nils-herrmann/clean_scirepeval huggingface dataset:
A Fine-Grained Approach to Scene Text Script Identification

This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.
Performance Optimization of Virtual Keyboards

Text entry has been a bottleneck of nontraditional computing devices. One of the promising methods is the virtual keyboard for touch screens. Correcting previous estimates on virtual keyboard efficiency in the literature, we estimated the potential performance of the existing QWERTY, FITALY, and OPTI designs of virtual keyboards to be in the neighborhood of 28, 36, and 38 words per minute (wpm), respectively. This article presents 2 quantitative design techniques to search for virtual keyboard layouts. The first technique simulated the dynamics of a keyboard with digraph springs between keys, which produced a Hooke keyboard with 41.6 wpm movement efficiency. The second technique used a Metropolis random walk algorithm guided by a “Fitts-digraph energy” objective function that quantifies the movement efficiency of a virtual keyboard. This method produced various Metropolis keyboards with different HUMAN-COMPUTER INTERACTION, 2002, Volume 17, pp. 89–XXX  Shumin Zhai is a human–computer interaction researcher with an interest in inventing and analyzing interaction methods and devices based on human performance insights and experimentation; he is a Research Staff Member in the User Sciences and Experience Research Department of the IBM Almaden Research Center. Michael Hunter is a graduate student of Computer Science at Brigham Young University; he is interested in designing graphical and haptic user interfaces. Barton A. Smith is an experimental scientist with an interest in machines, people, and society; he is manager of the Human Interface Research Group at the IBM Almaden Research Center. shapes and structures with approximately 42.5 wpm movement efficiency, which was 50% higher than QWERTY and 10% higher than OPTI. With a small reduction (41.16 wpm) of movement efficiency, we introduced 2 more design objectives that produced the ATOMIK layout. One was alphabetical tuning that placed the keys with a tendency from A to Z so a novice user could more easily locate the keys. The other was word connectivity enhancement so the most frequent words were easier to find, remember, and type.
Towards a core ontology for robotics and automation

In this article we introduce the main notions of our core ontology for the robotics and automation field, one of first results of the newly formed IEEE-RAS Working Group, named Ontologies for Robotics and Automation. It aims to provide a common ground for further ontology development in Robotics and Automation. Furthermore, we will discuss the main core ontology definitions as well as the ontology development process employed.
Optimal control strategies and cost-effectiveness analysis of a malaria model

The aim of this paper is to investigate the effectiveness and cost-effectiveness of three malaria preventive measures (use of treated bednets, spray of insecticides and a possible treatment of infective humans that blocks transmission to mosquitoes). For this, we consider a mathematical model for the transmission dynamics of the disease that includes these measures. We first consider the constant control parameters' case, we calculate the basic reproduction number and investigate the existence and stability of equilibria; the model is found to exhibit backward bifurcation. We then assess the relative impact of each of the constant control parameters measures by calculating the sensitivity index of the basic reproductive number to the model's parameters. In the time-dependent constant control case, we use Pontryagin's Maximum Principle to derive necessary conditions for the optimal control of the disease. We also calculate the Infection Averted Ratio (IAR) and the Incremental Cost-Effectiveness Ratio (ICER) to investigate the cost-effectiveness of all possible combinations of the three control measures. One of our findings is that the most cost-effective strategy for malaria control, is the combination of the spray of insecticides and treatment of infective individuals. This strategy requires a 100% effort in both treatment (for 20 days) and spray of insecticides (for 57 days). In practice, this will be extremely difficult, if not impossible to achieve. The second most cost-effective strategy which consists of a 100% use of treated bednets and 87% treatment of infective individuals for 42 and 100 days, respectively, is sustainable and therefore preferable.
Localized air-conditioning with occupancy control in an open office

Conditioning and ventilating open office spaces requires innovations as new buildings aim to be more intelligent, energy efficient and healthy. This paper explores the possibility of using a localized airflow to divide an open cubicle office into zones without partition walls. Computational fluid dynamics (CFD) model was used to simulate localized airflow in a cubicle office and both the energy and the indoor air quality concerns were addressed. The findings suggest that (1) localized airflow is plausible for zoning purposes, (2) localized airflow can result in both temperature and pollutant concentration segregations, (3) temperature segregations provide possible energy savings if coupled with occupancy-based HVAC control, and finally (4) limited air mixing between zones provide a novel way for better ventilation and indoor contaminant control.
Network-Based Visualization of Opinion Mining and Sentiment Analysis on Twitter

