nils-herrmann/clean_scirepeval huggingface dataset:
Green-lighting Movie Scripts : Revenue Forecasting and Risk Management

Major studios and independent production firms (Indies) often have to select or “greenlight” a portfolio of scripts to turn into movies. Despite the huge financial risk at stake, there is currently no risk management tool they can use to aid their decisions, even though such a tool is sorely needed. In this paper, we developed a forecasting and risk management tool, based on movies scripts, to aid movie studios and production firms in their green-lighting decisions. The methodology developed can also assist outside investors if they have access to the scripts. Building upon and extending the previous literature, we extracted three levels of textual information (genre/content, bag-of-words, and semantics) from movie scripts. We then incorporate these textual variables as predictors, together with the contemplated production budget, into a BART-QL (Bayesian Additive Regression Tree for Quasi-Linear) model to obtain the posterior predictive distributions, rather than point forecasts, of the box office revenues for the corresponding movies. We demonstrate how the predictive distributions of box office revenues can potentially be used to help movie producers intelligently select their movie production portfolios based on their risk preferences, and we describe an illustrative analysis performed for an independent production firm.
Machine learning the harness track: Crowdsourcing and varying race history

Racing prediction schemes have been with mankind a long time. From following crowd wisdom and betting on favorites to mathematical methods like the Dr. Z System, we introduce a different class of prediction system, the S&C Racing System that derives from machine learning. We demonstrate the S&C Racing system using Support Vector Regression (SVR) to predict finishes and analyzed it on fifteen months of harness racing data from Northfield Park, Ohio. We found that within the domain of harness racing, our system outperforms crowds and Dr. Z bettors in returns per dollar wagered on seven of the most frequently used wagers: Win $1.08 return, Place $2.30, Show $2.55, Exacta $19.24, Quiniela $18.93, Trifecta $3.56 and Trifecta Box $21.05. Furthermore, we also analyzed a range of race histories and found that a four race history maximized system accuracy and payout. The implications of this work suggest an informational inequality exists within the harness racing market that was exploited by S&C Racing. While interesting, the implications of machine learning in this domain shows promise.
A high-efficiency Class-E power amplifier with wide-range load in WPT systems

A high-efficiency power amplifier (PA) is important in a Megahertz wireless power transfer (WPT) system. It is attractive to apply the Class-E PA for its simple structure and high efficiency. However, the conventional design for Class-E PA can only ensure a high efficiency for a fixed load. It is necessary to develop a high-efficiency Class-E PA for a wide-range load in WPT systems. A novel design method for Class-E PA is proposed to achieve this objective in this paper. The PA achieves high efficiency, above 80%, for a load ranging from 10 to 100 Ω at 6.78 MHz in the experiment.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework

Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both data scarcity and data imbalance. The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation. Given a small set of network intrusion samples, it first derives a Poisson-Gamma joint probabilistic generative model to generate synthesised intrusion data using Monte Carlo methods. Those synthesised data are then augmented by deep generative neural networks through adversarial learning. Finally, it adopts the augmented intrusion data to train supervised models for detecting network intrusions. Comprehensive experimental validations on KDD Cup 99 dataset show that the proposed framework outperforms the existing learning based IDSs in terms of improved accuracy, precision, recall, and F1-score.
Megaman: Scalable Manifold Learning in Python

Manifold Learning (ML) is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus, ML algorithms are most applicable to highdimensional data and require large sample sizes to accurately estimate the manifold. Despite this, most existing manifold learning implementations are not particularly scalable. Here we present a Python package that implements a variety of manifold learning algorithms in a modular and scalable fashion, using fast approximate neighbors searches and fast sparse eigendecompositions. The package incorporates theoretical advances in manifold learning, such as the unbiased Laplacian estimator introduced by Coifman and Lafon (2006) and the estimation of the embedding distortion by the Riemannian metric method introduced by Perrault-Joncas and Meila (2013). In benchmarks, even on a single-core desktop computer, our code embeds millions of data points in minutes, and takes just 200 minutes to embed the main sample of galaxy spectra from the Sloan Digital Sky Survey— consisting of 0.6 million samples in 3750-dimensions—a task which has not previously been possible.
Adaptive Memory Networks

We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks. AMN processes the input story to extract entities and stores them in memory banks. Starting from a single bank, as the number of input entities increases, AMN learns to create new banks as the entropy in a single bank becomes too high. Hence, after processing an input-question(s) pair, the resulting network represents a hierarchical structure where entities are stored in different banks, distanced by question relevance. At inference, one or few banks are used, creating a tradeoff between accuracy and performance. AMN is enabled by dynamic networks that allow input dependent network creation and efficiency in dynamic mini-batching as well as our novel bank controller that allows learning discrete decision making with high accuracy. In our results, we demonstrate that AMN learns to create variable depth networks depending on task complexity and reduces inference times for QA tasks.
Ice-cap. A high-throughput method for capturing plant tissue samples for genotype analysis.

High-throughput genotype screening is rapidly becoming a standard research tool in the post-genomic era. A major bottleneck currently exists, however, that limits the utility of this approach in the plant sciences. The rate-limiting step in current high-throughput pipelines is that tissue samples from living plants must be collected manually, one plant at a time. In this article I describe a novel method for harvesting tissue samples from living seedlings that eliminates this bottleneck. The method has been named Ice-Cap to reflect the fact that ice is used to capture the tissue samples. The planting of seeds, growth of seedlings, and harvesting of tissue are all performed in a 96-well format. I demonstrate the utility of this system by using tissue harvested by Ice-Cap to genotype a population of Arabidopsis seedlings that is segregating a previously characterized mutation. Because the harvesting of tissue is performed in a nondestructive manner, plants with the desired genotype can be transferred to soil and grown to maturity. I also show that Ice-Cap can be used to analyze genomic DNA from rice (Oryza sativa) seedlings. It is expected that this method will be applicable to high-throughput screening with many different plant species, making it a useful technology for performing marker assisted selection.
Introduction to a Large-Scale General Purpose Ground Truth Database: Methodology, Annotation Tool and Benchmarks

This paper presents a large scale general purpose image database with human annotated ground truth. Firstly, an all-in-all labeling framework is proposed to group visual knowledge of three levels: scene level (global geometric description), object level (segmentation, sketch representation, hierarchical decomposition), and low-mid level (2.1D layered representation, object boundary attributes, curve completion, etc.). Much of this data has not appeared in previous databases. In addition, And-Or Graph is used to organize visual elements to facilitate top-down labeling. An annotation tool is developed to realize and integrate all tasks. With this tool, we’ve been able to create a database consisting of more than 636,748 annotated images and video frames. Lastly, the data is organized into 13 common subsets to serve as benchmarks for diverse evaluation endeavors.
Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.
Polarity Loss for Zero-shot Object Detection

Zero-shot object detection is an emerging research topic that aims to recognize and localize previously ‘unseen’ objects. This setting gives rise to several unique challenges, e.g., highly imbalanced positive vs. negative instance ratio, ambiguity between background and unseen classes and the proper alignment between visual and semantic concepts. Here, we propose an end-to-end deep learning framework underpinned by a novel loss function that puts more emphasis on difficult examples to avoid class imbalance. We call our objective the ‘Polarity loss’ because it explicitly maximizes the gap between positive and negative predictions. Such a margin maximizing formulation is important as it improves the visual-semantic alignment while resolving the ambiguity between background and unseen. Our approach is inspired by the embodiment theories in cognitive science, that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word dictionary) and the perception of the physical world (visual imagery). To this end, we learn to attend to a dictionary of related semantic concepts that eventually refines the noisy semantic embeddings and helps establish a better synergy between visual and semantic domains. Our extensive results on MS-COCO and Pascal VOC datasets show as high as 14× mAP improvement over state of the art.1
The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of "activity" as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people's independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application.
The linguistic realisation of information packaging

There is increasing awareness of the large degree of crosslinguistic diversity involved in the structural realisation of information packaging (or information structure). Whereas English and many Germanic languages primarily exploit intonation for informational purposes , in other languages, like Catalan, syntax plays the primary role in the realisation of information packaging and intonation is reduced to a secondary role. In yet another group of languages the primary structural correlate is morphology. This paper provides a contrastive analysis of the structural properties of information packaging in a number of languages. It also contains a discussion of some basic issues concerning information packaging and identiies a set of information-packaging primitives that are applied to the crosslinguistic facts.
The environment of childhood poverty.

Poor children confront widespread environmental inequities. Compared with their economically advantaged counterparts, they are exposed to more family turmoil, violence, separation from their families, instability, and chaotic households. Poor children experience less social support, and their parents are less responsive and more authoritarian. Low-income children are read to relatively infrequently, watch more TV, and have less access to books and computers. Low-income parents are less involved in their children's school activities. The air and water poor children consume are more polluted. Their homes are more crowded, noisier, and of lower quality. Low-income neighborhoods are more dangerous, offer poorer municipal services, and suffer greater physical deterioration. Predominantly low-income schools and day care are inferior. The accumulation of multiple environmental risks rather than singular risk exposure may be an especially pathogenic aspect of childhood poverty.
AN ONTOLOGICAL MODEL FOR REPRESENTING SEMANTIC LEXICONS: AN APPLICATION ON TIME NOUNS IN THE HOLY QURAN

صلاخلا ـ ة :  ىلع  قوفي ةيبرعلا ةغللاب نيثدحتملا ددع نأ نم مغرلا يتئام تنإ يف ةلوذبملا دوهجلا نأ لاإ ،صخش نويلم ةليلق ةيبوساحلا ةيبرعلا ةيوغللا رداصملا جا ادج  ب ةيبوساحلا ةيبرعلا مجاعملا لاجم يف ةصاخ . بلغأ نإ ةيفاآ تسيل يهف اذلو ،ةيبنجأ تاغلل امنإ ،ةيبرعلا ةغلل لصلأا يف ممصت مل ةدوجوملا دوهجلا يبرعلا عمتجملا تاجايتحا دسل .  فدهي حرتقم ضرع ىلإ ثحبلا اذه لأ جذومن ساح مجعم ةينقت ىلع ينبم يبو " يجولوتنلأا " اهيلع دمتعت يتلا ةيساسلأا تاينقتلا نم ةثيدح ةينقت يهو ، ةينقت " ةيللادلا بيولا " ام لاجم يف تاقلاعلاو ميهافملل يللادلا يفرعملا ليثمتلاب ىنعت ، .  دقو ءانب مت لأا جذومن ةيرظن ساسأ ىلع " ةيللادلا لوقحلا " تايوغللا لاجم يف ةفورعملا  ،  و ت م اهساسأ ىلع ينب يتلا تانايبلا ءاقتسا لأا جذومن نم " نامزلا ظافلأ " يف " ميركلا نآرقلا " ، يذلا اهلامآو اهيقر يف ةيبرعلا هيلإ تلصو ام قدأ دعي . اذه لثم رفوت نإ لأا جذومن اعفان نوكيس ةيبرعلا ةغلل ةيبرعلا ةغللا لاجم يف ةيبوساحلا تاقيبطتلل . مت دقو م ضرع ثحبلا اذه يف ءانب ةيجهنمل لصف لأا جذومن اهيلإ لصوتلا مت يتلا جئاتنلاو .
A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification

The success of sentence classification often depends on understanding both the syntactic and semantic properties of wordphrases. Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. In this paper, we propose a structureindependent ‘Gated Representation Alignment’ (GRA) model that blends a phrasefocused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6%) of finegrained sentiment ratings, when compared to other structure-independent baselines. We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanism and found that this is critical to the effectiveness of the CNN-RNN hybrid.
Designer Worlds : Procedural Generation of Infinite Terrain from Real-World Elevation Data

The standard way to procedurally generate random terrain for video games and other applications is to post-process the output of a fast noise generator such as Perlin noise. Tuning the post-processing to achieve particular types of terrain requires game designers to be reasonably well-trained in mathematics. A well-known variant of Perlin noise called value noise is used in a process accessible to designers trained in geography to generate geotypical terrain based on elevation statistics drawn from widely available sources such as the United States Geographical Service. A step-by-step process for downloading and creating terrain from realworld USGS elevation data is described, and an implementation in C++ is given.
AutonoVi-Sim: Autonomous Vehicle Simulation Platform with Weather, Sensing, and Traffic Control

