nils-herrmann/clean_scirepeval huggingface dataset:
A Hierarchy-to-Sequence Attentional Neural Machine Translation Model

Although sequence-to-sequence attentional neural machine translation NMT has achieved great progress recently, it is confronted with two challenges: learning optimal model parameters for long parallel sentences and well exploiting different scopes of contexts. In this paper, partially inspired by the idea of segmenting a long sentence into short clauses, each of which can be easily translated by NMT, we propose a hierarchy-to-sequence attentional NMT model to handle these two challenges. Our encoder takes the segmented clause sequence as input and explores a hierarchical neural network structure to model words, clauses, and sentences at different levels, particularly with two layers of recurrent neural networks modeling semantic compositionality at the word and clause level. Correspondingly, the decoder sequentially translates segmented clauses and simultaneously applies two types of attention models to capture contexts of interclause and intraclause for translation prediction. In this way, we can not only improve parameter learning, but also well explore different scopes of contexts for translation. Experimental results on Chinese–English and English–German translation demonstrate the superiorities of the proposed model over the conventional NMT model.
Fast Feature Pyramids for Object Detection

Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).
Stories with Help from Recurrent Neural Networks

Automated story generation has a long history of pursuit in artificial intelligence. Early approaches used hand-authored formal models of a particular story-world domain to generate narratives pertaining to that domain (Klein, Aeschlimann, and Balsiger 1973; Lebowitz 1985; Meehan 1977). With the advent of machine learning, more recent work has explored how to construct narrative models automatically from story corpora (Li et al. 2013; McIntyre and Lapata 2009; Swanson and Gordon 2012). This research has created a new potential for interactivity in narrative generation. Unlike previous approaches which lacked the breadth of knowledge required for open-domain storytelling, these systems leverage story data to interface with authors pursuing diverse narrative content. For example, Swanson and Gordon (Swanson and Gordon 2012) demonstrated an application where a user and automated agent took turns contributing sentences to a story. Their system used a case-based reasoning approach to retrieve a relevant continuation of the user’s sentence from a large database of stories. This research has given rise to a new type of story generation task, one of “narrative auto-completion”, where a system analyzes an ongoing narrative and generates a new contribution to the story. Analogous to existing automated writing aids like spelling and grammar correction, narrative auto-completion is applicable as a writing tool that suggests new ideas to authors. Recurrent Neural Networks (RNN) are a promising machine learning framework for language generation tasks. In natural language processing (NLP) tasks, RNNs are trained on sequences of text to model the conditional probability distribution of predicting a sequence unit (often a character or word) given the sequence up to that point. After training it is straightforward to generate new text by iteratively predicting the next unit based on the text generated so far. In this same way, a given text can be extended by predicting additional text in the sequence. For this reason an RNN is a suitable engine for an automated story writing assistant that takes an ongoing story as input for predicting a continuation of the story. In this thesis I explore the use of RNNs for this novel generation task, and show how this task affords a unique opportunity for the evaluation of generation systems.
GraphChi: Large-Scale Graph Computation on Just a PC

Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains challenging, especially to non-experts. In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel Parallel Sliding Windows algorithm, GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs, using just a single consumer-level computer. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. We build on the basis of Parallel Sliding Windows to propose a new data structure, Partitioned Adjacency Lists, which we use to design an online graph database, GraphChi-DB. We demonstrate that, on a single PC, GraphChi-DB can process over one hundred thousand graph updates per second, while simultaneously performing computation. GraphChi-DB compares favorably to existing graph databases, particularly on data that is much larger than the available memory. We evaluate our work both experimentally and theoretically. Based on the Parallel Sliding Windows algorithm, we propose new I/O efficient algorithms for solving fundamental graph problems. We also propose a novel algorithm for simulating billions of random walks in parallel on a single computer. By repeating experiments reported for existing distributed systems we show that, with only fraction of the resources, GraphChi can solve the same problems in a very reasonable time. Our work makes large-scale graph computation available to anyone with a modern PC.
Active lower limb prosthetics: a systematic review of design issues and solutions

This paper presents a review on design issues and solutions found in active lower limb prostheses. This review is based on a systematic literature search with a methodical search strategy. The search was carried out across four major technical databases and the retrieved records were screened for their relevance. A total of 21 different active prostheses, including 8 above-knee, 9 below-knee and 4 combined knee-ankle prostheses were identified. While an active prosthesis may help to restore the functional performance of an amputee, the requirements regarding the actuation unit as well as for the control system are high and the development becomes a challenging task. Regarding mechanical design and the actuation unit high force/torque delivery, high efficiency, low size and low weight are conflicting goals. The actuation principle and variable impedance actuators are discussed. The control system is paramount for a "natural functioning" of the prosthesis. The control system has to enable locomotion and should react to the amputee's intent. For this, multi-level control approaches are reviewed.
ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets

Determining mortality risk is important for critical decisions in Intensive Care Units (ICU). The need for machine learning models that provide accurate patient-specific prediction of mortality is well recognized. We present a new algorithm for ICU mortality prediction that is designed to address the problem of imbalance, which occurs, in the context of binary classification, when one of the two classes is significantly under–represented in the data. We take a fundamentally new approach in exploiting the class imbalance through a feature transformation such that the transformed features are easier to classify. Hypothesis testing is used for classification with a test statistic that follows the distribution of the difference of two χ random variables, for which there are no analytic expressions and we derive an accurate approximation. Experiments on a benchmark dataset of 4000 ICU patients show that our algorithm surpasses the best competing methods for mortality prediction.
Improved anomaly detection in surveillance videos based on a deep learning method

During the last few decades, surveillance cameras have been installed in different locations. Analysis of the information captured using these cameras can play effective roles in event prediction, online monitoring and goal-driven analysis applications including anomalies and intrusion detection. Nowadays, various Artificial Intelligence techniques have been used to detect anomalies, amongst them convolutional neural networks using deep learning techniques improved the detection accuracy significantly. The goal of this article is to propose a new method based on deep learning techniques for anomaly detection in video surveillance cameras. The proposed method has been evaluated in the UCSD dataset, and showed an increase in the accuracy of the anomaly detection.
Find me if you can: improving geographical prediction with social and spatial proximity

Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.
 Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users.
On Controllable Sparse Alternatives to Softmax

Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.
UIPicker: User-Input Privacy Identification in Mobile Applications

Identifying sensitive user inputs is a prerequisite for privacy protection. When it comes to today’s program analysis systems, however, only those data that go through well-defined system APIs can be automatically labelled. In our research, we show that this conventional approach is far from adequate, as most sensitive inputs are actually entered by the user at an app’s runtime: in our research, we inspect 17, 425 top apps from Google Play, and find that 35.46% of them involve sensitive user inputs. Manually marking them involves a lot of effort, impeding a large-scale, automated analysis of apps for potential information leaks. To address this important issue, we present UIPicker, an adaptable framework for automatic identification of sensitive user inputs. UIPicker is designed to detect the semantic information within the application layout resources and program code, and further analyze it for the locations where security-critical information may show up. This approach can support a variety of existing security analysis on mobile apps. We further develop a runtime protection mechanism on top of the technique, which helps the user make informed decisions when her sensitive data is about to leave the device in an unexpected way. We evaluate our approach over 200 randomly selected popular apps on GooglePlay. UIPicker is able to accurately label sensitive user inputs most of the time, with 93.6% precision and 90.1% recall.
Behaviorism, Cognitivism, Constructivism: Comparing Critical Features From an Instructional Design Perspective

The need for a bridge between basic learning research and educational practice has long been discussed. To ensure a strong connection between these two areas, Dewey (cited in Reigeluth, 1983) called for the creation and development of a “linking science”; Tyler (1978) a “middleman position”; and Lynch (1945) for employing an “engineering analogy” as an aid for translating theory into practice. In each case, the respective author highlighted the information and potential contributions of available learning theories, the pressing problems faced by those dealing with practical learning issues, and a general lack of using the former to facilitate solutions for the latter. Th e value of such a bridging function would be its ability to translate relevant aspects of the learning theories into optimal instructional actions. As described by Reigeluth (1983), the fi eld of Instructional Design performs this role. Instructional designers have been charged with “translating principles of learning and instruction into specifi cations for instructional materials and activities” (Smith & Ragan, 1993, p. 12). To achieve this goal, two sets of skills and knowledge are needed. First, the designer must understand the position of the practitioner. In this regard, the following questions would be relevant: What are The way we defi ne learning and what we believe about the way learning occurs has important implications for situations in which we want to facilitate changes in what people know and/or do. Learning theories provide instructional designers with verifi ed instructional strategies and techniques for facilitating learning as well as a foundation for intelligent strategy selection. Yet many designers are operating under the constraints of a limited theoretical background. This paper is an attempt to familiarize designers with three relevant positions on learning (behavioral, cognitive, and constructivist) which provide structured foundations for planning and conducting instructional design activities. Each learning perspective is discussed in terms of its specifi c interpretation of the learning process and the resulting implications for instructional designers and educational practitioners. The information presented here provides the reader with a comparison of these three diff erent viewpoints and illustrates how these diff erences might be translated into practical applications in instructional situations.
Antenna-on-package on low-cost organic substrate for 60 GHz wireless communication applications

This work proposes a Yagi-Uda array with fan-like driver for millimeter-wave application. The proposed antenna array is built on a low-cost and low-loss multilayer organic substrate for flip chip ball grid array (FCBGA) package. The package size is 16 × 8 × 0.44 mm3. The proposed four-element antenna array results in a peak gain of 12 – 14.5 dBi at operating frequency band. The proposed antenna achieves both broadband and high gain characteristics for millimeter-wave application. The proposed antenna array is also used to develop a low-cost 60-GHz switched-beam antenna array with butler matrix network. This work provides a cost-effective approach to implement a switched-beam antenna array for fifth-generation mobile communications (5G), IEEE 802.11ad, such as smart phone, laptops, and wireless router.
Agent-based modeling: methods and techniques for simulating human systems.

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.
Survey on modeling and indexing events in multimedia

Events have gained increasing interest in the area of multimedia in recent years. There have been many approaches published and research conducted on how to extract events from multimedia, represent it using appropriate models, and how to use events in end user applications. In this paper, we conduct an extensive analysis of existing event models along commonly identified aspects of events. In addition, we analyze how the different aspects of events relate to each other and how they can be applied together. Subsequently, we look into different approaches for how to index multimedia data. Finally, we elaborate on how to link the multimedia data with events in order to provide the basis for future event-based multimedia applications.
VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases

Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of dollars, not including the cost in morale and other more intangible detriments. The Aerospace Corporation is responsible for providing engineering assessments critical to the go/no-go decision for every Department of Defense (DoD) launch vehicle. These assessments are made by constantly monitoring streaming telemetry data in the hours before launch. For this demonstration, we will introduce VizTree, a novel time-series visualization tool to aid the Aerospace analysts who must make these engineering assessments. VizTree was developed at the University of California, Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry. Unlike other time series visualization tools, VizTree can scale to very large databases, giving it the potential to be a generally useful data mining and database tool.
Big data stream computing in healthcare real-time analytics

The healthcare industry is changing at a dramatic rate. There are multiple processes going on within the health sector. These processes not only impact the care of individuals but also help medical practitioners and the delivery of care and services. The industry can take advantage of big data analytics to ensure that all the multiple processes within the industry are running smoothly. Big data analytics is not just an opportunity but a necessity. Recently, big data stream computing has been studied in order to improve the quality of healthcare services and reduce costs by capability support prediction, thus making decisions in real-time. This paper proposes a generic architecture for big data healthcare analytic by using open sources, including Hadoop, Apache Storm, Kafka and NoSQL Cassandra. The combination of high throughput publish-subscribe messaging for streams, distributed real-time computing, and distributed storage system can effectively analyze a huge amount of healthcare data coming with a rapid rate.
Recurrent Residual Learning for Sequence Classification

In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) for modeling sequential data. We show that for sequence classification tasks, incorporating residual connections into recurrent structures yields similar accuracy to Long Short Term Memory (LSTM) RNN with much fewer model parameters. In addition, we propose two novel models which combine the best of both residual learning and LSTM. Experiments show that the new models significantly outperform LSTM.
Platform motion disturbances attenuation in a missile seeker subsystem using Internal Model Control

In this paper two degree of freedom Internal Model Controller (2-DOF IMC) is used to attenuate the platform (missile body) disturbance that couples to the gimbal inner axis (elevation axis) angular rate of the seeker i.e. the angular rate of the inner (elevation) axis of the seeker gimbal in the pitch plane. IMC is an efficient robust controller and its use in process control system is very common. It can track reference input and simultaneously it also rejects the external disturbances that enter into the system. In this paper these features have been utilized to attenuate the base disturbances and disturbance torque in the seeker system.
Design of a triangular fractal patch antenna with slit for IRNSS and GAGAN applications

In this paper a triangular fractal patch antenna with slit is designed for IRNSS and GAGAN applications using ADS software. India is intended to develop a satellite based navigation systems known as Indian Regional Navigational Satellite System (IRNSS) for positioning applications. Design of IRNSS antenna at user sector is indispensable. GPS Aided and Geo Augmented Navigation (GAGAN), a satellite based augmentation system for India, erect over the GPS system is anticipated to provide the flawless navigation support over the Asia-Pacific regions. The desired antenna has been deliberate on dielectric constant εr = 4.8 and substrate thickness h = 3.05 mm. The feed location of the antenna has been selected to produce the circular polarization. The self-similar property in antenna exhibits multi-band resonant frequencies. These specifications should be satisfied at the frequency L5 (1175 MHz), L1 (1575.42 MHz) and S (2492.08 MHz).