nils-herrmann/clean_scirepeval huggingface dataset:
A Dataset for Quality Assessment of Camera Captured Document Images

With the proliferation of cameras on mobile devices there is an increased desire to image document pages as an alternative to scanning. However, the quality of captured document images is often lower than its scanned equivalent due to hardware limitations and stability issues. In this context, automatic assessment of the quality of captured images is useful for many applications. Although there has been a lot of work on developing computational methods and creating standard datasets for natural scene image quality assessment, until recently quality estimation of camera captured document images has not been given much attention. One traditional quality indicator for document images is the Optical Character Recognition (OCR) accuracy. In this work, we present a dataset of camera captured document images containing varying levels of focal-blur introduced manually during capture. For each image we obtained the character level OCR accuracy. Our dataset can be used to evaluate methods for predicting OCR quality of captured documents as well as enhancements. In order to make the dataset publicly and freely available, originals from two existing datasets University of Washington dataset and Tobacco Database were selected. We present a case study with three recent methods for predicting the OCR quality of images on our dataset.
Customised Visualisations of Linked Open Data

This paper aims to tackle on Linked Open Data (LOD) customised visualisations. The work is part of an ongoing research on interfaces and tools for helping non-programmers to easily present, analyse and support sense making over large semantic dataset. The customisation is a key aspect of our work. Producing effective customised visualisations is still difficult due to the complexity of the existing tools and the limited set of options they offer, especially to those with little knowledge in LOD and semantic data models. How can we give users full control on the primitives of the visualisation and their properties, without requiring them to master Semantic Web technologies or programming languages? The paper presents a conceptual model that can be used as a reference to build tools for generating customisable infoviews. We used it to conduct a survey on existing tools in terms of customisation capabilities. Starting from the feedback collected in this phase, we will implement these customisation features into some prototypes we are currently experimenting on.
Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process

Influence maximization is a problem of finding a small set of highly influential users, also known as seeds, in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model and the Linear Threshold (LT) model to incorporate the time delay aspect of influence diffusion among individuals in social networks. We show that timecritical influence maximization under the time-delayed IC and LT models maintains desired properties such as submodularity, which allows a greedy approximation algorithm to achieve an approximation ratio of 1− 1/e. To overcome the inefficiency of the greedy algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures and directed acyclic subgraphs, while the second one converts the problem to one in the original models and then applies existing fast heuristic algorithms to it. Our simulation results demonstrate that our algorithms achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing fast heuristics that disregard the deadline constraint and delays in diffusion.  The copyright of that conference version is held by the Association for the Advancement of Artificial Intelligence (AAAI). †Part of this work was done while Wei Lu was a visiting student at Microsoft Research Asia. Support in part by The Natural Sciences and Engineering Research Council of Canada (NSERC). ‡This work was done while Ning Zhang was a visiting student at Microsoft Research Asia. 1 ar X iv :1 20 4. 30 74 v3 [ cs .S I] 1 3 Ju l 2 01 5
Opinion Dynamics and the Evolution of Social Power in Influence Networks

This paper studies the evolution of self appraisal, social power and interpersonal influences for a group of individuals who discuss and form opinions about a sequence of issues. Our empirical model combines the averaging rule by DeGroot to describe opinion formation processes and the reflected appraisal mechanism by Friedkin to describe the dynamics of individuals’ self appraisal and social power. Given a set of relative interpersonal weights, the DeGroot-Friedkin model predicts the evolution of the influence network governing the opinion formation process. We provide a rigorous mathematical formulation of the influence network dynamics, characterize its equilibria and establish its convergence properties for all possible structures of the relative interpersonal weights and corresponding eigenvector centrality scores. The model predicts that the social power ranking among individuals is asymptotically equal to their centrality ranking, that social power tends to accumulate at the top of the hierarchy, and that an autocratic (resp. democratic) power structure arises when the centrality scores are maximally non-uniform (resp. uniform).
A carpet cloak for visible light.

