Reviewerly/exHarmony huggingface dataset:
Defining the sound experience in Social VR

Companies and content creators have mostly focused on the visual side of virtual reality. However, sound has the ability to improve the immersivity and perceived quality of a visual display and virtual simulation in general. In Social VR, it is especially important to be immersed. Social VR is a way of communicating with other people through VR by integrating participants in the same environment and enabling them to interact with each other. This graduation project focused on the design of an immersive sound experience during a Social VR session between two friends watching TV together. The purpose of this project was to evaluate current implementations of the sound experience in Social VR and to improve that experience through user testing. A literature research was done, Social VR demos were tested, questionnaires were made, and two prototypes were developed and tested. The first prototype was made for a basic and passive Social VR experience. The second one was a more advanced prototype in which two users could speak with each other, watch TV, interact with buttons and see each other’s avatar.  Main findings: - Users expect the experience to feel like real life, with realistic visuals and sounds. The experience should be relaxing, immersing, and give the sense of being together. - Hearing yourself speak in VR through your headphones (sidetone) improves the experienc eand presence. - Users expect a home cinema experience for the TV, which can be done with a virtual 5.1 speaker setup without reverb. - Adding reverberation and spatialization to the voices improves co-presence and realism. - The reverberation of the room should be accurate as users could sense when it did not match their expectations.  - The environment should be a reasonably large size for a living room, it should be open and detailed. It should also have a suitable background noise such as rain.
Execution integrity without implicit trust of system software

When trusted application code in a TEE computes over results produced by an untrusted kernel and hypervisor [1, 2], it is difficult at best to reason about the secrecy and integrity properties achieved by the overall ensemble---to establish, despite the wide breadth of the Linux system call interface, that in-enclave code is immune to Iago attacks [3]. In this paper, we argue that an attractive use case for TEEs is tamper-proof audit: the TEE executes a trusted observer (TO) that allows efficient offline validation that application code running outside the TEE has executed as expected. We describe a TO design that inherently does not require any trust of system call results (and thus of the kernel or hypervisor), and DOG, a prototype TO implementation for Intel SGX that upholds application execution integrity, even for applications that do not fit within today's SGX virtual memory limits, and incurs modest execution overhead.
Vehicle Detection based on Deep Learning Heatmap Estimation.

The detection and localization of objects on point cloud provided by LiDAR sensors have various applications, especially for autonomous driving. As methods are being more efficient in terms of performance and execution speed, they operate in an end-to-end fashion, not allowing in case of errors the evaluation of which areas could cause failures. We propose in this paper a simple method that delivers from a simple statistical voxel encoding an intermediate top view heatmap that illustrates the global state of the scene seen by the network. Completely detected vehicles are represented on this map as elliptic blobs. Difficult cases such as occluded cars may not illustrate a complete spot, however their mark may still indicate that a possible hazard, allowing planification algorithms to decide a reduction of the velocity as something may suddenly appear. Furthermore, thanks to the alternative representation of objects, detections in terms of bounding boxes can be extracted even from the intermediate map. In addition to its implementation simplicity, the proposed architecture reaches high level of performance on the KITTI detection benchmark.
DATABASE OF CASES OF PRODUCTS AND SOFTWARES DEVELOPMENT IN THE HEALTHCARE AREA

In the development of products it is important to know the best practices and techniques employed as a way to be able to adapt them to better results.The healthcare area benefits from such knowledge, since there are many challenges in identifying users and engaging them in the project in order to meet their needs.This study presents a database of cases of products and softwares development in the healthcare area.A systematic bibliographic review was carried out on the Scopus and Web of Science databases and articles that contained cases of product and/or software development in the healthcare area and presented details of the users' involvement throughout the development process were selected.The database is made up of 203 articles, 160 of which relate to software development, 26 are product development cases (hardware) and 18 describe product development with software.This database serves as a reference for researchers and practitioners who want to seek evidence of best practice and ways to engage users in the development of healthcare products/softwares.
Smart Fault Tracing: Learning with Diagnostic Data for Predicting Vehicle States

Software applications, as the control centre of vehicle functionality, is becoming much important in recent years. The failure of downloading software applications can cause the loss of vehicle fun ...
IoT [Internet of Things]

Internet of things is a technology evolving with powerful mode in various applications like medical, infrastructure, landscaping, architecture, educational etc. The communication through internet of things is becoming cheap and by having powerful sensors almost all applications becoming speed and executing with accuracy. IoT  is compressed with several  machines which can communicate with other machines in development of clear IT atmosphere, flawless infrastructure development  like smart cities, effective water waste management and monitor cash flow management system.
LibGuides: LIS 2004 Research Strategies for College Students: Welcome to LIS 2004

