nils-herrmann/clean_scirepeval huggingface dataset:
Comparative evaluation of 15 kV SiC IGBT and 15 kV SiC MOSFET for 3-phase medium voltage high power grid connected converter applications

The advent of high voltage (HV) wide band-gap power semiconductor devices has enabled the medium voltage (MV) grid tied operation of non-cascaded neutral point clamped (NPC) converters. This results in increased power density, efficiency as well as lesser control complexity. The multi-chip 15 kV/40 A SiC IGBT and 15 kV/20 A SiC MOSFET are two such devices which have gained attention for MV grid interface applications. Such converters based on these devices find application in active power filters, STATCOM or as active front end converters for solid state transformers. This paper presents an experimental comparative evaluation of these two SiC devices for 3-phase grid connected applications using a 3-level NPC converter as reference. The IGBTs are generally used for high power applications due to their lower conduction loss while MOSFETs are used for high frequency applications due to their lower switching loss. The thermal performance of these devices are compared based on device loss characteristics, device heat-run tests, 3-level pole heat-run tests, PLECS thermal simulation based loss comparison and MV experiments on developed hardware prototypes. The impact of switching frequency on the harmonic control of the grid connected converter is also discussed and suitable device is selected for better grid current THD.
FPGA Design and Implementation of a Real-Time Stereo Vision System

Stereo vision is a well-known ranging method because it resembles the basic mechanism of the human eye. However, the computational complexity and large amount of data access make real-time processing of stereo vision challenging because of the inherent instruction cycle delay within conventional computers. In order to solve this problem, the past 20 years of research have focused on the use of dedicated hardware architecture for stereo vision. This paper proposes a fully pipelined stereo vision system providing a dense disparity image with additional sub-pixel accuracy in real-time. The entire stereo vision process, such as rectification, stereo matching, and post-processing, is realized using a single field programmable gate array (FPGA) without the necessity of any external devices. The hardware implementation is more than 230 times faster when compared to a software program operating on a conventional computer, and shows stronger performance over previous hardware-related studies.
PANDA: Facilitating Usable AI Development

Recent advances in artificial intelligence (AI) and machine learning have created a general perception that AI could be used to solve complex problems, and in some situations over-hyped as a tool that can be so easily used. Unfortunately, the barrier to realization of mass adoption of AI on various business domains is too high because most domain experts have no background in AI. Developing AI applications involves multiple phases, namely data preparation, application modeling, and product deployment. The effort of AI research has been spent mostly on new AI models (in the model training stage) to improve the performance of benchmark tasks such as image recognition. Many other factors such as usability, efficiency and security of AI have not been well addressed, and therefore form a barrier to democratizing AI. Further, for many real world applications such as healthcare and autonomous driving, learning via huge amounts of possibility exploration is not feasible since humans are involved. In many complex applications such as healthcare, subject matter experts (e.g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results. In this paper, we take a new perspective on developing AI solutions, and present a solution for making AI usable. We hope that this resolution will enable all subject matter experts (eg. Clinicians) to exploit AI like data scientists.
Joint Input-Label Embedding for Neural Text Classification

Neural text classification methods typically treat output classes as categorical labels which lack description and semantics. This leads to an inability to train them well on large label sets or to generalize to unseen labels and makes speed and parameterization dependent on the size of the label set. Joint inputlabel space methods ameliorate the above issues by exploiting label texts or descriptions, but often at the expense of weak performance on the labels seen frequently during training. In this paper, we propose a label-aware text classification model which addresses these issues without compromising performance on the seen labels. The model consists of a joint input-label multiplicative space and a labelset-size independent classification unit and is trained with cross-entropy loss to optimize accuracy. We evaluate our model on text classification for multilingual news and for biomedical text with a large label set. The label-aware model consistently outperforms both monolingual and multilingual classification models which do not leverage label semantics and previous joint input-label space models.
Learning from Delayed Outcomes with Intermediate Observations

Optimizing for long term value is desirable in many practical applications, e.g. recommender systems. The most common approach for long term value optimization is supervised learning using long term value as the target. Unfortunately, long term metrics take a long time to measure (e.g., will customers finish reading an ebook?), and vanilla forecasters cannot learn from examples until the outcome is observed. In practical systems where new items arrive frequently, such delay can increase the training-serving skew, thereby negatively affecting the model’s predictions for new products. We argue that intermediate observations (e.g., if customers read a third of the book in 24 hours) can improve a model’s predictions. We formalize the problem as a semi-stochastic model, where instances are selected by an adversary but, given an instance, the intermediate observation and the outcome are sampled from a factored joint distribution. We propose an algorithm that exploits intermediate observations and theoretically quantify how much it can outperform any prediction method that ignores the intermediate observations. Motivated by the theoretical analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if our assumptions are satisfied, and Residual Factored Forecaster (RFF) that is more robust to model mis-specification. Experiments on two real world datasets, a dataset derived from GitHub repositories and another dataset from a popular marketplace, show that RFF outperforms both FF as well as an algorithm that ignores intermediate observations.
The Multiplication of Viruses