We report an invisibility carpet cloak device, which is capable of making an object undetectable by visible light. The cloak is designed using quasi conformal mapping and is fabricated in a silicon nitride waveguide on a specially developed nanoporous silicon oxide substrate with a very low refractive index (n<1.25). The spatial index variation is realized by etching holes of various sizes in the nitride layer at deep subwavelength scale creating a local effective medium index. The fabricated device demonstrates wideband invisibility throughout the visible spectrum with low loss. This silicon nitride on low index substrate can also be a general scheme for implementation of transformation optical devices at visible frequencies.
On the Robust Control of Buck-Converter DC-Motor Combinations

The concepts of active disturbance rejection control and flatness-based control are used in this paper to regulate the response of a dc-to-dc buck power converter affected by unknown, exogenous, time-varying load current demands. The generalized proportional integral observer is used to estimate and cancel the time-varying disturbance signals. A key element in the proposed control for the buck converter-dc motor combination is that even if the control input gain is imprecisely known, the control strategy still provides proper regulation and tracking. The robustness of this method is further extended to the case of a double buck topology driving two different dc motors affected by different load torque disturbances. Simulation results are provided.
On the Design of Practical Fault-Tolerant SDN Controllers

The increase in the number of SDN-based deployments in production networks is triggering the need to consider fault-tolerant designs of controller architectures. Commercial SDN controller solutions incorporate fault tolerance, but there has been little discussion in the SDN community on the design of such systems and the tradeoffs involved. To fill this gap, we present a by-construction design of a fault-tolerant controller, and materialize it by proposing and formalizing a practical architecture for small to medium-sized networks. A central component of our design is a replicated shared database that stores all network state. Contrary to the more common primary-backup approaches, the proposed design guarantees a smooth transition in case of failures and avoids the need of an additional coordination service. Our preliminary results show that the performance of our solution fulfils the demands of the target networks. We hope this work to be a first step in what we consider a necessary discussion on how to build robust SDNs.
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Álvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.
Leaving so soon?: understanding and predicting web search abandonment rationales

Users of search engines often abandon their searches. Despite the high frequency of Web search abandonment and its importance to Web search engines, little is known about why searchers abandon beyond that it can be for good or bad reasons. In this paper, we ex-tend previous work by studying search abandonment using both a retrospective survey and an in-situ method that captures aban-donment rationales at abandonment time. We show that although satisfaction is a common motivator for abandonment, one-in-five abandonment instances does not relate to satisfaction. We also studied the automatic prediction of the underlying reason for ob-served abandonment. We used features of the query and the results, interaction with the result page (e.g., cursor movements, scrolling, clicks), and the full search session. We show that our classifiers can learn to accurately predict the reasons for observed search abandonment. Such accurate predictions help search providers estimate user satisfaction for queries without clicks, affording a more complete understanding of search engine performance.
Non-psychotropic plant cannabinoids: new therapeutic opportunities from an ancient herb.

Delta(9)-tetrahydrocannabinol binds cannabinoid (CB(1) and CB(2)) receptors, which are activated by endogenous compounds (endocannabinoids) and are involved in a wide range of physiopathological processes (e.g. modulation of neurotransmitter release, regulation of pain perception, and of cardiovascular, gastrointestinal and liver functions). The well-known psychotropic effects of Delta(9)-tetrahydrocannabinol, which are mediated by activation of brain CB(1) receptors, have greatly limited its clinical use. However, the plant Cannabis contains many cannabinoids with weak or no psychoactivity that, therapeutically, might be more promising than Delta(9)-tetrahydrocannabinol. Here, we provide an overview of the recent pharmacological advances, novel mechanisms of action, and potential therapeutic applications of such non-psychotropic plant-derived cannabinoids. Special emphasis is given to cannabidiol, the possible applications of which have recently emerged in inflammation, diabetes, cancer, affective and neurodegenerative diseases, and to Delta(9)-tetrahydrocannabivarin, a novel CB(1) antagonist which exerts potentially useful actions in the treatment of epilepsy and obesity.
Progressive Gaussian filtering using explicit likelihoods