LIS 2004 is a one-credit hour course. The course focuses on methods of accessing information resources available through the Internet. Adapted from: Author/Creator (Digital Rights Manager) Helene Gold [goldh], goldh@tcc.fl.edu Course introduction
Please don’t feed the models: Blogues da Ana e os limites da comunicação pública

In this work we analyze some pro-anorexia Portuguese-speaking blogs, attempting to systematize / categorize their contents, and discuss the implications of these spaces, as well as the communities around them, from the point of view of self-image representation, public communication and freedom of expression. After a brief review of the literature and approach to the subject in the medical and social sciences, the main characteristics of the disease are defined according to current medical criteria, susceptibility factors, prevention and prognosis. After this clarification, we proceed to the analysis of the empirical corpus, and discussion of the results.
NoSQL Database Selection Focused on Performance Criteria for Web-driven Applications

This paper delivers a comparative analysis of the performance of three of the NoSQL technologies in Web applications. These technologies are graph stores, key-value stores, and document stores. The ...
Causal Functions of English Discourse Markers (EDMs) in the Students’ Writing Hortatory Exposition Text Facing the Industry Era 4.0

Facing the industry era 4.0 students need to be able to make a good writing which is coherence and cohesion. Using the proper EDMs in sentences and paragraphs make them semantically and lexically accepted in written global communication. This study investigated the use of causal markers function in the Hortatory Exposition text by the third semester students of Universitas PGRI Semarang. According to Feng (2010), some causal markers function are summarized as follows; so, because, consequently, it follows, for, because, under the circumstances, for this reason, etc. The objectives of this study are to find out the causal function markers realized in Hortatory Exposition Text and to find out the most dominant functions realized. The data was statistically analyzed and categorized using a qualitative approach. The subjects were consisted of 37 respondents. Through collecting the Hortatory Exposition text written by each student, the writers analyzed the causal marker functions which were used in its text. The writers collected the texts and read them intensively. Afterwards we analyzed each sentence which was observed attentively, then found out the English Discourse Markers (EDMs) which was realized in its text. Finally some causals were classified in detail. Lastly, the result of the analysis shows that the causal markers function has 71 clauses. There are ten causal function markers used by the students, such as; still, because, because of, so, also cause, for the reason, because of that, in order to, it can cause, caused by. The most dominant causal marker function that used by the students in Hortatory Exposition text is “because”. There are 50 clauses that use “because” as the causal marker functions. It means that 70, 42% of the causal markers used in the text. This study shows that the text of hortatory written by students mostly common use the EDMs “because” since it is very familiar known by students rather then the other causal EDMs. The suggestion is that writing teachers need to plug and touch EDMs in their teaching material to support its writing coherence and cohesion.
POSITIVE EMOTION RECOGNITION THROUGH EYE TRACKING TECHNOLOGY

Currently, modern eye tracking technology has been utilized to examine the gazing behavior as human visual perception is very much linked to the psychological knowledge of mental processes. Hence, eye tracking technology is potential to reveal hidden cognitive processes of human behaviour. However, eye tracking understanding can be crucial to investigate as the analysis involved the nature feeling of positive stimuli affects subjects’ emotions and soul through the eye tracking device. This experiment therefore attempt to investigate and identify the feeling utilizing the eye tracking sensor. Audio-visual setup of media containing positive emotions attributes were exhibited to participants to gauge the feeling of valence and excitement. The experiment used Tobii TX300 eye tracker with 10 subjects participated. The positive emotions that apply in this experiment are Love, Joy, and Serenity. The results showed that both Joy and Inspiration stimuli fixation duration peaks the longest at 7000ms among the ten subjects tested. The experiment further found that the pupil dilated strongly to “Joy” and “Inspiration”. These fixation metrics results confirmed that the participants visual attention toward salient image content were differently processed which concluded the positive emotion recognition. The results sum up that eye tracking metrics is useful to implicitly uncover the feelings insight of human behavior.
LibGuides: Archaeology: Tutorial - Finding and using library resources: 5. Research databases

Online tutorial introducing you to the skills and techniques needed when searching for information
Apport de bruit de fond dans la détection des défauts dans le moteur asynchrone

