nils-herrmann/clean_scirepeval huggingface dataset:
Decoupled Novel Object Captioner

Image captioning is a challenging task where the machine automatically describes an image by sentences or phrases. It often requires a large number of paired image-sentence annotations for training. However, a pre-trained captioning model can hardly be applied to a new domain in which some novel object categories exist, i.e., the objects and their description words are unseen during model training. To correctly caption the novel object, it requires professional human workers to annotate the images by sentences with the novel words. It is labor expensive and thus limits its usage in real-world applications. In this paper, we introduce the zero-shot novel object captioning task where the machine generates descriptions without extra training sentences about the novel object. To tackle the challenging problem, we propose a Decoupled Novel Object Captioner (DNOC) framework that can fully decouple the language sequence model from the object descriptions. DNOC has two components. 1) A Sequence Model with the Placeholder (SM-P) generates a sentence containing placeholders. The placeholder represents an unseen novel object. Thus, the sequence model can be decoupled from the novel object descriptions. 2) A key-value object memory built upon the freely available detection model, contains the visual information and the corresponding word for each object. A query generated from the SM-P is used to retrieve the words from the object memory. The placeholder will further be filled with the correct word, resulting in a caption with novel object descriptions. The experimental results on the held-out MSCOCO dataset demonstrate the ability of DNOC in describing novel concepts.
Influence of the modulation method on the conduction and switching losses of a PWM converter system

The dependency of the conduction losses of a bridge leg of a PWM power converter system with a high pulse rate on the shape of the phase modulation functions is explored. This is done for modulation methods which are optimized with respect to minimum harmonic current RMS values. The results are compared to the results gained for simple sinusoidal modulation. Besides the conduction losses, the switching losses of the electric valves are calculated. Deviations from the classical sinusoidal modulation are only obtained for modulation methods for which the output voltage is formed by a cyclic change via only two active and a third nonswitching bridge leg. As the calculations show, these modulation methods allow a significant increase of the effective switching frequency. This effect is dependent on the phase angle between the fundamental of the converter output phase voltage and the converter output phase current; for this comparison, equal switching losses, as for the simple sinusoidal modulation, are assumed.<<ETX>>
How social influence can undermine the wisdom of crowd effect.

Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) ] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks. In the experiment, subjects could reconsider their response to factual questions after having received average or full information of the responses of other subjects. We compare subjects' convergence of estimates and improvements in accuracy over five consecutive estimation periods with a control condition, in which no information about others' responses was provided. Although groups are initially "wise," knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines the wisdom of crowd effect in three different ways. The "social influence effect" diminishes the diversity of the crowd without improvements of its collective error. The "range reduction effect" moves the position of the truth to peripheral regions of the range of estimates so that the crowd becomes less reliable in providing expertise for external observers. The "confidence effect" boosts individuals' confidence after convergence of their estimates despite lack of improved accuracy. Examples of the revealed mechanism range from misled elites to the recent global financial crisis.
Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies

The burden of entry into mobile crowdsensing (MCS) is prohibitively high for human-subject researchers who lack a technical orientation. As a result, the benefits of MCS remain beyond the reach of research communities (e.g., psychologists) whose expertise in the study of human behavior might advance applications and understanding of MCS systems. This paper presents Sensus, a new MCS system for human-subject studies that bridges the gap between human-subject researchers and MCS methods. Sensus alleviates technical burdens with on-device, GUI-based design of sensing plans, simple and efficient distribution of sensing plans to study participants, and uniform participant experience across iOS and Android devices. Sensing plans support many hardware and software sensors, automatic deployment of sensor-triggered surveys, and double-blind assignment of participants within randomized controlled trials. Sensus offers these features to study designers without requiring knowledge of markup and programming languages. We demonstrate the feasibility of using Sensus within two human-subject studies, one in psychology and one in engineering. Feedback from non-technical users indicates that Sensus is an effective and low-burden system for MCS-based data collection and analysis.
DEVELOPMENT OF A BROADBAND HORIZONTALLY POLARIZED OMNIDIRECTIONAL PLANAR ANTENNA AND ITS ARRAY FOR BASE STATIONS

A novel broadband horizontally polarized (HP) omnidirectional planar antenna is developed for mobile communications. The proposed antenna consists of four printed arc dipoles that form a circular loop for HP omnidirectional radiation. A broadband feeding network which includes four broadband baluns and an impedance matching circuit is designed to excite the four arc dipoles. An eight-element linear antenna array is developed for 2G/3G base stations. A broadband power divider is used to feed the antenna array. Experimental results show that the HP omnidirectional antenna element has a bandwidth of 31% (1.66–2.27GHz) while its array has a bandwidth of 34% (1.67–2.35GHz) and an omnidirectional antenna gain of ∼ 8 dBi. Both of the antenna element and its array have good omnidirectivity over the 10-dB return loss bandwidth. Simulated and measured results for the antenna element and its array are presented.
Scalable Bloom Filters