In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular Nonlinear Kalman Filters, e.g., the UKF, we do not rely on linearizing the measurement model. Instead, we take up the Gaussian progressive filtering approach introduced by the PGF 42 but explicitly rely on likelihood functions. Progression means, we incorporate the information of a new measurement gradually into the state estimate. The advantages of this filtering method are on the one hand the avoidance of sample degeneration and on the other hand an adaptive determination of the number of likelihood evaluations required for each measurement update. By this means, less informative measurements can be processed quickly, whereas measurements containing much information automatically receive more emphasis by the filter. These properties allow the new filter to cope with the demanding problem of very narrow likelihood functions in an efficient way.
E-TCAM: An Efficient SRAM-Based Architecture for TCAM

Ternary content addressable memories (TCAMs) perform high-speed search operation in a deterministic time. However, when compared with static random access memories (SRAMs), TCAMs suffer from certain limitations such as lowstorage density, relatively slow access time, low scalability, complex circuitry, and higher cost. One fundamental question is that can we utilize SRAM to combine it with additional logic to achieve the TCAM functionality? This paper proposes an efficient memory architecture, called E-TCAM, which emulates the TCAM functionality with SRAM. E-TCAM logically divides the classical TCAM table along columns and rows into hybrid TCAM subtables and then maps them to their corresponding memory blocks. During search operation, the memory blocks are accessed by their corresponding subwords of the input word and a match address is produced. An example design of 512×36 of E-TCAM has been successfully implemented on Xilinx Virtex-5, Virtex-6, and Virtex-7 field-programmable gate arrays (FPGAs). FPGA implementation results show that E-TCAM obtains 33.33 % reduction in block-RAMs, 71.07 % in slice registers, 77.16 % in lookup tables, 53.54 % in energy/bit/search, and offers 63.03 % improvement in speed, compared with the best available SRAM-based TCAM designs. Z. Ullah (B) · M. K. Jaiswal · R. C. C. Cheung Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong e-mail: mr.ullahzahid@gmail.com; zullah2-c@my.cityu.edu.hk M. K. Jaiswal e-mail: mkjaiswal2-c@my.cityu.edu.hk R. C. C. Cheung e-mail: r.cheung@cityu.edu.hk Circuits Syst Signal Process
Cryptanalysis of Microsoft's Point-to-Point Tunneling Protocol (PPTP)

The Point-to-Point Tunneling Protocol (PPTP) is used to secure PPP connections over TCP/IP links. In this paper we analyze Microsoft's Windows NT implementation of PPTP. We show how to break both the challenge/response authentication protocol (Microsoft CHAP) and the RC4 encryption protocol (MPPE), as well as how to attack the control channel in Microsoft's implementation. These attacks do not necessarily break PPTP, but only Microsoft's implementation of the protocol.
Human-Computer Interaction for Complex Pattern Recognition Problems

We review some applications of human-computer interaction that alleviate the complexity of visual recognition by partitioning it into human and machine tasks to exploit the differences between human and machine capabilities. Human involvement offers advantages, both in the design of automated pattern classification systems, and at the operational level of some image retrieval and classification tasks. Recent development of interactive systems has benefited from the convergence of computer vision and psychophysics in formulating visual tasks as computational processes. Computer-aided classifier design and exploratory data analysis are already well established in pattern recognition and machine learning, but interfaces and functionality are improving. On the operational side, earlier recognition systems made use of human talent only in preprocessing and in coping with rejects. Most current content-based image retrieval systems make use of relevance feedback without direct image interaction. In contrast, some visual object classification systems can exploit such interaction. They require, however, a domain-specific visible model that makes sense to both human and computer.
Electronically Reconfigurable Microwave Bandpass Filter

A new class of reconfigurable bandpass filter based on parallel-coupled switched delay line approach is presented in this paper. The approach allows both center frequency and passband bandwidth to be reconfigured electronically. In contrast to conventional approach, the effects of the switch losses are relatively insignificant and can be treated as an auxiliary effect and therefore the filter has low passband loss. Despite the loss performance, the filter provides excellent linearity performance with large power handling capability. Theoretical analysis is presented and the feasibility of the approach has been experimentally verified with microstrip circuit prototypes. The first filter prototype provides narrowband quasi-elliptic filter response with constant bandwidth (50-MHz) tuning across 45% tuning range. The minimum stopband rejection level is 45 dB. The second filter possesses passband-width 3:1 ratio tunability. The third filter provides both functionalities which allow center frequency and passband bandwidth to be reconfigured. All filters have a < 4-dB passband loss with an OIP3 of >20 dBm and are immune from power saturation effect. The measured results demonstrate improved performance in terms of linear and nonlinear characteristic compared with prior works.
Phase-shifted full-bridge PWM converter with clamping diodes and current transformer

Clamping diodes have been introduced into the zero-voltage-switched (ZVS) phase-shifted full-bridge (PSFB) pulse-width-modulated (PWM) converters to eliminate the voltage oscillation across the output rectifier diodes. However, the clamping diodes suffer from serious reverse recovery problem in output inductor discontinuous-current-mode (DCM) operation, impairing the reliability of the converter. A novel PSFB converter with clamping diodes and current transformer (CT) is proposed. The operation principles of the novel converter operating in output inductor CCM and DCM are analyzed in detail. The operation condition for the clamping diodes, the design considerations of the introduced CT and the output inductor are also given. Three 1200 W ZVS PSFB dc/dc modules, one being the conventional converter without CT, and the other two being the novel converters with different winding ratio of CT, are built for verification and comparison. Experimental results verify the effectiveness of the analysis and prove that the proposed converter has better reliability and higher efficiency compared with the conventional one.
An introduction to convex optimization for communications and signal processing

Convex optimization methods are widely used in the design and analysis of communication systems and signal processing algorithms. This tutorial surveys some of recent progress in this area. The tutorial contains two parts. The first part gives a survey of basic concepts and main techniques in convex optimization. Special emphasis is placed on a class of conic optimization problems, including second-order cone programming and semidefinite programming. The second half of the survey gives several examples of the application of conic programming to communication problems. We give an interpretation of Lagrangian duality in a multiuser multi-antenna communication problem; we illustrate the role of semidefinite relaxation in multiuser detection problems; we review methods to formulate robust optimization problems via second-order cone programming techniques
Smart Farm : Extending Automation To The Farm Level

With the advent of technology, the world around us is getting automated. Automatic systems are being favored over manual systems, as they are energy efficient and minimize the need for tedious manual labor. With agriculture being the primary economic sector of India and other developing countries, it is essential to automate it in order to increase efficiency. A typical farm requires a lot of labor. Automation can proficiently moderate the amount of manual labor, and make farming easier and faster, leading to more agricultural growth. The concept of automation is extended to the agricultural farms and farm houses. Numerous aspects of the farm are automated, which include auto-irrigation cycles and secure temperature controlled enclosures for livestock and farm products. In our paper, we implement automatic lighting system, auto-sprinkler system, in-house temperature control and security for farm houses. As temperature and motion sensitive devices will only work when required, such a system conserves energy effectively. The paper also presents features to enhance the security of the farm. Energy efficient farm automation is the need of the hour in an agro-based economy.
Recurrent Memory Array Structures Technical Report

The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves stateof-the-art performance on character level text prediction achieving 1.402 BPC† on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.
Entailment above the word level in distributional semantics

We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|=some dogs) to entailment involving unseen quantifiers (all cats|=several cats). Moreover, nominal and quantifier phrase entailment appears to be cued by different distributional correlates, as predicted by the type-based view of entailment in formal semantics.
A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research

A review of the literature suggests that few studies use formative indicator measurement models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective measurement models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of measurement model misspecification in the field, (d ) estimate the extent to which measurement model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for modeling formative indicator constructs.