nils-herrmann/clean_scirepeval huggingface dataset:
A Feature-Based Approach for Group-Wise Grid Map Registration

For autonomous vehicles and advanced driver assistance systems, information on the actual state of the environment is fundamental for localization and mapping tasks. Localization benefits from multiple observations of the same location at different times as these may provide important information on static and mobile objects. For efficient mapping, the environment may be explored in parallel. For these purposes, multiple observations represented by grid maps have to be aligned into one mutual frame. This paper addresses the problem of group-wise grid map registration using an image processing approach. For registration, a rotational-invariant descriptor is proposed in order to provide the correspondences of points of interest in radar-based occupancy grid maps. As pairwise registration of multiple grid maps suffers from bias, this paper proposes a graph-based approach for robust registration of multiple grid maps. This will facilitate highly accurate range sensor maps for the aforementioned purposes. Large-scale experiments show the benefit of the proposed methods and compare it to state-of-the-art algorithms on radar measurements.
On Training Targets for Supervised Speech Separation

Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation.
Towards Building a Knowledge Base of Monetary Transactions from a News Collection

We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques. The developed techniques allow for semi-automatic population of a financial knowledge base, which, in turn, may be used to support a range of data mining and exploration tasks. The key challenge we face in this domain is that the same event is often reported multiple times, with varying correctness of details. We address this challenge by first collecting all information pertinent to a given event from the entire corpus, then considering all possible representations of the event, and finally, using a supervised learning method, to rank these representations by the associated confidence scores. A main innovative element of our approach is that it jointly extracts and stores all attributes of the event as a single representation (quintuple). Using a purpose-built test set we demonstrate that our supervised learning approach can achieve 25% improvement in F1-score over baseline methods that consider the earliest, the latest or the most frequent reporting of the event.
Automatic Detection of Tone Mispronunciation in Mandarin

In this paper we present our study on detecting tone mispronunciations in Mandarin. Both template and HMM approaches are investigated. Schematic templates of pitch contours are shown to be impractical due to their larger pitch range of inter-, even intra-speaker variation. The statistical Hidden Markov Models (HMM) is used to generate a Goodness of Pronunciation (GOP) score for detection with an optimized threshold. To deal with the discontinuity issue of the F0 in speech, the multi-space distribution (MSD) modeling is used for building corresponding HMMs. Under an MSD-HMM framework, detection performance of different choices of features, HMM types and GOP measures are evaluated.
Field-stop layer optimization for 1200V FS IGBT operating at 200˚C

This paper is concerned with design considerations for enabling the operation of Field-Stop Insulated Gate Bipolar Transistors (FS IGBTs) at 200 C. It is found that through a careful optimization of the Field-Stop layer doping profile the device has a low leakage current and delivers a favorable tradeoff between the on-state voltage (Von) and turn-off loss (Eoff). An investigation of the adverse effects of increasing the junction temperature on the temperature-dependent properties of the FS IGBTs is also discussed herein.
Towards semantic context-aware drones for aerial scenes understanding

Visual object tracking with unmanned aerial vehicles (UAVs) plays a central role in the aerial surveillance. Reliable object detection depends on many factors such as large displacements, occlusions, image noise, illumination and pose changes or image blur that may compromise the object labeling. The paper presents a proposal for a hybrid solution that adds semantic information to the video tracking processing: along with the tracked objects, the scene is completely depicted by data from places, natural features, or in general Points of Interest (POIs). Each scene from a video sequence is semantically described by ontological statements which, by inference, support the object identification which often suffers from some weakness in the object tracking methods. The synergy between the tracking methods and semantic technologies seems to bridge the object labeling gap, enhance the understanding of the situation awareness, as well as critical alarming situations.
Face Recognition: A Hybrid Neural Network Approach

Faces represent complex, multidimensional, meaningful visual stimuli an d developing a computational model for face recognition is difficult [42]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sam pling, a self-organizing map neural network, and a convolutional neural network. The self-organizi ng map provides a quantization of the image samples into a topological space where inputs that are n e rby in the original space are also nearby in the output space, thereby providing dimensionality red uction and invariance to minor changes in the image sample, and the convolutional neural network prov ides for partial invariance to translation, rotation, scale, and deformation. The convolutional net work extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen -Loève transform in place of the self-organizing map, and a multi-layer perceptron in place of the con volutional network. The Karhunen-Loève transform performs almost as well (5.3% error versus 3 .8%). The multi-layer perceptron performs very poorly (40% error versus 3.8%). The method is cap able of rapid classification, requires only fast, approximate normalization and preprocessing, and cons istently exhibits better classification performance than the eigenfaces approach  on the database consider ed as the number of images per person in the training database is varied from 1 to 5. With 5 imag es per person the proposed method and eigenfaces result in 3.8% and 10.5% error respectively. The recogni zer provides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% of the examples. We use a database of 400 images of 40 individuals which cont ains quite a high degree of variability in expression, pose, and facial details. We analyze computati onal complexity and discuss how new classes could be added to the trained recognizer.
Can your paper evade the editor ’ s axe ? Towards an AI assisted peer review system

This work is an exploratory study of how we could progress a step towards an AI assisted peerreview system. The proposed approach is an ambitious attempt to automate the Desk-Rejection phenomenon prevalent in academic peer review. In this investigation we first attempt to decipher the possible reasons of rejection of a scientific manuscript from the editors desk. To seek a solution to those causes, we combine a flair of information extraction techniques, clustering, citation analysis to finally formulate a supervised solution to the identified problems. The projected approach integrates two important aspects of rejection: i) a paper being rejected because of out of scope and ii) a paper rejected due to poor quality. We extract several features to quantify the quality of a paper and the degree of in-scope exploring keyword search, citation analysis, reputations of authors and affiliations, similarity with respect to accepted papers. The features are then fed to standard machine learning based classifiers to develop an automated system. On a decent set of test data our generic approach yields promising results across 3 different journals. The study inherently exhibits the possibility of a redefined interest of the research community on the study of rejected papers and inculcates a drive towards an automated peer review system.
Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search

This paper presents a comparison between two feature selection methods, the Importance Score (IS) which is based on a greedy-like search and a genetic algorithm-based (GA) method, in order to better understand their strengths and limitations and their area of application. The results of our experiments show a very strong relation between the nature of the data and the behavior of both systems. The Importance Score method is more efficient when dealing with little noise and small number of interacting features, while the genetic algorithms can provide a more robust solution at the expense of increased computational effort.
Partially Observable Reinforcement Learning for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as ‘Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.
FA-RPN: Floating Region Proposals for Face Detection

We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals like iterative refinement, placement of fractional anchors and changing anchors which can be enabled without making any changes to the trained model. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.
Developmental dyslexia: genetic dissection of a complex cognitive trait

Developmental dyslexia, a specific impairment of reading ability despite adequate intelligence and educational opportunity, is one of the most frequent childhood disorders. Since the first documented cases at the beginning of the last century, it has become increasingly apparent that the reading problems of people with dyslexia form part of a heritable neurobiological syndrome. As for most cognitive and behavioural traits, phenotypic definition is fraught with difficulties and the genetic basis is complex, making the isolation of genetic risk factors a formidable challenge. Against such a background, it is notable that several recent studies have reported the localization of genes that influence dyslexia and other language-related traits. These investigations exploit novel research approaches that are relevant to many areas of human neurogenetics.
MauveDB: supporting model-based user views in database systems

Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.
Interoception: the sense of the physiological condition of the body

Converging evidence indicates that primates have a distinct cortical image of homeostatic afferent activity that reflects all aspects of the physiological condition of all tissues of the body. This interoceptive system, associated with autonomic motor control, is distinct from the exteroceptive system (cutaneous mechanoreception and proprioception) that guides somatic motor activity. The primary interoceptive representation in the dorsal posterior insula engenders distinct highly resolved feelings from the body that include pain, temperature, itch, sensual touch, muscular and visceral sensations, vasomotor activity, hunger, thirst, and 'air hunger'. In humans, a meta-representation of the primary interoceptive activity is engendered in the right anterior insula, which seems to provide the basis for the subjective image of the material self as a feeling (sentient) entity, that is, emotional awareness.
A Study on the Design Optimization of an AUV by Using Computational Fluid Dynamic Analysis

Autonomous Underwater Vehicles (AUV's) provide an important means for collecting detailed scientific information from the ocean depths. The hull resistance of an AUV is an important factor in determining the power requirements and range of the vehicle. This paper describes a design method using Computational Fluid Dynamics (CFD) for determining the hull resistance of an AUV under development. The CFD results reveal the distribution of hydrodynamic values (velocity, pressure, etc.) of the AUV with a ducted propeller. The optimization of the AUV hull profile for reducing the total resistance is also discussed in this paper. This paper demonstrates that shape optimization in conceptual design is possible by using a commercial CFD package. The optimum designs to minimize the drag force of the AUV were carried out, for a given object function and constraints.
Optimization of Artificial Bee Colony Algorithm for Clustering in Data Mining

Data mining is a powerful new technology, which aims at the extraction of hidden predictive information from large databases. Data pre-processing involves many tasks including detecting outliers, recovering incomplete data and correcting errors. Outlier detection is an important pre-processing task. It is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. It has many uses in applications like fraud detection, network intrusion detection and clinical diagnosis of diseases. Using clustering algorithms for outlier detection is a technique that is frequently used. The clustering algorithms consider outlier detection only to the point they do not interfere with the clustering process. In this paper, an efficient method has been proposed which is based on Fuzzy clustering using Artificial Bee Colony algorithm for detecting the outliers.
Bank of One: Empirical Analysis of Peer-to-Peer Financial Marketplaces

Peer to peer financial marketplaces provide a platform for individual lenders and borrowers to interact and transact. These marketplaces disintermediate the traditional financial services business models. In this exploratory paper we study the operation and effectiveness of one such marketplace: Prosper.com. We analyze six months of lender, borrower and loan repayment data to answer preliminary research questions about lender behavior, market effectiveness and antecedents of loan default. We show that lenders mostly behave rationally and charge appropriate risk premiums for antecedents of loan default. We also show that there are mismatches between risk premiums charged and relative importance of factors that drive loan default. We then explore the dynamic process of lenders adjusting their lending strategies to reduce these mismatches. We analyze the effectiveness of the group reputation used in the marketplace and show that it is not effective in promoting good borrower behavior. Our analysis provides a base for future research in this exciting and evolving context. Our results provide directions for practice applications as well as future research in design of financial marketplaces, investing and risk mitigation strategies and improving the effectiveness of peer-to-peer financial marketplaces.
Lightweight Authentication Scheme with Dynamic Group Members in IoT Environments

In IoT environments, the user may have many devices to connect each other and share the data. Also, the device will not have the powerful computation and storage ability. Many studies have focused on the lightweight authentication between the cloud server and the client in this environment. They can use the cloud server to help sensors or proxies to finish the authentication. But in the client side, how to create the group session key without the cloud capability is the most important issue in IoT environments. The most popular application network of IoT environments is the wireless body area network (WBAN). In WBAN, the proxy usually needs to control and monitor user's health data transmitted from the sensors. In this situation, the group authentication and group session key generation is needed. In this paper, in order to provide an efficient and robust group authentication and group session key generation in the client side of IoT environments, we propose a lightweight authentication scheme with dynamic group members in IoT environments. Our proposed scheme can satisfy the properties including the flexible generation of shared group keys, the dynamic participation, the active revocation, the low communication and computation cost, and no time synchronization problem. Also our scheme can achieve the security requirements including the mutual authentication, the group session key agreement, and prevent all various well-known attacks.
Drunk person identification using thermal infrared images

Drunk person identification is carried out using thermal infrared images. The features used for this purpose are simply the pixel values on specific points on the face of the person. It is proved that for a drunk person, the corresponding cluster in the feature space moves far away from its original position for the sober person. The concept behind the proposed approach is based on the physiology-based face identification. For demonstration purposes, Fisher Linear Discriminant approach is used for space dimensionality reduction. The feature space is found to be of very low dimensionality.
A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics

Facial micro-expressions are very brief, spontaneous facial expressions that appear on the face of humans when they either deliberately or unconsciously conceal an emotion. Micro-expression has shorter duration than macro-expression, which makes it more challenging for human and machine. Over the past ten years, automatic micro-expressions recognition has attracted increasing attention from researchers in psychology, computer science, security, neuroscience and other related disciplines. The aim of this paper is to provide the insights of automatic micro-expressions analysis and recommendations for future research. There has been a lot of datasets released over the last decade that facilitated the rapid growth in this field. However, comparison across different datasets is difficult due to the inconsistency in experiment protocol, features used and evaluation methods. To address these issues, we review the datasets, features and the performance metrics deployed in the literature. Relevant challenges such as the spatial temporal settings during data collection, emotional classes versus objective classes in data labelling, face regions in data analysis, standardisation of metrics and the requirements for real-world implementation are discussed. We conclude by proposing some promising future directions to advancing micro-expressions research.