nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning Approaches to Text Production

Text production is a key component of many NLP applications. In data-driven approaches, it is used for instance, to generate dialogue turns from dialogue moves [Wen et al., 2015, Wen et al., 2016, Novikova et al., 2017], to verbalise the content of Knowledge bases [Gardent et al., 2017a, Gardent et al., 2017b] or to generate natural English sentences from rich linguistic representations, such as dependency trees [Belz et al., 2011, Mille et al., 2018] or Abstract Meaning Representations [May and Priyadarshi, 2017, Konstas et al., 2017, Song et al., ]. In text-driven methods on the other hand, text production is at work in sentence compression [Knight and Marcu, 2000, Cohn and Lapata, 2008, Filippova and Strube, 2008, Pitler, 2010, Filippova et al., 2015, Toutanova et al., 2016]; sentence fusion [McKeown et al., 2010, Filippova, 2010, Thadani and McKeown, 2013]; paraphrasing [Dras, 1999, Barzilay and McKeown, 2001, Bannard and Callison-Burch, 2005, Wubben et al., 2010, Narayan et al., 2016, Dong et al., 2017, Mallinson et al., 2017]; sentence (or text) simplification [Siddharthan et al., 2004, Zhu et al., 2010, Woodsend and Lapata, 2011, Wubben et al., 2012, Narayan and Gardent, 2014, Xu et al., 2015, Narayan and Gardent, 2016, Zhang and Lapata, 2017, Narayan et al., 2017], text summarisation [Wan, 2010, Nenkova and McKeown, 2011, Woodsend and Lapata, 2010, Rush et al., 2015, Cheng and Lapata, 2016, Nallapati et al., 2016, Chen et al., 2016, Tan and Wan, 2017, See et al., 2017, Nallapati et al., 2017, Paulus et al., 2017, Yasunaga et al., 2017, Narayan et al., 2018a, Narayan et al., 2018b, Pasunuru and Bansal, 2018, Celikyilmaz et al., 2018] and end-to-end dialogue systems [Li et al., 2017]. Following the success of encoder-decoder models in modeling sequence-rewriting tasks such as machine translation [Sutskever et al., 2011, Bahdanau et al., 2014], deep learning models have successfully been applied to the various text production tasks. For instance, [Rush et al., 2015] utilize a local attention-based model for abstractive summarisation, [Shang et al., 2015] propose an encoder-decoder model for response generation,  2017] uses a hybrid of encoder-decoder model and pointer network [Vinyals et al., 2015] for story highlight generation, [Dong et al., 2017] exploits an encoder-decoder model for question rephrasing and [Konstas et al., 2017] for AMR generation. In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural models for text production. Each text production task raises a slightly different communication goal (e.g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation). We will outline the constraints specific to each subtasks and examine how the existing neural models account for them.
Let's Fix OpenGL

From windowing systems to virtual reality, real-time graphics code is ubiquitous. Programming models for constructing graphics software, however, have largely escaped the attention of programming languages researchers. This essay introduces the programming model of OpenGL, a ubiquitous API for real-time graphics applications, for a language-oriented audience. It highlights six broad problems with the programming model and connects them to traditions in PL research. The issues range from classic pitfalls, where established thinking can apply, to new open problems, where novel research is needed.
A High Gain Input-Parallel Output-Series DC/DC Converter With Dual Coupled Inductors

High voltage gain dc-dc converters are required in many industrial applications such as photovoltaic and fuel cell energy systems, high-intensity discharge lamp (HID), dc back-up energy systems, and electric vehicles. This paper presents a novel input-parallel output-series boost converter with dual coupled inductors and a voltage multiplier module. On the one hand, the primary windings of two coupled inductors are connected in parallel to share the input current and reduce the current ripple at the input. On the other hand, the proposed converter inherits the merits of interleaved series-connected output capacitors for high voltage gain, low output voltage ripple, and low switch voltage stress. Moreover, the secondary sides of two coupled inductors are connected in series to a regenerative capacitor by a diode for extending the voltage gain and balancing the primary-parallel currents. In addition, the active switches are turned on at zero current and the reverse recovery problem of diodes is alleviated by reasonable leakage inductances of the coupled inductors. Besides, the energy of leakage inductances can be recycled. A prototype circuit rated 500-W output power is implemented in the laboratory, and the experimental results shows satisfactory agreement with the theoretical analysis.
Code coverage for suite evaluation by developers

