nils-herrmann/clean_scirepeval huggingface dataset:
An Unsupervised Machine Learning Approach to Body Text and Table of Contents Extraction from Digital Scientific Articles

Scientific articles are predominantly stored in digital document formats, which are optimised for presentation, but lack structural information. This poses challenges to access the documents’ content, for example for information retrieval. We have developed a processing pipeline that makes use of unsupervised machine learning techniques and heuristics to detect the logical structure of a PDF document. Our system uses only information available from the current document and does not require any pre-trained model. Starting from a set of contiguous text blocks extracted from the PDF file, we first determine geometrical relations between these blocks. These relations, together with geometrical and font information, are then used categorize the blocks into different classes. Based on this logical structure we finally extract the body text and the table of contents of a scientific article. We evaluate our pipeline on a number of datasets and compare it with state-of-the-art document structure analysis approaches.
Verifying Music Tag Annotation Via Association Analysis

Music tags provide descriptive and rich information about a music piece, including its genre, artist, emotion, instrument, etc. While many work on automating it, at present, tag annotation is largely a manual process. It often involves judgements and opinions from people of different background and level of musical expertise. Therefore, the resulting tags are usually subjective, ambiguous, and errorprone. To deal with this situation, we seek automatic methods to verify and monitor this process. Furthermore, because multiple tags can annotate each music piece, our task lends itself to multi-label methods which capture the inherent associations among annotations in a given music repository. In this paper, we propose a novel approach to verify the quality of music tag annotations via association analysis. We demonstrate the effectiveness of our approach through a series of simulations using four publicly available music datasets. To our knowledge, our work is among the initial efforts in verifying music tag annotations.
75–85 GHz flip-chip phased array RFIC with simultaneous 8-transmit and 8-receive paths for automotive radar applications

This paper presents the first simultaneous 8-transmit and 8-receive paths 75-85 GHz phased array RFIC for FMCW automotive radars. The receive path has two separate I/Q mixers each connected to 4-element phased arrays for RF and digital beamforming. The chip also contains a build-in-self-test system (BIST) for the transmit and receive paths. Measurements on a flip-chip prototype show a gain >24 dB at 77 GHz, -25 dB coupling between adjacent channels in the transmit and receive paths (<;-45 dB between non-adjacent channels), and <;-50 dB coupling between the transmit and receive portions of the chip.
HURRICANE TRAJECTORY PREDICTION VIA A SPARSE RECURRENT NEURAL NETWORK

A proposed sparse recurrent neural network with flexible topology is used for trajectory prediction of the Atlantic hurricanes. For prediction of the future trajectories of a target hurricane, the most similar hurricanes to the target hurricane are found by comparing directions of the hurricanes. Then, the first and second differences of their positions over their life time are used for training the proposed network. Comparison of the obtained predictions with actual trajectories of Sandy and Humberto hurricanes show that our approach is quite promising for this aim.
HTTP-CoAP cross protocol proxy: an implementation viewpoint

In recent years there has been growing interest for the vision of a so-called Web of Things, which pursues the access to a wide variety of everyday objects through a regular web browser. We believe that this vision is right now at a key moment in its realization, analogous to the '90 for the World Wide Web. The Constrained Application Protocol (CoAP), a web protocol optimized for constrained networks and devices, will hopefully help wireless sensor nodes, a.k.a., smart objects, to become active citizens of the web. Indeed, CoAP has been designed to be easily mapped to HTTP. In this paper we discuss the HTTP mapping of CoAP, and we highlight its different facets and issues. We will describe what are the currently open issues, spanning the deployment of an HTTP-CoAP proxy to the security mapping issues. We also share the authors' experience gained in the design and implementation of two distinct HTTP-CoAP proxies.
A robust algorithm for point set registration using mixture of Gaussians

This paper proposes a novel and robust approach to the point set registration problem in the presence of large amounts of noise and outliers. Each of the point sets is represented by a mixture of Gaussians and the point set registration is treated as a problem of aligning the two mixtures. We derive a closed-form expression for the L/sub 2/distance between two Gaussian mixtures, which in turn leads to a computationally efficient registration algorithm. This new algorithm has an intuitive interpretation, is simple to implement and exhibits inherent statistical robustness. Experimental results indicate that our algorithm achieves very good performance in terms of both robustness and accuracy.
Building Energy Management Systems — Optimization of comfort and energy use

Building Energy Management Systems (BEMS) have been introduced in the built environment as a mechanism to increase the energy efficiency while maintaining the required comfort levels. BEMS also offers promising flexibility for Demand Side Management and Demand Responses (DSM&DR) to interact with Smart Grids. Through the use of intelligent control techniques, BEMS can be optimized to exploit the built environment' flexibility while ensuring the minimum comfort levels required by the building's user. This paper presents an analysis framework for BEMS based on a state of the art literature review. Furthermore, Simscape is introduced as a simulation tool that can model effectively sub-systems in the BEMS.
2-D periodic leaky-wave Antennas-part II: slot design

The far-field radiation patterns of a two-dimensional (2-D) periodic slot leaky-wave antenna (LWA) are studied. The antenna consists of a two-dimensional periodic array of slots in a conducting plane that is printed on top of a grounded dielectric slab. The antenna is excited by a simple source such as a dipole inside the slab. Reciprocity along with the spectral-domain method is used to calculate the far-field pattern, and the radiation characteristics of the structure are investigated. A comparison between the present periodic slot LWA and a 2-D periodic patch LWA discussed in Part I is given to show the advantages of the slot antenna for certain applications. The slot LWA can achieve high directivity patterns, and a circularly-polarized version of the antenna can achieve good circular-polarization at broadside.
Summarization Using Unsupervised Deep Learning

We present methods of extractive query-oriented single-document summarization using a deep auto-encoder (AE) to compute a feature space from the term-frequency (tf ) input. Our experiments explore both local and global vocabularies. We investigate the effect of adding small random noise to local tf as the input representation of AE, and propose an ensemble of such noisy AEs which we call the Ensemble Noisy Auto-Encoder (ENAE). ENAE is a stochastic version of an AE that adds noise to the input text and selects the top sentences from an ensemble of noisy runs. In each individual experiment of the ensemble, a different randomly generated noise is added to the input representation. This architecture changes the application of the AE from a deterministic feed-forward network to a stochastic runtime model. Experiments show that the AE using local vocabularies clearly provide a more discriminative feature space and improves the recall on average 11.2%. The ENAE can make further improvements, particularly in selecting informative sentences. To cover a wide range of topics and structures, we perform experiments on two different publicly available email corpora that are specifically designed for text summarization. We used ROUGE as a fully automatic metric in text summarization and we presented the average ROUGE-2 recall for all experiments.
PDMS-based triboelectric and transparent nanogenerators with ZnO nanorod arrays.

Vertically-grown ZnO nanorod arrays (NRAs) on indium tin oxide (ITO)-coated polyethylene terephthalate (PET), as a top electrode of nanogenerators, were investigated for the antireflective property as well as an efficient contact surface in bare polydimethysiloxane (PDMS)-based triboelectric nanogenerators. Compared to conventional ITO-coated PET (i.e., ITO/PET), the ZnO NRAs considerably suppressed the reflectance from 20 to 9.7% at wavelengths of 300-1100 nm, creating a highly transparent top electrode, as demonstrated by theoretical analysis. Also, the interval time between the peaks of generated output voltage under external pushing forces was significantly decreased from 1.84 to 0.19 s because the reduced contact area of the PDMS by discrete surfaces of the ZnO NRAs on ITO/PET causes a rapid sequence for triboelectric charge generation process including rubbing and separating. Therefore, the use of this top electrode enabled to operate the transparent PDMS-based triboelectric nanogenerator at high frequency of external pushing force. Under different external forces of 0.3-10 kgf, the output voltage and current were also characterized.
LTEV2Vsim: An LTE-V2V simulator for the investigation of resource allocation for cooperative awareness

With the increasing needs of the automotive industry, 3GPP is developing new functionalities in the long term evolution (LTE) cellular technology to specifically address vehicle-to-everything (V2X) connectivity, including in particular vehicle-to-vehicle (V2V) direct communications. At the basis of most enabled applications, either dealing with safety, traffic management or infotainment, it is of particular importance the cooperative awareness service, corresponding to messages sent periodically in broadcast by all vehicles to inform about their status and intentions. To optimize the usage of radio resources, allocations of packets are required to be smartly performed, following algorithms that currently represent a challenging open issue. Having in mind to face the design and validation of resource allocation in LTE-V2V networks when cooperative awareness is addressed, we implemented a simulator, called LTEV2Vsim, which will be shared with all the interested researchers. LTEV2Vsim is written in MATLAB and manages vehicles mobility either following simple models or more complex and realistic input files, and performs allocations following different implemented algorithms, both network controlled and autonomous. Finally, to demonstrate the possibilities of LTEV2Vsim, some example results are provided.
Feature Generating Networks for Zero-Shot Learning

Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets - CUB, FLO, SUN, AWA and ImageNet - in both the zero-shot learning and generalized zero-shot learning settings.
Exploiting semantic web knowledge graphs in data mining

Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many information systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations between them, which can be utilized in various natural language processing, information retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open  In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. Identifying, collecting and integrating useful background knowledge for a given data mining application can be a tedious and time consuming task. Furthermore, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. Therefore, in Part I, we evaluate unsupervised feature generation strategies from types and relations in knowledge graphs, which are used in different data mining tasks, i.e., classification, regression, and outlier detection. As the number of generated features grows rapidly with the number of instances in the dataset, we provide a strategy for feature selection in hierarchical feature space, in order to select only the most informative and most representative features for a given dataset. Furthermore, we provide an end-to-end tool for mining the Web of Linked Data, which provides functionalities for each step of the knowledge discovery process, i.e., linking local data to a Semantic Web knowledge graph, integrating features from multiple knowledge graphs, feature generation and selection, and building machine learning models. However, we show that such feature generation strategies often lead to high dimensional feature vectors even after
Discriminative key-component models for interaction detection and recognition

Not all frames are equal – selecting a subset of discriminative frames from a video can improve performance at detecting and recognizing human interactions. In this paper we present models for categorizing a video into one of a number of predefined interactions or for detecting these interactions in a long video sequence. The models represent the interaction by a set of key temporal moments and the spatial structures they entail. For instance: two people approaching each other, then extending their hands before engaging in a “handshaking” interaction. Learning the model parameters requires only weak supervision in the form of an overall label for the interaction. Experimental results on the UT-Interaction and VIRAT datasets verify the efficacy of these structured models for human interactions.
A survey on multiple object tracking algorithm

Visual Multiple Object Tracking (VMOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. There are many different approaches have been proposed to solve the problem. Compared with single object tracking which focuses on appearance model, motion model and other factors, multiple object tracking shares these common challenges, and has some more challenging tasks to be tackled, such as identifying among multiple objects, frequent occlusion due to the crowd, initializing and terminating of the object tracking, et al, which making it difficult to get a comprehensive understanding of this problem. In this paper, we try to make systematic review on VMOT. We review the recent advances in various aspects and introduce the classification of each aspect in detail. The main aspects include observation model and tracking algorithm. In each aspect, the existing methods are classified into different groups, and each group is discussed in details for their principles, advantages and drawbacks. This review work can provide researchers, especially new comers to the topic of VMOT, a general understanding of the state-of-the-arts, and help them to comprehend the aspects of a VMOT system and the inter-connection of these aspects.
Numerical Recipes 3rd Edition: The Art of Scientific Computing

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Comparing a Scalable SDN Simulation Framework Built on ns-3 and DCE with Existing SDN Simulators and Emulators

As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.
Natural language question answering over RDF: a graph data driven approach

RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a national language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.
Clinical Presentation of Klinefelter's Syndrome: Differences According to Age

The aim of the study was to establish the characteristics of presentation of 94 patients with Kinelfelter's syndrome (KS) referred to the endocrinologist at different ages. The diagnosis of KS was more frequent in the age group between 11 and 20 years (46.8%). Most of the patients (83.7%) showed the classic 47,XXY karyotype and 7.1% showed a 47,XXY/46,XY mosaicism. Half of the patients younger than 18 years presented mild neurodevelopmental disorders. The most frequent clinical findings were cryptorchidism in prepubertal patients, and small testes, cryptorchidism, and gynecomastia in pubertal patients. FSH, LH, AMH, and inhibin B levels were normal in prepubertal patients and became abnormal from midpuberty. Most adults were referred for small testes, infertility, and gynecomastia; 43.6% had sexual dysfunction. Testosterone levels were low in 45%. Mean stature was above the 50th percentile, and 62.5% had BMI ≥25.0 kg/m(2). In conclusion, the diagnosis of Klinefelter syndrome seems to be made earlier nowadays probably because pediatricians are more aware that boys and adolescents with neuro-developmental disorders and cryptorchidism are at increased risk. The increasing use of prenatal diagnosis has also decreased the mean age at diagnosis and allowed to get insight into the evolution of previously undiagnosed cases, which probably represent the mildest forms. In adults average height and weight are slightly higher than those in the normal population. Bone mineral density is mildly affected, more at the spine than at the femoral neck level, in less than half of cases.
Complications of botulinum toxin A use in facial rejuvenation.

The esthetic application of botulinum toxin type A (Botox) is a safe treatment modality; nevertheless complications can occur as a result of patient-and physician-related factors. Fortunately, adverse effects and undesirable sequelae after Botox injections are temporary. Complications may be more serious in patients who have more severe rhytids (which require more Botox), previous facial plastic surgery (altered anatomy), and those who have pre-existing neuromuscular disease. The physician can reduce complications by using proper injection techniques, appropriate regional Botox dosing, and by being conservative in the overall approach to Botox-mediated facial rejuvenation.