepBeliefSDK to support batching fully-connected layers. On NVIDIA SHIELD tablet, the application processes a frame and extracts 32 digits in approximately 60ms, and batching the fully-connected layers reduces the CNN runtime by another 12%.
Probabilistic Text Structuring: Experiments with Sentence Ordering

Ordering information is a critical task for natural language generation applications. In this paper we propose an approach to information ordering that is particularly suited for text-to-text generation. We describe a model that learns constraints on sentence order from a corpus of domainspecific texts and an algorithm that yields the most likely order among several alternatives. We evaluate the automatically generated orderings against authored texts from our corpus and against human subjects that are asked to mimic the model’s task. We also assess the appropriateness of such a model for multidocument summarization.
Collegial decision making based on social amplification leads to optimal group formation.

Group-living animals are often faced with choosing between one or more alternative resource sites. A central question in such collective decision making includes determining which individuals induce the decision and when. This experimental and theoretical study of shelter selection by cockroach groups demonstrates that choices can emerge through nonlinear interaction dynamics between equal individuals without perfect knowledge or leadership. We identify a simple mechanism whereby a decision is taken on the move with limited information and signaling and without comparison of available opportunities. This mechanism leads to optimal mean benefit for group individuals. Our model points to a generic self-organized collective decision-making process independent of animal species.
Otologic and Audiology Concerns of Microtia Repair.

Microtia is a congenital auricular deformity that commonly presents with associated congenital aural atresia. The most acute concern in these patients is concomitant hearing loss at birth. A team-based approach by plastic surgeons and otologists is necessary to address both the otologic and audiologic concerns of microtia and atresia. Hearing rehabilitation is imperative; yet it should not compromise the aesthetic components of reconstruction and vice versa. Here, the authors propose a framework to evaluate and manage patients with microtia and atresia with the goal of optimizing functional and cosmetic outcomes.
Privacy-preserving trajectory data publishing by local suppression

The pervasiveness of location-aware devices has spawned extensive research in trajectory data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing trajectory data among different parties often creates an obstacle for effective data mining. In this paper, we study the challenges of anonymizing trajectory data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low data utility in the resulting data and ineffective data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for trajectory data anonymization. The framework allows the adoption of various data utility metrics for different data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a trajectory database, both being the foundation of many trajectory data ∗Corresponding author Email addresses: ru_che@encs.concordia.ca (Rui Chen), fung@ciise.concordia.ca (Benjamin C. M. Fung), no_moham@encs.concordia.ca (Noman Mohammed), bcdesai@cs.concordia.ca (Bipin C. Desai)  2011 mining tasks. Our experiments on both synthetic and real-life data sets suggest that the framework is effective and efficient to overcome the challenges in trajectory data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the data utility in anonymous trajectory data.
A generic framework for privacy preserving deep learning

We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.
Locality Reconstruction Models for Book Representation

Books, as a representative of lengthy documents, convey rich semantics. Traditional document modeling methods, such as bag-of-words models, have difficulty capturing such rich semantics when only considering term-frequency features. In order to explore term spatial distributions over a book, a tree-structured book representation is investigated in this paper. Moreover, an efficient learning framework, Tree2Vector, is introduced for mapping tree-structured book data into vectorial space. In particular, we present two types of locality reconstruction (LR) models: Euclidean-type and cosine-type, during the transformation process of tree structures into vectorial representations. The LR is used for modeling the reconstruction process, in which each parent node in a tree is supposed to be reconstructed by its child nodes. The prominent advantage of this Tree2Vector framework is that it solely utilizes the local information within a single book tree. In addition, extensive experimental results demonstrate that Tree2Vector is able to deliver comparable or better performance in comparison to methods that consider the information of all trees in a database globally. Experimental results also suggest that cosine-type LR consistently performs better than Euclidean-type LR in applications of book and author recommendations.
Deep Neural Networks for Page Stream Segmentation and Classification

In this manuscript we propose a novel method for jointly page stream segmentation and multi-page document classification.The end goal is to classify a stream of pages as belonging to different classes of documents. We take advantage of the recent state-of-the-art results achieved using deep architectures in related fields such as document image classification, and we adopt similar models to obtain satisfying classification accuracies and a low computational complexity. Our contribution is twofold: first, the extraction of visual features from the processed documents is automatically performed by the chosen Convolutional Neural Network; second, the predictions of the same network are further refined using an additional deep model which processes them in a classic sliding-window manner to help finding and solving classification errors committed by the first network. The proposed pipeline has been evaluated on a publicly available dataset composed of more than half a million multi-page documents collected by an on-line loan comparison company, showing excellent results and high efficiency.
Efficient Transformerless MOSFET Inverter for a Grid-Tied Photovoltaic System

