nils-herrmann/clean_scirepeval huggingface dataset:
An effective algorithm for hyperparameter optimization of neural networks

A major challenge in designing neural network (NN) systems is to determine the best structure and parameters for the network given the data for the machine learning problem at hand. Examples of parameters are the number of layers and nodes, the learning rates, and the dropout rates. Typically, these parameters are chosen based on heuristic rules and manually fine-tuned, which may be very time-consuming, because evaluating the performance of a single parametrization of the NN may require several hours. This paper addresses the problem of choosing appropriate parameters for the NN by formulating it as a box-constrained mathematical optimization problem, and applying a derivative-free optimization tool that automatically and effectively searches the parameter space. The optimization tool employs a radial basis function model of the objective function (the prediction accuracy of the NN) to accelerate the discovery of configurations yielding high accuracy. Candidate configurations explored by the algorithm are trained to a small number of epochs, and only the most promising candidates receive full training. The performance of the proposed methodology is assessed on benchmark sets and in the context of predicting drug-drug interactions, showing promising results. The optimization tool used in this paper is open-source.
Dealing with the new user cold-start problem in recommender systems: A comparative review

The Recommender System (RS) is an efficient tool for decision makers that assists in the selection of appropriate items according to their preferences and interests. This system has been applied to various domains to personalize applications by recommending items such as books, movies, songs, restaurants, news articles and jokes, among others. An important issue problem, which occurs when there is a new user that has been registered to the system and no prior rating of this user is found in the rating table. In this paper, we first present a classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format. Next, some typical algorithms of these groups, such as MIPFGWC-CS, NHSM, FARAMS and HU–FCF, are described. Finally, these algorithms are implemented and validated on some benchmark RS datasets under various settings of the new user cold start. The experimental results indicate that NHSM achieves better accuracy and computational time than the relevant methods. & 2014 Elsevier Ltd. All rights reserved.
Wireless Communication Systems

This textbook introduces wireless communications from the viewpoint of wireless communication system designers. Existing textbooks on wireless communications pay more emphasis on fundamental principles and on the derivation of fundamental equations in the field. In this textbook, we try to pass to the readers as much knowledge that is most relevant to wireless system design as possible. We also give a comprehensive and state-of-the-art introduction to the enabling techniques for current and future generation wireless communications, such as CDMA, OFDM, smart antennas, MIMO, UWB, turbo and LDPC coding, cog-nitive radio, and wireless ad hoc/sensor networks. While some of the wireless communications textbooks have mentioned front-end subsystems, such as antennas and microwave/RF subsystems, this textbook is the first of its kind that gives details on how to design and select the antennas, microwave/RF subsystems , and A/D and D/A converters. This is most helpful for readers to get a clear knowledge of the whole wireless system. Also, this book is the first wireless communication textbook that gives a comprehensive introduction to speech coders and video coders that are used in wireless communication systems. This book is helpful for all academic and technical staff in the fields of telecommunications and wireless communications. Many examples are given to help the readers to understand the material covered in the book. Additional resources such as the MATLAB codes for some of the examples are available online at He has also published more than 30 papers, and has one U. S. patent pending. He is on the editorial boards of IET Signal Processing and Circuits, Systems & Signal Processing, and is on technical program committees of ICONIP 2009 and PIMRC'09. He has reviewed papers for dozens of journals and conferences. He is a member of the IEEE. Currently, his research interests are signal processing, wireless communications, RF systems, and neural networks. Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Contents Preface page xx List of Abbreviations xxiii 1 Introduction 1 1.1 The wireless age 1 1.2 Spectrum of electromagnetic waves 2 1.3 Block diagram of a communication system 3 1.4 Architecture of radio transceivers 3 1.4.1 Super-heterodyne transceivers 4 1.4.2 Direct-conversion transceivers 5 1.5 Orgaization of the book 8 2
Multi-Type Itemset Embedding for Learning Behavior Success

Contextual behavior modeling uses data from multiple contexts to discover patterns for predictive analysis. However, existing behavior prediction models often face difficulties when scaling for massive datasets. In this work, we formulate a behavior as a set of context items of different types (such as decision makers, operators, goals and resources), consider an observable itemset as a behavior success, and propose a novel scalable method, "multi-type itemset embedding", to learn the context items' representations preserving the success structures. Unlike most of existing embedding methods that learn pair-wise proximity from connection between a behavior and one of its items, our method learns item embeddings collectively from interaction among all multi-type items of a behavior, based on which we develop a novel framework, LearnSuc, for (1) predicting the success rate of any set of items and (2) finding complementary items which maximize the probability of success when incorporated into an itemset. Extensive experiments demonstrate both effectiveness and efficency of the proposed framework.
Shape from periodic texture using the spectrogram

Texture has long been recognized in computer vision as an important monocular shape cue, with texture gradients yielding information on surface orientation. A more recent trend is the analysis of images in terms of local spatial frequencies, where each pixel has associated with it its own spatial frequency distribution. This has proven to be a successful method of reasoning about and exploiting many imaging phenomena. Thinking about both shape-from-texture and local spatial frequency, it seems that texture gradients would cause systematic changes in local frequency, and that these changes could be analyzed to extract shape information. However, there does not yet exist a theory that connects texture, shape, and the detailed behavior of local spatial frequency. We show in this paper how local spatial frequency is related to the surface normal of a textured surface. We find that the Fourier power spectra of any two similarly textured patches on a plane are approximately related to each other by an affine transformation. The transformation parameters are a function of the plane’s surface normal. We use this relationship as the basis of a new algorithm for finding surface normals of textured shapes using the spectrogram, which is one type of local spatial frequency representation. We validate the relationship by testing the algorithm on real textures. By analyzing shape and texture in terms of the local spatial frequency representation, we can exploit the advantages of the representation for the shape-from-texture problem. Specifically, our algorithm requires no feature detection and can give correct results even when the texture is aliased.
Text Localization Based on Fast Feature Pyramids and Multi-Resolution Maximally Stable Extremal Regions

Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach during both feature computation and connected component extraction allows our method to identify uncommon text elements that are usually not detected by competing algorithms, while the adoption of approximated features and appropriately filtered connected components assures a low overall computational complexity of the proposed system.
Hesitant fuzzy analytic hierarchy process

Ordinary fuzzy sets have been recently extended to intuitionistic and hesitant fuzzy sets, which are frequently used for the solution of decision-making problems. All these extensions aim at better defining the membership values or functions of the considered parameters. In this paper we develop a hesitant fuzzy analytic hierarchy process method involving multi-experts' linguistic evaluations aggregated by ordered weighted averaging (OWA) operator. The developed hesitant fuzzy AHP method is applied to a multicriteria supplier selection problem.
Fully automatic image colorization based on Convolutional Neural Network

This paper deals with automatic image colorization. This is a very difficult task, since it is an ill-posed problem that usually requires user intervention to achieve high quality. A fully automatic approach is proposed that is able to produce realistic colorization of an input grayscale image. Motivated by the recent success of deep learning techniques in image processing, we propose a feed-forward, two-stage architecture based on Convolutional Neural Network that predicts the U and V color channels. Unlike most of the previous works, this paper presents a fully automatic colorization which is able to produce high-quality and realistic colorization even of complex scenes. Comprehensive experiments and qualitative and quantitative evaluations were conducted on the images of SUN database and on other images. We have found that Quaternion Structural Similarity (QSSIM) gives in some degree a good base for quantitative evaluation, that is why we chose QSSIM as an index-number for the quality of colorization.
Syntax Annotation for the GENIA Corpus

Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.
Distant Supervision for Relation Extraction with Hierarchical Attention and Entity Descriptions

Distant supervision for relation extraction is an effective method to find novel relational facts from plain text. However, distant supervision inevitably suffers from wrong label problem, and most existing methods generally focus on direct sentences containing entity pairs, but ignore massive information from background knowledge. To tackle these problems, we propose a novel hierarchical attention model to select valid instances and capture vital semantic information in them. Furthermore, we incorporate entity descriptions extracted from Wikipedia into the hierarchical attention model to provide supplementary background knowledge. The proposed architecture can not only combat the noise introduced by distant supervision, but also adequately extract latent and helpful background information. The experimental results on both Chinese and English datasets show that the proposed approach consistently achieves significant improvements on relation extraction as compared with strong baselines.
Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements.
Formal Query Generation for Question Answering over Knowledge Bases

Question answering (QA) systems often consist of several components such as Named Entity Disambiguation (NED), Relation Extraction (RE), and Query Generation (QG). In this paper, we focus on the QG process of a QA pipeline on a large-scale Knowledge Base (KB), with noisy annotations and complex sentence structures. We therefore propose SQG, a SPARQL Query Generator with modular architecture, enabling easy integration with other components for the construction of a fully functional QA pipeline. SQG can be used on large open-domain KBs and handle noisy inputs by discovering a minimal subgraph based on uncertain inputs, that it receives from the NED and RE components. This ability allows SQG to consider a set of candidate entities/relations, as opposed to the most probable ones, which leads to a significant boost in the performance of the QG component. The captured subgraph covers multiple candidate walks, which correspond to SPARQL queries. To enhance the accuracy, we present a ranking model based on Tree-LSTM that takes into account the syntactical structure of the question and the tree representation of the candidate queries to find the one representing the correct intention behind the question. SQG outperforms the baseline systems and achieves a macro F1-measure of 75% on the LC-QuAD dataset.
Direct and Indirect Discrimination Prevention Methods

Along with privacy, discrimination is a very import ant issue when considering the legal and ethical aspects of data mini ng. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those att ribu es are used for making decisions about them like giving them a job, loan, insu rance, etc. Discovering such potential biases and eliminating them from the traini ng data without harming their decision-making utility is therefore highly desirab le. For this reason, antidiscrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prev ention consists of inducing patterns that do not lead to discriminatory decisio n even if the original training datasets are inherently biased. In this chapter, by focusing on the discrimination prevention, we present a taxonomy for classifying a d examining discrimination prevention methods. Then, we introduce a group of p re-processing discrimination prevention methods and specify the different featur es of each approach and how these approaches deal with direct or indirect discr imination. A presentation of metrics used to evaluate the performance of those app ro ches is also given. Finally, we conclude our study by enumerating interesting fu ture directions in this research body.
Wildfire monitoring using a mixed air-ground mobile network

Forest fires are a challenging problem for many countries. They often cause economical lost and ecological damage, and they can sometimes even cost human lives. Finding hot spots immediately after a fire is an important part of fighting forest fires. The main objective is to obtain a temperature map of the burned area, to locate the most critical embers. This information can help firefighter managers make the correct decisions about ground crew movements. The pervasive application described in this article lets firefighters obtain images of hot spots directly from an unmanned aircraft and receive commands from their manager through a communication network. Every firefighter holds a personal electronic device (PED), which includes a touch screen, Wi-Fi connectivity, a GPS receiver, and temperature sensors. Because terrain conditions such as abrupt ravines, rocks, and dense vegetation can introduce obstacles to connectivity, a balloon with a Wi-Fi device is tethered to every firefighter's truck to improve communication. In addition, a fixed-wing unmanned aircraft augments the number of communication layers to three. This article studies the quality of this three-layered network in maintaining the application's bandwidth requirements.
Development of Autonomous Car—Part I: Distributed System Architecture and Development Process

An autonomous car is a self-driving vehicle that has the capability to perceive the surrounding environment and navigate itself without human intervention. For autonomous driving, complex autonomous driving algorithms, including perception, localization, planning, and control, are required with many heterogeneous sensors, actuators, and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, this paper applies distributed system architecture to the autonomous driving system, and proposes a development process and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. For the heterogeneous computing system of the distributed system, a system platform is presented, which provides a common development environment by minimizing the dependence between the software and the computing hardware. A time-triggered network protocol, FlexRay, is applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance. Part II of this paper will provide the evaluation of the development process and system platform by using an autonomous car, which has the ability to drive in an urban area.
Autoencoders, Unsupervised Learning, and Deep Architectures

To better understand deep architectures and unsupervised learning, uncluttered by hardware details, we develop a general autoencoder framework for the comparative study of autoencoders, including Boolean autoencoders. We derive several results regarding autoencoders and autoencoder learning, including results on learning complexity, vertical and horizontal composition, and fundamental connections between critical points and clustering. Possible implications for the theory of deep architectures are discussed.
Conditional molecular design with deep generative models

Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semisupervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by sampling from the generative distribution estimated by the model. We demonstrate the effectiveness of the proposed model by evaluating it on drug-like molecules. The model improves the performance of property prediction by exploiting unlabeled molecules and efficiently generates novel molecules fulfilling various target conditions.
The More You Know: Using Knowledge Graphs for Image Classification

One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.