nils-herrmann/clean_scirepeval huggingface dataset:
Benchmarking Web API Quality

Web APIs are increasingly becoming an integral part of web or mobile applications. As a consequence, performance characteristics and availability of the APIs used directly impact the user experience of end users. Still, quality of web APIs is largely ignored and simply assumed to be sufficiently good and stable. Especially considering geo-mobility of today’s client devices, this can lead to negative surprises at runtime. In this work, we present an approach and toolkit for benchmarking the quality of web APIs considering geo-mobility of clients. Using our benchmarking tool, we then present the surprising results of a geo-distributed 3-month benchmark run for 15 web APIs and discuss how application developers can deal with volatile quality both from an architectural and engineering point of view.
A SiGe BiCMOS 16-element phased-array transmitter for 60GHz communications

The demonstration of multi-Gb/s links in the 60GHz band has created new opportunities for wireless communications . Due to the directional nature of millimeter-wave (mm-Wave) propagation, beam steering enables longer-range non-line-of-sight (NLOS) links at these frequencies. A phased-array architecture is attractive for an integrated 60GHz transmitter (Tx) since it can attain both beam steering and higher EIRP through spatial combining. An all-RF 16-element 40-to-45GHz Tx for satellite applications , a 6-element 60GHz Tx with IF-path phase-shift [4], and a bi-directional 4-element 60GHz Tx/Rx with RF phase shifters  have been recently demonstrated in silicon. This work presents a fully-integrated phased-array Tx which supports multi-Gb/s NLOS IEEE 802.15.3c links. In addition to beamsteering, the IC has the following major features: an on-chip power sensor at each element, 3 temperature sensors, LO leakage and I/Q phase and amplitude adjustment, front-end OP1dB programmability, and an integrated modulator for pi/2-BPSK/MSK signaling (common mode in 802.15.3c). The IC integrates 2240 NPNs, 323,000 FETs and hundreds of transmission lines and is fabricated in the IBM 8HP 0.12µm SiGe BiCMOS process (fT = 200GHz).
Low-Profile Dual-Band Filtering Patch Antenna and Its Application to LTE MIMO System

This paper presents a low-profile dual-band filtering antenna element and its application to long-term evolution (LTE) multiple-input multiple-output (MIMO) system for wireless customer premise equipments (CPEs). The proposed element consists of two separate U-shaped patches operating at different frequencies and a multistub microstrip feed line. For size miniaturization, the smaller U-shaped patch is embedded in the larger one. In addition, the multistub feed line can generate two controllable resonant modes as well as two nulls in realized gain at the boresight direction. Since the modes of the patches and multistub feed line can be controlled individually, the two operating bands can be tuned to desired frequencies. Also, the radiation nulls in boresight gain can be controlled, high roll-off rate and out-of-band radiation rejection levels are thus obtained. For demonstration, a low profile (0.009λ0) dual-band filtering antenna element operating at 1.9 and 2.6 GHz for TD-LTE applications (B39 and B38-bands) is implemented. Dual-band bandpass responses and four radiation nulls are observed in the experiment. Measured in-band gains are 6.7 and 7.3 dBi, whereas out-of-band gains are less than -10 dBi. Based on this element, a four-element MIMO antenna is further designed for LTE CPEs, where low profile and high integration of multiple components are required. A low mutual coupling of less than -19.2 dB and the low envelope correlation coefficients of better than 0.2 are obtained with a small edge-to-edge spacing of 0.15λ0.
Microdeletion on 3p25 in a patient with features of 3p deletion syndrome.

The rare 3p deletion syndrome presents with a spectrum of anomalies caused by deletions of variable lengths within the short arm of chromosome 3. While most of these deletions involve the 3p terminus, interstitial deletions may also give rise to features of the syndrome. We have detected an interstitial deletion of 643 kb in a patient who displayed many of the typical 3p deletion features. This patient had a number of findings in common with a previously reported patient, who had a 1.6 Mb interstitial deletion, including cognitive handicap, seizures, and congenital heart defects. A 518 kb region of overlap containing 12 genes may prove to be a critical region for some of these features. The putative functions of several genes, such as CRELD1, SRGAP3, CAMK1, TADA3, and MTMR14 are discussed with respect to their potential involvement in the 3p deletion syndrome phenotype. We suggest that this 518 kb area of overlap may define a critical region, which when deleted, can give rise to the 3p deletion syndrome phenotype.
An expert evaluation of word-sized visualizations for analyzing eye movement data

Word-sized visualizations for eye movement data allow analysts to compare a variety of experiment conditions or participants at the sametime. Weimplementedasetofsuchword-sizedvisualizations as part of an analysis framework. We want to ﬁnd out which of the visualizations is most suitable for different analysis tasks. To this end, we applied the framework to data from an eye tracking study on the reading behavior of users studying metro maps. In anexpertevaluationwithﬁveanalysts,weidentiﬁeddistinguishing characteristics of the different word-sized visualizations.
Generating Questions Automatically from Informational Text

Good readers ask themselves questions during reading. Our goal is to scaffold this self-questioning strategy automatically to help children in grades 1-3 understand informational text. In previous work, we showed that instruction for self-questioning can be generated for narrative text. This paper tests the generality of that approach by applying it to informational text. We describe the modifications required, and evaluate the approach on informational texts from Project LISTEN’s Reading Tutor.
Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.
NEVESIM: event-driven neural simulation framework with a Python interface

NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.
Visual Programming Language for Thymio II Robot

This paper introduces a visual programming language (vpl) for Thymio II, an educational robot. Our vpl is intended for children in primary school and aims at making robotics programming approachable for young children by creating a close correspondence between the icons of the programming language and the design of the robot. Its two modes of operation — basic and advanced — allow children to learn programming at a level suitable for their current skill. Moreover, our vpl provides a live generation of textual code that eases the transition to textual programming for more advanced children. This paper describes the goals and the guiding principles behind the design of our vpl and demonstrates how an iterative development process with evaluations with children resulted in an improved vpl.
Market Mechanisms in Online Peer-to-Peer Lending

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Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings.

Mindfulness meditation practices (MMPs) are a subgroup of meditation practices which are receiving growing attention. The present paper reviews current evidence about the effects of MMPs on objective measures of cognitive functions. Five databases were searched. Twenty three studies providing measures of attention, memory, executive functions and further miscellaneous measures of cognition were included. Fifteen were controlled or randomized controlled studies and 8 were case-control studies. Overall, reviewed studies suggested that early phases of mindfulness training, which are more concerned with the development of focused attention, could be associated with significant improvements in selective and executive attention whereas the following phases, which are characterized by an open monitoring of internal and external stimuli, could be mainly associated with improved unfocused sustained attention abilities. Additionally, MMPs could enhance working memory capacity and some executive functions. However, many of the included studies show methodological limitations and negative results have been reported as well, plausibly reflecting differences in study design, study duration and patients' populations. Accordingly, even though findings here reviewed provided preliminary evidence suggesting that MMPs could enhance cognitive functions, available evidence should be considered with caution and further high quality studies investigating more standardized mindfulness meditation programs are needed.
Mass-media coverage , its influence on public awareness of climate-change issues , and implications for Japan ’ s national campaign to reduce greenhouse gas emissions

We analyse Japanese newspaper coverage of global warming from January 1998 to July 2007 and how public opinion during parts of that period were influenced by newspaper coverage. We show that a dramatic increase in newspaper coverage of global warming from January 2007 correlated with an increase in public concern for the issue. Before January 2007, we find that coverage of global warming had an immediate but short-term influence on public concern. With such transitory high levels of media coverage we suggest that for more effective communication of climate change, strategies aimed at maintaining mass-media coverage of global warming are required.
Smoothed Sarsa: Reinforcement learning for robot delivery tasks

Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actions in real time to accomplish these tasks efficiently. We introduce a novel reinforcement learning algorithm called Smoothed Sarsa that learns a good policy for these delivery tasks by delaying the backup reinforcement step until the uncertainty in the state estimate improves. The state space is modeled by a Dynamic Bayesian Network and updated using a Region-based Particle Filter. We take advantage of the fact that only discrete (topological) representations of entity locations are needed for decision-making, to make the tracking and decision making more efficient. Our experiments show that policy search leads to faster task completion times as well as higher total reward compared to a manually crafted policy. Smoothed Sarsa learns a policy orders of magnitude faster than previous policy search algorithms. We demonstrate our results on the Player/Stage simulator and on the Pioneer robot.
Multi-objective optimization with non-convex cost functions using fuzzy mechanism based continuous genetic algorithm

