nils-herrmann/clean_scirepeval huggingface dataset:
A Review on the Meaning of Cognitive Cities

Over the last years, the cognitive city paradigm has emerged as a promising solution to the challenges that megacities of the future will have to face. In this article, we provide a thorough review of the literature on Cognitive Cities. We put in place a clear and strict methodology that allows as to present a carefully curated set of articles that represent the foundations of the current understanding of the concept of cognitive city. Hence, this article is intended to serve as a reference for future studies in the field. We emphasise the ambiguities and overlapping meanings of the cognitive city term depending on the domain of study and the underlying philosophy. Also, we discuss some of the implications that cognitive cities might have on society pillars such as the healthcare sector, and we point out some of the main challenges for the adoption of the concept. Last but not least, we suggest some possible research lines that are to be pursued in the years to come.
Efficient online structured output learning for keypoint-based object tracking

Efficient keypoint-based object detection methods are used in many real-time computer vision applications. These approaches often model an object as a collection of keypoints and associated descriptors, and detection then involves first constructing a set of correspondences between object and image keypoints via descriptor matching, and subsequently using these correspondences as input to a robust geometric estimation algorithm such as RANSAC to find the transformation of the object in the image. In such approaches, the object model is generally constructed offline, and does not adapt to a given environment at runtime. Furthermore, the feature matching and transformation estimation stages are treated entirely separately. In this paper, we introduce a new approach to address these problems by combining the overall pipeline of correspondence generation and transformation estimation into a single structured output learning framework. Following the recent trend of using efficient binary descriptors for feature matching, we also introduce an approach to approximate the learned object model as a collection of binary basis functions which can be evaluated very efficiently at runtime. Experiments on challenging video sequences show that our algorithm significantly improves over state-of-the-art descriptor matching techniques using a range of descriptors, as well as recent online learning based approaches.
Circuit techniques for reducing the effects of op-amp imperfections: autozeroing, correlated double sampling, and chopper stabilization

In linear IC's fabricated in a low-voltage CMOS technology, the reduction of the dynamic range due to the dc offset and low,frequency noise of the ampli$ers becomes increasingly significant. Also, the achievable aniplijier gain is often quite low in such a technology, since cascoding may not be a practical circuit option due to the resulting reduction of the output signal swing. In this paper, some old and some new circuit techniques will be described for the compensation of the amplifier most important nonideal effects including the noise (mainly thermal and l l f noise), the input-referred dc offset voltage, as well as the finite gain resulting in a nonideal virtual ground at the input.
Clustering and projected clustering with adaptive neighbors

Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may not be the optimal one for data clustering and lead to the suboptimal results. In this paper, we propose a novel clustering model to learn the data similarity matrix and clustering structure simultaneously. Our new model learns the data similarity matrix by assigning the adaptive and optimal neighbors for each data point based on the local distances. Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the data similarity matrix, such that the connected components in the resulted similarity matrix are exactly equal to the cluster number. We derive an efficient algorithm to optimize the proposed challenging problem, and show the theoretical analysis on the connections between our method and the K-means clustering, and spectral clustering. We also further extend the new clustering model for the projected clustering to handle the high-dimensional data. Extensive empirical results on both synthetic data and real-world benchmark data sets show that our new clustering methods consistently outperforms the related clustering approaches.
A Performance Comparison of Loss Functions for Deep Face Recognition

The emergence of biometric tools and its increased usage in day to day devices has brought simplicity in the authentication process for the users as compared to the passwords and pattern locks being used. The ease of use of biometric reduces the manual work and helps in faster and automatic authentication. Among different biometric traits, the face is one which can be captured without much cooperation of users. Moreover, face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of Computer Vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. The loss functions play an important role in CNN training. Basically, it generates a huge loss, if the network does not perform well using the current parameter setting. In this paper, we present a performance comparison of different loss functions such as CrossEntropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing. The training and test results are analyzed in this paper.
A Branch and Bound Algorithm for Computing k-Nearest Neighbors

Computation of the k-nearest neighbors generally requires a large number of expensive distance computations. The method of branch and bound is implemented in the present algorithm to facilitate rapid calculation of the k-nearest neighbors, by eliminating the necesssity of calculating many distances. Experimental results demonstrate the efficiency of the algorithm. Typically, an average of only 61 distance computations were made to find the nearest neighbor of a test sample among 1000 design samples.
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors. However, their neighborhood aggregators neglect the unordered and unequal natures of an entity’s neighbors. To this end, we summarize the desired properties that may lead to effective neighborhood aggregators. We also introduce a novel aggregator, namely, Logic Attention Network (LAN), which addresses the properties by aggregating neighbors with both rulesand network-based attention weights. By comparing with conventional aggregators on two knowledge graph completion tasks, we experimentally validate LAN’s superiority in terms of the desired properties.
Integration of visual motion and locomotion in mouse visual cortex

Successful navigation through the world requires accurate estimation of one's own speed. To derive this estimate, animals integrate visual speed gauged from optic flow and run speed gauged from proprioceptive and locomotor systems. The primary visual cortex (V1) carries signals related to visual speed, and its responses are also affected by run speed. To study how V1 combines these signals during navigation, we recorded from mice that traversed a virtual environment. Nearly half of the V1 neurons were reliably driven by combinations of visual speed and run speed. These neurons performed a weighted sum of the two speeds. The weights were diverse across neurons, and typically positive. As a population, V1 neurons predicted a linear combination of visual and run speeds better than either visual or run speeds alone. These data indicate that V1 in the mouse participates in a multimodal processing system that integrates visual motion and locomotion during navigation.
Privacy-preserving data publishing for cluster analysis

Releasing person-specific data could potentially reveal sensitive information about individuals. k-anonymization is a promising privacy protection mechanism in data publishing. Although substantial research has been conducted on k-anonymization and its extensions in recent years, only a few prior works have considered releasing data for some specific purpose of data analysis. This paper presents a practical data publishing framework for generating a masked version of data that preserves both individual privacy and information usefulness for cluster analysis. Experiments on real-life data suggest that by focusing on preserving cluster structure in the masking process, the cluster quality is significantly better than the cluster quality of the masked data without such focus. The major challenge of masking data for cluster analysis is the lack of class labels that could be used to guide the masking process. Our approach converts the problem into the counterpart problem for classification analysis, wherein class labels encode the cluster structure in the data, and presents a framework to evaluate the cluster quality on the masked data.
Recognition of handwritten Roman Numerals using Tesseract open source OCR engine

The objective of the paper is to recognize handwritten samples of Roman numerals using Tesseract open source Optical Character Recognition (OCR) engine. Tesseract is trained with data samples of different persons to generate one user-independent language model, representing the handwritten Roman digit-set. The system is trained with 1226 digit samples collected form the different users. The performance is tested on two different datasets, one consisting of samples collected from the known users (those who prepared the training data samples) and the other consisting of handwritten data samples of unknown users. The overall recognition accuracy is obtained as 92.1% and 86.59% on these test datasets respectively.
Atanassov's Intuitionistic Fuzzy Programming Method for Heterogeneous Multiattribute Group Decision Making With Atanassov's Intuitionistic Fuzzy Truth Degrees

The aim of this paper is to develop a new Atanassov's intuitionistic fuzzy (A-IF) programming method to solve heterogeneous multiattribute group decision-making problems with A-IF truth degrees in which there are several types of attribute values such as A-IF sets (A-IFSs), trapezoidal fuzzy numbers, intervals, and real numbers. In this method, preference relations in comparisons of alternatives with hesitancy degrees are expressed by A-IFSs. Hereby, A-IF group consistency and inconsistency indices are defined on the basis of preference relations between alternatives. To estimate the fuzzy ideal solution (IS) and weights, a new A-IF programming model is constructed on the concept that the A-IF group inconsistency index should be minimized and must be not larger than the A-IF group consistency index by some fixed A-IFS. An effective method is developed to solve the new derived model. The distances of the alternatives to the fuzzy IS are calculated to determine their ranking order. Moreover, some generalizations or specializations of the derived model are discussed. Applicability of the proposed methodology is illustrated with a real supplier selection example.
Comparison of Sparse Representation and Fourier Discriminant Methods : Damage Location Classification in Indirect Lab-scale Bridge Structural Health Monitoring

This paper presents a novel method for interpreting data to improve the indirect structural health monitoring (SHM) of bridges. The research presented in the study is part of an ongoing study aimed at developing a novel SHM paradigm for the health assessment of bridges. In this paradigm, we envision the use of an instrumented vehicle that assesses a bridge’s dynamic characteristics while traveling across the bridge. These characteristics are then correlated to the health of the structure by means of advanced signal processing and pattern recognition approaches. In this paper, we present and compare two classification algorithms that locate the presence of damages at well-defined locations on the structure: sparse representation and the Fourier discriminant methods, and find that the sparse representation method provides superior classification accuracy.
Visualization of boundaries in volumetric data sets using LH histograms

A crucial step in volume rendering is the design of transfer functions that highlights those aspects of the volume data that are of interest to the user. For many applications, boundaries carry most of the relevant information. Reliable detection of boundaries is often hampered by limitations of the imaging process, such as blurring and noise. We present a method to identify the materials that form the boundaries. These materials are then used in a new domain that facilitates interactive and semiautomatic design of appropriate transfer functions. We also show how the obtained boundary information can be used in region-growing-based segmentation.
Advances toward reliable high density Cu-Cu interconnects by Cu-SiO2 direct hybrid bonding

Cu-SiO2 direct hybrid bonding is considered as one of the most promising approaches for matching the needs of three dimensional integrated circuits (3D-IC). In this paper we present the results of a complete morphological, electrical and reliability study conducted on four-layer copper structures realized by Cu-SiO2 direct hybrid bonding. Ultra-fine 7 μm pitch, 3 μm × 3 μm pads daisy chains with up to 30 160 connections are bonded with submicron accuracy, matching the interconnection needs in upcoming 3D circuits. A focus is paid on the influence of the temperature of annealing on the bonding quality and electrical performances. Thanks to SEM, FIB/SEM tomography, AFM, TEM and TEM-EDX analysis, the morphology of the bonded structures is described. Electrical parametric tests exhibit very low resistance structures, with high functional yields and low variability. State-of-the-art contact resistivity is obtained for 200 and 400 °C treatments. Unbiased HAST, temperature cycling and temperature storage tests demonstrate excellent reliability performances. Finally, an electromigration resistance comparative study is conducted on bonded copper lines with TaN/Ta and TiN diffusion barriers.
Homomorphic Tallying for the Estonian Internet Voting System

In this paper we study the feasibility of using homomorphic tallying in the Estonian Internet voting system. The paper analyzes the security benefits provided by homomorphic tallying, the costs introduced and the required changes to the voting system. We find that homomorphic tallying has several security benefits, such as improved ballot secrecy, public verifiability of the vote tallying server and the possibility for observers to recalculate the tally without compromising ballot secrecy. The use of modern elliptic curve cryptography allows homomorphic tallying to be implemented without a significant loss of performance.
Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages

In this paper, we report a study that examines the relationship between image-based computational analyses of web pages and users' aesthetic judgments about the same image material. Web pages were iteratively decomposed into quadrants of minimum entropy (quadtree decomposition) based on low-level image statistics, to permit a characterization of these pages in terms of their respective organizational symmetry, balance and equilibrium. These attributes were then evaluated for their correlation with human participants' subjective ratings of the same web pages on four aesthetic and affective dimensions. Several of these correlations were quite large and revealed interesting patterns in the relationship between low-level (i.e., pixel-level) image statistics and design-relevant dimensions.
Let's FOCUS: Mitigating Mobile Phone Use in College Classrooms

With the increasingly frequent appearance of mobile phones in college classrooms, there have been growing concerns regarding their negative aspects including distractive off-task multitasking. In this work, we design and evaluate Let’s FOCUS, a software-based intervention service that assists college students in self-regulating their mobile phone use in classrooms. Our preliminary survey study (with 47 professors and 283 students) reveals that it is critical to encourage voluntary participation by framing intervention as a learning tool and to raise awareness regarding appropriate mobile phone usage by establishing social norms in colleges. Let’s FOCUS introduces a virtual limiting space for each class (or a virtual classroom) where the students can explicitly restrict their mobile phone use voluntarily. Furthermore, it promotes students’ willing participation by leveraging social facilitation and context-aware reminders associated with virtual classrooms. We conducted a campus-wide campaign for approximately six weeks to evaluate the feasibility of the proposed approach. The results confirm that 379 students used the app to limit 9,335 hours of mobile phone usage over 233 classrooms. Let’s FOCUS was used in diverse learning contexts and for different purposes and its social learning and context-awareness features significantly motivated prolonged participation. We present the design considerations of software-based intervention.
Spatial-temporal wind field prediction by Artificial Neural Networks

The prediction of near surface wind speed is becoming increasingly vital for the operation of electrical energy grids as the capacity of installed wind power grows. The majority of predictive wind speed modeling has focused on point-based time-series forecasting. Effectively balancing demand and supply in the presence of distributed wind turbine electricity generation, however, requires the prediction of wind fields in space and time. Additionally, predictions of full wind fields are particularly useful for future power planning such as the optimization of electricity power supply systems. In this paper, we propose a composite artificial neural network (ANN) model to predict the 6-hour and 24-hour ahead average wind speed over a large area (~3.15×10 km). The ANN model consists of a convolutional input layer, a Long Short-Term Memory (LSTM) hidden layer, and a transposed convolutional layer as the output layer. We compare the ANN model with two non-parametric models, a null persistence model and a mean value model, and find that the ANN model has substantially smaller error than each of these models. Additionally, the ANN model also generally performs better than integrated autoregressive moving average models, which are trained for optimal performance in specific locations.
Overview of Control and Grid Synchronization for Distributed Power Generation Systems

Renewable energy sources like wind, sun, and hydro are seen as a reliable alternative to the traditional energy sources such as oil, natural gas, or coal. Distributed power generation systems (DPGSs) based on renewable energy sources experience a large development worldwide, with Germany, Denmark, Japan, and USA as leaders in the development in this field. Due to the increasing number of DPGSs connected to the utility network, new and stricter standards in respect to power quality, safe running, and islanding protection are issued. As a consequence, the control of distributed generation systems should be improved to meet the requirements for grid interconnection. This paper gives an overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines. In addition, control structures of the grid-side converter are presented, and the possibility of compensation for low-order harmonics is also discussed. Moreover, control strategies when running on grid faults are treated. This paper ends up with an overview of synchronization methods and a discussion about their importance in the control
Unsupervised Concept-to-text Generation with Hypergraphs

Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection (“what to say”) and surface realization (“how to say”) in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We represent our grammar compactly as a weighted hypergraph and recast generation as the task of finding the best derivation tree for a given input. Experimental evaluation on several domains achieves competitive results with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data.
A Parking Occupancy Detection Algorithm Based on AMR Sensor

Recently, with the explosive increase of automobiles in cities, parking problems are serious and even worsen in many cities. This paper proposes a new algorithm for parking occupancy detection based on the use of anisotropic magnetoresistive sensors. Parking occupancy detection is abstracted as binary pattern recognition problem. According to the status of the parking space, the recognition result contains two categories: vacant and occupied. The feature extraction method of the parking magnetic signal is proposed. In addition, the classification criteria are derived based on the distance discriminate analysis method. Eighty-two sensor nodes are deployed on the roadside parking spaces. By running the system for six months, we observed that the accuracy rate of the proposed parking occupancy detection algorithm is better than 98%.