nils-herrmann/clean_scirepeval huggingface dataset:
Non-Orthogonal Multiple Access Based Integrated Terrestrial-Satellite Networks

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max–min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.
Local Rademacher Complexity for Multi-Label Learning

We analyze the local Rademacher complexity of empirical risk minimization-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the singular values of the predictor in multi-label learning. Benefiting from the use of the local Rademacher complexity, our algorithm, therefore, has a sharper generalization error bound. Compared with methods that minimize over all singular values, concentrating on the tail singular values results in better recovery of the low-rank structure of the multi-label predictor, which plays an important role in exploiting label correlations. We propose a new conditional singular value thresholding algorithm to solve the resulting objective function. Moreover, a variance control strategy is employed to reduce the variance of variables in optimization. Empirical studies on real-world data sets validate our theoretical results and demonstrate the effectiveness of the proposed algorithm for multi-label learning.
Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.
CLAMS: Bringing Quality to Data Lakes

With the increasing incentive of enterprises to ingest as much data as they can in what is commonly referred to as "data lakes", and with the recent development of multiple technologies to support this "load-first" paradigm, the new environment presents serious data management challenges. Among them, the assessment of data quality and cleaning large volumes of heterogeneous data sources become essential tasks in unveiling the value of big data. The coveted use of unstructured and semi-structured data in large volumes makes current data cleaning tools (primarily designed for relational data) not directly adoptable.
 We present CLAMS, a system to discover and enforce expressive integrity constraints from large amounts of lake data with very limited schema information (e.g., represented as RDF triples). This demonstration shows how CLAMS is able to discover the constraints and the schemas they are defined on simultaneously. CLAMS also introduces a scale-out solution to efficiently detect errors in the raw data. CLAMS interacts with human experts to both validate the discovered constraints and to suggest data repairs.
 CLAMS has been deployed in a real large-scale enterprise data lake and was experimented with a real data set of 1.2 billion triples. It has been able to spot multiple obscure data inconsistencies and errors early in the data processing stack, providing huge value to the enterprise.
Robust Recovery of Signals From a Union of Subspaces

Traditional sampling theories consider the problem of reco nstructing an unknown signal x from a series of samples. A prevalent assumption which often guarantees a un ique signal consistent with the given measurements is thatx lies in a known subspace. Recently, there has been growing in terest in nonlinear but structured signal models, in which x is assumed to lie in a union of subspaces. An example is the cas e in whichx is a finite length vector that is sparse in a given basis. In this paper we develop a general f ramework for robust and efficient recovery of such signals from a given set of samples. More specifically, we tre at the case in whichx lies in a finite union of finite dimensional spaces and the samples are modelled as inner pro ducts with an arbitrary set of sampling functions. We first develop conditions under which unique and stable rec ov ry of x is possible, albeit with algorithms that have combinatorial complexity. To derive an efficient and ro bust recovery algorithm, we then show that our problem can be formulated as that of recovering a block sparse vector , namely a vector whose non-zero elements appear in fixed blocks. To solve this problem, we suggest minimizing a mixed l2/l1 norm subject to the measurement equations. We then develop equivalence conditions under wh ich t e proposed convex algorithm is guaranteed to recover the original signal. These results rely on the notio of block restricted isometry property (RIP), which is a generalization of the standard RIP used extensively in the context of compressed sensing. A special case of the proposed framework is that of recovering multiple measurem ent vectors (MMV) that share a joint sparsity pattern. Specializing our results to this context leads to new MMV rec overy methods as well as equivalence conditions under which the entire set can be determined efficiently.
A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization

In this work we investigate the usefulness of n-grams for document indexing in text categorization (TC). We call n-gram a set gk of n word stems, and we say that gk occurs in a document dj when a sequence of words appears in dj that, after stop word removal and stemming, consists exactly of the n stems in gk, in some order. Previous researches have investigated the use of n-grams (or some variant of them) in the context of specific learning algorithms, and thus have not obtained general answers on their usefulness for TC. In this work we investigate the usefulness of n-grams in TC independently of any specific learning algorithm. We do so by applying feature selection to the pool of all k-grams (k ≤ n), and checking how many n-grams score high enough to be selected in the top σ k-grams. We report the results of our experiments, using various feature selection measures and varying values of σ, performed on the Reuters-21578 standard TC benchmark. We also report results of making actual use of the selected n-grams in the context of a linear classifier induced by means of the Rocchio method.
Geographic Information Systems

datum Basis for measurement of position on the Earth’s surface. discrete object view Conceptualization of geographic phenomena as analogous to a tabletop littered with countable, possibly movable objects. ecological fallacy The fallacy of making inferences about individuals from aggregated data. field view Conceptualization of geographic phenomena as a set of functions over geographic space. geocoding Conversion of addresses, place names, or other geographic references to coordinates, such as latitude and longitude. modifiable areal unit problem The dependence of analysis of aggregate data on the precise reporting zones used for aggregation. polyline Representation of a line as a sequence of points connected by straight segments. spatial analysis A method of analysis whose results depend on the locations of the objects being analyzed. street centerline data set Representation of a street network as a collection of lines approximately centered along each street.
You Only Look Once: Unified, Real-Time Object Detection

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Support vector machine applications in the field of hydrology: A review

In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. SVMs introduced by Vapnik and others in the early 1990s are machine learning systems that utilize a hypothesis space of linear functions in a high dimensional feature space, trained with optimization algorithms that implements a learning bias derived from statistical learning theory. This paper reviews the state-of-the-art and focuses over a wide range of appliupport vector machines
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks

In this paper, we present improvements made to the TED-LIUM corpus we released in 2012. These enhancements fall into two categories. First, we describe how we filtered publicly available monolingual data and used it to estimate well-suited language models (LMs), using open-source tools. Then, we describe the process of selection we applied to new acoustic data from TED talks, providing additions to our previously released corpus. Finally, we report some experiments we made around these improvements.
AXIAL-FLUX PERMANENT-MAGNET MOTOR DESIGN FOR ELECTRIC VEHICLE DIRECT DRIVE USING SIZING EQUATION AND FINITE ELEMENT ANALYSIS

The design process of a double-sided slotted TORUS axialflux permanent-magnet (AFPM) motor suitable for direct drive of electric vehicle (EV) is presented. It used sizing equation and Finite Element Analysis (FEA). AFPM motor is a high-torque-density motor easily mounted compactly onto a vehicle wheel, fitting the wheel rim perfectly. A preliminary design is a double-sided slotted AFPM motor with 6 rotor poles for high torque-density and stable rotation. In determining the design requirements, a simple vehicle-dynamics model that evaluates vehicle performance through the typical cruising trip of an automobile was considered. To obtain, with the highest possible torque, the initial design parameters of the motor, AFPM’s fundamental theory and sizing equation were applied. Vector Field Opera-3D 14.0 commercial software ran the FEA of the motor design, evaluating and enhancing accuracy of the design parameters. Results of the FEA simulation were compared with those obtained from the sizing equation; at no-load condition, the flux density at every part of the motor agreed. The motor’s design meets all the requirements and limits of EV, and fits the shape and size of a classical-vehicle wheel rim. The design process is comprehensive and can be used for an arbitrary EV with an arbitrary cruising scenario.
Functional maps: a flexible representation of maps between shapes

We present a novel representation of maps between pairs of shapes that allows for efficient inference and manipulation. Key to our approach is a generalization of the notion of map that puts in correspondence real-valued functions rather than points on the shapes. By choosing a multi-scale basis for the function space on each shape, such as the eigenfunctions of its Laplace-Beltrami operator, we obtain a representation of a map that is very compact, yet fully suitable for global inference. Perhaps more remarkably, most natural constraints on a map, such as descriptor preservation, landmark correspondences, part preservation and operator commutativity become linear in this formulation. Moreover, the representation naturally supports certain algebraic operations such as map sum, difference and composition, and enables a number of applications, such as function or annotation transfer without establishing point-to-point correspondences. We exploit these properties to devise an efficient shape matching method, at the core of which is a single linear solve. The new method achieves state-of-the-art results on an isometric shape matching benchmark. We also show how this representation can be used to improve the quality of maps produced by existing shape matching methods, and illustrate its usefulness in segmentation transfer and joint analysis of shape collections.
CNN-based ranking for biomedical entity normalization

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.
Peekaboom: a game for locating objects in images

We introduce Peekaboom, an entertaining web-based game that can help computers locate objects in images. People play the game because of its entertainment value, and as a side effect of them playing, we collect valuable image metadata, such as which pixels belong to which object in the image. The collected data could be applied towards constructing more accurate computer vision algorithms, which require massive amounts of training and testing data not currently available. Peekaboom has been played by thousands of people, some of whom have spent over 12 hours a day playing, and thus far has generated millions of data points. In addition to its purely utilitarian aspect, Peekaboom is an example of a new, emerging class of games, which not only bring people together for leisure purposes, but also exist to improve artificial intelligence. Such games appeal to a general audience, while providing answers to problems that computers cannot yet solve.
Interactive Block Games for Assessing Children's Cognitive Skills: Design and Preliminary Evaluation

This paper presents design and results from preliminary evaluation of Tangible Geometric Games (TAG-Games) for cognitive assessment in young children. The TAG-Games technology employs a set of sensor-integrated cube blocks, called SIG-Blocks, and graphical user interfaces for test administration and real-time performance monitoring. TAG-Games were administered to children from 4 to 8 years of age for evaluating preliminary efficacy of this new technology-based approach.  Five different sets of SIG-Blocks comprised of geometric shapes, segmented human faces, segmented animal faces, emoticons, and colors, were used for three types of TAG-Games, including Assembly, Shape Matching, and Sequence Memory. Computational task difficulty measures were defined for each game and used to generate items with varying difficulty. For preliminary evaluation, TAG-Games were tested on 40 children. To explore the clinical utility of the information assessed by TAG-Games, three subtests of the age-appropriate Wechsler tests (i.e., Block Design, Matrix Reasoning, and Picture Concept) were also administered.  Internal consistency of TAG-Games was evaluated by the split-half reliability test. Weak to moderate correlations between Assembly and Block Design, Shape Matching and Matrix Reasoning, and Sequence Memory and Picture Concept were found. The computational measure of task complexity for each TAG-Game showed a significant correlation with participants' performance. In addition, age-correlations on TAG-Game scores were found, implying its potential use for assessing children's cognitive skills autonomously.
Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup

The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful.



We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new ('blind') articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.
Active learning in multimedia annotation and retrieval: A survey

Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data. It has been widely explored in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze the sample selection criteria. We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria: risk reduction, uncertainty, diversity, density and relevance. We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning. We also provide a discussion on several future trends in this research direction. In particular, we discuss cost analysis of human annotation and large-scale interactive multimedia annotation.
Design pattern mining enhanced by machine learning

Design patterns present good solutions to frequently occurring problems in object-oriented software design. Thus their correct application in a system's design may significantly improve its internal quality attributes such as reusability and maintainability. In software maintenance the existence of up-to-date documentation is crucial, so the discovery of as yet unknown design pattern instances can help improve the documentation. Hence a reliable design pattern recognition system is very desirable. However, simpler methods (based on pattern matching) may give imprecise results due to the vague nature of the patterns' structural description. In previous work we presented a pattern matching-based system using the Columbus framework with which we were able to find pattern instances from the source code by considering the patterns' structural descriptions only, and therefore we could not identify false hits and distinguish similar design patterns such as state and strategy. In the present work we use machine learning to enhance pattern mining by filtering out as many false hits as possible. To do so we distinguish true and false pattern instances with the help of a learning database created by manually tagging a large C++ system.
Moving beyond untagging: photo privacy in a tagged world

Photo tagging is a popular feature of many social network sites that allows users to annotate uploaded images with those who are in them, explicitly linking the photo to each person's profile. In this paper, we examine privacy concerns and mechanisms surrounding these tagged images. Using a focus group, we explored the needs and concerns of users, resulting in a set of design considerations for tagged photo privacy. We then designed a privacy enhancing mechanism based on our findings, and validated it using a mixed methods approach. Our results identify the social tensions that tagging generates, and the needs of privacy tools to address the social implications of photo privacy management.