nils-herrmann/clean_scirepeval huggingface dataset:
A Compiler Approach to Scalable Concurrent-Program Design

We describe a compilation system for the concurrent programming language Program Composition Notation (PCN). This notation provides a single-assignment programming model that permits concurrent-programming concerns such as decomposition, communication, synchronization, mapping, granularity, and load balancing to be addressed separately in a design. PCN is also extensible with programmer-defined operators, allowing common abstractions to be encapsulated and reused in different contexts.
The compilation system incorporates a concurrent-transformation system that allows abstractions to be defined through concurrent source-to-source transformations; these convert programmer-defined operators into a core notation. Run-time techniques allow the core notation to be compiled into a simple concurrent abstract machine which can be implemented in a portable fashion using a run-time library. The abstract machine provides a uniform treatment of single-assignment and mutable data structures, allowing data sharing between concurrent and sequential program segments and permitting integration of sequential C and Fortran code into concurrent programs.
This compilation system forms part of a program development toolkit that operates on a wide variety of networked workstations, multicomputers, and shared-memory multiprocessors. The toolkit has been used both to develop substantial applications and to teach introductory concurrent-programming classes, including a freshman course at Caltech.
Review of TDNN (time delay neural network) architectures for speech recognition

In this review, the TDNN architecture for speech recognition is described and its recognition performance for Japanese phonemes and phrases is explained. In comparative studies, it is shown that the TDNN yields superior phoneme recognition performance. The TDNN optimized for phoneme recognition, however, does not necessarily result in optimized word or phrase recognition performance, as overfitting to the specific phoneme data or recording conditions may occur. Care must therefore be taken to achieve robust integration, and several studies toward this goal will be reported.
Backprop KF: Learning Discriminative Deterministic State Estimators

Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles. However, generative models have limited capacity to handle rich sensory observations, such as camera images, since they must model the entire distribution over sensor readings. Discriminative models do not suffer from this limitation, but are typically more complex to train as latent variable models for state estimation. We present an alternative approach where the parameters of the latent state distribution are directly optimized as a deterministic computation graph, resulting in a simple and effective gradient descent algorithm for training discriminative state estimators. We show that this procedure can be used to train state estimators that use complex input, such as raw camera images, which must be processed using expressive nonlinear function approximators such as convolutional neural networks. Our model can be viewed as a type of recurrent neural network, and the connection to probabilistic filtering allows us to design a network architecture that is particularly well suited for state estimation. We evaluate our approach on tracking task with raw image inputs. The results show significant improvement over both standard generative approaches and regular recurrent neural networks.
Machine learning - a probabilistic perspective

“Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real-world case studies. The accompanying software package includes source code for many of the figures, making it both easy and very tempting to dive in and explore these methods for yourself. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data.” John Winn, Microsoft Research
Learning Graph Matching

As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility. The main research focus in this theme is about designing efficient algorithms for approximately solving the quadratic assignment problem, since it is NP-hard. In this paper we turn our attention to a different question: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the 'labels' are matches between them. Our experimental results reveal that learning can substantially improve the performance of standard graph matching algorithms. In particular, we find that simple linear assignment with such a learning scheme outperforms Graduated Assignment with bistochastic normalisation, a state-of-the-art quadratic assignment relaxation algorithm.
An Unsupervised Aspect-Sentiment Model for Online Reviews

With the increase in popularity of online review sites comes a corresponding need for tools capable of extracting the information most important to the user from the plain text data. Due to the diversity in products and services being reviewed, supervised methods are often not practical. We present an unsupervised system for extracting aspects and determining sentiment in review text. The method is simple and flexible with regard to domain and language, and takes into account the influence of aspect on sentiment polarity, an issue largely ignored in previous literature. We demonstrate its effectiveness on both component tasks, where it achieves similar results to more complex semi-supervised methods that are restricted by their reliance on manual annotation and extensive knowledge sources.
Opinion mining from student feedback data using supervised learning algorithms

This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machine learning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naïve Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.
Xbase: implementing domain-specific languages for Java

Xtext is an open-source framework for implementing external, textual domain-specific languages (DSLs). So far, most DSLs implemented with Xtext and similar tools focus on structural aspects such as service specifications and entities. Because behavioral aspects are significantly more complicated to implement, they are often delegated to general-purpose programming languages. This approach introduces complex integration patterns and the DSL's high level of abstraction is compromised.
 We present Xbase as part of Xtext, an expression language that can be reused via language inheritance in any DSL implementation based on Xtext. Xbase expressions provide both control structures and program expressions in a uniform way. Xbase is statically typed and tightly integrated with the Java type system. Languages extending Xbase inherit the syntax of a Java-like expression language as well as language infrastructure components, including a parser, an unparser, a linker, a compiler and an interpreter. Furthermore, the framework provides integration into the Eclipse IDE including debug and refactoring support.
 The application of Xbase is presented by means of a domain model language which serves as a tutorial example and by the implementation of the programming language Xtend. Xtend is a functional and object-oriented general purpose language for the Java Virtual Machine (JVM). It is built on top of Xbase which is the reusable expression language that is the foundation of Xtend.
Semantic annotation in historical documents

