nils-herrmann/clean_scirepeval huggingface dataset:
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

Recently, increasing attention has been drawn to training semantic segmentation models using synthetic data and computer-generated annotation. However, domain gap remains a major barrier and prevents models learned from synthetic data from generalizing well to real-world applications. In this work, we take the advantage of additional geometric information from synthetic data, a powerful yet largely neglected cue, to bridge the domain gap. Such geometric information can be generated easily from synthetic data, and is proven to be closely coupled with semantic information. With the geometric information, we propose a model to reduce domain shift on two levels: on the input level, we augment the traditional image translation network with the additional geometric information to translate synthetic images into realistic styles; on the output level, we build a task network which simultaneously performs depth estimation and semantic segmentation on the synthetic data. Meanwhile, we encourage the network to preserve the correlation between depth and semantics by adversarial training on the output space. We then validate our method on two pairs of synthetic to real dataset: Virtual KITTI→KITTI, and SYNTHIA→Cityscapes, where we achieve a significant performance gain compared to the non-adaptive baseline and methods without using geometric information. This demonstrates the usefulness of geometric information from synthetic data for cross-domain semantic segmentation.
Implications of Antisocial Parents

Antisocial behavior is a socially maladaptive and harmful trait to possess. This can be especially injurious for a child who is raised by a parent with this personality structure. The pathology of antisocial behavior implies traits such as deceitfulness, irresponsibility, unreliability, and an incapability to feel guilt, remorse, or even love. This is damaging to a child’s emotional, cognitive, and social development. Parents with this personality makeup can leave a child traumatized, empty, and incapable of forming meaningful personal relationships. Both genetic and environmental factors influence the development of antisocial behavior. Moreover, the child with a genetic predisposition to antisocial behavior who is raised with a parental style that triggers the genetic liability is at high risk for developing the same personality structure. Antisocial individuals are impulsive, irritable, and often have no concerns over their purported responsibilities. As a parent, this can lead to erratic discipline, neglectful parenting, and can undermine effective care giving. This paper will focus on the implications of parents with antisocial behavior and the impact that this behavior has on attachment as well as on the development of antisocial traits in children.
The Stator Voltage-Controlled Current Source Inverter Induction Motor Drive

The steady-state and dynamic performance of a stator voltage-controlled current source inverter (CSI) induction motor drive are presented. Commutation effects are neglected and the analytical results are based on the fundamental component. A synchronously rotating reference frame linearized model in terms of a set of nondimensional parameters, based on the rotor transient time constant, is developed. It is shown that the control scheme is capable of stabilizing the drive over a region identical to the statically stable region of a conventional voltage-fed induction motor. A simple approximate expression for the drive dominant poles under no-load conditions and graphical representations of the drive dynamics under load conditions are presented. The effect of parameter variations on the drive dynamic response can be evaluated from these results. An analog simulation of the drive is developed, and the results confirm the small signal analysis of the drive system. In addition the steady-state results of the analog simulation are compared with experimental results, as well as with corresponding values obtained from a stator referred equivalent circuit. The comparison indicates good correspondence under load conditions and the limitation of applying the equivalent circuit for no-load conditions without proper recognition of the system losses.
A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings. The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The method relies on two phases: an off-line phase, and an on-line phase. During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients. The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon. The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence. The developed method is tested on benchmark data taken from the “NASA prognostics data repository” related to several experiments of failures on bearings done under different operating conditions. Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper. The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs. Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions. In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure. Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance.
Modeling and Control of Three-Port DC/DC Converter Interface for Satellite Applications

This paper presents the control strategy and power management for an integrated three-port converter, which interfaces one solar input port, one bidirectional battery port, and an isolated output port. Multimode operations and multiloop designs are vital for such multiport converters. However, control design is difficult for a multiport converter to achieve multifunctional power management because of various cross-coupled control loops. Since there are various modes of operation, it is challenging to define different modes and to further implement autonomous mode transition based on the energy state of the three power ports. A competitive method is used to realize smooth and seamless mode transition. Multiport converter has plenty of interacting control loops due to integrated power trains. It is difficult to design close-loop controls without proper decoupling method. A detailed approach is provided utilizing state-space averaging method to obtain the converter model under different modes of operation, and then a decoupling network is introduced to allow separate controller designs. Simulation and experimental results verify the converter control design and power management during various operational modes.
Optimising motor adaptation in childhood obesity.

Childhood obesity is a major public health concern. According to the World Health Organization, more than 22 million children worldwide are classified as overweight ( 2009). In Australia, the most recent data available show that 4.5% of boys and 5.5% of girls ages 2–18 years old are obese (Magarey & Daniels, 2001). Cutoffs for body mass index, weight in kilograms divided by height in metres squared, ‡ 30 kg/m2 for obesity are universally accepted for adults. International cutoffs for obesity designed for children (Cole, Bellizzi, Flegal & Dietz, 2000) use age, gender and body mass index to define obesity (e.g. the cutoff for a 2-year-old boy is 20.09 kg/m2, whereas the cutoff for a 171⁄2-year-old girl is 29.84 kg/m2). Current research on the role of occupational therapy in addressing childhood obesity has focussed on weight loss, weight gain prevention, or increases in physical activity by restructuring environments and routines (Ziviani, Desha, Poulsen & Whiteford, 2010). However, weight loss is not immediate. Examining how to maintain children’s safety during weight loss is important. Obesity affects children’s ability to maintain safety (Bazelmans et al., 2004) while performing their occupations. Impairments in motor adaptation, altering actions to cope with continuously changing environments, result in increased safety risks for children who are obese. They also influence occupational performance, ‘the ability to perceive, desire, recall, plan and carry out roles, routines, tasks, and subtasks for the purpose of self-maintenance, productivity, leisure and rest in response to demands of
Defining and applying prediction performance metrics on a recurrent NARX time series model

Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output. Some prediction metrics are also proposed to assess the quality of predictions. This metrics enable to compare different prediction schemes and provide an objective way to measure how changes in training or prediction model (Neural network architecture) affect the quality of predictions. Results show that the proposed NARX approach consistently outperforms the prediction obtained by the RNN neural network.
Deep Bottleneck Classifiers in Supervised Dimension Reduction

Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, which gives a low dimensional representation for the data when the full network is trained to minimize reconstruction error. We propose using a deep bottlenecked neural network in supervised dimension reduction. Instead of trying to reproduce the data, the network is trained to perform classification. Pretraining with restricted Boltzmann machines is combined with supervised finetuning. Finetuning with supervised cost functions has been done, but with cost functions that scale quadratically. Training a bottleneck classifier scales linearly, but still gives results comparable to or sometimes better than two earlier supervised methods.
EEG signal analysis for BCI application using fuzzy system

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.
Robust PCA for Anomaly Detection in Cyber Networks

This paper uses network packet capture data to demonstrate how Robust Principal Component Analysis (RPCA) can be used in a new way to detect anomalies which serve as cyber-network attack indicators. The approach requires only a few parameters to be learned using partitioned training data and shows promise of ameliorating the need for an exhaustive set of examples of different types of network attacks. For Lincoln Labs DARPA intrusion detection data set, the method achieves low false-positive rates while maintaining reasonable truepositive rates on individual packets. In addition, the method correctly detected packet streams in which an attack which was not previously encountered, or trained on, appears.
Compact Ka-band Cassegrain antenna with multimode monopulse tracking feed for satcom-on-the-move applications

High data rate satellite communications in Ka-band are an important and ongoing area of research and development. Especially compact antennas with high gain are of interest for bidirectional land mobile scenarios. In this paper, we describe a volume-efficient Cassegrain reflector antenna fed from a novel PCB-based multimode monopulse feed chain. The structure provides a dual circular polarisation communication mode as well as the TM01 mode for multimode monopulse tracking , . The antenna is operational at 20GHz and 30 GHz, to cover the downlink and the uplink frequency ranges in Ka-band. This work contributes to the development of a ground-segment satellite user terminal for land-mobile communications in disaster scenarios.
A Novel Metal-Mountable Electrically Small Antenna for RFID Tag Applications With Practical Guidelines for the Antenna Design

A novel dual-layer electrically small radio-frequency-identification (RFID) tag antenna is proposed for metallic object applications. With a proximity-coupled feed method, two rotationally symmetric loaded via-patches are fed through an embedded dual-element planar inverted-F antenna (PIFA) array. With this configuration, the antenna volume is reduced to 0.08 λ×0.04 λ×0.007 λ while the measured antenna gain is 0.08 dBi at the frequency of 923 MHz. Studies demonstrate that the proposed antenna is a good candidate for ultra-high-frequency RFID tags to be mounted on metallic surfaces, especially in size-constrained scenarios. Meanwhile, a figure of merit, namely, NBG, is presented, with which a comparison among electrically small tag antennas is carried out. Finally, several guidelines are given out to facilitate the miniaturization of RFID tag antennas for metallic object applications.
Television Drama Viewing and Romantic Beliefs : Considering Parasocial Interaction and Attachment Style

This study examined the relationships between attachment styles, drama viewing, parasocial interaction, and romantic beliefs. A survey of students revealed that drama viewing was weakly but negatively related to romantic beliefs, controlling for parasocial interaction and attachment styles. Instead, parasocial interaction mediated the effect of drama viewing on romantic beliefs: Those who viewed television dramas more heavily reported higher levels of parasocial interaction, which lead to stronger romantic beliefs. Regarding the attachment styles, anxiety was positively related to drama viewing and parasocial interaction, while avoidance was negatively related to parasocial interaction and romantic beliefs. These findings indicate that attachment styles and parasocial interaction are important for the association of drama viewing and romantic beliefs.
Cloud security is not (just) virtualization security: a short paper

Cloud infrastructure commonly relies on virtualization. Customers provide their own VMs, and the cloud provider runs them often without knowledge of the guest OSes or their configurations. However, cloud customers also want effective and efficient security for their VMs. Cloud providers offering security-as-a-service based on VM introspection promise the best of both worlds: efficient centralization and effective protection. Since customers can move images from one cloud to another, an effective solution requires learning what guest OS runs in each VM and securing the guest OS without relying on the guest OS functionality or an initially secure guest VM state.
 We present a solution that is highly scalable in that it (i) centralizes guest protection into a security VM, (ii) supports Linux and Windows operating systems and can be easily extended to support new operating systems, (iii) does not assume any a-priori semantic knowledge of the guest, (iv) does not require any a-priori trust assumptions into any state of the guest VM. While other introspection monitoring solutions exist, to our knowledge none of them monitor guests on the semantic level required to effectively support both white- and black-listing of kernel functions, or allows to start monitoring VMs at any state during run-time, resumed from saved state, and cold-boot without the assumptions of a secure start state for monitoring.
Examensarbete Machine Learning for detection of barcodes and OCR

Machine learning can be utilized in many different ways in the field of automatic manufacturing and logistics. In this thesis supervised machine learning have been utilized to train a classifiers for detection and recognition of objects in images. The techniques AdaBoost and Random forest have been examined, both are based on decision trees. The thesis has considered two applications: barcode detection and optical character recognition (OCR). Supervised machine learning methods are highly appropriate in both applications since both barcodes and printed characters generally are rather distinguishable. The first part of this thesis examines the use of machine learning for barcode detection in images, both traditional 1D-barcodes and the more recent Maxi-codes, which is a type of two-dimensional barcode. In this part the focus has been to train classifiers with the technique AdaBoost. The Maxi-code detection is mainly done with Local binary pattern features. For detection of 1D-codes, features are calculated from the structure tensor. The classifiers have been evaluated with around 200 real test images, containing barcodes, and shows promising results. The second part of the thesis involves optical character recognition. The focus in this part has been to train a Random forest classifier by using the technique point pair features. The performance has also been compared with the more proven and widely used Haar-features. Although, the result shows that Haar-features are superior in terms of accuracy. Nevertheless the conclusion is that point pairs can be utilized as features for Random forest in OCR.
Single-trial lie detection using a combined fNIRS-polygraph system

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into "true" and "lie" classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph.
Compatible and Diverse Fashion Image Inpainting

Visual compatibility is critical for fashion analysis, yet is missing in existing fashion image synthesis systems. In this paper, we propose to explicitly model visual compatibility through fashion image inpainting. To this end, we present Fashion Inpainting Networks (FiNet), a two-stage image-to-image generation framework that is able to perform compatible and diverse inpainting. Disentangling the generation of shape and appearance to ensure photorealistic results, our framework consists of a shape generation network and an appearance generation network. More importantly, for each generation network, we introduce two encoders interacting with one another to learn latent code in a shared compatibility space. The latent representations are jointly optimized with the corresponding generation network to condition the synthesis process, encouraging a diverse set of generated results that are visually compatible with existing fashion garments. In addition, our framework is readily extended to clothing reconstruction and fashion transfer, with impressive results. Extensive experiments with comparisons with state-of-the-art approaches on fashion synthesis task quantitatively and qualitatively demonstrate the effectiveness of our method.
Domestication, Genomics and the Future for Banana

Cultivated bananas and plantains are giant herbaceous plants within the genus Musa. They are both sterile and parthenocarpic so the fruit develops without seed. The cultivated hybrids and species are mostly triploid (2n = 3x = 33; a few are diploid or tetraploid), and most have been propagated from mutants found in the wild. With a production of 100 million tons annually, banana is a staple food across the Asian, African and American tropics, with the 15 % that is exported being important to many economies.



There are well over a thousand domesticated Musa cultivars and their genetic diversity is high, indicating multiple origins from different wild hybrids between two principle ancestral species. However, the difficulty of genetics and sterility of the crop has meant that the development of new varieties through hybridization, mutation or transformation was not very successful in the 20th century. Knowledge of structural and functional genomics and genes, reproductive physiology, cytogenetics, and comparative genomics with rice, Arabidopsis and other model species has increased our understanding of Musa and its diversity enormously.



There are major challenges to banana production from virulent diseases, abiotic stresses and new demands for sustainability, quality, transport and yield. Within the genepool of cultivars and wild species there are genetic resistances to many stresses. Genomic approaches are now rapidly advancing in Musa and have the prospect of helping enable banana to maintain and increase its importance as a staple food and cash crop through integration of genetical, evolutionary and structural data, allowing targeted breeding, transformation and efficient use of Musa biodiversity in the future.
Expanding Technological Frames Towards Mediated Collaboration - Groupware Adoption in Virtual Learning Teams

This paper provides an in-depth analysis of the technological and social factors that led to the successful adoption of groupware by a virtual team in a educational setting. Drawing on a theoretical framework based on the concept of technological frames, we conducted an action research study to analyse the chronological sequence of events in groupware adoption. We argue that groupware adoption can be conceptualised as a three-step process of expanding and aligning individual technological frames towards groupware. The first step comprises activities that bring knowledge of new technological opportunities to the participants. The second step involves facilitating the participants to articulate and evaluate their work practices and their use of tech© Scandinavian Journal of Information Systems, 2006, 18(2):29-68 nology. The third and final step deals with the participants' commitment to, and practical enactment of, groupware technology. The alignment of individual technological frames requires the articulation and re-evaluation of experience with collaborative practice and with the use of technology. One of the key findings is that this activity cannot take place at the outset of groupware adoption.