Visualizing the result of users' opinion mining on twitter using social network graph can play a crucial role in decision-making. Available data visualizing tools, such as NodeXL, use a specific file format as an input to construct and visualize the social network graph. One of the main components of the input file is the sentimental score of the users' opinion. This motivates us to develop a free and open source system that can take the opinion of users in raw text format and produce easy-to-interpret visualization of opinion mining and sentiment analysis result on a social network. We use a public machine learning library called LingPipe Library to classify the sentiments of users' opinion into positive, negative and neutral classes. Our proposed system can be used to analyze and visualize users' opinion on the network level to determine sub-social structures (sub-groups). Moreover, the proposed system can also identify influential people in the social network by using node level metrics such as betweenness centrality. In addition to the network level and node level analysis, our proposed method also provides an efficient filtering mechanism by either time and date, or the sentiment score. We tested our proposed system using user opinions about different Samsung products and related issues that are collected from five official twitter accounts of Samsung Company. The test results show that our proposed system will be helpful to analyze and visualize the opinion of users at both network level and node level.
Efficient Subgraph Skyline Search Over Large Graphs

Subgraph search is very useful in many real-world applications. However, users may be overwhelmed by the masses of matches. In this paper, we propose subgraph skyline search problem, denoted as S3, to support more complicated analysis over graph data. Specifically, given a large graph G and a query graph q, we want to find all the subgraphs g in G, such that g is graph isomorphic to q and not dominated by any other subgraphs. In order to improve the efficiency, we devise a hybrid feature encoding incorporating both structural and numeric features. Moreover, we present some optimizations based on partitioning strategy. We also propose a skylayer index to facilitate the dynamic subgraph skyline computation. Extensive experiments over real dataset confirm the effectiveness and efficiency of our algorithm.
A Dual-Core Multi-Threaded Xeon Processor with 16MB L3 Cache

A dual-core 64b Xeonreg MP processor is implemented in a 65nm 8M process. The 435mm2 die has 1.328B transistors. Each core has two threads and a unified 1MB L2 cache. The 16MB unified, 16-way set-associative L3 cache implements both sleep and shut-off leakage reduction modes
Learning to live with the pain: acceptance of pain predicts adjustment in persons with chronic pain

When patients find their pain unacceptable they are likely to attempt to avoid it at all costs and seek readily available interventions to reduce or eliminate it. These efforts may not be in their best interest if the consequences include no reductions in pain and many missed opportunities for more satisfying and productive functioning. The purpose of this study was to examine acceptance of pain. One hundred and sixty adults with chronic pain provided responses to a questionnaire assessing acceptance of pain, and a number of other questionnaires assessing their adjustment to pain. Correlational analyses showed that greater acceptance of pain was associated with reports of lower pain intensity, less pain-related anxiety and avoidance, less depression, less physical and psychosocial disability, more daily uptime, and better work status. A relatively low correlation between acceptance and pain intensity showed that acceptance is not simply a function of having a low level of pain. Regression analyses showed that acceptance of pain predicted better adjustment on all other measures of patient function, independent of perceived pain intensity. These results are preliminary. Further study will be needed to show for whom and under what circumstances, accepting some aspects of the pain experience may be beneficial.
Modern information technology in an old workforce: Toward a strategic research agenda