Bloom Filters provide space-efficient storage of sets at the cost of a probability of false positives on membership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements without increasing the false positive probability. This leads typically to a conservative assumption regarding maximum set size, possibly by orders of magnitude, and a consequent space waste. This paper proposes Scalable Bloom Filters, a variant of Bloom Filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability.
Building contingency planning for closed-loop supply chains with product recovery

Contingency planning is the first stage in developing a formal set of production planning and control activities for the reuse of products obtained via return flows in a closed-loop supply chain. The paper takes a contingency approach to explore the factors that impact production planning and control for closed-loop supply chains that incorporate product recovery. A series of three cases are presented, and a framework developed that shows the common activities required for all remanufacturing operations. To build on the similarities and illustrate and integrate the differences in closed-loop supply chains, Hayes and Wheelwright’s product–process matrix is used as a foundation to examine the three cases representing Remanufacture-to-Stock (RMTS), Reassemble-to-Order (RATO), and Remanufacture-to-Order (RMTO). These three cases offer end-points and an intermediate point for closed-loop supply operations. Since they represent different positions on the matrix, characteristics such as returns volume, timing, quality, product complexity, test and evaluation complexity, and remanufacturing complexity are explored. With a contingency theory for closed-loop supply chains that incorporate product recovery in place, past cases can now be reexamined and the potential for generalizability of the approach to similar types of other problems and applications can be assessed and determined.
Color-Guided Depth Map Super Resolution Using Convolutional Neural Network

With the development of 3-D applications, such as 3-D reconstruction and object recognition, accurate and high-quality depth map is urgently required. Recently, depth cameras have been affordable and widely used in daily life. However, the captured depth map always owns low resolution and poor quality, which limits its practical application. This paper proposes a color-guided depth map super resolution method using convolutional neural network. First, a dual-stream convolutional neural network, which integrates the color and depth information simultaneously, is proposed for depth map super resolution. Then, the optimized edge map generated by the high resolution color image and low resolution depth map is used as additional information to refine the object boundary in the depth map. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.
Scene text localization using extremal regions and Corner-HOG feature

This paper presents a text detection method based on Extremal Regions (ERs) and Corner-HOG feature. Local Histogram of Oriented Gradient (HOG) extracted around corners (Corner-HOG) is used to effectively prune the non-text components in the component tree. Experimental results show that the Corner-HOG based pruning method can discard an average of 83.06% of all ERs in an image while preserving a recall of 90.51% of the text components. The remaining ERs are then grouped into text lines and candidate text lines are verified using black-white transition feature and the covariance descriptor of HOG. Experimental results on the 2011 Robust Reading Competition dataset show that the proposed text detection method provides promising performance.
Learning Sparse Representations for Human Action Recognition

This paper explores the effectiveness of sparse representations obtained by learning a set of overcomplete basis (dictionary) in the context of action recognition in videos. Although this work concentrates on recognizing human movements-physical actions as well as facial expressions-the proposed approach is fairly general and can be used to address other classification problems. In order to model human actions, three overcomplete dictionary learning frameworks are investigated. An overcomplete dictionary is constructed using a set of spatio-temporal descriptors (extracted from the video sequences) in such a way that each descriptor is represented by some linear combination of a small number of dictionary elements. This leads to a more compact and richer representation of the video sequences compared to the existing methods that involve clustering and vector quantization. For each framework, a novel classification algorithm is proposed. Additionally, this work also presents the idea of a new local spatio-temporal feature that is distinctive, scale invariant, and fast to compute. The proposed approach repeatedly achieves state-of-the-art results on several public data sets containing various physical actions and facial expressions.
An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.
Effect of a treatment strategy of tight control for rheumatoid arthritis (the TICORA study): a single-blind randomised controlled trial

Present treatment strategies for rheumatoid arthritis include use of disease-modifying antirheumatic drugs, but a minority of patients achieve a good response. We aimed to test the hypothesis that an improved outcome can be achieved by employing a strategy of intensive outpatient management of patients with rheumatoid arthritis--for sustained, tight control of disease activity--compared with routine outpatient care.



We designed a single-blind, randomised controlled trial in two teaching hospitals. We screened 183 patients for inclusion. 111 were randomly allocated either intensive management or routine care. Primary outcome measures were mean fall in disease activity score and proportion of patients with a good response (defined as a disease activity score <2.4 and a fall in this score from baseline by >1.2). Analysis was by intention-to-treat.



One patient withdrew after randomisation and seven dropped out during the study. Mean fall in disease activity score was greater in the intensive group than in the routine group (-3.5 vs -1.9, difference 1.6 [95% CI 1.1-2.1], p<0.0001). Compared with routine care, patients treated intensively were more likely to have a good response (definition, 45/55 [82%] vs 24/55 [44%], odds ratio 5.8 [95% CI 2.4-13.9], p<0.0001) or be in remission (disease activity score <1.6; 36/55 [65%] vs 9/55 [16%], 9.7 [3.9-23.9], p<0.0001). Three patients assigned routine care and one allocated intensive management died during the study; none was judged attributable to treatment.



A strategy of intensive outpatient management of rheumatoid arthritis substantially improves disease activity, radiographic disease progression, physical function, and quality of life at no additional cost.
Air-ground Matching: Appearance-based GPS-denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera-equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image-based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel airground image-matching algorithm to search the airborne image of the MAV within a ground-level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral three-dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground-level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision-based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus outperforming conventional visual placerecognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.
Uncovering the Bitcoin Blockchain: An Analysis of the Full Users Graph

BITCOIN is a novel decentralized cryptocurrency system which has recently received a great attention from a wider audience. An interesting and unique feature of this system is that the complete list of all the transactions occurred from its inception is publicly available. This enables the investigation of funds movements to uncover interesting properties of the BITCOIN economy. In this paper we present a set of analyses of the user graph, i.e. the graph obtained by an heuristic clustering of the graph of BITCOIN transactions. Our analyses consider an up-to-date BITCOIN blockchain, as in December 2015, after the exponential explosion of the number of transactions occurred in the last two years. The set of analyses we defined includes, among others, the analysis of the time evolution of BITCOIN network, the verification of the "rich get richer" conjecture and the detection of the nodes which are critical for the network connectivity.
Toxicities of the anti-PD-1 and anti-PD-L1 immune checkpoint antibodies.

J. Naidoo1*, D. B. Page2, B. T. Li3, L. C. Connell3, K. Schindler4, M. E. Lacouture5,6, M. A. Postow3,6 & J. D. Wolchok3,6 Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore; Providence Portland Medical Center and Earl A. Chiles Research Institute, Portland; Department of Medicine and Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Dermatology, Medical University of Vienna, Vienna, Austria; Dermatology Service, Memorial Sloan Kettering Cancer Center, New York; Department of Medicine, Weill Cornell Medical College, New York, USA
Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs.

When a meta-analysis on results from experimental studies is conducted, differences in the study design must be taken into consideration. A method for combining results across independent-groups and repeated measures designs is described, and the conditions under which such an analysis is appropriate are discussed. Combining results across designs requires that (a) all effect sizes be transformed into a common metric, (b) effect sizes from each design estimate the same treatment effect, and (c) meta-analysis procedures use design-specific estimates of sampling variance to reflect the precision of the effect size estimates.
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering

Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learningbased approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.
A unified small signal analysis of DC-DC converters with Average Current Mode Control

A small signal analysis of DC-DC converters with Average Current Mode Control (ACMC) in Continuous Conduction Mode (CCM) is performed in a unified manner. Design-oriented equations are derived for all three types of DC-DC converters. These equations are such that they are expressed explicitly with converter circuit and operating parameters, making them easy to be directly applicable to practical design purposes. Experimental results of an ACMC Boost converter are given to validate the model prediction with good correlation.
Cyber Network Mission Dependencies

Cyber assets are critical to mission success in every arena of the Department of Defense. Because all DoD missions depend on cyber infrastructure, failure to secure network assets and assure the capabilities they enable will pose a fundamental risk to any defense mission. The impact of a cyber attack is not well understood by warfighters or leadership. It is critical that the DoD develop better cognizance of Cyber Network Mission Dependencies (CNMD). This report identifies the major drivers for mapping missions to network assets, introduces existing technologies in the mission-mapping landscape, and proposes directions for future development.
ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT

Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.
A Low-Cost and Efficient Permanent-Magnet-Assisted Synchronous Reluctance Motor Drive

In this paper, various key points in the rotor design of a low-cost permanent-magnet-assisted synchronous reluctance motor (PMa-SynRM) are introduced and their effects are studied. Finite-element approach has been utilized to show the effects of these parameters on the developed average electromagnetic torque and total d-q inductances. One of the features considered in the design of this motor is the magnetization of the permanent magnets mounted in the rotor core using the stator windings. This feature will cause a reduction in cost and ease of manufacturing. Effectiveness of the design procedure is validated by presenting simulation and experimental results of a 1.5-kW prototype PMa-SynRM