In Software Abstractions Daniel Jackson introduces an approach tosoftware design that draws on traditional formal methods but exploits automated tools to find flawsas early as possible. This approach--which Jackson calls "lightweight formal methods" or"agile modeling"--takes from formal specification the idea of a precise and expressivenotation based on a tiny core of simple and robust concepts but replaces conventional analysis basedon theorem proving with a fully automated analysis that gives designers immediate feedback. Jacksonhas developed Alloy, a language that captures the essence of software abstractions simply andsuccinctly, using a minimal toolkit of mathematical notions. This revised edition updates the text,examples, and appendixes to be fully compatible with the latest version of Alloy (Alloy 4).The designer can use automated analysis not only to correct errors but also tomake models that are more precise and elegant. This approach, Jackson says, can rescue designersfrom "the tarpit of implementation technologies" and return them to thinking deeply aboutunderlying concepts. Software Abstractions introduces the key elements: a logic,which provides the building blocks of the language; a language, which adds a small amount of syntaxto the logic for structuring descriptions; and an analysis, a form of constraint solving that offersboth simulation (generating sample states and executions) and checking (finding counterexamples toclaimed properties).
Impact of Feature Selection Techniques for Tweet Sentiment Classification

Sentiment analysis of tweets is a powerful application of mining social media sites that can be used for a variety of social sensing tasks. Common feature engineering techniques frequently result in a large numbers of features being generated to represent tweets. Many of these features may degrade classifier performance and increasing computational cost. Feature selection techniques can be used to select an optimal subset of features, reducing the computational cost of training a classifier, and potentially improving classification performance. Despite its benefits, feature selection has received little attention within the tweet sentiment domain. We study the impact of ten filter-based feature selection techniques on classification performance, using ten feature subset sizes and four different learners. Our experimental results demonstrate that feature selection can significantly improve classification performance in comparison to not using feature selection. Additionally, both choice of ranker and feature subset size significantly impact classifier performance. To the best of our knowledge, this is the first work which extensively studies feature selections effect on tweet sentiment classification.
DESIGN OF 2 X 4 MICROSTRIP MONOPULSE PATCH ANTENNA IN

The design of array antenna is vital study for today’s Wireless communication system to achieve higher gain, highly directional beam and also to counteract the effect of fading while signal propagates through various corrupted environments. In this paper, the design and analysis of a 2x4 microstrip patch antenna array is introduced and a rat-race coupler is incorporated. The antenna array is designed to function in the C-band and is used to receive signals from the telemetry link of an Unmanned Air Vehicle. The transmitter in the aircraft radiates two other directional beams adjacent to the main lobe, called the left lobe (L) and the right lobe (R). The rat race coupler generates the sum and difference patterns by adding and subtracting the left lobe signals with the right lobe signals respectively to generate L+R and L-R signals. The array of square patch antenna provides frequency close to the designed operating frequency with an acceptable Directivity and Gain. The proposed antenna array is a high gain, low-cost, low weight Ground Control Station (GCS) antenna. This paper, aims at a VSWR less than 2 and bandwidth greater than 50 MHz and a high antenna gain. The simulation has been done by using Advanced Design System (A.D.S) software.  2x4 microstrip patch antenna, Rat-race coupler, Inset feed, Square patch antenna
VirtualWorlds as Proxy for Multi-object Tracking Analysis

Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called "Virtual KITTI", automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.
Coding and Computational Thinking in Early Childhood : The Impact of ScratchJr in Europe

In recent years, there has been an increased effort to introduce coding and computational thinking in early childhood education. In accordance with the international trend, programming has become an increasingly growing focus in European education. With over 9.5 million iOS downloads, ScratchJr is the most popular freely available introductory programming language for young children (ages 5-7). This paper provides an overview of ScratchJr, and the powerful ideas from computer science it is designed to teach. In addition, data analytics are presented to show trends of usage in Europe and and how it compares to the rest of the world. Data reveals that countries with robust computer science initiatives such as the UK and the Nordic countries have high usage of ScratchJr.
Statistical learning and language acquisition.

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants' ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants' sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. WIREs  This article is categorized under: Linguistics > Language Acquisition Psychology > Language.
Managing Data Quality in Business Intelligence Applications

Business Intelligence (BI) solutions commonly aim at assisting decision-making processes by providing a comprehensive view over a company’s core business data and suitable abstractions thereof. Decision-making based on BI solutions therefore builds on the assumption that providing users with targeted, problemspecific fact data enables them to make informed and, hence, better decisions in their everyday businesses. In order to really provide users with all the necessary details to make informed decisions, we however believe that – in addition to conventional reports – it is essential to also provide users with information about the quality, i.e. with quality metadata, regarding the data from which reports are generated. Identifying a lack of support for quality metadata management in conventional BI solutions, in this paper we propose the idea of quality-aware reports and a possible architecture for quality-aware BI, able to involve the users themselves into the quality metadata management process, by explicitly soliciting and exploiting user feedback.
A Capsule based Approach for Polyphonic Sound Event Detection