As the workforce ages rapidly in industrialized countries, a phenomenon known as the graying of the workforce, new challenges arise for firms as they have to juggle this dramatic demographical change (Trend 1) in conjunction with the proliferation of increasingly modern information and communication technologies (ICTs) (Trend 2). Although these two important workplace trends are pervasive, their interdependencies have remained largely unexplored. While Information Systems (IS) research has established the pertinence of age to IS phenomena from an empirical perspective, it has tended to model the concept merely as a control variable with limited understanding of its conceptual nature. In fact, even the few IS studies that used the concept of age as a substantive variable have mostly relied on stereotypical accounts alone to justify their age-related hypotheses. Further, most of these studies have examined the role of age in the same phenomenon (i.e., initial adoption of ICTs), implying a marked lack of diversity with respect to the phenomena under investigation. Overall, IS research has yielded only limited insight into the role of age in phenomena involving ICTs. In this essay, we argue for the importance of studying agerelated impacts more carefully and across various IS phenomena, and we enable such research by providing a research agenda that IS scholars can use. In doing so, we hope that future research will further both our empirical and conceptual understanding of the managerial challenges arising from the interplay of a graying workforce and rapidly evolving ICTs.
Visualization Techniques for Digital forensics : A Survey Mr .

Digital crimes is big problem due to large numbers of data access and insufficient attack analysis techniques so there is the need for improvements in existing digital forensics techniques. With growing size of storage capacity these digital forensic investigations are getting more difficult. Visualization allows for displaying large amounts of data at once. Integrated visualization of data distribution bars and rules, visualization of behaviour and comprehensive analysis, maps allow user to analyze different rules and data at different level, with any kind of anomaly in data. Data mining techniques helps to improve the process of visualization. These papers give comprehensive review on various visualization techniques with various anomaly detection techniques.
Key elements to enable millimeter wave communications for 5G wireless systems

Current cellular spectrum at below 3 GHz bands is experiencing severe shortage and cannot keep up with the dramatic proliferation of mobile traffic in the near future, requiring the search for innovative solutions to enable the 5G era. mmWave communications, with a possible gigabit-per-second data rate, have attracted great attention as a candidate for 5G broadband cellular communication networks. However, a complete characterization of mmWave links for 5G wireless networks still remains elusive and there are many challenges and research areas that need to be addressed. In this work we discuss several key elements to enable mmWave communications in 5G: · Channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous and rain are explained. · The hybrid (digital plus analog) beamforming architecture in mmWave system is discussed. · The blockage effect in mmWave communications due to penetration loss and possible approaches are presented. · The application of mmWave transmission with narrow beams in non-orthogonal device-todevice communication is proposed. · mmWave transmission in the booster cell of heterogeneous anchor-booster networks. · mmWave transmission for small cell backhaul is further discussed.
Direct versus indirect line of sight (LOS) stabilization

Two methods are analyzed for inertially stabilizing the pointing vector defining the line of sight (LOS) of a two-axis gimbaled laser tracker. Mounting the angular rate and acceleration sensors directly on the LOS axes is often used for precision pointing applications. This configuration impacts gimbal size, and the sensors must be capable of withstanding high angular slew rates. With the other stabilization method, sensors are mounted on the gimbal base, which alleviates some issues with the direct approach but may be less efficient, since disturbances are not measured in the LOS coordinate frame. This paper investigates the impact of LOS disturbances and sensor noise on the performance of each stabilization control loop configuration. It provides a detailed analysis of the mechanisms by which disturbances are coupled to the LOS track vector for each approach, and describes the advantages and disadvantages of each. It concludes with a performance comparison based upon simulated sensor noise and three sets of platform disturbance inputs ranging from mild to harsh disturbance environments.
Vocabulary Selection Strategies for Neural Machine Translation

Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence. Recent work on improving the efficiency of neural translation models adopted a similar strategy by restricting the output vocabulary to a subset of likely candidates given the source. In this paper we experiment with context and embedding-based selection methods and extend previous work by examining speed and accuracy trade-offs in more detail. We show that decoding time on CPUs can be reduced by up to 90% and training time by 25% on the WMT15 English-German and WMT16 English-Romanian tasks at the same or only negligible change in accuracy. This brings the time to decode with a state of the art neural translation system to just over 140 words per seconds on a single CPU core for English-German.
Optimised De Bruijn patterns for one-shot shape acquisition

