Reviewerly/exHarmony huggingface dataset:
Embedded Narratives in Procedurally Generated Environments

Procedural Content Generation can help alleviate the workloads of designers, with the drawback of reducing their control over the final product. At the same time, many games have a growing focus on conveying a narrative using environmental storytelling which requires a high degree of control to embed in a virtual environment. This paper seeks to reconcile the apparent conflict in these two approaches, and explores a method in which Procedural Content Generation can be used to create virtual environments with embedded narratives. This is done using a Space-Time Drama Manager to dynamically place narrative elements in the virtual environment. A prototype game was developed and evaluated in a between-groups test (n = 69). Participants in the control condition all experienced the same environment, while those in the experimental condition each experienced a different procedurally generated environment. No significant difference was found for any of the measures. This was attributed to the fact that it is possible to convey an embedded narrative consistently to players, even when the environment which contains the narrative elements is procedurally generated, yielding a different experience for each participant.
Sequence Prediction Using Spectral RNNs

Fourier methods have a long and proven track record as an excellent tool in data processing. As memory and computational constraints gain importance in embedded and mobile applications, we propose to combine Fourier methods and recurrent neural network architectures. The short-time Fourier transform allows us to efficiently process multiple samples at a time. Additionally, weight reductions trough low pass filtering is possible. We predict time series data drawn from the chaotic Mackey-Glass differential equation and real-world power load and motion capture data. (Source code available at https://github.com/v0lta/Spectral-RNN).
Depth Guided Attention for Person Re-identification

Person re-identification is an important video-surveillance task for recognizing people from different non-overlapping camera views. Recently it has gained significant attention upon the introduction of different sensors (i.e. depth cameras) that provide the additional information irrespective of the visual features. Despite recent advances with deep learning models, state-of-the-art re-identification approaches fail to leverage the sensor-based additional information for robust feature representations. Most of these state-of-the-art approaches rely on complex dedicated attention-based architectures for feature fusion and thus become unsuitable for real-time deployment. In this paper, a new deep learning method is proposed for depth guided re-identification. The proposed method takes into account the depth-based additional information in the form of an attention mechanism, unlike state-of-the-art methods of complex architectures. Experimental evaluations on a depth-based benchmark dataset suggest the superiority of our proposed approach over the considered baseline as well as with the state-of-the-art.
Memory Attention: Robust Alignment Using Gating Mechanism for End-to-End Speech Synthesis

Recent end-to-end (e2e) speech synthesis systems usually employ attention techniques to align an input text sequence against a mel-spectrogram sequence. Attention-based e2e approach has shown state-of-the-art performance in speech synthesis. However, generating stable and robust attention alignment to avoid some serious failures such as repeating, missing, and mumbling phones is still an ongoing challenge. In order to mitigate these alignment failures, we propose a novel attention method called memory attention for e2e speech synthesis, which is inspired by the gating mechanism of the long-short term memory (LSTM). Leveraging the sequence modeling power of the gating techniques, memory attention can produce a stable alignment by controlling the amount of content-based and location-based information. For performance evaluation, we compared our proposed memory attention algorithm with various conventional attention techniques in single speaker and emotional speech synthesis scenarios. From the experimental results, we conclude that memory attention can robustly generate various stylish speech.
Research Progress on Ship Anomaly Detection Based on Big Data

The purpose of ship behavior anomaly detection is to identify and monitor some non-expected behaviors of ships, so as to improve the navigation safety of ships. Its research is of great significance to the safety guarantee of maritime navigation, intelligent monitoring of sea areas and the development of port management. This paper summarizes and evaluates the research progress of ship anomaly detection based on big data and points out the future development trend. First of all, the concept of ship abnormal behavior is introduced, and the process of data-driven ship abnormal detection and its data basis are described in detail. Secondly, the data-driven ship anomaly detection methods are divided into statistical method, machine learning method and neural network method, and their research status and existing problems are reviewed respectively. Finally, focusing on maritime big data, temporal and spatial correlation of scenarios, online real-time anomaly detection and other aspects, the current problems and challenges in the study of ship anomaly detection are discussed, and the future research direction is introduced.
Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt &amp; pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from ([Formula: see text]) to ([Formula: see text]) or ([Formula: see text]) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.
IT security in secondary CS education