The unipolar sinusoidal pulse width modulation full-bridge transformerless photovoltaic (PV) inverter can achieve high efficiency by using latest superjunction metal-oxide-semiconductor field-effect transistor (MOSFET) together with silicon carbide (SiC) diodes. However, the MOSFETs aSiCre limited to use in transformerless PV inverter due to the low reverse-recovery characteristics of the body diode. In this paper, a family of new transformerless PV inverter topology for a single-phase grid-tied operation is proposed using superjunction MOSFETs and SiC diodes as no reverse-recovery issues are required for the main power switches for unity power operation. The added clamping branch clamps the freewheeling voltage at the half of dc input voltage during the freewheeling period. As a result, the common-mode voltage kept constant during the whole grid period that reduces the leakage current significantly. In addition, dead time is not necessary for main power switches at both the high-frequency commutation and the grid zero crossing instant, results low-current distortion at output. Finally, a 1-kW prototype is built and tested to verify the theoretical analysis. The experimental results show 98.5% maximum efficiency and 98.32% European efficiency. Furthermore, to show the effectiveness, the proposed topology is compared with the other transformerless topologies.
A 79 GHz differentially fed grid array antenna

This paper presents a planar grid array antenna with a 100 Ω differential microstrip line feed on a single layer of standard soft substrate. The antenna operates in the 79 GHz frequency band for automotive radar applications. Its single row design offers a narrow beam in elevation and a wide beam in azimuth. Together with the differential microstrip line feeding, the antenna is suitable for differential multichannel MMICs in the frequency range.
Conversion of LDR image to HDR-like image through high-level synthesis tool for FPGA implementation

High dynamic range (HDR) of an image will increase the visibility of the image. Due to the reflection, the image may lose its visibility in some specific area. In the image, we call it highlight. We cannot reveal the original color and information in the highlight area. By removing this highlight, we can increase the visibility of the image that expands the dynamic range of the image. In this paper, we convert our algorithm by high-level synthesis (HLS) tool to the synthesizable hardware language. Our target is to show that our algorithm for HDR-like image can be implemented on FPGA. We also compare our HLS results with simulation result.
IGBT and Diode Behavior During Short-Circuit Type 3

A short circuit during inverter operation can result in the so-called short-circuit types 2 and 3. The short-circuit type 3 results in an interaction with several current commutations between the Insulate-Gate Bipolar Transistor (IGBT) and the antiparallel diode. The switched ON IGBT, conducting no current before the short circuit occurs, has no plasma inside and can block any voltage. It behaves like a voltage-controlled current source, which can stress the diode by commutating the complete short-circuit current into the diode. An avalanche arises inside the diode, which can lead to a destruction of the diode. The current through the diode determines dv/dt and makes the diode a dominating semiconductor during the short circuit. The current source behavior of the IGBT can be verified with semiconductor simulations and measurements on a high-power 3.3-kV IGBT. In the simulations, the IGBT is replaced by a current source, which gives similar results. For the measurements, the gate-emitter voltage is varied to change the IGBT current without influencing the collector-emitter voltage.
Novel Six-Slot Four-Pole Axial Flux-Switching Permanent Magnet Machine for Electric Vehicle

This paper proposes several novel 6-slot/4-pole axial flux-switching machine topologies for electric and hybrid electric vehicles. The proposed topologies include stator shifting, rotor shifting, and a combination of shifting both the stator and the rotor. The proposed topologies eliminate the even harmonics of the flux linkage that create unbalanced flux linkage in the traditional 6-slot/4-pole machine. Fundamental frequency will be reduced by 60% compared with a 12-slot/10-pole axial flux-switching machine without unbalanced flux linkage. An analytical method is used to find the offset angle between stator a-axis and the rotor pole axis to realize the elimination of even harmonics of the flux linkage. The analytical method is verified by finite-element analysis and experiments. The results confirm that the proposed novel topologies efficiently eliminate the even harmonics of the flux linkage in the 6-slot/4-pole machine.
Neural Embeddings of Graphs in Hyperbolic Space

Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted signi cant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in highdimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the at Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry signi cantly improves performance on downstream tasks for several real-world public datasets.
FUZZY Based PID Controller for Speed Control of D . C . Motor Using LabVIEW

This paper presents an implementation of self-tuned PID controller (FPID) for speed control of DC motor based on LabVIEW (Laboratory Virtual Instrument Engineering Workbench Environment). The algorithms of fuzzy PID controller (FPID) and conventional PID controller (CPID) are implemented using PID and Fuzzy Logic simulation toolkits of the Lab View environment. The simulation results demonstrate that the designed self-tuned PID controller realize a perfect speed tracking with lesser rise time and settling time, minimum steady state error and give better performance compared to conventional PID controller.  DC Motor, Ziegler-Nichols Tuning, Speed Control, Fuzzy Logic and Fuzzy plus PID controller, LabVIEW.
Why did my car just do that ? Explaining semi-autonomous driving actions to improve driver understanding , trust , and performance

