nils-herrmann/clean_scirepeval huggingface dataset:
Deep Discriminative Representation Learning with Attention Map for Scene Classification

Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.
Real-Time Earthquake Detection Using Convolutional Neural Network and Social Data

Twitter has become a potential data source to explore useful information mentioned by users. For instance, Twitter tweets related to an earthquake will be utilized to detect temporal occurrence as well as location information by a humanitarian organization. Therefore, we proposed a Convolution Neural Network (CNN) based method to determine informative tweet and the real-time event detection algorithm to detect the timely occurrence of the given event. In this study, CNN model trained from the tweets related to the earthquake in the past labeled by crowdsourcing plays a role as the classifier to predict an earthquake keyword related tweet is informative or not. Then these informative tweets are considered as input streaming data of time detection phase. Our system with the aid of CNN module can detect the earthquake after it happens in the level of tolerance and ensure earlier than an announcement from official disaster website of government.
Credit Risk Assessment Using Statistical and MachineLearning: Basic Methodology and Risk Modeling Applications

Risk assessment of financial intermediaries is an area of renewed interest due to the financial crises of the 1980’s and 90’s. An accurate estimation of risk, and its use in corporate or global financial risk models, could be translated into a more efficient use of resources. One important ingredient to accomplish this goal is to find accurate predictors of individual risk in the credit portfolios of institutions. In this context we make a comparative analysis of different statistical and machine learning modeling methods of classification on a mortgage loan dataset with the motivation to understand their limitations and potential. We introduced a specific modeling methodology based on the study of error curves. Using state-of-the-art modeling techniques we built more than 9,000 models as part of the study. The results show that CART decision-tree models provide the best estimation for default with an average 8.31% error rate for a training sample of 2,000 records. As a result of the error curve analysis for this model we conclude that if more data were available, approximately 22,000 records, a potential 7.32% error rate could be achieved. Neural Networks provided the second best results with an average error of 11.00%. The K-Nearest Neighbor algorithm had an average error rate of 14.95%. These results outperformed the standard Probit algorithm which attained an average error rate of 15.13%. Finally we discuss the possibilities to use this type of accurate predictive model as ingredients of institutional and global risk models.
A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina

ÐThis paper describes a robust hierarchical algorithm for fully-automatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computer-aided instrumentation. Central to the new algorithm is a 12-parameter interimage transformation derived by modeling the retina as a rigid quadratic surface with unknown parameters, imaged by an uncalibrated weak perspective camera. The parameters of this model are estimated by matching vascular landmarks extracted by an algorithm that recursively traces the blood vessel structure. The parameter estimation technique, which could be generalized to other applications, is a hierarchy of models and methods: an initial match set is pruned based on a zeroth order transformation estimated as the peak of a similarity-weighted histogram; a first order, affine transformation is estimated using the reduced match set and least-median of squares; and the final, second order, 12-parameter transformation is estimated using an M-estimator initialized from the first order estimate. This hierarchy makes the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions. Before final convergence of the M-estimator, feature positions are refined and the correspondence set is enhanced using normalized sum-of-squared differences matching of regions deformed by the emerging transformation. Experiments involving 3,000 image pairs (I; HPR Â I; HPR pixels) from 16 different healthy eyes were performed. Starting with as low as 20 percent overlap between images, the algorithm improves its success rate exponentially and has a negligible failure rate above 67 percent overlap. The experiments also quantify the reduction in errors as the model complexities increase. Final registration errors less than a pixel are routinely achieved. The speed, accuracy, and ability to handle small overlaps compare favorably with retinal image registration techniques published in the literature.
SOF: a semi-supervised ontology-learning-based focused crawler

The rapid increase in the volume of data available on the Internet makes it increasingly impractical for a crawler to index the whole Web. Instead, many intelligent crawlers, known as ontology-based semantic focused crawlers, have been designed by making use of Semantic Web technologies for topiccentered Web information crawling. Ontologies, however, have constraints of validity and time, which may influence the performance of the crawlers. Ontology-learning-based focused crawlers are therefore designed to automatically evolve ontologies by integrating ontology learning technologies. Nevertheless, surveys indicate that the existing ontology-learning-based focused crawlers do not have the capability to automatically enrich the content of ontologies, which makes these crawlers unreliable in the open and heterogeneous Web environment. Hence, in this paper, we propose a framework for a novel semi-supervised ontology-learning-based focused (SOF) crawler, the SOF crawler, which embodies a series of schemas for ontology generation and Web information formatting, a semi-supervised ontology learning framework, and a hybrid Web page classification approach aggregated by a group of support vector machine models. A series of tests are implemented to evaluate the technical feasibility of this proposed framework. The conclusion and the future work are summarized in the final section.
Diffusion-Convolutional Neural Networks

We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graphstructured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data that is invariant under isomorphism, as well as polynomial-time prediction and learning that can be represented as tensor operations and efficiently implemented on a GPU. Through several experiments with real structured datasets, we demonstrate that DCNNs are able to outperform probabilistic relational models and kernel-on-graph methods at relational node classification tasks.
Integrated bioethanol and protein production from brown seaweed Laminaria digitata.

A wild-growing glucose-rich (i.e. 56.7% glucose content) brown seaweed species Laminaria digitata, collected from the North Coast of Denmark in August 2012, was used as the feedstock for an integrated bioethanol and protein production. Glutamic acid and aspartic acid are the two most abundant amino acids in the algal protein, both with proportional content of 10% in crude protein. Only minor pretreatment of milling was used on the biomass to facilitate the subsequent enzymatic hydrolysis and fermentation. The Separate Hydrolysis and Fermentation (SHF) resulted in obviously higher ethanol yield than the Simultaneous Saccharification and Fermentation (SSF). High conversion rate at maximum of 84.1% glucose recovery by enzymatic hydrolysis and overall ethanol yield at maximum of 77.7% theoretical were achieved. Protein content in the solid residues after fermentation was enriched by 2.7 fold, with similar distributions of amino acids, due to the hydrolysis of polymers in the seaweed cell wall matrix.
A Low-Profile Third-Order Bandpass Frequency Selective Surface

We demonstrate a new class of low-profile frequency selective surfaces (FSS) with an overall thickness of lambda/24 and a third-order bandpass frequency response. The proposed FSS is a three layer structure composed of three metal layers, separated by two electrically thin dielectric substrates. Each layer is a two-dimensional periodic structure with sub-wavelength unit cell dimensions and periodicity. The unit cell of the proposed FSS is composed of a combination of resonant and non-resonant elements. It is shown that this arrangement acts as a spatial bandpass filter with a third-order bandpass response. However, unlike traditional third-order bandpass FSSs, which are usually obtained by cascading three identical first-order bandpass FSSs a quarter wavelength apart from one another and have thicknesses in the order of lambda/2 , the proposed structure has an extremely low profile and an overall thickness of about lambda/24 , making it an attractive choice for conformal FSS applications. As a result of the miniaturized unit cells and the extremely small overall thickness of the structure, the proposed FSS has a reduced sensitivity to the angle of incidence of the EM wave compared to traditional third-order frequency selective surfaces. The principles of operation along with guidelines for the design of the proposed FSS are presented in this paper. A prototype of the proposed third-order bandpass FSS is also fabricated and tested using a free space measurement system at C band.
Work strain, health, and absenteeism: a meta-analysis.

Work strain has been argued to be a significant cause of absenteeism in the popular and academic press. However, definitive evidence for associations between absenteeism and strain is currently lacking. A theory focused meta-analysis of 275 effects from 153 studies revealed positive but small associations between absenteeism and work strain, psychological illness, and physical illness. Structural equation modeling results suggested that the strain-absence connection may be mediated by psychological and physical symptoms. Little support was received for the purported volitional distinction between absence frequency and time lost absence measures on the basis of illness. Among the moderators examined, common measurement, midterm and stable sources of variance, and publication year received support.
The promising future of microalgae: current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products

Microalgae have recently attracted considerable interest worldwide, due to their extensive application potential in the renewable energy, biopharmaceutical, and nutraceutical industries. Microalgae are renewable, sustainable, and economical sources of biofuels, bioactive medicinal products, and food ingredients. Several microalgae species have been investigated for their potential as value-added products with remarkable pharmacological and biological qualities. As biofuels, they are a perfect substitute to liquid fossil fuels with respect to cost, renewability, and environmental concerns. Microalgae have a significant ability to convert atmospheric CO2 to useful products such as carbohydrates, lipids, and other bioactive metabolites. Although microalgae are feasible sources for bioenergy and biopharmaceuticals in general, some limitations and challenges remain, which must be overcome to upgrade the technology from pilot-phase to industrial level. The most challenging and crucial issues are enhancing microalgae growth rate and product synthesis, dewatering algae culture for biomass production, pretreating biomass, and optimizing the fermentation process in case of algal bioethanol production. The present review describes the advantages of microalgae for the production of biofuels and various bioactive compounds and discusses culturing parameters.
Workspace characterization for concentric tube continuum robots

Concentric tube robots exhibit complex workspaces due to the way their component tubes bend and twist as they interact with one another. This paper explores ways to compute and characterize their workspaces. We use Monte Carlo random samples of the robot's joint space and a discrete volumetric workspace representation, which can describe both reachability and redundancy. Experiments on two physical prototypes are provided to illustrate the proposed approach.
A high precision micropower operational amplifier

An operational amplifier configuration implemented as a true micropower high precision op amp is described. It includes a well controlled and predictable DC biasing network that is insensitive to variations in temperature, supply voltages, and process. Also, it permits single supply operation. Excellent DC precision characteristics, comparable to or better than the very best precision op amps currently available, are realized yet at micropower levels. By simply increasing the biasing currents, a version of this design operates in general purpose applications without any degradation in its high precision characteristics. Thus, the AC performance levels of general purpose op amps are attained at a fraction of supply current. This device is fabricated using a standard bipolar IC process; an ion-implanted JFET is added to simplify biasing.
Robust face recognition based on weighted DeepFace

In this paper, we present a new face recognition algorithm based on weighted deep face learning. Our proposed method composes of two steps: face detection and face feature extraction. The aim of face detection is to find an accurate face position. The face alignment is then applied by finding the facial landmarks in the face rectangle. With the help of face alignment the error rate of face recognition can be reduced. Deep learning is employed to extract distinctive features of the face components. We create the weights for each face feature by optimizing the within class variations with respect to between class similarity measures. We achieved the lowest total error rate of 0.01429% on XM2VTS database. We also accomplished the 98.61% of accuracy on LFW database. For realtime face recognition we achieved 99.17% for our own video database.
Entity Extraction, Linking, Classification, and Tagging for Social Media: A Wikipedia-Based Approach

Many applications that process social data, such as tweets, must extract entities from tweets (e.g., “Obama” and “Hawaii” in “Obama went to Hawaii”), link them to entities in a knowledge base (e.g., Wikipedia), classify tweets into a set of predefined topics, and assign descriptive tags to tweets. Few solutions exist today to solve these problems for social data, and they are limited in important ways. Further, even though several industrial systems such as OpenCalais have been deployed to solve these problems for text data, little if any has been published about them, and it is unclear if any of the systems has been tailored for social media. In this paper we describe in depth an end-to-end industrial system that solves these problems for social data. The system has been developed and used heavily in the past three years, first at Kosmix, a startup, and later at WalmartLabs. We show how our system uses a Wikipedia-based global “real-time” knowledge base that is well suited for social data, how we interleave the tasks in a synergistic fashion, how we generate and use contexts and social signals to improve task accuracy, and how we scale the system to the entire Twitter firehose. We describe experiments that show that our system outperforms current approaches. Finally we describe applications of the system at Kosmix and WalmartLabs, and lessons learned.
Little-JIL/Juliette: a process definition language and interpreter

Little-JIL, a language for programming coordination in processes is an executable, high-level language with a formal (yet graphical) syntax and rigorously defined operational semantics. The central abstraction in Little-JIL is the “step,” which is the focal point for coordination, providing a scoping mechanism for control, data, and exception flow and for agent and resource assignment. Steps are organized into a static hierarchy, but can have a highly dynamic execution structure including the possibility of recursion and concurrency.
Little-JIL is based on two main hypotheses. The first is that coordination structure is separable from other process language issues. Little-JIL provides rich control structures while relying on separate systems for resource, artifact, and agenda management. The second hypothesis is that processes are executed by agents that know how to perform their tasks but benefit from coordination support. Accordingly, each Little-JIL step has an execution agent (human or automated) that is responsible for performing the work of the step.
This approach has proven effective in supporting the clear and concise expression of agent coordination for a wide variety of software, workflow, and other processes.
Flexible 16 Antenna Array for Microwave Breast Cancer Detection

Radar-based microwave imaging has been widely studied for breast cancer detection in recent times. Sensing dielectric property differences of tissues has been studied over a wide frequency band for this application. We design single- and dual-polarization antennas for wireless ultrawideband breast cancer detection systems using an inhomogeneous multilayer model of the human breast. Antennas made from flexible materials are more easily adapted to wearable applications. Miniaturized flexible monopole and spiral antennas on a 50-μm Kapton polyimide are designed, using a high-frequency structure simulator, to be in contact with biological breast tissues. The proposed antennas are designed to operate in a frequency range of 2-4 GHz (with reflection coefficient (S11) below -10 dB). Measurements show that the flexible antennas have good impedance matching when in different positions with different curvature around the breast. Our miniaturized flexible antennas are 20 mm × 20 mm. Furthermore, two flexible conformal 4 × 4 ultrawideband antenna arrays (single and dual polarization), in a format similar to that of a bra, were developed for a radar-based breast cancer detection system. By using a reflector for the arrays, the penetration of the propagated electromagnetic waves from the antennas into the breast can be improved by factors of 3.3 and 2.6, respectively.
Identifying the Scan and Attack Infrastructures Behind Amplification DDoS Attacks

Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic.
 In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\% certainty.
Piaget ’ s Constructivism , Papert ’ s Constructionism : What ’ s the difference ?

What is the difference between Piaget's constructivism and Papert’s “constructionism”? Beyond the mere play on the words, I think the distinction holds, and that integrating both views can enrich our understanding of how people learn and grow. Piaget’s constructivism offers a window into what children are interested in, and able to achieve, at different stages of their development. The theory describes how children’s ways of doing and thinking evolve over time, and under which circumstance children are more likely to let go of—or hold onto— their currently held views. Piaget suggests that children have very good reasons not to abandon their worldviews just because someone else, be it an expert, tells them they’re wrong. Papert’s constructionism, in contrast, focuses more on the art of learning, or ‘learning to learn’, and on the significance of making things in learning. Papert is interested in how learners engage in a conversation with [their own or other people’s] artifacts, and how these conversations boost self-directed learning, and ultimately facilitate the construction of new knowledge. He stresses the importance of tools, media, and context in human development. Integrating both perspectives illuminates the processes by which individuals come to make sense of their experience, gradually optimizing their interactions with the world
Link-level access cloud architecture design based on SDN for 5G networks

The exponential growth of data traffic and connected devices, and the reduction of latency and costs, are considered major challenges for future mobile communication networks. The satisfaction of these challenges motivates revisiting the architecture of these networks. We propose an SDN-based design of a hierarchical architecture for the 5G packet core. In this article we focus on the design of its access cloud with the goal of providing low latency and scalable Ethernet-like support to terminals and MTC devices including mobility management. We examine and address its challenges in terms of network scalability and support for link-level mobility. We propose a link-level architecture that forwards frames from and to edge network elements (AP and routers) with a label that identifies the APs through which the terminal is reachable. An SDN local controller tracks and updates the users' location information at the edge network elements. Additionally, we propose to delegate in SDN local controllers the handling of non-scalable operations, such as broadcast and multicast messages, and network management procedures.