There is a steady increase of digital networking in our world, e. g. in the form of "smart" devices as part of the internet-of-things or more recently with daily video conferences. This progress is accompanied by weekly media reports on security incidents affecting the IT we use on a daily basis. In order to provide orientation in this seemingly "insecure" digital world, it is necessary for every digital citizen to have a basic knowledge of the field of IT security. Current curricula for secondary CS education already try to cover some aspects of IT security - however, practical reports suggest that some important security-related topics have not yet been considered. Based on this assumption, we use a Qualitative Content Analysis to compare international as well as German secondary school curricula with an up-to-date body of knowledge of IT security to identify the gap to knowledge areas in current curricula.
Evaluation Matrix for Smart Machine-Learning Algorithm Choice

In Machine Learning, algorithm choice greatly affects the performance on a problem. Different advantages and disadvantages have to be taken into account in view of the specific use case. For critical applications, like in the medical field, model accuracy is paramount, while in the environment of embedded systems high computational efficiency may be of greater importance. Generally, expert knowledge is used to estimate a suitable algorithm for a defined application. However, the use of Machine Learning and Data Mining through non expert users is increasing.To simplify the process of algorithm choice for these inexperienced users, we propose an evaluation matrix for ranking machine learning algorithms, based on efficiency criteria. These criteria consist of different metrics, like accuracy, model complexity or scalability, which then in turn can be weighted for the underlying problem and evaluated to yield a use case specific algorithm ranking.Therefor efficiency criteria are discussed and assessment methods are defined. Resulting is a practicable evaluation matrix, which can be adapted to specific use cases through choice of importance weighting on the different efficiency criteria. The concept is of a modular nature, additional efficiency criteria can easily be included. Also, the set of algorithms considered in the analysis can be expanded through the established assessment methods.
Creating Directed Weighted Network of Terms Based on Analysis of Text Corpora

One of the most urgent problems of natural language processing -formalization and creation ontological models of subject domains based on the text corpora is considered. In this work, a new approach for determining the weights of links in the network of terms that correspond to certain concepts of the considered subject domain is considered. In particular, applying the proposed approach for determining the weights of links in the network of terms, the terminological ontology of the subject domain that related to an ecological footprint was created as an approbation. Further analysis of the created model made it possible to determine the most influential and significant links between the corresponding nodes in the network of terms that in turn correspond to certain concepts of the considered subject domain. The proposed and considered approaches and methods were programmed and using the software for modeling and visualization of graphs - Gephi the built directed networks of terms were visualized for better visual perception. The weighted directed networks of terms built according to the proposed approach can be used for automatically creating terminological ontologies of subject domains with the participation of experts. Also, the research result can be used to create personal search interfaces for users of information retrieval systems and also can be used in navigation systems in data-bases. It should help users of such systems simplify the process of searching the relevant information.
Investigating Airplane Crash Data with Watson Analytics and Cognos Analytics

Over the last century, the world has seen a number of airplane crashes both in the sky and on the ground. The development of technology aims to decrease technological difficulties and human errors. However, fatalities and aircraft damages caused by crashes do not cease to exist.
Usability Evaluation for the Integration of Library Data Analysis and an Interactive Artwork by Sensing Technology

In this research, we propose innovative ideas for digital art combined with educational applications for the library. It also presents the library’s information services to be more humane and interesting. By taking interactive devices as the basis, this research integrates college library borrowing data and an interactive artwork in order to perform an information visualization service for a user in the library by using their own smart phone. This research uses an instant data operation to carry out the information inquiry applications to present three years of library borrowing data, from 2013 to 2015, obtained from the Hsih Shin University library Data Access Protocol. Finally, a usability evaluation for the interactive art is conducted. To testify that this interactive art design meets the principle of user acceptance, a questionnaire survey is used for the statistical verification. The research findings show that a positive explanation value was achieved for user acceptance. The research model proposed by this research acts as a usability evaluation of the integrated application of an interactive artwork and library information service. The research model also corresponded to the user’s emotional feedback in the HCI research field. The value of this research is that we provide usability-verified models for cross-domain applications.
Exploration of Establishing Multimedia and Computer Network-aided Classroom Teaching Model for College English and American Literature Course

Abstract The British and American literature course is a professional theory course required for senior English major students in colleges and universities. It is of great significance for cultivating the humanistic quality and comparative awareness of culture and literature in students. However, many colleges and universities pay insufficient attention to English and American literature course of English majors. There are general problems such as relatively few class hours, obsolete teaching contents, backward teaching means, low interest of students in learning, and poor teaching effects, etc. In this paper, a new model of English and American literature classroom teaching is established based on multimedia, computer network, and other modern educational means to make active exploration and research and change the current situation.
Accelerating Deep Learning with Millions of Classes

Deep learning has achieved remarkable success in many classification tasks because of its great power of representation learning for complex data. However, it remains challenging when extending to classification tasks with millions of classes. Previous studies are focused on solving this problem in a distributed fashion or using a sampling-based approach to reduce the computational cost caused by the softmax layer. However, these approaches still need high GPU memory in order to work with large models and it is non-trivial to extend them to parallel settings. To address these issues, we propose an efficient training framework to handle extreme classification tasks based on Random Projection. The key idea is that we first train a slimmed model with a random projected softmax classifier and then we recover it to the original classifier. We also show a theoretical guarantee that this recovered classifier can approximate the original classifier with a small error. Later, we extend our framework to parallel settings by adopting a communication reduction technique. In our experiments, we demonstrate that the proposed framework is able to train deep learning models with millions of classes and achieve above $$10{\times }$$ speedup compared to existing approaches.
Strict NMS: Pedestrian Detection in Crowd Scenes

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel NonMaximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose strict-NMS, which applies a higher suppression threshold to an instance; (2) we apply Mask boot module to the Fast-RCNN to improve the detect capacity. (3) We achieve state of the art results on the CityPersons benchmarks.
The ICT Design for Modern Education Technology and Applications

The current trend towards automation and data exchange in manufacturing technologies, known as Industry 4.0, has also an impact on engineering education. This has a significant influence on the field of research on information communication technology (ICT) for education. It means that the digitalization of teaching processes requires not only suitable supporting software tools but also a suitable infrastructure for managing the flow and transmission of educational knowledge within this infrastructure (clouds, remote desktops, virtual spaces, personal computers). In this context, the authors aim to fill a gap in universal ICT support for teaching, and the state of the art in ICT in general. This gap involves reassessing the actual issues in modern education technology and applications, and requires a more universal approach, because global software or cloud and Web services are not automatically suitable solutions for use in personalized teaching situations where the key players are teachers, i.e. individuals. An approach (also supported within the international research Consortium V4+ACARDC), is illustrated by examples such as testing clouds (cloud content management), virtual spaces, and the development of educational packages and supporting software for visually impaired people.
MobHide: App-Level Runtime Data Anonymization on Mobile

Developers of mobile apps gather a lot of user’s personal information at runtime by exploiting third-party analytics libraries, without keeping the owner (i.e., the user) of such information in the loop. We argue that this is somehow paradoxical. To overcome this limitation, in this paper, we discuss a methodology (i.e., MobHide), allowing the user to choose a different privacy level for each app installed on her device. According to the user’s preferences, MobHide anonymizes the data collected by the analytics libraries before sending them to the app developers, through a fruitful combination of data anonymization techniques. More in detail, the methodology enables to i) analyze all the network traffic generated by the invocation of analytics libraries, ii) anonymize the personal and device data using a generalization technique, and the events related to the user’s behavior by exploiting local differential privacy, and iii) send the anonymized data to the developers.We empirically assessed the viability of the approach on Android, by implementing the methodology as an Android app, i.e., HideDroid, that relies on the VPN service provided by Google to intercept all network requests. Our preliminary experiments - carried out on a real app (i.e., Duolingo) - are promising, and suggest that runtime data anonymization on mobile is feasible nowadays, as it negligibly impacts the app performance.
Deep kernel supervised hashing for node classification in structural networks

Node classification in structural networks has been proven to be useful in many real world applications. With the development of network embedding, the performance of node classification has been greatly improved. However, nearly all the existing network embedding based methods are hard to capture the actual category features of a node because of the linearly inseparable problem in low-dimensional space; meanwhile they cannot incorporate simultaneously network structure information and node label information into network embedding. To address the above problems, in this paper, we propose a novel Deep Kernel Supervised Hashing (DKSH) method to learn the hashing representations of nodes for node classification. Specifically, a deep multiple kernel learning is first proposed to map nodes into suitable Hilbert space to deal with linearly inseparable problem. Then, instead of only considering structural similarity between two nodes, a novel similarity matrix is designed to merge both network structure information and node label information. Supervised by the similarity matrix, the learned hashing representations of nodes simultaneously preserve the two kinds of information well from the learned Hilbert space. Extensive experiments show that the proposed method significantly outperforms the state-of-the-art baselines over three real world benchmark datasets.
Software Fault Prediction Using Random Forests

In this paper, we present a software fault prediction model using random forests. Software fault prediction identifies the faulty regions in a software product early in its lifecycle and hence improves the quality attributes such as reliability of the software. Random forest is an ensemble learning method for classification. Random forests contain many of decision trees and the result is the function of these decision trees. The proposed approach is applied on software defect prediction datasets collected from PROMISE software engineering repository and evaluated the performance using precision, recall, accuracy, and F-measure. The results of random forest model are compared with other models such as support vector machines, backpropagation neural networks, decision trees. Based on comparison, it is observed that random forest model is superior to the other models.
PUMP FIT: An Augmented Reality Application Which Helps People with Their Workout More Efficiently

Project Pump fit focuses on the ways how to improve and make gym exercises, workouts more effective and less strainful. By using the method of Augmented reality(AR) in which a virtual instructor will be showing how to do the exercise and will correct if the person is doing it wrong. Giving a more close monitoring to each individual making it more efficient and useful.
Situated Learning of Soft Skills with an Interactive Agent in Virtual Reality via Multimodal Feedback

Nowadays, customer virtual reality (VR) devices can offer high-quality presence experiences in a relatively low cost, which opens opportunities for popularizing situated learning. We investigate situated learning of soft skills, in particular, elevator pitches, through simulating an interactive agent with multimodal feedback in VR. A virtual agent acts as a coach and offers both verbal and nonverbal feedback to users. We developed a prototype system and conducted a quantitative experiment to investigate the potential of situated learning in VR, especially on how users perceive the virtual agent. We summarize design considerations on developing interactive agents with multimodal feedback in VR to facilitate situated learning of soft skills.
Predictive Analysis of Resource Usage Data in Academic Libraries using the VADER Sentiment Algorithm

In any educational campus, a well-equipped library is the most essential requirement, as it is a complete storehouse of information - comprising books, magazines, articles, research papers, and other important documents; not only in print but also on digital media. The most prominent change observed in the library's design of educational institutions in recent days is automation. Library automation allows the librarians easy cataloging of books and maintaining proper records. In big institutions, a pool of resource usage data is generated. This data can be productively used to understand the learning approach of students by professors. Hence, the purpose is to apply sentiment analysis on it for predicting the use of books and resources that would help the qualitative up-gradation of the library. This paper presents the analysis of the data for the renewal of books and resources in the existing work. The progress of the work to date is discussed in the paper.