This study explores, in the context of semiautonomous driving, how the content of the verbalized message accompanying the car’s autonomous action affects the driver’s attitude and safety performance. Using a driving simulator with an auto-braking function, we tested different messages that provided advance explanation of the car’s imminent autonomous action. Messages providing only “how” information describing actions (e.g., “The car is braking”) led to poor driving performance, whereas “why” information describing reasoning for actions (e.g., “Obstacle ahead”) was preferred by drivers and led to better driving performance. Providing both “how and why” resulted in the safest driving performance but increased negative feelings in drivers. These results suggest that, to increase overall safety, car makers need to attend not only to the design of autonomous actions but also to the right way to explain these actions to the drivers.
A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization and Mapping (SLAM) capabilities, and to facilitate research on this important topic at a low entry barrier. Up to four cameras are interfaced through a modern ARM-FPGA system, along with an Inertial Measurement Unit (IMU) providing high-quality rate gyro and accelerometer measurements, calibrated and hardware-synchronized with the images. This facilitates a tight fusion of visual and inertial cues that leads to a level of robustness and accuracy which is difficult to achieve with purely visual SLAM systems. In addition to raw data, the sensor head provides FPGA-pre-processed data such as visual keypoints, reducing the computational complexity of SLAM algorithms significantly and enabling employment on resource-constrained platforms. Sensor selection, hardware and firmware design, as well as intrinsic and extrinsic calibration are addressed in this work. Results from a tightly coupled reference visual-inertial motion estimation framework demonstrate the capabilities of the presented system.
Numerical Study of p-n-Doped Poly-Silicon Shield Gate Trench MOSFET With Reduced Output Capacitance

This letter proposes a new shield gate (SG) trench MOSFET structure, which utilizes a p-n-doped polysilicon refill as the SG electrode to minimize the total output capacitance or charge. TCAD simulation is carried out to compare the proposed and conventional device structures. It is shown that the proposed structure offers 30% lower <inline-formula> <tex-math notation="LaTeX">$\text{Q}_{\text {OSS}}$ </tex-math></inline-formula> and 18.2% reduction of inductive switching loss while retaining other attractive characteristics of the SG trench MOSFET.
Software architecture-centric methods and agile development

The agile software development paradigm and plan-driven approaches each have their strengths and shortcomings. The former emphasizes rapid, flexible development, while the latter emphasizes project and process infrastructure. Many practitioners, particularly of agile methods, tend-to view software architecture in light of the plan-driven side of the spectrum. They think that architecture-centric methods are too much work, equating them with high-ceremony processes emphasizing document production. But many elements make up a successful development approach, including process, product, technology, people, and tools. Software architecture is part of product quality and isn't tied to a particular process, technology, culture, or tool. This article explores the relationship and synergies between architecture-centric design and analysis methods and the extreme programming framework. We chose to focus on XP because it's one of the most mature and best-known agile practices.
Collaboratively Training Sentiment Classifiers for Multiple Domains

We propose a collaborative multi-domain sentiment classification approach to train sentiment classifiers for multiple domains simultaneously. In our approach, the sentiment information in different domains is shared to train more accurate and robust sentiment classifiers for each domain when labeled data is scarce. Specifically, we decompose the sentiment classifier of each domain into two components, a global one and a domain-specific one. The global model can capture the general sentiment knowledge and is shared by various domains. The domain-specific model can capture the specific sentiment expressions in each domain. In addition, we extract domain-specific sentiment knowledge from both labeled and unlabeled samples in each domain and use it to enhance the learning of domain-specific sentiment classifiers. Besides, we incorporate the similarities between domains into our approach as regularization over the domain-specific sentiment classifiers to encourage the sharing of sentiment information between similar domains. Two kinds of domain similarity measures are explored, one based on textual content and the other one based on sentiment expressions. Moreover, we introduce two efficient algorithms to solve the model of our approach. Experimental results on benchmark datasets show that our approach can effectively improve the performance of multi-domain sentiment classification and significantly outperform baseline methods.
Design, Analysis, and Optimization of Ironless Stator Permanent Magnet Machines

This paper presents a methodology for the design, analysis, and graphical optimization of ironless brushless permanent magnet machines primarily for generator applications. Magnetic flux in this class of electromagnetic machine tends to be 3-D due to the lack of conventional iron structures and the absence of a constrained magnetic flux path. The proposed methodology includes comprehensive geometric, magnetic and electrical dimensioning followed by detailed 3-D finite element (FE) modeling of a base machine for which parameters are determined. These parameters are then graphically optimized within sensible volumetric and electromagnetic constraints to arrive at improved design solutions. This paper considers an ironless machine design to validate the 3-D FE model to optimize power conversion for the case of a low-speed, ironless stator generator. The machine configuration investigated in this paper has concentric arrangement of the rotor and the stator, solenoid-shaped coils, and a simple mechanical design considered for ease of manufacture and maintenance. Using performance and material effectiveness as the overriding optimization criteria, this paper suggests optimal designs configurations featuring two different winding arrangements, i.e., radial and circumferentially mounted. Performance and material effectiveness of the studied ironless stator designs are compared to published ironless machine configurations.
The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.