nils-herrmann/clean_scirepeval huggingface dataset:
Tag-aware recommender systems by fusion of collaborative filtering algorithms

Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.
Computer-assisted pop-up design for children: computationally enriched paper engineering

Computationally-enriched crafts are activities that blend the advantages of computational media with the affective, social, and cognitive affordances of children's crafts. In this paper, we describe a design application, Popup Workshop, whose purpose is to introduce children to the craft (and engineering discipline) of pop-up design in paper. We describe the fundamental ideas behind computational crafts in general, present our application in its current implementation and offer a scenario for its use, explore the particular ways in which pop-up design serves as fertile ground for integrating computation and tangible design, and discuss our early pilot-tests with elementary-school children, as well as ongoing and related work.
A Blind Baud-Rate ADC-Based CDR

This paper proposes a 10-Gb/s blind baud-rate ADC-based CDR. The blind baud-rate operation is made possible by using a 2UI integrate-and-dump filter, which creates intentional ISI in adjacent bit periods. The blind samples are interpolated to recover center-of-the-eye samples for a speculative Mueller-Muller PD and a 2-tap DFE operation. A test chip, fabricated in 65-nm CMOS, implements a 10-Gb/s CDR with a measured high-frequency jitter tolerance of 0.19 UIPP and ±300 ppm of frequency offset.
Mundanely miraculous: the robot in healthcare

As both hero and villain, robots have played prominent roles in media such as films and books. Now, robots are no longer hidden away from the public conscious in fictive worlds or real-life factories. Robots are becoming a real part of our everyday encounters in environments such as healthcare settings. In this paper, we describe a discourse analysis of 60 YouTube videos that showcase robots in healthcare activities. Our narrative weaves three discourses that construct visions of the healthcare robot: (1) the miraculous robot as the robot that enhances patient care; (2) the mundane robot as the innocuous robot that integrates into the workflow seamlessly; and (3) the preternatural robot as the robot that is miraculous but never mundane. We propose several contrary visions to this dominant narrative of healthcare robots as a framework for future fieldwork that, we argue, should investigate the institutions of robotics.
Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation

Most recent approaches to monocular 3D human pose estimation rely on Deep Learning. They typically involve training a network to regress from an image to either 3D joint coordinates directly, or 2D joint locations from which the 3D coordinates are inferred by a model-fitting procedure. The former takes advantage of 3D cues present in the images but rarely models uncertainty. By contrast, the latter often models 2D uncertainty, for example in the form of joint location heatmaps, but discards all the image information, such as texture, shading and depth cues, in the fitting step. In this paper, we therefore propose to jointly model 2D uncertainty and leverage 3D image cues in a regression framework for monocular 3D human pose estimation. To this end, we introduce a novel two-stream deep architecture. One stream focuses on modeling uncertainty via probability maps of 2D joint locations and the other exploits 3D cues by directly acting on the image. We then study different approaches to fusing their outputs to obtain the final 3D prediction. Our experiments evidence in particular that our late-fusion mechanism improves upon the state-of-theart by a large margin on standard 3D human pose estimation benchmarks.
Demystifying automotive safety and security for semiconductor developer

Advances in both semiconductor and automotive industry are today enabling the next generation of vehicles with significant electronics content than ever before. Consumers can now avail vehicle offerings in the form of Electric and Hybrid Electric Vehicles (EV/HEV) that have improved fuel efficiency, provide enhanced driver-passenger comfort and experience through Advance Driver Assistance Systems (ADAS) and car infotainment systems, and more. Increasing electronics, software content, and connectivity drive two consumer concerns — “functional safety” and “security” — to the forefront. In this tutorial, we dissect these concerns from an end application perspective and translate the system level requirements and standards into semiconductor development requirements. We indicate both current and emerging practices, and touch upon areas requiring new or optimal design and electronic design automation (EDA) solutions. While functional safety is the primary focus for deep-dive in this tutorial, we also examine key facets of automotive security which is now emerging as a critical area for further understanding and standardization.
Microphone Array Driven Speech Recognition: Influence of Localization on the Word Error Rate

Interest within the automatic speech recognition (ASR) research community has recently focused on the recognition of speech captured with one or more microphones located in the far field, rather than being mounted on a headset and positioned next to the speaker’s mouth. Far field ASR is a natural application for beamforming techniques using an array of microphones. A prerequisite for applying such techniques, however, is a reliable means of speaker localization. In this work, we compare the accuracy of source localization systems based on only audio features, only video features, as well as a combination of audio and video features using speech data collected during seminars held by actual speakers. We also investigate the influence of source localization accuracy on the word error rate (WER) of a far field ASR system, comparing the WERs obtained with position estimates from several automatic source localizers with those obtained from true speaker positions. Our results reveal that accurate speaker localization is crucial for minimizing the error rate of a far field ASR system.
Deep Learning using Linear Support Vector Machines

Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. For classification tasks, most of these “deep learning” models employ the softmax activation function for prediction and minimize cross-entropy loss. In this paper, we demonstrate a small but consistent advantage of replacing the softmax layer with a linear support vector machine. Learning minimizes a margin-based loss instead of the cross-entropy loss. While there have been various combinations of neural nets and SVMs in prior art, our results using L2-SVMs show that by simply replacing softmax with linear SVMs gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and the ICML 2013 Representation Learning Workshop’s face expression recognition challenge.
The "visual cliff".

