nils-herrmann/clean_scirepeval huggingface dataset:
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-theart performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.
Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization

Learning the sparse Gaussian Markov Random Field, or conversely, estimating the sparse inverse covariance matrix is an approach to uncover the underlying dependency structure in data. Most of the current methods solve the problem by optimizing the maximum likelihood objective with a Laplace prior L1 on entries of a precision matrix. We propose a novel objective with a regularization term which penalizes an approximate product of the Cholesky decomposed precision matrix. This new reparametrization of the penalty term allows efficient coordinate descent optimization, which in synergy with an active set approach results in a very fast and efficient method for learning the sparse inverse covariance matrix. We evaluated the speed and solution quality of the newly proposed SCHL method on problems consisting of up to 24,840 variables. Our approach was several times faster than three state-of-the-art approaches. We also demonstrate that SCHL can be used to discover interpretable networks, by applying it to a high impact problem from the health informatics domain.
Time-series detection of perspiration as a liveness test in fingerprint devices

Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets.
Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (BiLSTM) along dependency paths (DP). We make a “common root” assumption to extend DP representations of cross-sentence links. In the final comparison to two stateof-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge and manually annotated attributes of entities (class, tense, polarity, etc.).
Zero quiescent current, delay adjustable, power-on-reset circuit

A power-on-reset (POR) circuit is extremely important in digital and mixed-signal ICs. It is used to initialize critical nodes of digital circuitry inside the chip during power-on. A novel POR circuit having zero quiescent current and an adjustable delay is proposed and demonstrated. The circuit has been designed in 0.5μm CMOS process to work for a supply voltage ranging from 1.8V to 5.5V. The circuit generates a POR signal after a predetermined delay, after the supply voltage crosses a predefined threshold voltage. This delay can be increased or decreased via programmable fuses. The POR threshold does not depend upon the supply ramp rate. The Brown-Out (BO) voltage for the proposed circuit matches the minimum supply voltage required by the digital circuitry inside the IC. The circuit consumes zero steady state current, making it ideal for low power applications.
A shape-based similarity measure for time series data with ensemble learning

This paper introduces a shape-based similarity measure, called the angular metric for shape similarity (AMSS), for time series data. Unlike most similarity or dissimilarity measures, AMSS is based not on individual data points of a time series but on vectors equivalently representing it. AMSS treats a time series as a vector sequence to focus on the shape of the data and compares data shapes by employing a variant of cosine similarity.  To deal with the potential drawback, ensemble learning is adopted, which integrates data smoothing when AMSS is used for classification. Evaluative experiments reveal distinct properties of AMSS and its effectiveness when applied in the ensemble framework as compared to existing measures.
Transition Flight Modeling of a Fixed-Wing VTOL UAV

This paper describes a complete six-degreeof-freedom nonlinear mathematical model of a tilt rotor unmanned aerial vehicle (UAV). The model is specifically tailored for the design of a hover to forward flight and forward flight to hover transition control system. In that respect, the model includes the aerodynamic effect of propeller-induced airstream which is a function of cruise speed, tilt angle and angle of attack. The cross-section area and output velocity of the propeller-induced airstream are calculated with momentum theory. The projected area on the UAV body that is affected by the propeller-induced airstream is specified and 2D aerodynamic analyses are performed for the airfoil profile of this region. Lookup-tables are generated and implemented in the nonlinear mathematical model. In addition, aerodynamic coefficients of the airframe are calculated by using CFD method and these data are embedded B. Yuksek ( ) · A. Vuruskan · U. Ozdemir · M. A. Yukselen · G. Inalhan Faculty of Aeronautics and Astronautics, Istanbul Technical University, 34469 Istanbul, Turkey e-mail: yuksekb@itu.edu.tr A. Vuruskan e-mail: vuruskan@itu.edu.tr U. Ozdemir e-mail: ugur.ozdemir@itu.edu.tr M. A. Yukselen e-mail: yukselen@itu.edu.tr G. Inalhan e-mail: inalhan@itu.edu.tr into the nonlinear model as a lookup-table form. In the transition flight regime, both aerodynamic and thrust forces act on the UAV body and the superimposed dynamics become very complex. Hence, it is important to define a method for hover-to-cruise and cruise-to-hover transitions. To this end, both transition scenarios are designed and a state-schedule is developed for flight velocity, angle of attack, and thrust levels of each of the thrust-propellers. This transition state schedule is used as a feedforward state for the flight control system. We present the simulation results of the transition control system and show the successful transition of TURAC in experiment.
Learning Multi-Modal Navigation for Unmanned Ground Vehicles