We present AutonoVi-Sim, a novel high-fidelity simulation platform for autonomous driving data generation and driving strategy testing. AutonoVi-Sim is a collection of high-level extensible modules which allows the rapid development and testing of vehicle configurations and facilitates construction of complex traffic scenarios. Autonovi-Sim supports multiple vehicles with unique steering or acceleration limits, as well as unique tire parameters and dynamics profiles. Engineers can specify the specific vehicle sensor systems and vary time of day and weather conditions to generate robust data and gain insight into how conditions affect the performance of a particular algorithm. In addition, AutonoVi-Sim supports navigation for non-vehicle traffic participants such as cyclists and pedestrians, allowing engineers to specify routes for these actors, or to create scripted scenarios which place the vehicle in dangerous reactive situations. Autonovi-Sim facilitates training of deep-learning algorithms by enabling data export from the vehicle's sensors, including camera data, LIDAR, relative positions of traffic participants, and detection and classification results. Thus, AutonoVi-Sim allows for the rapid prototyping, development and testing of autonomous driving algorithms under varying vehicle, road, traffic, and weather conditions. In this paper, we detail the simulator and provide specific performance and data benchmarks.
A Robust and Efficient Pre-Silicon Validation Environment for Mixed-Signal Circuits on Intel's Test Chips

In the past, it was possible to validate analog CMOS circuits through transistor-level (schematic netlist) simulation. As manufacturing processes grow in complexity, the ever-increasing amount of design-for-test/manufacturability/yield/quality (DFx) circuitry renders transistor-level simulation impractical; and digital behavioral simulation ignores crucial analog interactions. This paper presents a more robust and efficient methodology for pre-silicon validation of such mixed-signal circuits. We adopt a top-down strategy and model all blocks at the behavioral level. However, we also represent select analog blocks at the transistor level. This strategy precludes the need for what is currently a weakness in mixed-signal validation - equivalence checking between analog behavioral models and analog schematics. In addition, this strategy enables performance validation to be seamlessly integrated with functional validation. Further, by leveraging the industry standard languages of SPICE, System Verilog and Verilog-AMS, we are able to build and simulate all modeling and validation constructs with a single tool. On Intel's most recent test chip, our validation methodology found more bugs with fewer person-hours than previous attempts with purely digital or transistor-level validation.
A new algorithm for the assignment problem

We propose a new algorithm for the classical assignment problem. The algorithm resembles in some ways the Hungarian method but differs substantially in other respects. The average computational complexity of an efficient implementation of the algorithm seems to be considerably better than the one of the Hungarian method. In a large number of randomly generated problems the algorithm has consistently outperformed an efficiently coded version of the Hungarian method by a broad margin. The factor of improvement increases with the problem dimension N and reaches an order of magnitude for N equal to several hundreds.
Deep Learning at Alibaba

In this talk, I will focus on the applications and the latest development of deep learning technologies at Alibaba. More specifically, I will discuss (a) how to handle high dimensional data in deep learning and its application to recommender system, (b) the development of deep learning models for transfer learning and its application to image classification, (c) the development of combinatorial optimization techniques for DNN model compression and its application to large-scale image classification and object detection, and (d) the exploration of deep learning technique for combinatorial optimization and its application to the packing problem in shipping industry. I will conclude my talk with a discussion of new directions for deep learning that are under development at Alibaba.
CogNitive 5G networks: Comprehensive operator use cases with machine learning for management operations

This paper presents a novel cognitive management architecture developed within the H2020 CogNet project to manage 5G networks. We also present the instantiation of this architecture for two Operator use cases, namely ‘SLA enforcement’ and ‘Mobile Quality Predictor’. The SLA enforcement use case tackles the SLA management with machine learning techniques, precisely, LSTM (Long Short Term Memory). The second use case, Mobile Quality Predictor, proposes a framework using machine learning to enable an accurate bandwidth prediction for each mobile subscriber in real-time. A problem statement, stakeholders, an instantiation of the cognitive management architecture, a related work as well as an evaluation results are presented for each use case.