Polyphonic sound event detection (polyphonic SED) is an interesting but challenging task due to the concurrence of multiple sound events. Recently, SED methods based on convolutional neural networks (CNN) and recurrent neural networks (RNN) have shown promising performance. Generally, CNN are designed for local feature extraction while RNN are used to model the temporal dependency among these local features. Despite their success, it is still insufficient for existing deep learning techniques to separate individual sound event from their mixture, largely due to the overlapping characteristic of features. Motivated by the success of Capsule Networks (CapsNet), we propose a more suitable capsule based approach for polyphonic SED. Specifically, several capsule layers are designed to effectively select representative frequency bands for each individual sound event. The temporal dependency of capsule's outputs is then modeled by a RNN. And a dynamic threshold method is proposed for making the final decision based on RNN outputs. Experiments on the TUT-SED Synthetic 2016 dataset show that the proposed approach obtains an F1-score of 68.8% and an error rate of 0.45, outperforming the previous state-of-the-art method of 66.4% and 0.48, respectively.
Theory and Practice of Bloom Filters for Distributed Systems

Many network solutions and overlay networks utilize probabilistic techniques to reduce information processing and networking costs. This survey article presents a number of frequently used and useful probabilistic techniques. Bloom filters and their variants are of prime importance, and they are heavily used in various distributed systems. This has been reflected in recent research and many new algorithms have been proposed for distributed systems that are either directly or indirectly based on Bloom filters. In this survey, we give an overview of the basic and advanced techniques, reviewing over 20 variants and discussing their application in distributed systems, in particular for caching, peer-to-peer systems, routing and forwarding, and measurement data summarization.
Fab: content-based, collaborative recommendation.(Special Section: Recommender Systems)

Fab is a recommendation system designed to help users sift through the enormous amount of information available in the World Wide Web. Operational since Dec. 1994, this system combines the content-based and collaborative methods of recommendation in a way that exploits the advantages of the two approaches while avoiding their shortcomings. Fab’s hybrid structure allows for automatic recognition of emergent issues relevant to various groups of users. It also enables two scaling problems, pertaining to the rising number of users and documents, to be addressed.
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds

In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce grouping techniques which define point neighborhoods in the initial world space and the learned feature space. Neighborhoods are important as they allow to compute local or global point features depending on the spatial extend of the neighborhood. Additionally, we incorporate dedicated loss functions to further structure the learned point feature space: the pairwise distance loss and the centroid loss. We show how to apply these mechanisms to the task of 3D semantic segmentation of point clouds and report state-of-the-art performance on indoor and outdoor datasets. ar X iv :1 81 0. 01 15 1v 2 [ cs .C V ] 8 D ec 2 01 8 2 F. Engelmann et al.
iSeeYou: Disabling the MacBook Webcam Indicator LED

The ubiquitous webcam indicator LED is an important privacy feature which provides a visual cue that the camera is turned on. We describe how to disable the LED on a class of Apple internal iSight webcams used in some versions of MacBook laptops and iMac desktops. This enables video to be captured without any visual indication to the user and can be accomplished entirely in user space by an unprivileged (non-root) application. The same technique that allows us to disable the LED, namely reprogramming the firmware that runs on the iSight, enables a virtual machine escape whereby malware running inside a virtual machine reprograms the camera to act as a USB Human Interface Device (HID) keyboard which executes code in the host operating system. We build two proofs-of-concept: (1) an OS X application, , which demonstrates capturing video with the LED disabled; and (2) a virtual machine escape that launches Terminal.app and runs shell commands. To defend against these and related threats, we build an OS X kernel extension, iSightDefender, which prohibits the modification of the iSight’s firmware from user space.
Increased bone formation in osteocalcin-deficient mice

VERTEBRATES constantly remodel bone. The resorption of preexisting bone by osteoclasts and the formation of new bone by osteoblasts is strictly coordinated to maintain bone mass within defined limits. A few molecular determinants of bone remodelling that affect osteoclast activity1–3 have been characterized, but the molecular determinants of osteoblast activity are unknown. To investigate the role of osteocalcin, the most abundant osteoblast-specific non-collagenous protein4, we have generated osteocalcin-deficient mice. These mice develop a phenotype marked by higher bone mass and bones of improved functional quality. Histomorphometric studies done before and after ovariectomy showed that the absence of osteocalcin leads to an increase in bone formation without impairing bone resorption. To our knowledge, this study provides the first evidence that osteocalcin is a determinant of bone formation.
Security Considerations around End-to-End Security in the IP-based Internet of Things

The IP-based Internet of Things refers to the interconnection of smart objects in a Low-power and Lossy Network (LLN) with the Internet by means of protocols such as 6LoWPAN or CoAP. The provisioning of an end-to-end security connection is the key to ensure basic functionalities such as software updates or network access. This is, however, very challenging due to the asymmetry of the devices in the system (smart objects are resource constrained when compared with traditional Internet devices), and the interaction of possibly different security protocols such as TLS and DTLS. This paper describes the security and threat model for this scenario to arrive at the following conclusion: end-to-end security in the IP-based Internet of Things is more than a simple end-to-end handshake and requires additional measures to protect the LLN while performing the handshake. We further describe some simple solutions and give hints for further work.
Deep learning for healthcare: review, opportunities and challenges

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.