nils-herrmann/clean_scirepeval huggingface dataset:
Collective Opinion Target Extraction in Chinese Microblogs

Microblog messages pose severe challenges for current sentiment analysis techniques due to some inherent characteristics such as the length limit and informal writing style. In this paper, we study the problem of extracting opinion targets of Chinese microblog messages. Such fine-grained word-level task has not been well investigated in microblogs yet. We propose an unsupervised label propagation algorithm to address the problem. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. Topics in microblogs are identified by hashtags or using clustering algorithms. Experimental results on Chinese microblogs show the effectiveness of our framework and algorithms.
Clustering short text using Ncut-weighted non-negative matrix factorization

Non-negative matrix factorization (NMF) has been successfully applied in document clustering. However, experiments on short texts, such as microblogs, Q&A documents and news titles, suggest unsatisfactory performance of NMF. An major reason is that the traditional term weighting schemes, like binary weight and tfidf, cannot well capture the terms' discriminative power and importance in short texts, due to the sparsity of data. To tackle this problem, we proposed a novel term weighting scheme for NMF, derived from the Normalized Cut (Ncut) problem on the term affinity graph. Different from idf, which emphasizes discriminability on document level, the Ncut weighting measures terms' discriminability on term level. Experiments on two data sets show our weighting scheme significantly boosts NMF's performance on short text clustering.
An MRF Model for Binarization of Natural Scene Text

Inspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by the value of the energy function. We minimize the energy function, i.e. find the optimal binarization, using an iterative graph cut scheme. Our model is robust to variations in foreground and background colours as we use a Gaussian Mixture Model in the energy function. In addition, our algorithm is efficient to compute, and adapts to a variety of document images. We show results on word images from the challenging ICDAR 2003 dataset, and compare our performance with previously reported methods. Our approach shows significant improvement in pixel level accuracy as well as OCR accuracy.
Towards time-optimal race car driving using nonlinear MPC in real-time

This paper addresses the real-time control of autonomous vehicles under a minimum traveling time objective. Control inputs for the vehicle are computed from a nonlinear model predictive control (MPC) scheme. The time-optimal objective is reformulated such that it can be tackled by existing efficient algorithms for real-time nonlinear MPC that build on the generalized Gauss-Newton method. We numerically validate our approach in simulations and present a real-world hardware setup of miniature race cars that is used for an experimental comparison of different approaches.
Deceiving Others: Distinct Neural Responses of the Prefrontal Cortex and Amygdala in Simple Fabrication and Deception with Social Interactions

Brain mechanisms for telling lies have been investigated recently using neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography. Although the advent of these techniques has gradually enabled clarification of the functional contributions of the prefrontal cortex in deception with respect to executive function, the specific roles of subregions within the prefrontal cortex and other brain regions responsible for emotional regulation or social interactions during deception are still unclear. Assuming that the processes of falsifying truthful responses and deceiving others are differentially associated with the activities of these regions, we conducted a positron emission tomography experiment with 2 (truth, lie) 2 (honesty, dishonesty) factorial design. The main effect of falsifying the truthful responses revealed increased brain activity of the left dorsolateral and right anterior prefrontal cortices, supporting the interpretation of previous studies that executive functions are related to making untruthful responses. The main effect of deceiving the interrogator showed activations of the ventromedial prefrontal (medial orbitofrontal) cortex and amygdala, adding new evidence that the brain regions assumed to be responsible for emotional processing or social interaction are active during deceptive behavior similar to that in real-life situations. Further analysis revealed that activity of the right anterior prefrontal cortex showed both effects of deception, indicating that this region has a pivotal role in telling lies. Our results provide clear evidence of functionally dissociable roles of the prefrontal subregions and amygdala for human deception.
Synthesizing memory models from framework sketches and Litmus tests

A memory consistency model specifies which writes to shared memory a given read may see. Ambiguities or errors in these specifications can lead to bugs in both compilers and applications. Yet architectures usually define their memory models with prose and litmus tests—small concurrent programs that demonstrate allowed and forbidden outcomes. Recent work has formalized the memory models of common architectures through substantial manual effort, but as new architectures emerge, there is a growing need for tools to aid these efforts. 
 This paper presents MemSynth, a synthesis-aided system for reasoning about axiomatic specifications of memory models. MemSynth takes as input a set of litmus tests and a framework sketch that defines a class of memory models. The sketch comprises a set of axioms with missing expressions (or holes). Given these inputs, MemSynth synthesizes a completion of the axioms—i.e., a memory model—that gives the desired outcome on all tests. The MemSynth engine employs a novel embedding of bounded relational logic in a solver-aided programming language, which enables it to tackle complex synthesis queries intractable to existing relational solvers. This design also enables it to solve new kinds of queries, such as checking if a set of litmus tests unambiguously defines a memory model within a framework sketch. 
 We show that MemSynth can synthesize specifications for x86 in under two seconds, and for PowerPC in 12 seconds from 768 litmus tests. Our ambiguity check identifies missing tests from both the Intel x86 documentation and the validation suite of a previous PowerPC formalization. We also used MemSynth to reproduce, debug, and automatically repair a paper on comparing memory models in just two days.
SETD5 gene variant associated with mild intellectual disability - a case report.