In this paper, a fuzzy mechanism based continuous genetic algorithm is employed to optimize the non-convex multi-objective problem for allocating power generation cost to all the generating units of the electrical system considering system constraints. Here, the total system cost for generation is optimized by considering Economic load dispatch and Environmental Dispatch simultaneously. The valve point loading effect is also considered in the proposed multi-objective problem to obtain Non-Convex Environmental Economic Dispatch problem. This biobjective problem is transformed as single objective problem considering price penalty factor. The proposed technique gives the best compromised solution with the highest rank out of the existing Pareto optimal solution set. The performance of the proposed method is confirmed and validated on two test systems having three and six generating units (IEEE-30 bus system). To show the dominance of the method the obtained results are further compared with the recently published result.
A Multi-Modal Approach to Infer Image Affect

e group aect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. e state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. is paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the rst time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.
What is Your Organization 'Like'?: A Study of Liking Activity in the Enterprise

The 'like' button, introduced by Facebook several years ago, has become one of the most prominent icons of social media. Similarly to other popular social media features on the web, enterprises have also recently adopted it. In this paper, we present a first comprehensive study of liking activity in the enterprise. We studied the logs of an enterprise social media platform within a large global organization along a period of seven months, in which 393,720 'likes' were performed. In addition, we conducted a survey of 571 users of the platform's 'like' button. Our evaluation combines quantitative and qualitative analysis to inspect what employees like, why they use the 'like' button, and to whom they give their 'likes'.
Research Commentary - The Digital Transformation of Healthcare: Current Status and the Road Ahead

As the United States expends extraordinary efforts towards the digitization of its healthcare system, and as policy makers across the globe look to information technology as a means of making healthcare systems safer, more affordable, and more accessible, a rare and remarkable opportunity has emerged for the information systems research community to leverage its in-depth knowledge to both advance theory and influence practice and policy. Although Health IT (HIT) has tremendous potential for improving quality and reducing cost in healthcare, significant challenges need to be overcome to fully realize this potential. In this commentary, we survey the landscape of existing studies on HIT to provide an overview of the current status of HIT research. We then identify three major areas that warrant further research: 1) HIT design, implementation, and meaningful use, 2) measurement and quantification of HIT payoff and impact, and 3) extending the traditional realm of HIT. We discuss specific research questions in each domain and suggest appropriate methods to approach them. We encourage IS scholars to become active participants in the global discourse on healthcare transformation through information technology.
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy

Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems — the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models. In this paper, we study the combination of these two techniques and show that the performance of low-precision networks can be significantly improved by using knowledge distillation techniques. Our approach, Apprentice, achieves state-of-the-art accuracies using ternary precision and 4-bit precision for variants of ResNet architecture on ImageNet dataset. We present three schemes using which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline.
Delving into adversarial attacks on deep policies

Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs. random noise. We present a novel method for reducing the number of times adversarial examples need to be injected for a successful attack, based on the value function. We further explore how re-training on random noise and FGSM perturbations affects the resilience against adversarial examples.
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data

We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting “machines reading regression data” in reach.
Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems

We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a 1−ε fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor ε approaches 0 when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast ε approaches 0, is near optimal: We give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain.
 Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a 1−ε fraction of the optimal profit obtainable w.r.t. the worst distribution in the adversarial sequence. Further, if the algorithm is given one number per distribution, namely the optimal profit possible for each of the adversary’s distribution, then we design an algorithm that achieves a 1−ε fraction of the weighted average of the optimal profit of each distribution the adversary picks.
 In the offline setting we give a fast algorithm to solve very large linear programs (LPs) with both packing and covering constraints. We give algorithms to approximately solve (within a factor of 1+ε) the mixed packing-covering problem with O(γ m log (n/Δ)/ε2) oracle calls where the constraint matrix of this LP has dimension n× m, the success probability of the algorithm is 1−Δ, and γ quantifies how significant a single request is when compared to the sum total of all requests.
 We discuss implications of our results to several special cases including online combinatorial auctions, network routing, and the adwords problem.
Cryptography on FPGAs: State of the Art Implementations and Attacks

In the last decade, it has become aparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as FPGAs become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.