A robot that operates efficiently in a team with a human in an unstructured outdoor environment must be able to translate commands from a modality that is intuitive to its operator into actions. This capability is especially important as robots become ubiquitous and interact with untrained users. For this to happen, the robot must be able to perceive the world as humans do, so that the nuances of natural language and human perception are appropriately reflected in the actions taken by the robot. Traditionally, this has been done with separate perception, language processing, and planning blocks unified by a grounding system. The grounding system relates abstract symbols in the command to concrete representations in perception that can be placed into a metric or topological map upon which one can execute a planner. These modules are trained separately, often with different performance specifications, and are connected with restrictive interfaces to ease development and debugging (i.e., point objects with discrete attributes and a limited command language), but which also limits the kinds of information one module can transfer to another. The tremendous success of deep learning has revolutionized traditional lines of research in computer vision, such as object detection and scene labeling. Some of the most recent work goes even further, bringing together state of the art techniques in natural language processing with image understanding in what is called visual question answering, or VQA. Symbol grounding, multi-step reasoning, and comprehension of spatial relations are already elements of these systems, all contained in a single differentiable deep learning architecture, eliminating the need for well-defined interfaces between modules and the simplifying assumptions that go with them. Building upon this work, we propose a technique to transform a natural language command and a static aerial image, into a cost map suitable for planning. With this technique, we take a step towards unifying language, perception, and planning in a single, end-to-end trainable system. Further, we propose a synthetic benchmark based upon the CLEVR dataset, which can be used to compare the strengths weakness of the comprehension abilities of various planning algorithms in the context of an unbiased environment with virtually unlimited data. Finally, we propose some extensions to the system as steps towards practical robotics applications.
NailTactors: eyes-free spatial output using a nail-mounted tactor array

This paper investigates the feasibility of using a nail-mounted array of tactors, NailTactors, as an eyes-free output device. By rim-attached eccentric-rotating-mass (ERM) vibrators to artificial nails, miniature high-resolution tactile displays were realized as an eyes-free output device. To understand how to deliver rich signals to users for valid signal perception, three user studies were conducted. The results suggest that users can not only recognized absolute and relative directional cues, but also recognized numerical characters in EdgeWrite format with an overall 89% recognition rate. Experiments also identified the optimal placement of ERM actuators for maximizing information transfer.
Evaluation of Selected Dielectric Elastomers for use in an Artificial Muscle Actuator

This paper presents the results of a series of experiments designed to measure the suitability of selected dielectric elastomers as artificial muscle actuators. The paper aims to differentiate between three widely used dielectric elastomers, 3M VHB-4910, BJB Enterprises TC-5005 and NuSil Technology’s CF19-2186. The materials are compared in terms of their ease of manufacture, strain, electrical properties and hysteresis. The effect and the importance of prestraining each material is also considered. A conclusion is drawn on the basis of these comparisons and recommendations are given for the design of actuators utilising these materials.
Developing Non-goal Dialog System Based on Examples of Drama Television

This paper presents a design and experiments of developing a non-goal dialog system by utilizing human-to-human conversation examples from drama television. The aim is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) filtering and constructing a dialog example database from the drama conversations, and (2) retrieving a proper system response by finding the best dialog example based on the current user query. Subjective evaluation from a small user study is also discussed.
Towards improving bug tracking systems with game mechanisms