This simple apparatus is used to investigate depth perception in different animals. All species thus far tested seem able to perceive and avoid a sharp drop as soon as they can move about. Human infants at the creeping and toddling stage are notoriously prone to falls from more or less high places. They must be kept from going over the brink by side panels on their cribs, gates on stairways and the vigilance of adults. As their muscular coordination matures they begin to avoid such accidents on their own. Common sense might suggest that the child learns to recognize falling-off places by experience–that is, by falling and hurting himself. But is experience really the teacher? Or is the ability to perceive and avoid a brink part of the child's original endowment? Answers to these questions will throw light on the genesis of space perception in general. Height perception is a special case of distance perception: information in the light reaching the eye provides stimuli that can be utilized for the discrimination both of depth and of receding distance on the level. At what stage of development can an animal respond effectively to these stimuli? Does the onset of such response vary with animals of different species and habitats? At Cornell University we have been investigating these problems by means of a simple experimental setup that we call a visual cliff. The cliff is a simulated one and hence makes it possible not only to control the optical and other stimuli (auditory and tactual, for instance) but also to protect the experimental subjects. It consists of a board laid across a large sheet of heavy glass which is supported a foot or more above the floor. On one side of the board a sheet of patterned material is placed flush against the undersurface of the glass, giving the glass the appearance as well as the substance of solidity. On the other side a sheet of the same material is laid upon the floor; this side of the board thus becomes the visual cliff . The Classic Visual Cliff Experiment This young explorer has the good sense not to crawl out onto an apparently unsupported surface, even when Mother beckons from the other side. Rats, pups, kittens, and chicks also will not try to walk across to the other side. (So don't bother asking why the chicken crossed the visual cliff.) …
The Role of Smart Meters in Enabling Real-Time Energy Services for Households : The Italian Case

The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.
High breakdown Voltage undoped AlGaN-GaN power HEMT on sapphire substrate and its demonstration for DC-DC converter application

Undoped AlGaN-GaN power high electron mobility transistors (HEMTs) on sapphire substrate with 470-V breakdown voltage were fabricated and demonstrated as a main switching device for a high-voltage dc-dc converter. The fabricated power HEMT realized a high breakdown voltage with a field plate structure and a low on-state resistance of 3.9 m/spl Omega//spl middot/cm/sup 2/, which is 10 /spl times/ lower than that of conventional Si MOSFETs. The dc-dc converter operation of a down chopper circuit was demonstrated using the fabricated device at the input voltage of 300 V. These results show the promising possibilities of the AlGaN-GaN power HEMTs on sapphire substrate for future switching power devices.
OBA2: An Onion approach to Binary code Authorship Attribution

A critical aspect of malware forensics is authorship analysis. The successful outcome of such analysis is usually determined by the reverse engineer's skills and by the volume and complexity of the code under analysis. To assist reverse engineers in such a tedious and error-prone task, it is desirable to develop reliable and automated tools for supporting the practice of malware authorship attribution. In a recent work, machine learning was used to rank and select syntax-based features such as n-grams and ow graphs. The experimental results showed that the top ranked features were unique for each author, which was regarded as an evidence that those features capture the author's programming styles. In this paper, however, we show that the uniqueness of features does not necessarily correspond to authorship. Speci cally, our analysis demonstrates that many unique features selected using this method are clearly unrelated to the authors' programming styles, for example, unique IDs or random but unique function names generated by the compiler; furthermore, the overall accuracy is generally unsatisfactory. Motivated by this discovery, we propose a layered Onion Approach for Binary Authorship Attribution called OBA2. The novelty of our approach lies in the three complementary layers: preprocessing, syntax-based attribution, and semantic-based attribution. Experiments show that our method produces results that not only are more accurate but have a meaningful connection to the authors' styles.
Gray links in the use of requirements traceability

The value of traceability is in its use. How do different software engineering tasks affect the tracing of the same requirement? In this paper, we answer the question via an empirical study where we explicitly assign the participants into 3 trace-usage groups of one requirement: finding its implementation for verification and validation purpose, changing it within the original software system, and reusing it toward another application. The results uncover what we call "gray links"--around 20% of the total traces are voted to be true links with respect to only one task but not the others. We provide a mechanism to identify such gray links and discuss how they can be leveraged to advance the research and practice of value-based requirements traceability.
Fast concurrent queues for x86 processors

Conventional wisdom in designing concurrent data structures is to use the most powerful synchronization primitive, namely compare-and-swap (CAS), and to avoid contended hot spots. In building concurrent FIFO queues, this reasoning has led researchers to propose combining-based concurrent queues.
 This paper takes a different approach, showing how to rely on fetch-and-add (F&A), a less powerful primitive that is available on x86 processors, to construct a nonblocking (lock-free) linearizable concurrent FIFO queue which, despite the F&A being a contended hot spot, outperforms combining-based implementations by 1.5x to 2.5x in all concurrency levels on an x86 server with four multicore processors, in both single-processor and multi-processor executions.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.
Analyzing and predicting viral tweets

Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks.
 This paper addresses important questions concerning the spread of information on Twitter.  Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.
Measuring the effectiveness of answers in Yahoo! Answers

This study investigates the ways in which effectiveness of answers in Yahoo! Answers, one of the largest community question answering sites (CQAs), is related to question types and answerer reputation. Effective answers are defined as those that are detailed, readable, superior in quality, and contributed promptly. Five question types that were studied include factoid, list, definition, complex interactive, and opinion. Answerer reputation refers to the past track record of answerers in the community.  The dataset comprises 1,459 answers posted in Yahoo! Answers in response to 464 questions that were distributed across the five question types. The analysis was done using factorial analysis of variance.  The results indicate that factoid, definition and opinion questions were comparable in attracting high quality as well as readable answers. Although reputed answerers generally fared better in offering detailed and high quality answers, novices were found to submit more readable responses. Moreover, novices were more prompt in answering factoid, list and definition questions.  By analyzing variations in answer effectiveness with a twin-focus on question types and answerer reputation, this study explores a strand of CQA research that has hitherto received limited attention. The findings offer insights to users and designers of CQAs.
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions

Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students' facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees of engagement (Cohen's κ = 0.96). When fine discrimination is required (four distinct levels) the reliability decreases, but is still quite high ( κ = 0.56). Furthermore, we found that engagement labels of 10-second video clips can be reliably predicted from the average labels of their constituent frames (Pearson r=0.85), suggesting that static expressions contain the bulk of the information used by observers. We used machine learning to develop automatic engagement detectors and found that for binary classification (e.g., high engagement versus low engagement), automated engagement detectors perform with comparable accuracy to humans. Finally, we show that both human and automatic engagement judgments correlate with task performance. In our experiment, student post-test performance was predicted with comparable accuracy from engagement labels ( r=0.47) as from pre-test scores ( r=0.44).
The Diabetes Quality Improvement Project: moving science into health policy to gain an edge on the diabetes epidemic.

As the worldwide diabetes epidemic continues to unfold, some experts have asked whether the war against it is being lost (1–3). In the U.S., blindness, kidney failure, amputations, and cardiovascular disease resulting from diabetes not only markedly reduce quality and length of life but also cost nearly $100 billion annually (4–7). Fortunately, a vast body of research has clearly established that several effective treatments and practices may substantially reduce or prevent this burden (8– 19). These interventions, if broadly implemented, can allow us to use our health efforts and resources more effectively and efficiently to minimize the diabetes burden. Judging from the scant data available, however, the level of care currently delivered to populations with diabetes often may not produce the possible health-related gains (20,21). To assess more completely the level of diabetes care delivered in the U.S., we need standardized uniform performance measures that can assess quality of care accurately and reliably. These measures will enhance uptake of research into practice and may ultimately improve diabetes care and clinical outcomes. In the early to mid1990s, many organizations developed performance measures for diabetes care, but the result was that providers were often required to collect and report many different, sometimes conflicting, measures, depending on their care delivery system. It was recognized that a national consensus on measures could enhance this process and provide a method for assessing care within and across health care settings while providing a meaningful mechanism for quality improvement (QI). In this review, we describe the Diabetes Quality Improvement Project (DQIP), which has developed and implemented a comprehensive set of national measures for evaluation and QI.
Survey on routing in data centers: insights and future directions

Recently, a series of data center network architectures have been proposed. The goal of these works is to interconnect a large number of servers with significant bandwidth requirements. Coupled with these new DCN structures, routing protocols play an important role in exploring the network capacities that can be potentially delivered by the topologies. This article conducts a survey on the current state of the art of DCN routing techniques. The article focuses on the insights behind these routing schemes and also points out the open research issues hoping to spark new interests and developments in this field.