nils-herrmann/clean_scirepeval huggingface dataset:
Boosted decision trees for word recognition in handwritten document retrieval

Recognition and retrieval of historical handwritten material is an unsolved problem. We propose a novel approach to recognizing and retrieving handwritten manuscripts, based upon word image classification as a key step. Decision trees with normalized pixels as features form the basis of a highly accurate AdaBoost classifier, trained on a corpus of word images that have been resized and sampled at a pyramid of resolutions. To stem problems from the highly skewed distribution of class frequencies, word classes with very few training samples are augmented with stochastically altered versions of the originals. This increases recognition performance substantially. On a standard corpus of 20 pages of handwritten material from the George Washington collection the recognition performance shows a substantial improvement in performance over previous published results (75% vs 65%). Following word recognition, retrieval is done using a language model over the recognized words. Retrieval performance also shows substantially improved results over previously published results on this database. Recognition/retrieval results on a more challenging database of 100 pages from the George Washington collection are also presented.
Implementing database operations using SIMD instructions

Modern CPUs have instructions that allow basic operations to be performed on several data elements in parallel. These instructions are called SIMD instructions, since they apply a single instruction to multiple data elements. SIMD technology was initially built into commodity processors in order to accelerate the performance of multimedia applications. SIMD instructions provide new opportunities for database engine design and implementation. We study various kinds of operations in a database context, and show how the inner loop of the operations can be accelerated using SIMD instructions. The use of SIMD instructions has two immediate performance benefits: It allows a degree of parallelism, so that many operands can be processed at once. It also often leads to the elimination of conditional branch instructions, reducing branch mispredictions.We consider the most important database operations, including sequential scans, aggregation, index operations, and joins. We present techniques for implementing these using SIMD instructions. We show that there are significant benefits in redesigning traditional query processing algorithms so that they can make better use of SIMD technology. Our study shows that using a SIMD parallelism of four, the CPU time for the new algorithms is from 10% to more than four times less than for the traditional algorithms. Superlinear speedups are obtained as a result of the elimination of branch misprediction effects.
Development of indoor positioning system with ultrasonic and infrared signals

Position measurement is one of the primal problems in wide range of applications such as air/land/marine vehicles tracking, automation systems, and equipment tracking in medical sector, robotics and sensor networks. Location information in outdoor application can be easily obtained from well-known systems such as GPS and GLONASS. Since GPS signal is not available in indoor area, several positioning systems have been developed for. But there is not a consensus for indoor positioning systems like the outdoor. In this study, a positioning system that uses ultrasonic and infrared signals is designed and implemented. The system is tested in 4m2 coverage range and it is observed that the maximum absolute error is less than +/-2 cm.
Learning part-of-speech taggers with inter-annotator agreement loss

In natural language processing (NLP) annotation projects, we use inter-annotator agreement measures and annotation guidelines to ensure consistent annotations. However, annotation guidelines often make linguistically debatable and even somewhat arbitrary decisions, and interannotator agreement is often less than perfect. While annotation projects usually specify how to deal with linguistically debatable phenomena, annotator disagreements typically still stem from these “hard” cases. This indicates that some errors are more debatable than others. In this paper, we use small samples of doublyannotated part-of-speech (POS) data for Twitter to estimate annotation reliability and show how those metrics of likely interannotator agreement can be implemented in the loss functions of POS taggers. We find that these cost-sensitive algorithms perform better across annotation projects and, more surprisingly, even on data annotated according to the same guidelines. Finally, we show that POS tagging models sensitive to inter-annotator agreement perform better on the downstream task of chunking.
A comparative study of classifier ensembles for bankruptcy prediction

The aim of bankruptcy prediction in the areas of data mining and machine learning is to develop an effective model which can provide the higher prediction accuracy. In the prior literature, various classification techniques have been developed and studied, in/with which classifier ensembles by combining multiple classifiers approach have shown their outperformance over many single classifiers. However, in terms of constructing classifier ensembles, there are three critical issues which can affect their performance. The first one is the classification technique actually used/adopted, and the other two are the combination method to combine multiple classifiers and the number of classifiers to be combined, respectively. Since there are limited, relevant studies examining these aforementioned disuses, this paper conducts a comprehensive study of comparing classifier ensembles by three widely used classification techniques including multilayer perceptron (MLP) neural networks, support vector machines (SVM), and decision trees (DT) based on two well-known combination methods including bagging and boosting and different numbers of combined classifiers. Our experimental results by three public datasets show that DT ensembles composed of 80–100 classifiers using the boosting method perform best. The Wilcoxon signed ranked test also demonstrates that DT ensembles by boosting perform significantly different from the other classifier ensembles. Moreover, a further study over a real-world case by a Taiwan bankruptcy dataset was conducted, which also demonstrates the superiority of DT ensembles by boosting over the others.
A Framework for Analysis of Road Accidents

The road accident data analysis use data mining and machine learning techniques, focusing on identifying factors that affect the severity of an accident. There are a variety of reasons that contribute to accidents. Some of them are internal to the driver but many are external. For example, adverse weather conditions like fog, rainfall or snowfall cause partial visibility and it may become difficult as well as risky to drive on such roads. It is expected that the findings from this paper would help civic authorities to take proactive actions on likely crash prone weather and traffic conditions.
Unbiased scalable softmax optimization

Recent neural network and language models rely on softmax distributions with an extremely large number of categories. Since calculating the softmax normalizing constant in this context is prohibitively expensive, there is a growing literature of efficiently computable but biased estimates of the softmax. In this paper we propose the first unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints (and no extra work is required at the end of each epoch). We show that our proposed unbiased methods comprehensively outperform the state-of-the-art on seven real world datasets.
Meta-Learning in Distributed Data Mining Systems: Issues and Approaches

Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some p rincipled fashion multiple classifiers computed separately over different databas es. This study, describes meta-learning and presents the JAM system (Java Ag ents for Meta-learning), an agent-based meta-learning system for large-scale data mining applications. Specifically, it identifies and addresses several impor tant desiderata for distributed data mining systems that stem from thei r additional complexity compared to centralized or host-based systems. Distributed systems may need to deal with heterogenous platforms, with multiple databases and (possibly) different schemas, with the design and imp lementation of scalable and effective protocols for communicating among the data sites, and the selective and efficient use of the information that is gat hered from other peer data sites. Other important problems, intrinsic wi thin ∗Supported in part by an IBM fellowship. data mining systems that must not be ignored, include, first, the abil ity to take advantage of newly acquired information that was not previously av ailable when models were computed and combine it with existing models, and second, the flexibility to incorporate new machine learning methods and d ta mining technologies. We explore these issues within the context of JAM and evaluate various proposed solutions through extensive empirical st udie .
Geodesic flow kernel for unsupervised domain adaptation

In real-world applications of visual recognition, many factors - such as pose, illumination, or image quality - can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods.
Static Analysis of Executables to Detect Malicious Patterns

Malicious code detection is a crucial component of any defense mechanism. In this paper, we present a unique viewpoint on malicious code detection. We regard malicious code detection as an obfuscation-deobfuscation game between malicious code writers and researchers working on malicious code detection. Malicious code writers attempt to obfuscate the malicious code to subvert the malicious code detectors, such as anti-virus software. We tested the resilience of three commercial virus scanners against code-obfuscation attacks. The results were surprising: the three commercial virus scanners could be subverted by very simple obfuscation transformations! We present an architecture for detecting malicious patterns in executables that is resilient to common obfuscation transformations. Experimental results demonstrate the efficacy of our prototype tool, SAFE (a static analyzer for executables).
Recognizing Textual Entailment via Multi-task Knowledge Assisted LSTM

Recognizing Textual Entailment (RTE) plays an important role in NLP applications like question answering, information retrieval, etc. Most previous works either use classifiers to employ elaborately designed features and lexical similarity or bring distant supervision and reasoning technique into RTE task. However, these approaches are hard to generalize due to the complexity of feature engineering and are prone to cascading errors and data sparsity problems. For alleviating the above problems, some work use LSTM-based recurrent neural network with word-by-word attention to recognize textual entailment. Nevertheless, these work did not make full use of knowledge base (KB) to help reasoning. In this paper, we propose a deep neural network architecture called Multi-task Knowledge Assisted LSTM (MKAL), which aims to conduct implicit inference with the assistant of KB and use predicate-topredicate attention to detect the entailment between predicates. In addition, our model applies a multi-task architecture to further improve the performance. The experimental results show that our proposed method achieves a competitive result compared to the previous work.
Topology adaptive graph convolutional networks

Convolution acts as a local feature extractor in convolutional neural networks (CNNs). However, the convolution operation is not applicable when the input data is supported on an irregular graph such as with social networks, citation networks, or knowledge graphs. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network that generalizes CNN architectures to graph-structured data and provides a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution, replacing the square filter for the grid-structured data in traditional CNNs. The outputs are the weighted sum of these filters’ outputs, extraction of both vertex features and strength of correlation between vertices. It can be used with both directed and undirected graphs. The proposed TAGCN not only inherits the properties of convolutions in CNN for gridstructured data, but it is also consistent with convolution in traditional signal processing. We apply TAGCN to semi-supervised learning problems for graph vertex classification; experiments on a number of data sets demonstrate that our method outperforms the existing graph convolutional neural networks and achieves stateof-the-art performance for each data set tested.
A Bayesian Classification Model for Fraud Detection over ATM Platforms Milgo , Carolyne

The banking system relies greatly on the use of Automated Teller Machines, debit and credit cards as a vital element of its payment processing systems. The efficient functioning of payment processing systems allows transactions to be completed safely and on time thereby contributing to operational performance. Customer accounts are equally exposed to risk with respect to fraud. The major security risk identified is identity and access management of customer funds. This research sought to use classification techniques to implement a novel fraud detection solution to establish legitimacy of customer transactions. It also sought to identify the main security issues encountered with use of ATM cards and establish internal control mechanisms which can be implemented to deter possibility of card fraud. The data was obtained from a local bank and was consequently preprocessed and fed into WEKA which was used to develop the training model which was to be used for classification of incoming transactions.
Jointly Modeling Deep Video and Compositional Text to Bridge Vision and Language in a Unified Framework

Recently, joint video-language modeling has been attracting more and more attention. However, most existing approaches focus on exploring the language model upon on a fixed visual model. In this paper, we propose a unified framework that jointly models video and the corresponding text sentences. The framework consists of three parts: a compositional semantics language model, a deep video model and a joint embedding model. In our language model, we propose a dependency-tree structure model that embeds sentence into a continuous vector space, which preserves visually grounded meanings and word order. In the visual model, we leverage deep neural networks to capture essential semantic information from videos. In the joint embedding model, we minimize the distance of the outputs of the deep video model and compositional language model in the joint space, and update these two models jointly. Based on these three parts, our system is able to accomplish three tasks: 1) natural language generation, and 2) video retrieval and 3) language retrieval. In the experiments, the results show our approach outperforms SVM, CRF and CCA baselines in predicting Subject-VerbObject triplet and natural sentence generation, and is better than CCA in video retrieval and language retrieval tasks.
A new ultrawideband printed monopole antenna: the planar inverted cone antenna (PICA)

A new antenna, the planar inverted cone antenna (PICA), provides ultrawideband (UWB) performance with a radiation pattern similar to monopole disk antennas , but is smaller in size. Extensive simulations and experiments demonstrate that the PICA antenna provides more than a 10:1 impedance bandwidth (for VSWR<2) and supports a monopole type omnidirectional pattern over 4:1 bandwidth. A second version of the PICA with two circular holes changes the current flow on the metal disk and extends the high end of the operating frequency range, improving the pattern bandwidth to 7:1.
Strategies and struggles with privacy in an online social networking community

Online social networking communities such as Facebook and MySpace are extremely popular. These sites have changed how many people develop and maintain relationships through posting and sharing personal information. The amount and depth of these personal disclosures have raised concerns regarding online privacy. We expand upon previous research on users’ under-utilization of available privacy options by examining users’ current strategies for maintaining their privacy, and where those strategies fail, on the online social network site Facebook. Our results demonstrate the need for mechanisms that provide awareness of the privacy impact of users’ daily interactions.
Ravana: controller fault-tolerance in software-defined networking

Software-defined networking (SDN) offers greater flexibility than traditional distributed architectures, at the risk of the controller being a single point-of-failure. Unfortunately, existing fault-tolerance techniques, such as replicated state machine, are insufficient to ensure correct network behavior under controller failures. The challenge is that, in addition to the application state of the controllers, the switches maintain hard state that must be handled consistently. Thus, it is necessary to incorporate switch state into the system model to correctly offer a "logically centralized" controller.
 We introduce Ravana, a fault-tolerant SDN controller platform that processes the control messages transactionally and exactly once (at both the controllers and the switches). Ravana maintains these guarantees in the face of both controller and switch crashes. The key insight in Ravana is that replicated state machines can be extended with lightweight switch-side mechanisms to guarantee correctness, without involving the switches in an elaborate consensus protocol. Our prototype implementation of Ravana enables unmodified controller applications to execute in a fault-tolerant fashion. Experiments show that Ravana achieves high throughput with reasonable overhead, compared to a single controller, with a failover time under 100ms.
Audio Analysis using the Discrete Wavelet Transform

The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the temporal and spectral properties of non-stationary signals li ke audio. In this paper we describe some applications of the DWT to the problem of extracting information from non-speech audio. More specifically automatic classification of various types of audio using the DWT is described and compared with other traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes of music is presented. Both synthetic and real world stimuli were used to evaluate the performance of the beat detection algorithm.  audio analysis, wavelets, classification, beat extraction
Filtered channel features for pedestrian detection

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.
Complex Embeddings for Simple Link Prediction

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction