nils-herrmann/clean_scirepeval huggingface dataset:
A novel classification method for paper-reviewer recommendation

Reviewer recommendation problem in the research field usually refers to invite experts to comment on the quality of papers, proposals, etc. How to effectively and accurately recommend reviewers for the submitted papers and proposals is a meaningful and still tough task. At present, many unsupervised recommendation methods have been researched to solve this task. In this paper, a novel classification method named Word Mover’s Distance–Constructive Covering Algorithm (WMD–CCA, for short) is proposed to solve the reviewer recommendation problem as a classification issue. A submission or a reviewer is described by some tags, such as keywords, research interests, and so on. First, the submission or the reviewer is represented as some vectors by a word embedding method. That is to say, each tag describing a submission or a reviewer is represented as a vector. Second, the Word Mover’s Distance (WMD, for short) method is used to measure the minimum distances between submissions and reviewers. Actually, the papers usually have research field information, and utilizing them well might improve the reviewer recommendation accuracy. So finally, the reviewer recommendation task is transformed into a classification problem which is solved by a supervised learning method- Constructive Covering Algorithm (CCA, for short). Comparative experiments are conducted with 4 public datasets and a synthetic dataset from Baidu Scholar, which show that the proposed method WMD–CCA effectively solves the reviewer recommendation task as a classification issue and improves the recommendation accuracy.
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution

The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain different types of information which make different contributions to image reconstruction. However, most CNN-based models lack discriminative ability for different types of information and deal with them equally, which results in the representational capacity of the models being limited. On the other hand, as the depth of neural networks grows, the long-term information coming from preceding layers is easy to be weaken or lost in late layers, which is adverse to super-resolving image. To capture more informative features and maintain long-term information for image superresolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features. In each FMM module, we construct a set of channelwise and spatial attention residual (CSAR) blocks and stack them in a chain structure to dynamically modulate multi-level features in a global-and-local manner. This feature modulation strategy enables the high contribution information to be enhanced and the redundant information to be suppressed. Meanwhile, for long-term information persistence, a gated fusion (GF) node is attached at the end of the FMM module to adaptively fuse hierarchical features and distill more effective information via the dense skip connections and the gating mechanism. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over the stateof-the-art methods.
Improved Microaneurysm Detection using Deep Neural Networks

In this work, we propose a novel microaneurysm (MA) detection for early dieabetic ratinopathy screening using color fundus images. Since MA usually the first lesions to appear as a indicator of diabetic retinopathy, accurate detection of MA is necessary for treatment. Each pixel of the image is classified as either MA or non-MA using deep neural network with dropout training procedure using maxout activation function. No preprocessing step or manual feature extraction is required. Substantial improvements over standard MA detection method based on pipeline of preprocessing, feature extraction, classification followed by postprocessing is achieved. The presented method is evaluated in publicly available Retinopathy Online Challenge (ROC) and Diaretdb1v2 database and achieved state-of-the-art accuracy.
Clustering Time Series Using Unsupervised-Shapelets

Time series clustering has become an increasingly important research topic over the past decade. Most existing methods for time series clustering rely on distances calculated from the entire raw data using the Euclidean distance or Dynamic Time Warping distance as the distance measure. However, the presence of significant noise, dropouts, or extraneous data can greatly limit the accuracy of clustering in this domain. Moreover, for most real world problems, we cannot expect objects from the same class to be equal in length. As a consequence, most work on time series clustering only considers the clustering of individual time series "behaviors," e.g., individual heart beats or individual gait cycles, and contrives the time series in some way to make them all equal in length. However, contriving the data in such a way is often a harder problem than the clustering itself. In this work, we show that by using only some local patterns and deliberately ignoring the rest of the data, we can mitigate the above problems and cluster time series of different lengths, i.e., cluster one heartbeat with multiple heartbeats. To achieve this we exploit and extend a recently introduced concept in time series data mining called shapelets. Unlike existing work, our work demonstrates for the first time the unintuitive fact that shapelets can be learned from unlabeled time series. We show, with extensive empirical evaluation in diverse domains, that our method is more accurate than existing methods. Moreover, in addition to accurate clustering results, we show that our work also has the potential to give insights into the domains to which it is applied.
Modeling HTTP/2 Speed from HTTP/1 Traces

With the standardization of HTTP/2, content providers want to understand the benefits and pitfalls of transitioning to the new standard. Using a large dataset of HTTP/1.1 resource timing data from production traffic on Akamai’s CDN, and a model of HTTP/2 behavior, we obtain the distribution of performance differences between the protocol versions for nearly 280,000 downloads. We find that HTTP/2 provides significant performance improvements in the tail, and, for websites for which HTTP/2 does not improve median performance, we explore how optimizations like prioritization and push can improve performance, and how these improvements relate to page structure.
An Algebraic Theory of Graph Reduction

Wc show how membership in classes of graphs definable m monwhc second-order ]oglc and of bounded treewldth can be decided by finite sets of terminating reduction rules. The method is constructive in the sense that wc describe an algorlthm that wdl produce, from J formula in monxhc second-order Ioglc and an mleger k such that the class dcfmed by the formul~ IS of treewidth s k, a set of rewrite rules that rcducxs any member of the elms to one of’ firrltely many graphs, in a number of steps bounded by the size c~f the graph. This rcductmn syjtem ymlds an algorlthm that runs m time linear m the size of the graph. We illustrate our results with rcductlon systems that recognux some families of outerplanar and planar graphs. Categories .ind  Nonnurnerical Algorithms and Pr[>blems—cotyz/] ~trczt~o/zs O;Zdzscrctc strut tztrcs; F.-1 3 [Mathematical Logic and Formal Languages]; Formal Languages-ckmscs defu~ed by ~runzn~ars c~ruutomafa. G 2.2 [Discrete Mathematics]; Graph Thco~–grup/~ alcqortrhms, trees General Terms Algorithm\, Languages, Perform~nce. Theory Additlonai
Improving Duckworth lewis method by using machine learning

There has been lot of research has been done in the area of sports with prediction. Machine learning algorithm has been applied on most of the data to predict its outcome. In cricket also there are many research has been done to find out the outcome of the match. But there are a special case in the cricket where statistic plays an important role. When the cricket match is interrupted by the rain and then again it starts during the shortage of the time it is difficult to predict the run and overs remaining in the cricket match.To over come this issue Duck worth lewis method has been introduced. In this paper we are going to discuss the short come of the method of Duck worth lewis method. In this research We are going to improve the model of duck worth lewis method with machine learning models. At end we are going to compare the Duck worth lewis method, Duck worth lewis method with run rate as a variable and Improved version of duck worth lewis method.
Title Design and realization of a smart battery management system

Battery management system (BMS) emerges a decisive system component in battery-powered applications, such as (hybrid) electric vehicles and portable devices. However, due to the inaccurate parameter estimation of aged battery cells and multi-cell batteries, current BMSs cannot control batteries optimally, and therefore affect the usability of products. In this paper, we proposed a smart management system for multi-cell batteries, and discussed the development of our research study in three directions: i) improving the effectiveness of battery monitoring and current sensing, ii) modeling the battery aging process, and iii) designing a self-healing circuit system to compensate performance variations due to aging and other variations.
When Cognitive Radio meets the Internet of Things?

Internet of Things (IoT) is a world wide network of interconnected objects. IoT capable objects will be interconnected through wired and wireless communication technologies. However, cost-effectiveness issues and accessibility to remote users make wireless communication as a feasible solution. A majority of possibilities have been proposed but many of these suffer from vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase spectrum. Thus trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT. Additionally, ubiquitous objects with cognitive capabilities will be able to make intelligent decisions to achieve interference-free and on-demand services. The main goal of this paper is to discuss how CR technology can be helpful for the IoT paradigm. More precisely, in this paper, we highlight CR functionalities, specially spectrum sensing in conjunction with cloud services to serve as self-reconfigurable IoT solutions for a number of applications.
Novel compact spider microstrip antenna with new Defected Ground Structure

Two novel Defected Ground Structures (DGS) were first proposed, which have better results than that of the dumbbell (published shape). Using the general model of DGS, its equivalent parameters were extracted. The two new proposed shapes of DGS were then used to design a novel compact spider microstrip antenna to minimize its area. The size of the developed antenna was reduced to about 90.5% of that of the conventional one. This antenna with two different novel shapes of DGS was designed and simulated by using the ready-made software package Zeland-IE3D. Finally, it was fabricated by using thin film and photolithographic technique and measured by using vector network analyzer. Good agreements were found between the simulated and measured results.
Machine Learning Algorithms in Bipedal Robot Control

Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early stages, learning techniques have demonstrated promising potential to build adaptive control systems for bipedal robots. This paper gives a review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control. The effects and limitations of different learning techniques are discussed through a representative selection of examples from the literature. Guidelines for future research on learning control of bipedal robots are provided in the end.
Dependency Parsing and Dialogue Systems : an investigation of dependency parsing for commercial application

In this thesis, we investigate dependency parsing for commercial application, namely for future integration in a dialogue system. To do this, we conduct several experiments on dialogue data to assess parser performance on this domain, and to improve this performance over a baseline. This work makes the following contributions: first, the creation and manual annotation of a gold-standard data set for dialogue data; second, a thorough error analysis of the data set, comparing neural network parsing to traditional parsing methods on this domain; and finally, various domain adaptation experiments show how parsing on this data set can be improved over a baseline. We further show that dialogue data is characterized by questions in particular, and suggest a method for improving overall parsing on these constructions.
A PRINCIPAL AXIS TRANSFORMATION FOR NON-HERMITIAN MATRICES CARL ECKART AND GALE YOUNG The availability of the principal axis transformation for hermitian

each two nonparallel elements of G cross each other. Obviously the conclusions of the theorem do not hold. The following example will show that the condition that no two elements of the collection G shall have a complementary domain in common is also necessary. In the cartesian plane let M be a circle of radius 1 and center at the origin, and iVa circle of radius 1 and center at the point (5, 5). Let d be a collection which contains each continuum which is the sum of M and a horizontal straight line interval of length 10 whose left-hand end point is on the circle M and which contains no point within M. Let G2 be a collection which contains each continuum which is the sum of N and a vertical straight line interval of length 10 whose upper end point is on the circle N and which contains no point within N. Let G = Gi+G2 . No element of G crosses any other element of G, but uncountably many have a complementary domain in common with some other element of the collection. However, it is evident that no countable subcollection of G covers the set of points each of which is common to two continua of the collection G. I t is not known whether or not the condition that each element of G shall separate some complementary domain of every other one can be omitted.
A scrum-based approach to CMMI maturity level 2 in web development environments

Scrum has become one of the most popular agile methodologies, either alone or combined with other agile practices. Besides, CMMI (Capability Maturity Model Integration) is accepted as a suitable model to measure the maturity of the organizations when developing or acquiring software. Although these two approaches are often considered antagonist, the use of an agile approach to reach certain CMMI maturity levels may result beneficial to organizations that develop Web systems, since they would take the advantages of both approaches. In Web community, this union may be very interesting, because agile approaches fits with the special needs of Web development, and they could be a useful tool for companies getting a certain grade of maturity. This work analyzes the goals of CMMI maturity level 2 and the feasibility of achieving them using the practices proposed by Scrum, trying to assess whether the use of this methodology is suitable for meeting the CMMI generic and specific goals or not. Finally, and based on this analysis, this paper raises a possible extension of Scrum, based on agile techniques, to accommodate the CMMI maturity level 2.
Three-way substrate integrated waveguide (SIW) power divider design

In this paper, an X-band 1×3 substrate integrated waveguide (SIW) power divider design is presented. The designed SIW power divider provides equal amplitude with uniform phase distribution at the each output port. It has also a satisfactory operating bandwidth and low insertion loss. Moreover, the return loss is approximately 25 dB at the design frequency as shown in the EM simulation results.
A Novel Ultra-Lightweight Multiband Rectenna on Paper for RF Energy Harvesting in the Next Generation LTE Bands

This paper introduces a novel compact ultralightweight multiband RF energy harvester fabricated on a paper substrate. The proposed rectenna is designed to operate in all recently released LTE bands (range 0.79–0.96 GHz; 1.71–2.17 GHz; and 2.5–2.69 GHz). High compactness and ease of integration between antenna and rectifier are achieved by using a topology of nested annular slots. The proposed rectifier features an RF-to-dc conversion efficiency in the range of 5%–16% for an available input power of −20 dBm in all bands of interest, which increases up to 11%–30% at −15 dBm. The rectenna has been finally tested both in laboratory and in realistic scenarios featuring a superior performance to other state-of-the-art RF harvesters on flexible substrates.
Highly compact 1T-1R architecture (4F2 footprint) involving fully CMOS compatible vertical GAA nano-pillar transistors and oxide-based RRAM cells exhibiting excellent NVM properties and ultra-low power operation

For the first time, nano-meter-scaled 1T-1R non-volatile memory (NVM) architecture comprising of RRAM cells built on vertical GAA nano-pillar transistors, either junction-less or junction-based, is systematically investigated. Transistors are fabricated using fully CMOS compatible technology and RRAM cells are stacked onto the tip of the nano-pillars (with a diameter down to ~37nm) to achieve a compact 4F2 footprint. In addition, through this platform, different RRAM stacks comprising CMOS friendly materials are studied, and it is found that TiN/Ni/HfO2/n+-Si RRAM cells show excellent switching properties in either bipolar or unipolar mode, including (1) ultra-low switching current/power: SET ~20nA/85nW and RESET ~200pA/700pW, (2) multi-level switchability, (3) good endurance, >105, (4) satisfactory retention, 10 years at 85oC; and (5) fast switching speed ~50ns. Moreover, this vertical (gate-all-around) GAA nano-pillar based 1T-1R architecture provides a more direct and flexible test vehicle to verify the scalability and functionality of RRAM candidates with a dimension close to actual application.
Shuffle Index: Efficient and Private Access to Outsourced Data

Data outsourcing and cloud computing have been emerging at an ever-growing rate as successful approaches for allowing users and companies to rely on external services for storing and managing data. As data and access to them are not under the control of the data owner, there is a clear need to provide proper confidentiality protection. Such requirements concern the confidentiality not only of the stored data (content) but also of the specific accesses (or patterns of them) that users make on such data.
 In this article, we address these issues and propose an approach for guaranteeing content, access, and pattern confidentiality in a data outsourcing scenario. The proposed solution is based on the definition of a shuffle index structure, which adapts traditional B +-trees and, by applying a combination of techniques (covers, caches, and shuffling), ensures confidentiality of the data and of queries over them, protecting each single access as well as sequences thereof. The proposed solution also supports update operations over the data, while making reads and writes not recognizable as such by the server. We show that the shuffle index exhibits a limited performance cost, thus resulting effectively usable in practice.
Multi-Objective Optimization of 50 kW/85 kHz IPT System for Public Transport

Inductive power transfer (IPT) is an attractive solution for the automated battery charging of public transport electric vehicles (EVs) with low maintenance requirements. This paper presents the design of a 50 kW/85 kHz contactless EV charger, with a focus on the IPT transmitter and receiver coils. The multi-objective magnetics optimization reveals the Pareto tradeoffs and performance limitations in terms of high efficiency, high power density, and low stray field for high-power IPT systems without moving mechanical parts. A full-scale hardware prototype is realized and experimentally investigated. The dc-dc conversion efficiency, including all the power electronics stages, is measured as 95.8% at 50 kW power transfer across an air gap of 160 mm (coil dimensions 410 × 760 × 60 mm3). With 150 mm coil misalignment in the worst case direction, the dc-dc efficiency drops to 92%. The measurements of the magnetic stray field show compliance with ICNIRP 2010 at 800 mm distance from the IPT coil center.
Multimodal language grounding for improved human-robot collaboration: exploring spatial semantic representations in the shared space of attention

There is an increased interest in artificially intelligent technology that surrounds us and takes decisions on our behalf. This creates the need for such technology to be able to communicate with humans and understand natural language and non-verbal behaviour that may carry information about our complex physical world. Artificial agents today still have little knowledge about the physical space that surrounds us and about the objects or concepts within our attention. We are still lacking computational methods in understanding the context of human conversation that involves objects and locations around us. Can we use multimodal cues from human perception of the real world as an example of language learning for robots? Can artificial agents and robots learn about the physical world by observing how humans interact with it and how they refer to it and attend during their conversations? This PhD project’s focus is on combining spoken language and non-verbal behaviour extracted by multi-party dialogue in order to increase context awareness and spatial understanding for artificial agents.
Characterizing perceptual artifacts in compressed video streams

To achieve optimal video quality under bandwidth and power constraints, modern video coding techniques employ lossy coding schemes, which often create compression artifacts that may lead to degradation of perceptual video quality. Understanding and quantifying such perceptual artifacts play important roles in the development of effective video compression, streaming and quality enhancement systems. Moreover, the characteristics of compression artifacts evolve over time due to the continuous adoption of novel coding structures and strategies during the development of new video compression standards. In this paper, we reexamine the perceptual artifacts created by standard video compression, summarizing commonly observed spatial and temporal perceptual distortions in compressed video, with emphasis on the perceptual temporal artifacts that have not been well identified or accounted for in previous studies. Furthermore, a floating effect detection method is proposed that not only detects the existence of floating, but also segments the spatial regions where floating occurs∗.