One of the key challenges of developers testing code is determining a test suite's quality -- its ability to find faults. The most common approach is to use code coverage as a measure for test suite quality, and diminishing returns in coverage or high absolute coverage as a stopping rule. In testing research, suite quality is often evaluated by a suite's ability to kill mutants (artificially seeded potential faults). Determining which criteria best predict mutation kills is critical to practical estimation of test suite quality. Previous work has only used small sets of programs, and usually compares multiple suites for a single program. Practitioners, however, seldom compare suites --- they evaluate one suite. Using suites (both manual and automatically generated) from a large set of real-world open-source projects shows that evaluation results differ from those for suite-comparison: statement (not block, branch, or path) coverage predicts mutation kills best.
Dual-Band Base Station Array Using Filtering Antenna Elements for Mutual Coupling Suppression

This paper presents a novel dual-band base station antenna array using filtering antenna elements for size miniaturization. It consists of two 1 × 6 subarrays arranged side by side, which are designed for Digital Cellular System (DCS: 1710-1880 MHz) and Wideband Code Division Multiple Access (WCDMA: 1920-2170 MHz) applications. The two subarrays are composed of filtering antenna elements with high in-band radiation efficiency and out-of-band radiation rejection levels. The radiation of the DCS subarray is suppressed in the WCDMA band and vice versa. Mutual coupling between the two subarrays, therefore, can be suppressed and high isolation can be obtained with reduced subarray spacing. For demonstration, a dual-band filtering antenna array is designed and fabricated. The overall width of the array is only 206 mm, which is much narrower than that of typical industrial products (~290 mm). An isolation of 35 dB is obtained between the two subarrays without any decoupling network. The measured antenna gains are about 14.2 and 14.5 dBi for DCS and WCDMA bands, respectively, and the 3-dB beamwidths of the horizontal radiation patterns are 65° ± 5°. In addition, null filling below the main beam in the vertical radiation patterns is realized by elaborately designing a feed network to manipulate the output magnitude and phase of each array element. The proposed array is suitable for potential base station applications.
Detecting Depression with Audio/Text Sequence Modeling of Interviews

Medical professionals diagnose depression by interpreting the responses of individuals to a variety of questions, probing lifestyle changes and ongoing thoughts. Like professionals, an effective automated agent must understand that responses to queries have varying prognostic value. In this study we demonstrate an automated depression-detection algorithm that models interviews between an individual and agent and learns from sequences of questions and answers without the need to perform explicit topic modeling of the content. We utilized data of 142 individuals undergoing depression screening, and modeled the interactions with audio and text features in a Long-Short Term Memory (LSTM) neural network model to detect depression. Our results were comparable to methods that explicitly modeled the topics of the questions and answers which suggests that depression can be detected through sequential modeling of an interaction, with minimal information on the structure of the interview.
Integrating occlusion culling with parallel LOD for rendering complex 3D environments on GPU

In many research domains, such as mechanical engineering, game development and virtual reality, a typical output model is usually produced in a multi-object manner for efficient data management. To have a complete description of the whole model, tens of thousands of, or even millions of, such objects are created, which makes the entire dataset exceptionally complex. Consequently, visualizing the model becomes a computationally intensive process that impedes a real-time rendering and interaction.
Variational shape approximation

A method for concise, faithful approximation of complex 3D datasets is key to reducing the computational cost of graphics applications. Despite numerous applications ranging from geometry compression to reverse engineering, efficiently capturing the geometry of a surface remains a tedious task. In this paper, we present both theoretical and practical contributions that result in a novel and versatile framework for geometric approximation of surfaces. We depart from the usual strategy by casting shape approximation as a variational geometric partitioning problem. Using the concept of geometric proxies, we drive the distortion error down through repeated clustering of faces into best-fitting regions. Our approach is entirely discrete and error-driven, and does not require parameterization or local estimations of differential quantities. We also introduce a new metric based on normal deviation, and demonstrate its superior behavior at capturing anisotropy.
Web-STAR: Towards a Visual Web-Based IDE for a Story Comprehension System

In this work, we present Web-STAR, an online platform for story understanding built on top of the STAR (STory comprehension through ARgumentation) reasoning engine. This platform includes a web-based IDE, integration with the STAR system and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of “social” features like public story sharing with a built-in commenting system, a public repository for sharing stories with the community and collaboration tools that can be used from both project team members for development and educators for teaching. Moreover, we discuss the ongoing work on adding new features and functionality to this platform.
Yet another MicroArchitectural Attack: : exploiting I-Cache

MicroArchitectural Attacks (MA), which can be considered as a special form of Side-Channel Analysis, exploit microarchitectural functionalities of processor implementations and can compromise the security of computational environments even in the presence of sophisticated protection mechanisms like virtualization and sandboxing. This newly evolving research area has attracted significant interest due to the broad application range and the potentials of these attacks. Cache Analysis and Branch Prediction Analysis were the only types of MA that had been known publicly. In this paper, we introduce Instruction Cache (I-Cache) as yet another source of MA and present our experimental results which clearly prove the practicality and danger of I-Cache Attacks.
Starfish: A Self-tuning System for Big Data Analytics

Hadoop is a MAD system for data analytics  Magnetism: attracts all sources of data  Agility: adapts in sync with rapid data evolution  Depth: supports complex analytics needs Starfish makes Hadoop MADDER and Self-Tuning  Data-lifecycle-awareness: achieves good performance throughout data lifecycle  Elasticity: adjusts resources and operational costs  Robustness: provides availability and predictability Just-in-Time Job
An integrated GPU power and performance model

GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as GPUs has increased significantly.
 Hence, in this paper, we propose an integrated power and performance (IPP) prediction model for a GPU architecture to predict the optimal number of active processors for a given application. The basic intuition is that when an application reaches the peak memory bandwidth, using more cores does not result in performance improvement.
 We develop an empirical power model for the GPU. Unlike most previous models, which require measured execution times, hardware performance counters, or architectural simulations, IPP predicts execution times to calculate dynamic power events. We then use the outcome of IPP to control the number of running cores. We also model the increases in power consumption that resulted from the increases in temperature.
 With the predicted optimal number of active cores, we show that we can save up to 22.09%of runtime GPU energy consumption and on average 10.99% of that for the five memory bandwidth-limited benchmarks.
How To Exchange Secrets with Oblivious Transfer

As this paper puts forward the notion of “Oblivious Transfers” and is a well-known and frequently cited paper, I felt I should typeset the manuscript, and here is the result. While typesetting, I tried to stick to the original manuscript as much as possible. However, there were some cases, such as a few typos or punctuation marks, which were changed. As in many papers on cryptography, Alice and Bob play the role of participants of the given cryptographic protocols. For the sake of readability, Alice’s and Bob’s messages were printed in red and blue ink, respectively. This work was carefully proofread by my colleague Y. Sobhdel (sobhdel@ce.sharif.edu). Thanks also to H. M. Moghaddam for mentioning a minor mistake in an earlier version. That said, I will be thankful if you inform me of any possible mistakes.
Entity Linking meets Word Sense Disambiguation: a Unified Approach

Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of-the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org
A CMOS inverter-based class-AB pseudo differential amplifier for HF applications

This paper presents a CMOS inverter-based class-AB pseudo differential amplifier for HF applications using new simple rail-to-rail CMFB circuit. The proposed circuit employs two CMOS inverters and the complementary common-mode feedback (CMFB) consisting of current mode common-mode detector and transimpedance amplifiers. The circuit has been designed using 0.18 µm CMOS technology under 1 V supply, and the simulation results shows that the rail to rail output swing is achieved with low common-mode gain (−15 dB). The output swing of the circuit is 0.7 V. The power dissipation of the circuit is 96 µW.
Learning to Cooperate via Policy Search

Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to value-based methods for partially observable environments. In this paper, we provide a gradient-based distributed policysearch method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain.
A codebook-free and annotation-free approach for fine-grained image categorization

Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual appearances. Most of the state-of-the-art basic-level object classification algorithms have difficulties in this challenging problem. One reason for this can be attributed to the popular codebook-based image representation, often resulting in loss of subtle image information that are critical for fine-grained classification. Another way to address this problem is to introduce human annotations of object attributes or key points, a tedious process that is also difficult to generalize to new tasks. In this work, we propose a codebook-free and annotation-free approach for fine-grained image categorization. Instead of using vector-quantized codewords, we obtain an image representation by running a high throughput template matching process using a large number of randomly generated image templates. We then propose a novel bagging-based algorithm to build a final classifier by aggregating a set of discriminative yet largely uncorrelated classifiers. Experimental results show that our method outperforms state-of-the-art classification approaches on the Caltech-UCSD Birds dataset.
Understanding MOSFET mismatch for analog design

Despite the significance of matched devices in analog circuit design, mismatch modeling for design application has been lacking. This paper addresses misconceptions about MOSFET mismatch for analog design. t mismatch does not follow a simplistic 1 ( area) law, especially for wide/short and narrow/long devices, which are common geometries in analog circuits. Further, t and gain factor are not appropriate parameters for modeling mismatch. A physically based mismatch model can be used to obtain dramatic improvements in prediction of mismatch. This model is applied to MOSFET current mirrors to show some nonobvious effects over bias, geometry, and multiple-unit devices.
The Neurobiological Mechanisms of Generalized Anxiety Disorder and Chronic Stress

Two classification systems are now at the forefront of clinical psychiatric research: (1) Diagnostic and Statistical Manual, Fifth Edition and (2) the National Institutes of Mental Health Research Domain Criteria. Herein, we propose that these two classification systems are complementary rather than mutually exclusive, and when combined provide important information for understanding aspects of the pathophysiology related to Generalized Anxiety Disorder (GAD). The neurobiological literature for GAD and one relevant research domain criteria component, sustained threat, are reviewed from multiple units of analysis (genetic, neuroimaging, neuroendocrine, and psychophysiological). It is hypothesized that generating a comprehensive, biologically based understanding of the relationship between GAD, sustained threat, and the measureable units of analysis will provide information critical to design the most effective treatments.
Survey of Consistent Network Updates

Computer networks have become a critical infrastructure. As such, networks should not only meet strict requirements in terms of correctness, availability, and performance, but they should also be very flexible and support fast updates, e.g., due to a change in the security policy, increasing traffic, or failures. In this paper, we present a structured survey of mechanisms and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed. We also explain the relationship between consistent network update problems and classic algorithmic optimization ones. While our survey is mainly motivated by the advent of Software-Defined Networks (SDNs) and their primary need for correct and efficient update techniques, the fundamental underlying problems are not new, and we provide a historical perspective of the subject as well. CCS Concepts: rGeneral and reference → Surveys and overviews; rNetworks → Network algorithms; rComputer systems organization→ Distributed architectures;
Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search

Similarity indices for high-dimensional data are very desirable for building content-based search systems for featurerich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate similarity search. A significant drawback of these approaches is the requirement for a large number of hash tables in order to achieve good search quality. This paper proposes a new indexing scheme called multi-probe LSH that overcomes this drawback. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by and improves upon recent theoretical work on entropy-based LSH designed to reduce the space requirement of the basic LSH method. We have implemented the multi-probe LSH method and evaluated the implementation with two different high-dimensional datasets. Our evaluation shows that the multi-probe LSH method substantially improves upon previously proposed methods in both space and time efficiency. To achieve the same search quality, multi-probe LSH has a similar timeefficiency as the basic LSH method while reducing the number of hash tables by an order of magnitude. In comparison with the entropy-based LSH method, to achieve the same search quality, multi-probe LSH uses less query time and 5 to 8 times fewer number of hash tables.
Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in realtime, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.