Coded structured light is an optical technique based on active stereovision which allows shape acquisition. By projecting a suitable set of light patterns onto the surface of an object and capturing images with a camera, a large number of correspondences can be found and 3D points can be reconstructed by means of triangulation. One-shot techniques are based on projecting an unique pattern so that moving objects can be measured. A major group of techniques in this field define coloured multi-slit or stripe patterns in order to obtain dense reconstructions. The former type of patterns is suitable for locating intensity peaks in the image while the latter is aimed to locate edges. In this paper, we present a new way to design coloured stripe patterns so that both intensity peaks and edges can be located without loss of accuracy and reducing the number of hue levels included in the pattern. The results obtained by the new pattern are quantitatively and qualitatively compared to similar techniques. These results also contribute to a comparison between the peak-based and edge-based reconstruction strategies. q 2005 Elsevier B.V. All rights reserved.
Learning a Parametric Embedding by Preserving Local Structure

The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on three datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.
Directivity Improvement of Vivaldi Antenna Using Double-Slot Structure

The conventional Vivaldi antenna is known for its ultrawideband characteristic, but low directivity. In order to improve the directivity, a double-slot structure is proposed to design a new Vivaldi antenna. The two slots are excited in uniform amplitude and phase by using a T-junction power divider. The double-slot structure can generate plane-like waves in the E-plane of the antenna. As a result, directivity of the double-slot Vivaldi antenna is significantly improved by comparison to a conventional Vivaldi antenna of the same size. The measured results show that impedance bandwidth of the double-slot Vivaldi antenna is from 2.5 to 15 GHz. Gain and directivity of the proposed antenna is considerably improved at the frequencies above 6 GHz. Furthermore, the main beam splitting at high frequencies of the conventional Vivaldi antenna on thick dielectric substrates is eliminated by the double-slot structure.
A state of the art in structured light patterns for surface profilometry

Shape reconstruction using coded structured light is considered one of the most reliable techniques to recover object surfaces. Having a calibrated projector–camera pair, a light pattern is projected onto the scene and imaged by the camera. Correspondences between projected and recovered patterns are found and used to extract 3D surface information. This paper presents an up-to-date review and a new classification of the existing techniques. Some of these techniques have been implemented and compared, obtaining both qualitative and quantitative results. The advantages and drawbacks of the different patterns and their potentials are discussed. & 2010 Elsevier Ltd. All rights reserved.
Understanding User Revisions When Using Information Systems Features: Adaptive System Use and Triggers

Beaudry and Pinsonneault (2005) IT related coping behaviors System users choose different adaptation strategies based on a combination of primary appraisal (i.e., a user’s assessment of the expected consequences of an IT event) and secondary appraisal (i.e., a user’s assessment of his/her control over the situation). Users will perform different actions in response to a combination of cognitive and behavioral efforts, both of which have been categorized as either problemor emotion-focused. Whole
Heterogeneous Network Embedding via Deep Architectures

Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination of heterogeneous contents and structures. The creation of a multidimensional embedding of such data opens the door to the use of a wide variety of off-the-shelf mining techniques for multidimensional data. Despite the importance of this problem, limited efforts have been made on embedding a network of scalable, dynamic and heterogeneous data. In such cases, both the content and linkage structure provide important cues for creating a unified feature representation of the underlying network. In this paper, we design a deep embedding algorithm for networked data. A highly nonlinear multi-layered embedding function is used to capture the complex interactions between the heterogeneous data in a network. Our goal is to create a multi-resolution deep embedding function, that reflects both the local and global network structures, and makes the resulting embedding useful for a variety of data mining tasks. In particular, we demonstrate that the rich content and linkage information in a heterogeneous network can be captured by such an approach, so that similarities among cross-modal data can be measured directly in a common embedding space. Once this goal has been achieved, a wide variety of data mining problems can be solved by applying off-the-shelf algorithms designed for handling vector representations. Our experiments on real-world network datasets show the effectiveness and scalability of the proposed algorithm as compared to the state-of-the-art embedding methods.
Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories

We develop a framework to incorporate unlabeled data in the Error-Correcting Output Coding (ECOC) setup by decomposing multiclass problems into multiple binary problems and then use Co-Training to learn the individual binary classification problems. We show that our method is especially useful for classification tasks involving a large number of categories where Co-training doesn’t perform very well by itself and when combined with ECOC, outperforms several other algorithms that combine labeled and unlabeled data for text classification in terms of accuracy, precision-recall tradeoff, and efficiency.