This paper presents an approach to proceed with semantic annotation in historical documents from the 19th century that discuss the constitution of the mother language, the Portuguese Language in Brazil. The objective is to generate a group of semantically annotated documents in agreement with a domain ontology. To provide this domain ontology, the Linguistic Instrument Ontology was employed, and it supported the process of automatic semantic annotation. The results obtained with the annotation were analyzed in comparison with the Gold Standard and they presented an elevated level of coincidence, between 0.86 and 1.00 for the F1-score measure. Besides that, it was possible to locate new documents about the discussed domain in a sample of the Revistas Brazileiras. These results prove the efficacy of the approach of automatic semantic annotation.
Blind Deconvolution Using Convex Programming

We consider the problem of recovering two unknown vectors, w and x, of length L from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension N and the other with dimension K. Although the observed convolution is nonlinear in both w and x, it is linear in the rank-1 matrix formed by their outer product wx*. This observation allows us to recast the deconvolution problem as low-rank matrix recovery problem from linear measurements, whose natural convex relaxation is a nuclear norm minimization program. We prove the effectiveness of this relaxation by showing that, for “generic” signals, the program can deconvolve w and x exactly when the maximum of N and K is almost on the order of L. That is, we show that if x is drawn from a random subspace of dimension N, and w is a vector in a subspace of dimension K whose basis vectors are spread out in the frequency domain, then nuclear norm minimization recovers wx* without error. We discuss this result in the context of blind channel estimation in communications. If we have a message of length N, which we code using a random L x N coding matrix, and the encoded message travels through an unknown linear time-invariant channel of maximum length K, then the receiver can recover both the channel response and the message when L ≳ N + K, to within constant and log factors.
Copper pillar shape and related stress simulation studies in flip chip packages

One of the major benefits of copper pillar interconnect is to reduce the pillar footprint in comparison to that for traditional solder bumps. This increases the space available for escape routing between neighboring pillars as compared to traditional solder bumps and, in many cases, permits the reduction of the number of substrate layers and, in turn, reduces the cost of the flip chip package solution. In this paper, we have used finite element analysis to simulate thermo-mechanical stresses in oblong-shaped copper pillars, a configuration which further increases the space available for escape routing. We pay particular attention to the maximum tensile stress at the surface of the dielectric layers on the chip and the solder. We compare three different types of copper pillar cross section geometries: circular, oblong-shape in the radial direction and oblong-shape in the anti-radial direction as well as diameters of polyimide passivation opening under the copper pillar. We find the lowest stress is obtained by using oblong shaped copper pillars in the radial direction in conjunction with a small opening of the polyimide passivation.
A Multiband Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE Applications

A multiband dual-polarized omnidirectional antenna for 2G/3G/Long Term Evolution (LTE) mobile communications is proposed in this letter, which consists of horizontal polarization (HP) and vertical polarization (VP) element with separate feeds. The VP element consists of three polygonal radiation patches with three equally spaced legs shorted to the ground plane. The HP element consists of three wideband slot loop structures, which is nested on the top of the VP element. Three slot loop structures provide a 360° coverage for HP and enhance its bandwidth. Both the simulated and measured results indicate that the frequency bands of 1650–2900 MHz for HP and 780–2700 MHz for VP can be achieved. The reflection of VP improves the gain of the HP element at least 1 dBi after nesting. The gain of HP element is more than 3 dBi for LTE, and the gain of VP element is more than 5 dBi in the LTE band and 1.5 dBi in the 2G band. Port isolation larger than 30 dB and low-gain variation levels are also obtained. The proposed antenna can be applied in mobile communications.
Interacting with spatially augmented reality

We present the notion of projector-based spatially augmented reality (SAR), and explore how it can be used as an effective user interface to enable users to easily and naturally interact with their real physical environment and the virtual environment. In SAR, the user's physical environment is illuminated with images projected from the projectors. We then describe a framework that can easily incorporate different types of interactions on a continuum of display surfaces and input devices. More specifically, in the framework, we first establish an understanding of the 3D relationship between the user, the projectors and the display surfaces. Then, with the help of some new calibration and rendering techniques, only a simple procedure is needed to effectively illuminate the display surfaces. We describe various underlying techniques and discuss the results in the context of three different applications.
Fast-join: An efficient method for fuzzy token matching based string similarity join

String similarity join that finds similar string pairs between two string sets is an essential operation in many applications, and has attracted significant attention recently in the database community. A significant challenge in similarity join is to implement an effective fuzzy match operation to find all similar string pairs which may not match exactly. In this paper, we propose a new similarity metrics, called “fuzzy token matching based similarity”, which extends token-based similarity functions (e.g., Jaccard similarity and Cosine similarity) by allowing fuzzy match between two tokens. We study the problem of similarity join using this new similarity metrics and present a signature-based method to address this problem. We propose new signature schemes and develop effective pruning techniques to improve the performance. Experimental results show that our approach achieves high efficiency and result quality, and significantly outperforms state-of-the-art methods.
Storage and Querying of E-Commerce Data

New generation of e-commerce applications require data sch emas that are constantly evolving and sparsely populated. The co nventional horizontal row representation fails to meet these re quir ments. We represent objects in a vertical format storing an o bject as a set of tuples. Each tuple consists of an object identifier and attribute name-value pair. Schema evolution is now easy. Ho wever, writing queries against this format becomes cumberso me. We create a logical horizontal view of the vertical representa tion and transform queries on this view to the vertical table. We pres ent alternative implementations and performance results that s ow the effectiveness of the vertical representation for sparse da ta. We also identify additional facilities needed in database systems to support these applications well.
Boosting with Abstention

We present a new boosting algorithm for the key scenario of binary classification with abstention where the algorithm can abstain from predicting the label of a point, at the price of a fixed cost. At each round, our algorithm selects a pair of functions, a base predictor and a base abstention function. We define convex upper bounds for the natural loss function associated to this problem, which we prove to be calibrated with respect to the Bayes solution. Our algorithm benefits from general margin-based learning guarantees which we derive for ensembles of pairs of base predictor and abstention functions, in terms of the Rademacher complexities of the corresponding function classes. We give convergence guarantees for our algorithm along with a linear-time weak-learning algorithm for abstention stumps. We also report the results of several experiments suggesting that our algorithm provides a significant improvement in practice over two confidence-based algorithms.
Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition

Like other problems in computer vision, offline handwritten Chinese character recognition (HCCR) has achieved impressive results using convolutional neural network (CNN)-based methods. However, larger and deeper networks are needed to deliver state-of-the-art results in this domain. Such networks intuitively appear to incur high computational cost, and require the storage of a large number of parameters, which render them unfeasible for deployment in portable devices. To solve this problem, we propose a Global Supervised Low-rank Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve the problems of speed and storage capacity. We design a nine-layer CNN for HCCR consisting of 3,755 classes, and devise an algorithm that can reduce the network’s computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0.21% drop in accuracy. In tests, the proposed algorithm can still surpass the best single-network performance reported thus far in the literature while requiring only 2.3 MB for storage. Furthermore, when integrated with our effective forward implementation, the recognition of an offline character image takes only 9.7 ms on a CPU. Compared with the state-of-the-art CNN model for HCCR, ∗Corresponding author Email addresses: xiaoxuefengchina@gmail.com (Xuefeng Xiao), lianwen.jin@gmail.com (Lianwen Jin)  2017
Single motor actuated peristaltic wave generator for a soft bodied worm robot

This paper presents the design and development of a single motor actuated peristaltic worm robot with three segments using a bio-inspired method of locomotion with one actuator that achieves optimised worm like peristaltic motion. Each segment consists of two solid circular disks that have a tendon connected through to the drive mechanism using a Bowden cable and a soft rubber skin that deforms under the compression of the tendon. Our hypothesis that a tuned peristaltic waveform can achieve improved performance of locomotion distance and clamping strength is proven using an initial test platform capable of demonstrating varying waveform types with multiple actuators. Three experiments were undertaken: (i) moving along a flat surface, (ii) moving through a confined tunnel (iii) moving through a confined tunnel whilst pulling a payload. Results from these experiments have identified the optimal parameters for a smart gearbox capable of achieving the same optimal peristaltic waveform signal as the more complex test platform but with only one actuator driving all three worm segments. Unlike other examples of peristaltic worm robots, this example uses a control method embedded within the mechanics of the design removing excessive number of actuators which contributes to miniaturisation, reduces power consumption and simplifies the overall design.
Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms

This paper presents our vision of Human Computer Interaction (HCI): "Tangible Bits." Tangible Bits allows users to "grasp & manipulate" bits in the center of users’ attention by coupling the bits with everyday physical objects and architectural surfaces. Tangible Bits also enables users to be aware of background bits at the periphery of human perception using ambient display media such as light, sound, airflow, and water movement in an augmented space. The goal of Tangible Bits is to bridge the gaps between both cyberspace and the physical environment, as well as the foreground and background of human activities. This paper describes three key concepts of Tangible Bits: interactive surfaces; the coupling of bits with graspable physical objects; and ambient media for background awareness. We illustrate these concepts with three prototype systems – the metaDESK, transBOARD and ambientROOM – to identify underlying research issues.
Compact UWB Bandpass Filter With Dual Notch Bands Using Open Circuited Stubs

In this letter, an ultra-wideband (UWB) bandpasss filter with multiple notch bands is presented. The UWB bandpass characteristic is achieved using a modified distributed highpass filter (HPF) and suppressing higher order harmonics of the HPF by realizing defected stepped impedance resonator. The dual band notches at 5.75 and 8.05 GHz are obtained by embedding two open stubs on the main microstrip line. The in band and out of band performance obtained from fullwave electromagnetic simulation, equivalent circuit model and measurement are in good agreement.
Robust Face Recognition via Sparse Representation

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.