Through an experiment carried out at the ISTA Ndolo Mechanics Laboratory, we want to note that noise, generally considered a disruptive signal in signal processing, can provide useful information in the maintenance Rotating machines. We used an asynchronous engine that we operated it in the Holy State (defect-free) and in the state of progressive defects. The generated signals were recorded and analyzed using LabView and Matlab software, which we were able to show the defect-related change in spectral mapping.
Spectroscopy as a Non-Destructive Method to Study Terrestrial Dust Particles: Case Study from United Arab Emirates

The aim of this study is to evaluate the effects of physical and chemical sand characteristics, such as moisture content, organic content, and magnetic content on spectral signals using spectro-radiometer and Hyper-spectral camera. JMP Pro software is used to determine the correlation between moisture content percentage, organic content percentage, magnetic content percentage and reflection percentage. All samples have a fine sand texture with a pH ranging from 7.76 to 8.88 and electrical conductivity varies between 900.1 μS to 1539 μS. Results show that organic content ranges from 0.501% to 4.094%, moisture content has a range from 0% to 1.219%, and magnetic varies between 1.062% and 2.907%. Heavy metals (Ba, Cd, Cr, Mn, Ni, Pb, and Zn) are detected in all samples with lower concentration than the maximum limit sit by U.S Environmental Protection Agency. X-ray diffraction shows that all samples contain quartz and graphite.
Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor

The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to detect prostate cancer using eosin and hematoxylin stained histopathological images. In this work the above problem is approached as follows: the optical density of each whole slide image is calculated and its eosin and hematoxylin concentration components estimated. Then, hand-crafted features, which are expected to capture the expertise of pathologists, are extracted from patches of these two concentration components. Finally, patches are classified using a Deep Gaussian Process on the extracted features. The new approach outperforms current state of the art shallow as well as deep classifiers like InceptionV3, Xception and VGG19 with an AUC value higher than 0.98.
Prioritized Experience Replay based on the Wasserstein Metric in Deep Reinforcement Learning: The regularizing effect of modelling return distributions

This thesis tests the hypothesis that distributional deep reinforcement learning (RL) algorithms get an increased performance over expectation based deep RL because of the regularizing effect of fitting a more complex model. This hypothesis was tested by comparing two variations of the distributional QR-DQN algorithm combined with prioritized experience replay. The first variation, called QR-W, prioritizes learning the return distributions. The second one, QR-TD, prioritizes learning the Q-Values. These algorithms were be tested with a range of network architectures. From too large architectures which are prone to overfitting, to smaller ones prone to underfitting. To verify the findings the experiment was done in two environments. As hypothesised, QR-W performed better on the networks prone to overfitting, and QR-TD performed better on networks prone to underfitting. This suggests that fitting distributions has a regularizing effect, which at least partially explains the performance of distributional algorithms. To compare QR-TD and QR-W to conventional benchmarks from literature they were tested in the Enduro environment from the arcade learning environment proposed by Bellemare. QR-W outperformed the state-of-the-art algorithms IQN and Rainbow in a quarter of the training time.
Modeling Adaptive Learning Agents for Domain Knowledge Transfer

The implementation of intelligent agents in industrial applications is often prevented by the high cost of adopting such a system to a particular problem domain. This paper states the thesis that when learning agents are applied to work environments that require domain-specific experience, the agent benefits if it can be further adapted by a supervising domain expert. Closely interacting with the agent, a domain expert should be able to understand its decisions and update the underlying knowledge base as needed. The result would be an agent with individualized knowledge that comes in part from the domain experts. The model of such an adaptive learning agent must take into account the problem domain, the design of the learning agent and the perception of the domain user. Therefore, already in the modeling phase, more attention must be paid to make the learning element of the agent adaptable by an operator. Domain modeling and meta-modeling methods could help to make inner processes of the agent more accessible. In addition, the knowledge gained should be made reusable for future agents in similar environments. To begin with, the existing methods for modeling agent systems and the underlying concepts will be evaluated, based on the requirements for different industrial scenarios. The methods are then compiled into a framework that allows for the description and modeling of such systems in terms of adaptability to a problem domain. Where necessary, new methods or tools will be introduced to close the gap between inconsistent modeling artifacts. The framework shall then be used to build learning agents for real-life scenarios and observe their application in a case study. The results will be used to assess the quality of the adapted knowledge base and compare it to a manual knowledge modeling process.
Providing Mass Context to a Pretrained Deep Convolutional Neural Network for Breast Mass Classification