The recent advent of exome sequencing has allowed for the identification of pathogenic gene variants responsible for a variety of diseases that were previously clinically diagnosed, with no underlying molecular etiology. Among these conditions, intellectual disability is a prevalent heterogeneous condition, presenting itself in a large spectrum of intensity, in some cases associated with congenital malformations, behavioral and various other intellectual development alterations. Here we report on a 36-year-old male patient, with a mild intellectual disability that remained undiagnosed at the molecular level for all his life. Using Nextera Exome Sequencing, a Chr3:9.517.294 A>AC (c.3848_3849insC) SETD5 gene insertion was found. This rare variant was classified as likely pathogenic due to its frameshift nature in the gene, in which loss-of-function mutations have been previously reported to cause intellectual disability, as well as a 3p25.3 microdeletion phenotype. It is possible that this variant shows partial activity, due to its gene localization, which would explain the patient's mild phenotype when compared with other reports.
Simplified 3.3V tolerance circuit for 2.5V I/O design in PCI-X signaling environment

A low voltage interface circuit with a high voltage tolerance enables devices with different power supply levels to be efficiently coupled together without significant leakage current or damage to the circuits. The interface circuit includes an impedance control circuit, an output buffer, an input buffer, an isolation circuit and a pull up protection circuit. When a high voltage is applied to the IO pad, the pull-up protection circuit drives the gate of the pull up transistor to the high IO pad voltage to ensure that no current flows to the positive supply voltage. Also the isolation circuit couples the high IO pad voltage to the body of the pull-up transistor to prevent leakage current through parasitic diodes formed by the pull-up transistor.
Convolution Aware Initialization

Initialization of parameters in deep neural networks has been shown to have a big impact on the performance of the networks (Mishkin & Matas, 2015). The initialization scheme devised by He et al, allowed convolution activations to carry a constrained mean which allowed deep networks to be trained effectively (He et al., 2015a). Orthogonal initializations and more generally orthogonal matrices in standard recurrent networks have been proved to eradicate the vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of current initialization schemes do not take fully into account the intrinsic structure of the convolution operator. Using the duality of the Fourier transform and the convolution operator, Convolution Aware Initialization builds orthogonal filters in the Fourier space, and using the inverse Fourier transform represents them in the standard space. With Convolution Aware Initialization we noticed not only higher accuracy and lower loss, but faster convergence. We achieve new state of the art on the CIFAR10 dataset, and achieve close to state of the art on various other tasks.
Geolocation for Twitter: Timing Matters

Automated geolocation of social media messages can benefit a variety of downstream applications. However, these geolocation systems are typically evaluated without attention to how changes in time impact geolocation. Since different people, in different locations write messages at different times, these factors can significantly vary the performance of a geolocation system over time. We demonstrate cyclical temporal effects on geolocation accuracy in Twitter, as well as rapid drops as test data moves beyond the time period of training data. We show that temporal drift can effectively be countered with even modest online model updates.
Image Encryption Using Chaotic Maps: A Survey

As the exchange of data over the open networks and Internet is rapidly growing, security of the data becomes a major concern. One possible solution to this problem is to encrypt the data. The data can be text, image, audio, video etc.. In today's world most of the multimedia applications involve images. Earlier image encryption techniques like AES,DES,RSA etc. exhibit low levels of security and also weak anti attack ability. This problem was overcome by using chaos based cryptography. The chaotic systems are very sensitive to initial conditions and control parameters which make them suitable for image encryption. Many works have been done in the field of chaos based image encryption. In this survey paper an attempt has been made to review the aspects and approaches of the design used for image encryption.
Review of battery cell balancing techniques

A highly reliable and efficient battery management system (BMS) is crucial for applications that are powered by electrochemical power. Cell balancing is one of the most important features of a BMS. Cell balancing techniques help to distribute energy evenly among battery cells. Without cell balancing, a portion of the capacity or energy in the battery bank will be wasted, especially for long battery string which operates in frequent recycling condition. In this paper, some popular cell balancing techniques are described and categorized according to the way of processing redundant energy in battery cells.
Toward curricular guidelines for cybersecurity

This session reports on a workshop convened by the ACM Education Board with funding by the US National Science Foundation and invites discussion from the community on the workshop findings. The topic, curricular directions for cybersecurity, is one that resonates in many departments considering how best to prepare graduates to face the challenges of security issues in employment and future research. The session will include presentation of the workshop context and conclusions, but will be open to participant discussion. This will be the first public presentation of the results of the workshop and the first opportunity for significant response.
Grounded Learning of Color Semantics with Autoencoders

Humans learn language by grounding word meaning in the physical world. Recent efforts in natural language processing have attempted to model such multimodal learning by incorporating visual information into word and sentence representations. Here, we explore the task of grounding lexical color descriptions in their visual referents. We propose an RNN-based autoencoder model to learn vector representations of sentences that reflect their associated color values. Our model effectively learns a joint visual-lexical space that demonstrates compositionality and generalizes to unseen color names. As a demonstration of such a space learned, we show that our model can predict captions from color representations and color representations from captions. In addition to successfully modeling color language, this work provides a novel framework for grounded language learning.
GPGPU Enabled Ray Directed Adaptive Volume Visualization for High Density Scans

This paper presents an open source implementation of a volume visualizer capable of rendering large scale simulated and tomographic volume datasets on everyday commodity hardware. The ability to visualize this data enables a wide range of analysis in many scientific disciplines, such as medicine, biology, engineering, and physics. The implementation presented takes advantage of a hierarchical space filling curve to efficiently query only a subset of the data required for accurate visualization. A major advantage of this implementation is that it is open source, and works on a wide range of visualization devices by using the Unity game engine, a cross platform engine capable of targeting over 25 platforms [24]. This work's unique contribution is utilizing a space filling curve for multiple bricks in the volume to permit adaptive streaming at different resolutions.
Exploring romantic relationships on social networking sites using the self-expansion model

Several hypotheses were derived from the self-expansion model (Aron & Aron, 1986 ) concerning romantic relationships and social networking sites (SNSs). A sample of 276 participants responded to questions about their relationshi p history and SNS uses and a subset of those (N = 149) responded to additional questions about a current romantic partner. Results suggest that past self-expansion leaves a residue shown by more interests. This finding was moderated by overall Facebook use. Particular Facebook behaviors such as tagging one’s partner in status updates, appearing together in photographs, and listing similar interests on profiles are indicative of self-expansion processes typically found in romantic
Learning a Variational Network for Reconstruction of Accelerated MRI Data

To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.



Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data.



The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4.



Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.  2018.
The OMG-Emotion Behavior Dataset

This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion)1 by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.1https://www2.informatik.uni-hamburg.de/wtm/OMG-EmotionChallenge/
Peak-End Effects on Player Experience in Casual Games

The peak-end rule is a psychological heuristic observing that people's retrospective assessment of an experience is strongly influenced by the intensity of the peak and final moments of that experience. We examine how aspects of game player experience are influenced by peak-end manipulations to the sequence of events in games that are otherwise objectively identical. A first experiment examines players' retrospective assessments of two games (a pattern matching game based on Bejeweled and a point-and-click reaction game) when the sequence of difficulty is manipulated to induce positive, negative and neutral peak-end effects. A second experiment examines assessments of a shootout game in which the balance between challenge and skill is similarly manipulated. Results across the games show that recollection of challenge was strongly influenced by peak-end effects; however, results for fun, enjoyment, and preference to repeat were varied -- sometimes significantly in favour of the hypothesized effects, sometimes insignificant, but never against the hypothesis.
Choosing the right crowd: expert finding in social networks

Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks.
 This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.
Development of prosocial, individualistic, and competitive orientations: theory and preliminary evidence.

The authors adopt an interdependence analysis of social value orientation, proposing that prosocial, individualistic, and competitive orientations are (a) partially rooted in different patterns of social interaction as experienced during the periods spanning early childhood to young adulthood and (b) further shaped by different patterns of social interaction as experienced during early adulthood, middle adulthood, and old age. Congruent with this analysis, results revealed that relative to individualists and competitors, prosocial individuals exhibited greater levels of secure attachment (Studies 1 and 2) and reported having more siblings, especially sisters (Study 3). Finally, the prevalence of prosocials increased--and the prevalence of individualists and competitors decreased--from early adulthood to middle adulthood and old age (Study 4).
Rethink energy accounting with cooperative game theory

Energy accounting determines how much a software principal contributes to the total system energy consumption. It is the foundation for evaluating software and for operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track all hardware usage by software in a heterogeneous multicore system like modern smartphones and tablets.
 In this work, we argue that energy accounting should be formulated as a cooperative game and that the Shapley value provides the ultimate ground truth for energy accounting policies. We reveal the important flaws of existing energy accounting policies based on the Shapley value theory and provide Shapley value-based energy accounting, a practical approximation of the Shapley value, for battery-powered mobile systems. We evaluate this approximation against existing energy accounting policies in two ways: (i) how well they identify the top energy consuming applications, and (ii) how effective they are in system energy management. Using a prototype based on Texas Instruments Pandaboard and smartphone workload, we experimentally demonstrate existing energy accounting policies can deviate by 400% in attributing energy consumption to running applications and can be up to 25% less effective in system energy management when compared to Shapley value-based energy accounting.