Low bug report quality and human conflicts pose challenges to keep bug tracking systems productive. This work proposes to address these issues by applying game mechanisms to bug tracking systems. We investigate the use of game mechanisms in Stack Overflow, an online community organized to resolve computer programming related problems, for which the improvements we seek for bug tracking systems also turn out to be relevant. The results of our Stack Overflow investigation show that its game mechanisms could be used to address these issues by motivating contributors to increase contribution frequency and quality, by filtering useful contributions, and by creating an agile and dependable moderation system. We proceed by mapping these mechanisms to open-source bug tracking systems, and find that most benefits are applicable. Additionally, our results motivate tailoring a reward and reputation system and summarizing bug reports as future directions for increasing the benefits of game mechanisms in bug tracking systems.
Playful palette: an interactive parametric color mixer for artists

We present Playful Palette, a color picker interface for digital paint programs that derives intuition from oil paint and watercolor palettes, but extends them with digital features. A Playful Palette is a set of blobs of color that blend together to create gradients and gamuts. They can be directly manipulated to explore arrangements and harmonies. All edits are non-destructive, and an infinite history allows previous palettes to be revisited and modified, recoloring the painting. The Playful Palette design is motivated by a pilot study of how artists use paint palettes, and we evaluate the final design with a set of traditional and digital media painters to demonstrate that Playful Palette is effective both at enabling artists' color tasks, and at amplifying their creativity.
CoreDet: a compiler and runtime system for deterministic multithreaded execution

The behavior of a multithreaded program does not depend only on its inputs. Scheduling, memory reordering, timing, and low-level hardware effects all introduce nondeterminism in the execution of multithreaded programs. This severely complicates many tasks, including debugging, testing, and automatic replication. In this work, we avoid these complications by eliminating their root cause: we develop a compiler and runtime system that runs arbitrary multithreaded C/C++ POSIX Threads programs deterministically.
 A trivial non-performant approach to providing determinism is simply deterministically serializing execution. Instead, we present a compiler and runtime infrastructure that ensures determinism but resorts to serialization rarely, for handling interthread communication and synchronization. We develop two basic approaches, both of which are largely dynamic with performance improved by some static compiler optimizations. First, an ownership-based approach detects interthread communication via an evolving table that tracks ownership of memory regions by threads. Second, a buffering approach uses versioned memory and employs a deterministic commit protocol to make changes visible to other threads. While buffering has larger single-threaded overhead than ownership, it tends to scale better (serializing less often). A hybrid system sometimes performs and scales better than either approach individually.
 Our implementation is based on the LLVM compiler infrastructure. It needs neither programmer annotations nor special hardware. Our empirical evaluation uses the PARSEC and SPLASH2 benchmarks and shows that our approach scales comparably to nondeterministic execution.
Trust-aware crowdsourcing with domain knowledge

The rise of social network and crowdsourcing platforms makes it convenient to take advantage of the collective intelligence to estimate true labels of questions of interest. However, input from workers is often noisy and even malicious. Trust is used to model workers in order to better estimate true labels of questions. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. Finally, we demonstrate that our model is superior to state-of-the-art by testing it on multiple real-world datasets.
Dune: Safe User-level Access to Privileged CPU Features

Dune is a system that provides applications with direct but safe access to hardware features such as ring protection, page tables, and tagged TLBs, while preserving the existing OS interfaces for processes. Dune uses the virtualization hardware in modern processors to provide a process, rather than a machine abstraction. It consists of a small kernel module that initializes virtualization hardware and mediates interactions with the kernel, and a user-level library that helps applications manage privileged hardware features. We present the implementation of Dune for 64bit x86 Linux. We use Dune to implement three userlevel applications that can benefit from access to privileged hardware: a sandbox for untrusted code, a privilege separation facility, and a garbage collector. The use of Dune greatly simplifies the implementation of these applications and provides significant performance advantages.
Modeling and predicting the popularity of online news based on temporal and content-related features

As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity.
SparkClouds: Visualizing Trends in Tag Clouds

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.
STDP-based spiking deep neural networks for object recognition

Previous studies have shown that spike-timingdependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated – using rate-based neural networks trained with back-propagation – that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first ∗Corresponding author.  (ST) timothee.masquelier@cnrs.fr (TM). layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We think that the combination of STDP with latency coding is key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions.