nils-herrmann/clean_scirepeval huggingface dataset:
Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases

A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.
Towards Better Analysis of Deep Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.
Wide Cell Pitch LPT(II)-CSTBT™(III) technology rating up to 6500 V for low loss

In this paper, an advanced High Voltage (HV) IGBT technology, which is focused on low loss and is the ultimate device concept for HV IGBT, is presented. CSTBTTM technology utilizing “ULSI technology” and “Light Punch-Through (LPT) II technology” (i.e. narrow Wide Cell Pitch LPT(II)-CSTBT(III)) for the first time demonstrates breaking through the limitation of HV IGBT's characteristics with voltage ratings ranging from 2500 V up to 6500 V. The improved significant trade-off characteristic between on-state voltage (VCE(sat)) and turn-off loss (EOFF) is achieved by means of a “narrow Wide Cell Pitch CSTBT(III) cell”. In addition, this device achieves a wide operating junction temperature (@218 ∼ 448K) and excellent short circuit behavior with the new cell and vertical designs. The LPT(II) concept is utilized for ensuring controllable IGBT characteristics and achieving a thin N− drift layer. Our results cover design of the Wide Cell Pitch LPT(II)-CSTBT(III) technology and demonstrate high total performance with a great improvement potential.
Use of Fourier and Karhunen-Loeve Decomposition for Fast Pattern Matching With a Large Set of Templates

We present a fast pattern matching algorithm with a large set of templates. The algorithm is based on the typical template matching speeded up by the dual decomposition; the Fourier transform and the Karhunen-Loeve transform. The proposed algorithm is appropriate for the search of an object with unknown distortion within a short period. Patterns with different distortion differ slightly from each other and are highly correlated. The image vector subspace required for effective representation can be defined by a small number of eigenvectors derived by the Karhunen-Loeve transform. A vector subspace spanned by the eigenvectors is generated, and any image vector in the subspace is considered as a pattern to be recognized. The pattern matching of objects with unknown distortion is formulated as the process to extract the portion of the input image, find the pattern most similar to the extracted portion in the subspace, compute normalized correlation between them at each location in the input image, and find the location with the best score. Searching for objects with unknown distortion requires vast computation. The formulation above makes it possible to decompose highly correlated reference images into eigenvectors, as well as to decompose images in frequency domain, and to speed up the process significantly. Index Terms —Template matching, pattern matching, Karhunen-Loeve transform, Fourier transform, eigenvector. —————————— ✦ ——————————
Fully on-chip temperature, process, and voltage sensors

A process, voltage, and temperature (PVT) sensor without a voltage/current analog-to-digital converter (ADC) or bandgap reference is proposed for high accuracy, low power, and wide voltage range portable applications. Conventional temperature sensors rely on voltage/current ADC for digital output code conversion. The proposed temperature sensor generates a clock frequency proportional to the measured temperature, and converts the frequency into a corresponding digital code. The generated digital code is still under the influence of PVT variations. Two distinct sensors for voltage and process monitoring are also proposed to enhance temperature sensor environmental variation immunity. The property of zero temperature coefficient (ZTC) bias point is used to remove temperature effect. The proposed wide voltage range low power PVT sensor is designed in UMC 65nm bulk CMOS technology. It is capable of operating over a wide voltage range within 0.3V~1V. The power consumption is no more than 3.7μW at 0.3V supply voltage and a high sample rate of 10k samples/sec. The temperature error is merely −0.8~0.8ºC.
Compositional real-time scheduling framework

Our goal is to develop a compositional real-time scheduling framework so that global (system-level) timing properties can be established by composing independently (specified and) analyzed local (component-level) timing properties. The two essential problems in developing such a framework are: (1) to abstract the collective real-time requirements of a component as a single real-time requirement and (2) to compose the component demand abstraction results into the system-level real-time requirement. In our earlier work, we addressed the problems using the Liu and Layland periodic model. In this paper, we address the problems using another well-known model, a bounded-delay resource partition model, as a solution model to the problems. To extend our framework to this model, we develop an exact feasibility condition for a set of bounded-delay tasks over a bounded-delay resource partition. In addition, we present simulation results to evaluate the overheads that the component demand abstraction results incur in terms of utilization increase. We also present utilization bound results on a bounded-delay resource model.
GreenThumb: superoptimizer construction framework

Developing an optimizing compiler backend remains a laborious process, especially for nontraditional ISAs that have been appearing recently. Superoptimization sidesteps the need for many code transformations by searching for the most optimal instruction sequence semantically equivalent to the original code fragment. Even though superoptimization discovers the best machine-specific code optimizations, it has yet to become widely-used. We propose GreenThumb, an extensible framework that reduces the cost of constructing superoptimizers and provides a fast search algorithm that can be reused for any ISA, exploiting the unique strengths of enumerative, stochastic, and symbolic (SAT-solver-based) search algorithms. To extend GreenThumb to a new ISA, it is only necessary to implement an emulator for the ISA and provide some ISA-specific search utility functions.
SmartParking: A Secure and Intelligent Parking System

Parking is costly and limited in almost every major city in the world. Innovative parking systems for meeting near-term parking demand are needed. This paper proposes a novel, secure, and intelligent parking system (SmartParking) based on secured wireless network and sensor communication. From the point of users' view, SmartParking is a secure and intelligent parking service. The parking reservation is safe and privacy preserved. The parking navigation is convenient and efficient. The whole parking process will be a non-stop service. From the point of management's view, SmartParking is an intelligent parking system. The parking process can be modeled as birth-death stochastic process and the prediction of revenues can be made. Based on the prediction, new business promotion can be made, for example, on-sale prices and new parking fees. In SmartParking, new promotions can be published through wireless network. We address hardware/software architecture, implementations, and analytical models and results. The evaluation of this proposed system proves its efficiency.
A multichannel convolutional neural network for cross-language dialog state tracking

The fifth Dialog State Tracking Challenge (DSTC5) introduces a new cross-language dialog state tracking scenario, where the participants are asked to build their trackers based on the English training corpus, while evaluating them with the unlabeled Chinese corpus. Although the computer-generated translations for both English and Chinese corpus are provided in the dataset, these translations contain errors and careless use of them can easily hurt the performance of the built trackers. To address this problem, we propose a multichannel Convolutional Neural Networks (CNN) architecture, in which we treat English and Chinese language as different input channels of one single CNN model. In the evaluation of DSTC5, we found that such multichannel architecture can effectively improve the robustness against translation errors. Additionally, our method for DSTC5 is purely machine learning based and requires no prior knowledge about the target language. We consider this a desirable property for building a tracker in the cross-language context, as not every developer will be familiar with both languages.
An Analysis of the Real Interest Rate Under Regime Shifts %

Résumé This study considers the time series behavior of the U.S. real interest rate from 1961 to 1986. We provide a statistical characterization of the series using the methodology of Hamilton (1989), by allowing three possible regimes affecting both the mean and variance of the series. The results suggest that the ex-post real interest rate is essentially random around a mean that is different for the periods 1961-1973, 1973-1980 and 1980-1986. The variance of the process is also different in these episodes being higher in both the 1973-1980 and 1980-1986 sub-periods. The inflation rate series is also analyzed using a three regime framework and again our results show interesting patterns with shifts in both mean and variance. Various model selection tests are run and both an ex-ante real interest rate and an expected inflation series are constructed. Finally, we make clear how our results can explain some recent findings in the literature. Cette étude s'intéresse au comportement des séries du taux d'intérêt réel américain de 1961 à 1986. En utilisant la méthodologie d'Hamilton (1989), la modélisation statistique des séries se fait en postulant trois régimes possibles affectant la moyenne et la variance de celles-ci. Les résultats suggèrent que le taux d'intérêt réel ex-post est essentiellement un processus non corrélé et centré sur une moyenne qui diffère sur les périodes 1961-1973, 1973-1980 et 1980-1986. La variance du processus est aussi différente pour chacune de ces périodes, étant plus élevée dans les sous périodes 1973-1980 et 1980-1986. Les séries du taux d'inflation sont aussi analysées à la lumière de ce modèle à trois régimes et les résultats traduisent encore un comportement intéressant de celles-ci, avec des changements dans la moyenne et la variance. Différents tests de spécification sont utilisés et des séries, à la fois du taux d'intérêt réel ex-ante et de l'inflation anticipée, sont construites. Enfin, Il est montré comment ces résultats peuvent expliquer certaines conclusion récentes de la littérature.
Klotski: Reprioritizing Web Content to Improve User Experience on Mobile Devices

Despite web access on mobile devices becoming commonplace, users continue to experience poor web performance on these devices. Traditional approaches for improving web performance (e.g., compression, SPDY, faster browsers) face an uphill battle due to the fundamentally conflicting trends in user expectations of lower load times and richer web content. Embracing the reality that page load times will continue to be higher than user tolerance limits for the foreseeable future, we ask: How can we deliver the best possible user experience? To this end, we present KLOTSKI, a system that prioritizes the content most relevant to a user’s preferences. In designing KLOTSKI, we address several challenges in: (1) accounting for inter-resource dependencies on a page; (2) enabling fast selection and load time estimation for the subset of resources to be prioritized; and (3) developing a practical implementation that requires no changes to websites. Across a range of user preference criteria, KLOTSKI can significantly improve the user experience relative to native websites.
How Many Workers to Ask?: Adaptive Exploration for Collecting High Quality Labels

Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism to obtain labels for system development and evaluation. Successful deployment of crowdsourcing at scale involves adjusting many variables, a very important one being the number of workers needed per human intelligence task (HIT). We consider the crowdsourcing task of learning the answer to simple multiple-choice HITs, which are representative of many relevance experiments. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same HIT. A stopping rule is an algorithm that, given a HIT, decides for any given set of worker answers to stop and output an answer or iterate and ask one more worker. In contrast to other solutions that try to estimate worker performance and answer at the same time, our approach assumes the historical performance of a worker is known and tries to estimate the HIT difficulty and answer at the same time. The difficulty of the HIT decides how much weight to give to each worker's answer. In this paper we investigate how to devise better stopping rules given workers' performance quality scores. We suggest adaptive exploration as a promising approach for scalable and automatic creation of ground truth. We conduct a data analysis on an industrial crowdsourcing platform, and use the observations from this analysis to design new stopping rules that use the workers' quality scores in a non-trivial manner. We then perform a number of experiments using real-world datasets and simulated data, showing that our algorithm performs better than other approaches.
Auto Level Color Correction for Underwater Image Matching Optimization

The absorption of the light by sea water and light scattering by small particles of underwater environment has become an obstacle of underwater vision researches with camera. It gives impact to the limitation of visibility distances camera in the sea water. The research of 3D reconstruction requires image matching technique to find out the keypoints of image pairs. SIFT is one of the image matching technique where the quality of image matching depends on the quality of the image. This research proposed HSV conversion image with auto level color correction to increase the number of SIFT image matching. The experimental results show the number of image matching is increase until 4 %.
Secure QR code system

Quick Response (QR) codes are two dimensional barcodes that can be used to efficiently store small amount of data. They are increasingly used in all life fields, especially with the wide spread of smart phones which are used as QR code scanners. While QR codes have many advantages that make them very popular, there are several security issues and risks that are associated with them. Running malicious code, stealing users' sensitive information and violating their privacy and identity theft are some typical security risks that a user might be subject to in the background while he/she is just reading the QR code in the foreground. In this paper, a security system for QR codes that guarantees both users and generators security concerns is implemented. The system is backward compatible with current standard used for encoding QR codes. The system is implemented and tested using an Android-based smartphone application. It was found that the system introduces a little overhead in terms of the delay required for integrity verification and content validation.
Cold-start news recommendation with domain-dependent browse graph

Online social networks and mash-up services create opportunities to connect different web services otherwise isolated. Specifically in the case of news, users are very much exposed to news articles while performing other activities, such as social networking or web searching. Browsing behavior aimed at the consumption of news, especially in relation to the visits coming from other domains, has been mainly overlooked in previous work. To address that, we build a BrowseGraph out of the collective browsing traces extracted from a large viewlog of Yahoo News (0.5B entries), and we define the ReferrerGraph as its subgraph induced by the sessions with the same referrer domain. The structural and temporal properties of the graph show that browsing behavior in news is highly dependent on the referrer URL of the session, in terms of type of content consumed and time of consumption. We build on this observation and propose a news recommender that addresses the cold-start problem: given a user landing on a page of the site for the first time, we aim to predict the page she will visit next. We compare 24 flavors of recommenders belonging to the families of content-based, popularity-based, and browsing-based models. We show that the browsing-based recommender that takes into account the referrer URL is the best performing, achieving a prediction accuracy of 48% in conditions of heavy data sparsity.
The Theory of Characteristic Modes Revisited: A Contribution to the Design of Antennas for Modern Applications

The objective of this paper is to summarize the work that has been developed by the authors for the last several years, in order to demonstrate that the Theory of Characteristic Modes can be used to perform a systematic design of different types of antennas. Characteristic modes are real current modes that can be computed numerically for conducting bodies of arbitrary shape. Since characteristic modes form a set of orthogonal functions, they can be used to expand the total current on the surface of the body. However, this paper shows that what makes characteristic modes really attractive for antenna design is the physical insight they bring into the radiating phenomena taking place in the antenna. The resonance frequency of modes, as well as their radiating behavior, can be determined from the information provided by the eigenvalues associated with the characteristic modes. Moreover, by studying the current distribution of modes, an optimum feeding arrangement can be found in order to obtain the desired radiating behavior.
Solving Euclidean Distance Matrix Completion Problems Via Semidefinite Programming

Given a partial symmetric matrixA with only certain elements specified, the Euclidean distance matrix completion problem (EDMCP) is to find the unspecified elements of A that makeA a Euclidean distance matrix (EDM). In this paper, we follow the successful approach in [20] and solve the EDMCP by generalizing the completion problem to allow for approximate completions. In particular, we introduce a primal-dual interiorpoint algorithm that solves an equivalent (quadratic objective function) semidefinite programming problem (SDP). Numerical results are included which illustrate the efficiency and robustness of our approach. Our randomly generated problems consistently resulted in low dimensional solutions when no completion existed.
Challenges in Enterprise Search

Concerted research effort since the nineteen fifties has lead to effective methods for retrieval of relevant documents from homogeneous collections of text, such as newspaper archives, scientific abstracts and CD-ROM encyclopaedias. However, the triumph of the Web in the nineteen nineties forced a significant paradigm shift in the Information Retrieval field because of the need to address the issues of enormous scale, fluid collection definition, great heterogeneity, unfettered interlinking, democratic publishing, the presence of adversaries and most of all the diversity of purposes for which Web search may be used. Now, the IR field is confronted with a challenge of similarly daunting dimensions – how to bring highly effective search to the complex information spaces within enterprises. Overcoming the challenge would bring massive economic benefit, but victory is far from assured. The present work characterises enterprise search, hints at its economic magnitude, states some of the unsolved research questions in the domain of enterprise search need, proposes an enterprise search test collection and presents results for a small but interesting subproblem.
Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination

The proliferation of cyber-physical systems introduces the fourth stage of industrialization, commonly known as Industry 4.0. The vertical integration of various components inside a factory to implement a flexible and reconfigurable manufacturing system, i.e., smart factory, is one of the key features of Industry 4.0. In this paper, we present a smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop-floor objects such as machines, conveyers, and products. Then, we provide a classification of the smart objects into various types of agents and define a coordinator in the cloud. The autonomous decision and distributed cooperation between agents lead to high flexibility. Moreover, this kind of self-organized system leverages the feedback and coordination by the central coordinator in order to achieve high efficiency. Thus, the smart factory is characterized by a self-organized multi-agent system assisted with big data based feedback and coordination. Based on this model, we propose an intelligent negotiation mechanism for agents to cooperate with each other. Furthermore, the study illustrates that complementary strategies can be designed to prevent deadlocks by improving the agents’ decision making and the coordinator’s behavior. The simulation results assess the effectiveness of the proposed negotiation mechanism and deadlock prevention strategies.