Breast cancer is one of the most common cancers among women in the world, and the average error rate among radiologists during diagnosis is 30%. Computer-aided medical diagnosis aims to assist doct ...
The Influence of Using Poster Presentation Towards Students Speaking Ability (Quasi Experimental Research at The Eleven Grade of SMAN 1 Pandeglang in The Academic Year of 2018019)

Pupung Bugi Pertiwi Pamela. 152301769. 2019. The Influence of Using Poster Presentation Towards Students Speaking Ability (Quasi Experimental Research at The Eleven Grade of SMAN 1 Pandeglang in The Academic Year of 2018019) This research investigates the influence of poster presentation in speaking ability. This research was to find out whether teacher speaking by using poster presentation to improve students speaking ability, especially in speaking analytical exposition text. This research used two classes pre-test and post-test design quantitative research method. This research involves 73 students as sample among in eleven grade of SMAN 1 Pandeglang in academic year 2018/2019 population. The first class XI MIPA 3 as control class and the second class XI MIPA 5 as experimental class. The research findings of the last result showed that tobservatio the writer got from the calculation is higher than than ttable both at significance level 5% and 1%. Significance 5% tobservation = 5,63 and ttable = 1,67 so significance 1% tobservation 5,63 and ttable = 2,39, In addition the result of the observations showed that the students more motivate in learning English, especially in speaking subject. The result of this research shows that the students speaking analytical exposition text using poster presentation achieve better performance than those who do not use it.
Proactively Guiding Patients Through ADL via Knowledge-Based and Context-Driven Activity Recognition.

Assisted Ambient Living (AAL) focuses on self-sufficiency, assisting disabled people to perform activities of daily living (ADL) by automating assistive actions in smart environments. Importantly, AAL provides opportunities for dynamically guiding patients with a cognitive decline through an ADL. Activity recognition is a pivotal task since it allows detecting when an ADL is started by recognizing its constituent activities. When dealing cognitive decline, activity recognition should also be able to detect when activities are performed incorrectly-e.g., performed out-of-order, at the wrong location or time, or with the wrong objects (e.g., utensils) - which is nevertheless not a common goal in activity recognition. Moreover, it should be able to cope with non-uniform ways of performing the ADL that are nevertheless correct. We present a novel knowledge-driven activity recognition approach, which employs semantic reasoning to recognize both correct and incorrect actions, based on the ADL workflow as well as associated environment context.
Deep Tile Coder: an Efficient Sparse Representation Learning Approach with applications in Reinforcement Learning.

Recent work has shown that sparse representations -- where only a small percentage of units are active -- can significantly reduce interference. Those works, however, relied on relatively complex regularization or meta-learning approaches, that have only been used offline in a pre-training phase. In this work, we pursue a direction that achieves sparsity by design, rather than by learning. Specifically, we design an activation function that produces sparse representations deterministically by construction, and so is more amenable to online training. The idea relies on the simple approach of binning, but overcomes the two key limitations of binning: zero gradients for the flat regions almost everywhere, and lost precision -- reduced discrimination -- due to coarse aggregation. We introduce a Fuzzy Tiling Activation (FTA) that provides non-negligible gradients and produces overlap between bins that improves discrimination. We first show that FTA is robust under covariate shift in a synthetic online supervised learning problem, where we can vary the level of correlation and drift. Then we move to the deep reinforcement learning setting and investigate both value-based and policy gradient algorithms that use neural networks with FTAs, in classic discrete control and Mujoco continuous control environments. We show that algorithms equipped with FTAs are able to learn a stable policy faster without needing target networks on most domains.
A 1096fps Hardware Architecture for Fast Training in Object Tracking

In recent years, Discriminative Correlation Filter based methods have significantly outperformed the state-of-the art in tracking accuracy. However, the high-complexity training process makes it hard for the tracking task to keep both high accuracy and speed. In this work, the training algorithm is optimized to significantly reduce its computation with acceptable accuracy loss. Then a dedicated hardware is designed to further accelerate the training process with high accuracy. First, time constraints are released to turn serial module into parallel module; Second, the symmetry and sparsity of regularization filter kernel is utilized to reduce 80% computation of regularization convolution; Third, the computation of inner product module in training is reduced by turning complex numbers calculations into real and imaginary numbers calculations respectively. In conclusion, about 24.19% computation of training process is reduced and 4.30% parallel processing time is saved to get a 1.32x hardware resources improvement and 1.05x speedup than the original process. The simulation results show that the throughput of this hardware achieves 1096fps at 250 MHz, which is especially suitable for tracking tasks with high speed and accuracy requirement.