nils-herrmann/clean_scirepeval huggingface dataset:
Scene Text Synthesis for Efficient and Effective Deep Network Training

A large amount of annotated training images is critical for training accurate and robust deep network models but the collection of a large amount of annotated training images is often time-consuming and costly. Image synthesis alleviates this constraint by generating annotated training images automatically by machines which has attracted increasing interest in the recent deep learning research. We develop an innovative image synthesis technique that composes annotated training images by realistically embedding foreground objects of interest (OOI) into background images. The proposed technique consists of two key components that in principle boost the usefulness of the synthesized images in deep network training. The first is context-aware semantic coherence which ensures that the OOI are placed around semantically coherent regions within the background image. The second is harmonious appearance adaptation which ensures that the embedded OOI are agreeable to the surrounding background from both geometry alignment and appearance realism. The proposed technique has been evaluated over two related but very different computer vision challenges, namely, scene text detection and scene text recognition. Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.
Numerically Grounded Language Models for Semantic Error Correction

Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction. Current approaches generally focus on relatively shallow semantics and do not account for numeric quantities. Our approach uses language models grounded in numbers within the text. Such groundings are easily achieved for recurrent neural language model architectures, which can be further conditioned on incomplete background knowledge bases. Our evaluation on clinical reports shows that numerical grounding improves perplexity by 33% and F1 for semantic error correction by 5 points when compared to ungrounded approaches. Conditioning on a knowledge base yields further improvements.
Perception on the Usage of Mobile Assisted Language Learning ( MALL ) in English as a Second Language ( ESL ) Learning among Vocational College Students

Mobile phones are viewed as significant aids to English language learning. Numerous studies have been conducted in different contexts on mobile assisted language learning (MALL) that indicate the benefits of mobile devices for English language learning. Nevertheless, few studies on MALL had been conducted on among vocational college students in Malaysia. Therefore, this study was aimed to investigate the perception on the usage of MALL in English as a Second Language (ESL) among private vocational college students. Data were collected from a survey questionnaire adapted from Technology Acceptance Model (TAM). The results show the respondents have positive perception on the usage of MALL. Majority of the respondents showed overall agreement on both constructs perceived usefulness (PU) and perceived ease of use (PEoU) of MALL. They believed the usage of MALL will enhance the teaching and learning process. This evidence of acceptance has implication for educators and curriculum designers to exploit the mobile phone for autonomous and interactive ESL learning beyond the classroom context. It is hoped that MALL will be used as one of the teaching aids that could assist educators to teach English as a Second Language (ESL) more effectively.
Autonomous Intersection Management For Semi-Autonomous Vehicles

Recent advances in autonomous vehicle technology will open the door to highly efficient transportation systems in the future, as demonstrated by Autonomous Intersection Management, an intersection control protocol designed for fully autonomous vehicles. We, however, anticipate there will be a long transition period during which most vehicles have some but not all capabilities of fully autonomous vehicles. This chapter introduces a new protocol called Semi-Autonomous Intersection Management, which allows vehicles with partiallyautonomous features such as adaptive cruise control to enter an intersection from different directions simultaneously. Our experiments show that this protocol can greatly decrease traffic delay when most vehicles are semi-autonomous. Our incremental deployment study reveals that traffic delay keeps decreasing as more and more vehicles employ features of autonomy.
A Method and Tool Support for Model-based Semi-automated Failure Modes and Effects Analysis of Engineering Designs

Limitations in scope but also difficulties with the efficiency and scalability of present algorithms se em to have so far limited the industrial uptake of existi ng automated FMEA technology. In this paper, we descri be a new tool for the automatic synthesis of FMEAs which builds upon our earlier work on fault tree synthesi s. The tool constructs FMEAs from engineering diagrams (e. g. developed in Matlab-Simulink) that have been augmen ted with information about component failures. To gener at a system FMEA, the tool first generates a “forest” of interconnected system fault trees by traversing the system model. This “forest” is then mechanically translate d into a simple table of direct relationships between compon ent and system failures, effectively a system FMEA. We describe the architecture of the tool and demonstra te its application on a steer-by-wire prototype. We also d iscuss its performance and show that this approach could l ea to efficient ways of generating useful analyses from d esign representations. .
Development of an Autonomous Remote Access Water Quality Monitoring System

Due to the vast increase in global industrial output, rural to urban drift and the over-utilization of land and sea resources, the quality of water available to people has deteriorated greatly. Before the sensor based approach to water quality monitoring, water quality was tested by collecting the samples of water and experimentally analyzing it in the laboratories. However, in today, with time being a scarce resource, the traditional method of water quality testing is not efficient anymore. To tackle this issue, several electronic (microcontroller and sensor based) water quality monitoring systems were developed in the past decade. However, an in depth study of this current water quality testing technology shows that there are some limitations that should be taken into consideration. Therefore, an automatic, remote, and low cost water quality monitoring system has been developed. This system consists of a core microcontroller, multiple sensors, GSM module, LCD display screen, and an alarm subsystem. The quality of water is read from the physical world through the water quality testing sensors and sent to the microcontroller. The data is then analyzed by the microcontroller and the result is displayed on the LCD screen on the device. At the same time, another copy of the sensor readings is sent remotely to the user’s mobile phone in the form of SMS. If an abnormal water quality parameter is detected by any sensor, the alarm system will turn on the respective red LED for that parameter and the buzzer will give warning sound. At the same time, the abnormality of the water parameter is reported to the user through SMS. The system is aimed to be used for wide applications and by all categories of users. It can facilitate the process of water quality monitoring autonomously and with low cost; to help people improve their quality of drinking water, household water supplies and aquaculture farms, especially in rural areas where residents do not have access to standardized water supply and suffer from different diseases caused by contaminated water.
WhatsApp network forensics: Decrypting and understanding the WhatsApp call signaling messages

WhatsApp is a widely adopted mobile messaging application with over 800 million users. Recently, a calling feature was added to the application and no comprehensive digital forensic analysis has been performed with regards to this feature at the time of writing this paper. In this work, we describe how we were able to decrypt the network traffic and obtain forensic artifacts that relate to this new calling feature which included the: a) WhatsApp phone numbers, b) WhatsApp server IPs, c) WhatsApp audio codec (Opus), d) WhatsApp call duration, and e) WhatsApp's call termination. We explain the methods and tools used to decrypt the traffic as well as thoroughly elaborate on our findings with respect to the WhatsApp signaling messages. Furthermore, we also provide the community with a tool that helps in the visualization of the WhatsApp protocol messages.
An Improved DC-Link Voltage Fast Control Scheme for a PWM Rectifier-Inverter System

This paper presents an improved dc-link voltage fast control strategy based on energy balance for a pulse width modulation (PWM) rectifier-inverter system to reduce the fluctuation of dc-link voltage. A conclusion is drawn that the energy in dc-link capacitor cannot be kept constant in dynamic process when the system operates in rectifier mode, even in ideal case. Meanwhile, the minimum dc-link voltage deviation is analyzed. Accordingly, a predictive dc-link voltage control scheme based on energy balance is proposed, while the grid current is regulated by deadbeat predictive control. A prediction method for output power is also introduced. Furthermore, a small-signal model with the control delay is adopted to analyze the stability and robustness of the control strategy. The simulation and experimental results demonstrate both good dynamic and steady-state performances of the rectifier-inverter system with the proposed control scheme.
Analysis and detection of low quality information in social networks

With social networks like Facebook, Twitter and Google+ attracting audiences of millions of users, they have been an important communication platform in daily life. This in turn attracts malicious users to the social networks as well, causing an increase in the incidence of low quality information. Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My PhD thesis work focuses on the analysis and detection of low quality information in social networks. We introduce social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. For evolutionary study of low quality information, we present the results on large-scale study on Web spam and email spam over a long period of time. Furthermore, we provide activity-based detection approaches to filter out low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumor. Our framework and detection techniques show promising results in analyzing and detecting low quality information in social networks.
Asymmetric-Circular Shaped Slotted Microstrip Antennas for Circular Polarization and RFID Applications

Novel asymmetric-circular shaped slotted microstrip patch antennas with slits are proposed for circularly polarized (CP) radiation and radio frequency identification (RFID) reader applications. A single-feed configuration based asymmetric-circular shaped slotted square microstrip patches are adopted to realize the compact circularly polarized microstrip antennas. The asymmetric-circular shaped slot(s) along the diagonal directions are embedded symmetrically onto a square microstrip patch for CP radiation and small antenna size. The CP radiation can be achieved by slightly asymmetric (unbalanced) patch along the diagonal directions by slot areas. Four symmetric-slits are also embedded symmetrically along the orthogonal directions of the asymmetric-circular shaped slotted patch to further reduce antenna size. The operating frequency of the antenna can be tuned by varying the slit length while keeping the CP radiation unchanged. The measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz with 17.0 MHz impedance bandwidth is achieved for the antenna on a RO4003C substrate. The overall antenna size is 0.27λo × 0.27λo × 0.0137λo at 900 MHz.
A Statistical Model of Human Pose and Body Shape

Generation and animation of realistic humans is an essential part of many projects in today’s media industry. Especially, the games and special effects industry heavily depend on realistic human animation. In this work a unified model that describes both, human pose and body shape is introduced which allows us to accurately model muscle deformations not only as a function of pose but also dependent on the physique of the subject. Coupled with the model’s ability to generate arbitrary human body shapes, it severely simplifies the generation of highly realistic character animations. A learning based approach is trained on approximately 550 full body 3D laser scans taken of 114 subjects. Scan registration is performed using a non-rigid deformation technique. Then, a rotation invariant encoding of the acquired exemplars permits the computation of a statistical model that simultaneously encodes pose and body shape. Finally, morphing or generating meshes according to several constraints simultaneously can be achieved by training semantically meaningful regressors.
A Deep Learning Approach for Subject Independent Emotion Recognition from Facial Expressions

This paper proposes Deep Learning (DL) models for emotion recognition from facial expressions. We have focused on two “deep” neural models: Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN). For each of these DL neural models, we have chosen several architectures. We have considered both the case of subject independent emotion recognition and also that of subject dependent emotion recognition. One has selected the Support Vector Machine (SVM) as a benchmark algorithm. We have chosen the JAFFE database to evaluate the above proposed models for person independent/dependent facial expression recognition. Using DL approach, we have obtained a subject-independent emotion recognition score of 65.22%, corresponding to an increase of 6% over the best score given by the considered benchmark methods. For person dependent emotion recognition, the DL model leads to the recognition score of 95.71%, representing an increase of 3% over the best of chosen benchmark methods.  Facial expression, Deep Learning (DL), Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), subject independent/dependent emotion recognition
Bucket Renormalization for Approximate Inference

Probabilistic graphical models are a key tool in machine learning applications. Computing the partition function, i.e., normalizing constant, is a fundamental task of statistical inference but it is generally computationally intractable, leading to extensive study of approximation methods. Iterative variational methods are a popular and successful family of approaches. However, even state of the art variational methods can return poor results or fail to converge on difficult instances. In this paper, we instead consider computing the partition function via sequential summation over variables. We develop robust approximate algorithms by combining ideas from mini-bucket elimination with tensor network and renormalization group methods from statistical physics. The resulting “convergence-free” methods show good empirical performance on both synthetic and real-world benchmark models, even for difficult instances.
Establishing and maintaining semantically rich traceability : a metamodelling aproach

This thesis addresses the problem of model-to-model traceability in Model Driven Engineering (MDE). A MDE process typically involves models expressed in different modelling languages that capture different views of the system under development. To enhance automation, consistency and coherency, establishing and maintaining semantically rich traceability links between models used throughout the software development lifecycle is of paramount importance. This thesis deals with the various challenges associated with providing traceability support in the context of MDE by defining a domain-specific, model-based traceability approach, which supports the main traceability activities in a rigorous and semi-automatic manner. To evaluate the validity of the thesis proposition, a reference implementation has been provided. The results obtained from the application of the proposed approach to various case-studies and examples have confirmed the feasibility and benefits of such an approach.
Why Do Those With Long-Term Substance Use Disorders Stop Abusing Substances? A Qualitative Study

Although a significant proportion of adults recover from substance use disorders (SUDs), little is known about how they reach this turning point or why they stop using. The purpose of the study was to explore the factors that influence reasoning and decision making about quitting substance use after a long-term SUD. Semistructured interviews were conducted with 18 participants, each of whom had been diagnosed with a SUD and had been abstinent for at least 5 years. A resource group of peer consultants in long-term recovery from SUDs contributed to the study's planning, preparation, and initial analyses. Participants recalled harmful consequences and significant events during their years of substance use. Pressure and concern from close family members were important in their initial efforts to abstain from substance use. Being able to imagine a different life, and the awareness of existing treatment options, promoted hope and further reinforced their motivation to quit. Greater focus on why those with SUDs want to quit may help direct treatment matching; treatment completion may be more likely if the person's reasons for seeking help are addressed.
Maintaining Integrity: How Nurses Navigate Boundaries in Pediatric Palliative Care.

To explore how nurses manage personal and professional boundaries in caring for seriously ill children and their families.



Using a constructivist grounded theory approach, a convenience sample of 18 registered nurses from four practice sites was interviewed using a semi-structured interview guide.



Nurses across the sites engaged in a process of maintaining integrity whereby they integrated two competing, yet essential, aspects of their nursing role - behaving professionally and connecting personally. When skillful in both aspects, nurses were satisfied that they provided high-quality, family-centered care to children and families within a clearly defined therapeutic relationship. At times, tension existed between these two aspects and nurses attempted to mitigate the tension. Unsuccessful mitigation attempts led to compromised integrity characterized by specific behavioral and emotional indicators. Successfully mitigating the tension with strategies that prioritized their own needs and healing, nurses eventually restored integrity. Maintaining integrity involved a continuous effort to preserve completeness of both oneself and one's nursing practice.



Study findings provide a theoretical conceptualization to describe the process nurses use in navigating boundaries and contribute to an understanding for how this specialized area of care impacts health care providers.



Work environments can better address the challenges of navigating boundaries through offering resources and support for nurses' emotional responses to caring for seriously ill children. Future research can further refine and expand the theoretical conceptualization of maintaining integrity presented in this paper and its potential applicability to other nursing specialties.
Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression

A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spat iahpectral representations [lo], , which provide a complete image description in terms of locally windowed 2-D spectral coordinates embedded within global 2-D spatial coordinates. Because intrinsic redundancies within images a re extracted, the resulting image codes can be very compact. However, these conjoint transforms are inherently difficult to compute because t e elementary expansion functions a re not orthogonal. One o r t h o g o n k i n g approach developed for 1-D signals by Bastiaans [SI, based on biorthonormal expansions, is restricted by constraints on the conjoint sampling rates and invariance of the windowing function, as well as by the fact that the auxiliary orthogonalizing functions are nonlocal infinite series. In the present “neural network” approach, based upon interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights, the network finds coefficients for complete conjoint 2-D Gabor transforms without these restrictive conditions. For arbitrary noncomplete transforms, in which the coefficients might be interpreted simply as signifying the presence of certain features in the image, the network finds optimal coefficients in the sense of minimal mean-squared-error in representing the image. I n one algebraically complete scheme permitting exact reconstruction, the network finds expansion coefficients that reduce entropy from 7.57 in the pixel representation to 2.55 in the complete 2-D Gabor transform. In “wavelet” expansions based on a biologically inspired log-polar ensemble of dilations, rotations, and translations of a single underlying 2-D Gabor wavelet template, image compression is illustrated with ratios up to 20: 1. Also demonstrated is image segmentation based on the clustering of coefficients in the complete 2-D Gabor transform. This coefficient-finding network for implementing useful nonorthogonal image transforms may also have neuroscientific relevance, because the network layers with fixed weights use empirical 2-D receptive field profiles obtained from orientation-selective neurons in cat visual cortex as the weighting functions, and the resulting transform mimics the biological visual strategy of embedding angular and spectral analysis within global spatial coordinates.