nils-herrmann/clean_scirepeval huggingface dataset:
An iterative Kalman smoother for robust 3D localization on mobile and wearable devices

In this paper, we introduce an Iterative Kalman Smoother (IKS) for tracking the 3D motion of a mobile device in real-time using visual and inertial measurements. In contrast to existing Extended Kalman Filter (EKF)-based approaches, smoothing can better approximate the underlying nonlinear system and measurement models by re-linearizing them. Additionally, by iteratively optimizing over all measurements available, the IKS increases the convergence rate of critical parameters (e.g., IMU-camera clock drift) and improves the positioning accuracy during challenging conditions (e.g., scarcity of visual features). Furthermore, and in contrast to existing inverse filters, the proposed IKS's numerical stability allows for efficient 32-bit implementations on resource-constrained devices, such as cell phones and wearables. We validate the IKS for performing vision-aided inertial navigation on Google Glass, a wearable device with limited sensing and processing, and demonstrate positioning accuracy comparable to that achieved on cell phones. To the best of our knowledge, this work presents the first proof-of-concept real-time 3D indoor localization system on a commercial-grade wearable computer.
The Pyramid Method: Incorporating human content selection variation in summarization evaluation

Human variation in content selection in summarization has given rise to some fundamental research questions: How can one incorporate the observed variation in suitable evaluation measures? How can such measures reflect the fact that summaries conveying different content can be equally good and informative? In this article, we address these very questions by proposing a method for analysis of multiple human abstracts into semantic content units. Such analysis allows us not only to quantify human variation in content selection, but also to assign empirical importance weight to different content units. It serves as the basis for an evaluation method, the Pyramid Method, that incorporates the observed variation and is predictive of different equally informative summaries. We discuss the reliability of content unit annotation, the properties of Pyramid scores, and their correlation with other evaluation methods.
Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12

This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each label, followed by a selection based on the probability of the entity/attribute pair, on that domain. The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition. In the opinion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features. Working in unconstrained mode, our results for slot 1 were assessed with precision between 57% and 63%, and recall varying between 42% and 47%. In sentiment polarity, sentiue’s result accuracy was approximately 79%, reaching the best score in 2 of the 3 domains.
Artificial Intelligence in Clothing Fashion

“Clothes make the man,” said Mark Twain. This article presents a survey of the literature on Artificial Intelligence applications to clothing fashion. An AIbased stylist model is proposed based on fundamental fashion theory and the early work of AI in fashion. This study examines three essential components of a complete styling task as well as previously launched applications and earlier research work. Additionally, the implementation and performance of Neural Networks, Genetic Algorithms, Support Vector Machines and other AI methods used in the fashion domain are discussed in detail. This article explores the focus of previous studies and provides a general overview of the usage of AI techniques in the fashion domain.
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning

Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets.
Deep Architectures for Automated Seizure Detection in Scalp EEGs

Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign variants (e.g., slowing). Commercially available systems suffer from unacceptably high false alarm rates. Deep learning algorithms that employ high dimensional models have not previously been effective due to the lack of big data resources. In this paper, we use the TUH EEG Seizure Corpus to evaluate a variety of hybrid deep structures including Convolutional Neural Networks and Long Short-Term Memory Networks. We introduce a novel recurrent convolutional architecture that delivers 30% sensitivity at 7 false alarms per 24 hours. We have also evaluated our system on a held-out evaluation set based on the Duke University Seizure Corpus and demonstrate that performance trends are similar to the TUH EEG Seizure Corpus. This is a significant finding because the Duke corpus was collected with different instrumentation and at different hospitals. Our work shows that deep learning architectures that integrate spatial and temporal contexts are critical to achieving state of the art performance and will enable a new generation of clinically-acceptable technology.
Video-based heart rate measurement using head motion tracking and ICA

A noninvasive and easy-operation measurement of the heart rate has great potential in home healthcare. We present a simple and high running efficiency method for measuring heart rate from a video. By only tracking one feature point which is selected from a small ROI (Region of Interest) in the head area, we extract trajectories of this point in both X-axis and Y-axis. After a series of processes including signal filtering, interpolation, the Independent Component Analysis (ICA) is used to obtain a periodic signal, and then the heart rate can be calculated. We evaluated on 10 subjects and compared to a commercial heart rate measuring instrument (YUYUE YE680B) and achieved high degree of agreement. A running time comparison experiment to the previous proposed motion-based method is carried out and the result shows that the time cost is greatly reduced in our method.
Generalized Deformable Spatial Pyramid: Geometry-preserving dense correspondence estimation

We present a Generalized Deformable Spatial Pyramid (GDSP) matching algorithm for calculating the dense correspondence between a pair of images with large appearance variations. The main challenges of the problem generally originate in appearance dissimilarities and geometric variations between images. To address these challenges, we improve the existing Deformable Spatial Pyramid (DSP)  model by generalizing the search space and devising the spatial smoothness. The former is leveraged by rotations and scales, and the latter simultaneously considers dependencies between high-dimensional labels through the pyramid structure. Our spatial regularization in the high-dimensional space enables our model to effectively preserve the meaningful geometry of objects in the input images while allowing for a wide range of geometry variations such as perspective transform and non-rigid deformation. The experimental results on public datasets and challenging scenarios show that our method outperforms the state-of-the-art methods both qualitatively and quantitatively.
UWB CMOS Monocycle Pulse Generator

A low-complexity fully integrated ultrawideband (UWB) monocycle pulse generator realized in 90-nm CMOS technology by ST-Microelectronics is presented. The circuit provides a monocycle pulse when activated by a negative edge of an external trigger signal provided by a microcontroller by exploiting the operating principle of nonlinear waveform shapers. This pulse generator represents a building block of an innovative wearable system-on-chip UWB radar on silicon for cardiopulmonary monitoring. On-chip measurements show that the pulse generator provides monocycle pulses with a duration time equal to 380 ps and a peak-to-peak amplitude of 660 mV (including the losses of the microprobes, cables, and electrostatic-discharge-protected pads), which are in very good agreement with the postlayout simulations. The power consumption is 19.8 mW from a 1.2-V power supply.
A large scale exploratory analysis of software vulnerability life cycles

Software systems inherently contain vulnerabilities that have been exploited in the past resulting in significant revenue losses. The study of vulnerability life cycles can help in the development, deployment, and maintenance of software systems. It can also help in designing future security policies and conducting audits of past incidents. Furthermore, such an analysis can help customers to assess the security risks associated with software products of different vendors. In this paper, we conduct an exploratory measurement study of a large software vulnerability data set containing 46310 vulnerabilities disclosed since 1988 till 2011. We investigate vulnerabilities along following seven dimensions: (1) phases in the life cycle of vulnerabilities, (2) evolution of vulnerabilities over the years, (3) functionality of vulnerabilities, (4) access requirement for exploitation of vulnerabilities, (5) risk level of vulnerabilities, (6) software vendors, and (7) software products. Our exploratory analysis uncovers several statistically significant findings that have important implications for software development and deployment.
Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain

We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
Neural Network-Based Abstract Generation for Opinions and Arguments

ive 2.8 3.1 3.3 5.0% Our System 3.6 4.8 4.2 18.0% Human Abstract (reference) 4.2 4.8 4.5 65.5% Sample Summaries • Movie: The Neverending Story •  A magical journey about the power of a young boy’s imagination to save a dying fantasy land, The Neverending Story remains a much-loved kids adventure. •  It pokes along at times and lapses occasionally into dark moments of preachy philosophy, but this is still a charming, amusing and harmless film for kids.  The Neverending Story is a silly fantasy movie that often shows its age . • Our System: The Neverending Story is an entertaining children’s adventure, with heart and imagination to spare.
Tetrahedral Mobile Robot with Novel Ball Shape Wheel

In this paper, a mobile robot with a tetrahedral shape for its basic structure is presented as a thrown robot for search and rescue robot application. The Tetrahedral Mobile Robot has its body in the center of the whole structure. The driving parts that produce the propelling force are located at each corner. As a driving wheel mechanism, we have developed the "Omni-Ball" with one active and two passive rotational axes, which are explained in detail. An actual prototype model has been developed to illustrate the concept and to perform preliminary motion experiments, through which the basic performance of the Tetrahedral Mobile Robot was confirmed
Collaborative Detection of DDoS Attacks over Multiple Network Domains

This paper presents a new distributed approach to detecting DDoS (distributed denial of services) flooding attacks at the traffic-flow level The new defense system is suitable for efficient implementation over the core networks operated by Internet service providers (ISPs). At the early stage of a DDoS attack, some traffic fluctuations are detectable at Internet routers or at the gateways of edge networks. We develop a distributed change-point detection (DCD) architecture using change aggregation trees (CAT). The idea is to detect abrupt traffic changes across multiple network domains at the earliest time. Early detection of DDoS attacks minimizes the floe cling damages to the victim systems serviced by the provider. The system is built over attack-transit routers, which work together cooperatively. Each ISP domain has a CAT server to aggregate the flooding alerts reported by the routers. CAT domain servers collaborate among themselves to make the final decision. To resolve policy conflicts at different ISP domains, a new secure infrastructure protocol (SIP) is developed to establish mutual trust or consensus. We simulated the DCD system up to 16 network domains on the Cyber Defense Technology Experimental Research (DETER) testbed, a 220-node PC cluster for Internet emulation experiments at the University of Southern California (USC) Information Science Institute. Experimental results show that four network domains are sufficient to yield a 98 percent detection accuracy with only 1 percent false-positive alarms. Based on a 2006 Internet report on autonomous system (AS) domain distribution, we prove that this DDoS defense system can scale well to cover 84 AS domains. This security coverage is wide enough to safeguard most ISP core networks from real-life DDoS flooding attacks.
Multi-resolution on compressed sets of clauses

This paper presents a system based on new operators for handling sets of propositional clauses represented by means of ZBDDs. The high compression power of such data structures allows efficient encodings of structured instances. A specialized operator for the distribution of sets of clauses is introduced and used for performing multi-resolution on clause sets. Cut eliminations between sets of clauses of exponential size may then be performed using polynomial size data structures. The ZRES system, a new implementation of the Davis-Putnam procedure of 1960, solves two hard problems for resolution, that are currently out of the scope of the best SAT provers.
On-line Clustering for Real-Time Topic Detection in Social Media Streaming Data

The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Worldwide, more and more people report and distribute up-to-date information about almost any topic. At the same time, there is an increasing interest in information that can be gathered from this data. The popularity of new services and technologies that produce and consume data streams imposes new challenges on the analysis, namely, in terms of handling high volumes of noisy data in real-time. Since social media analysis is concerned with investigating current topics and actual events around the world, there is a pronounced need to detect topics in the data and to directly display their occurrence to analysts or other users. In this paper, we present an on-line clustering approach, which builds on traditional data mining methods to address the new requirements of data stream mining: (a) fast incremental processing of incoming stream objects, (b) compactness of data representation, and (c) efficient identification of changes in evolving clustering models.
Robust 3D Face Shape Reconstruction from Single Images via Two-Fold Coupled Structure Learning and Off-the-Shelf Landmark Detectors

In this paper, we propose a robust method for monocular face shape reconstruction (MFSR) using a sparse set of facial landmarks that are detected by most of the off-theshelf landmark detectors. Different from the classical shape-from-shading framework, we formulate the MFSR problem as a Two-Fold Coupled Structure Learning (2FCSL) process, which consists of learning a regression between two subspaces spanned by 3D sparse landmarks and 2D sparse landmarks, and a coupled dictionary learned on 3D sparse and dense shape using K-SVD. To handle variations in face pose, we explicitly incorporate pose estimation in our method. Extensive experiments on both synthetic and real data from two challenging datasets using manual and automatic landmarks indicate that our method achieves promising performance and is robust to pose variations and landmark localization noise.
Drawing and Recognizing Chinese Characters with Recurrent Neural Network

Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.
Anti-forensic resilient memory acquisition

Memory analysis has gained popularity in recent years proving to be an effective technique for uncovering malware in compromised computer systems. The process of memory acquisition presents unique evidentiary challenges since many acquisition techniques require code to be run on a potential compromised system, presenting an avenue for anti-forensic subversion. In this paper, we examine a number of simple anti-forensic techniques and test a representative sample of current commercial and free memory acquisition tools. We find that current tools are not resilient to very simple anti-forensic measures. We present a novel memory acquisition technique, based on direct page table manipulation and PCI hardware introspection, without relying on operating system facilities making it more difficult to subvert. We then evaluate this technique’s further vulnerability to subversion by considering more advanced anti-forensic attacks. a 2013 Johannes Stüttgen and Michael Cohen.
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.
Lateralization of auditory language functions: A dynamic dual pathway model

Spoken language comprehension requires the coordination of different subprocesses in time. After the initial acoustic analysis the system has to extract segmental information such as phonemes, syntactic elements and lexical-semantic elements as well as suprasegmental information such as accentuation and intonational phrases, i.e., prosody. According to the dynamic dual pathway model of auditory language comprehension syntactic and semantic information are primarily processed in a left hemispheric temporo-frontal pathway including separate circuits for syntactic and semantic information whereas sentence level prosody is processed in a right hemispheric temporo-frontal pathway. The relative lateralization of these functions occurs as a result of stimulus properties and processing demands. The observed interaction between syntactic and prosodic information during auditory sentence comprehension is attributed to dynamic interactions between the two hemispheres.
Stress , Self-Efficacy , Social Support , and Coping Strategies in University Students

The goal of this study was to examine the relationship of self-efficacy, social support, and coping strategies with stress levels of university students. Seventy-five Education students completed four questionnaires assessing these variables. Significant correlations were found for stress with total number of coping strategies and the use of avoidance-focused coping strategies. As well, there was a significant correlation between social support from friends and emotion-focused coping strategies. Gender differences were found, with women reporting more social support from friends than men. Implications of these results for counselling university students are discussed.