Reviewerly/exHarmony huggingface dataset:
Relaxed Asymmetric Deep Hashing Learning: Point-to-Angle Matching

Due to the powerful capability of the data representation, deep learning has achieved a remarkable performance in supervised hash function learning. However, most of the existing hashing methods focus on point-to-point matching that is too strict and unnecessary. In this article, we propose a novel deep supervised hashing method by relaxing the matching between each pair of instances to a point-to-angle way. Specifically, an inner product is introduced to asymmetrically measure the similarity and dissimilarity between the real-valued output and the binary code. Different from existing methods that strictly enforce each element in the real-valued output to be either +1 or -1, we only encourage the output to be close to its corresponding semantic-related binary code under the cross-angle. This asymmetric product not only projects both the real-valued output and the binary code into the same Hamming space but also relaxes the output with wider choices. To further exploit the semantic affinity, we propose a novel Hamming-distance-based triplet loss, efficiently making a ranking for the positive and negative pairs. An algorithm is then designed to alternatively achieve optimal deep features and binary codes. Experiments on four real-world data sets demonstrate the effectiveness and superiority of our approach to the state of the art.
InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models

Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.
Compositional uncertainty in deep Gaussian processes.

Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations. However, exact Bayesian inference is intractable for DGPs, motivating the use of various approximations. We show that the application of simplifying mean-field assumptions across the hierarchy leads to the layers of a DGP collapsing to near-deterministic transformations. We argue that such an inference scheme is suboptimal, not taking advantage of the potential of the model to discover the compositional structure in the data. To address this issue, we examine alternative variational inference schemes allowing for dependencies across different layers and discuss their advantages and limitations.
The Influence of Display Parameters and Display Devices over Spatial Ability Test Answers in Virtual Reality Environments

This manuscript analyzes the influence of display parameters and display devices over the spatial skills of the users in virtual reality environments. For this, the authors of this manuscript developed a virtual reality application which tests the spatial skills of the users. 240 students used an LG desktop display and 61 students used the Gear VR for the tests. Statistical data are generated when the users do the tests and the following factors are logged by the application and evaluated in this manuscript: virtual camera type, virtual camera field of view, virtual camera rotation, contrast ratio parameters, the existence of shadows and the device used. The probabilities of correct answers were analyzed based on these factors by logistic regression (logit) analysis method. The influences and interactions of all factors were analyzed. The perspective camera, lighter contrast ratio, no or large camera rotations and the use of the Gear VR greatly and positively influenced the probability of correct answers on the tests. Therefore, for the assessment of spatial ability in virtual reality, the use of these parameters and device present the optimal user-centric human–computer interaction practice.
Magnetic Resonance Image Segmentation Based on Multi-Scale Convolutional Neural Network

The incidence of nervous system and soft tissue is getting higher, and nuclear magnetic resonance is the preferred method of examination and is widely used.The segmentation of brain magnetic resonance (MR) images is the key to the subsequent operations such as three-dimensional reconstruction, quantitative analysis of normal tissues and diseased tissues.The accuracy of image segmentation is critical in the doctor's assessment with the location, shape, and size of the lesion tissue, as well as the determination of the disease and the correct diagnosis plan.The results of this study indicate that the multi-scale convolutional neural network (MSCNN) model can segment brain tumors accurately and effectively.Through multi-scale input, this paper overcomes the need to select specific input scales according to the size of the tumor, accommodate more neighborhood information from various angles, and adapt to different tumor sizes, thus improving the segmentation accuracy of brain tumors.Based on the same accuracy, the segmentation speed is accelerated to ensure the real-time segmentation further.This method can effectively segment the brain lesion tissue in the nuclear magnetic resonance image, which improves the generalization ability.It can be used for identifying the brain lesion tissue of the nuclear magnetic resonance medical image.
A Comparative Study Between Wired and Wireless Virtual Reality Setups

Virtual Reality (VR) through head-mounted displays (HMDs) can be delivered via multiple setups such as smartphones, standalone VR or VR Workstations. The VR Workstation setup delivers the best performance of them all; however, as a drawback up until recently, it required cables to power up the VR equipment. The introduction of wireless solutions for VR Workstations came to solve one of the disadvantages of this setup. However, the impact of the wireless solution versus the HMD cables was not yet properly investigated. In this paper, we study the impact of using a wired vs wireless HMD on Presence, Cybersickness, and Game Experience. We conducted a quasi-experimental between-subjects study with 68 participants assigned to the following three groups that were balanced regarding gender and sample size: Cable (participants used a wired HMD), Cable + Help (a researcher assisted the participants with the HMD cable during the experience to reduce the participants' awareness of it), and Wireless (untethered HMD). Results showed no significant differences in presence and cybersickness. The game experience was significantly different, with Cable + Help performing better than Cable and Wireless. We conclude that sense of presence using wireless solutions is equated to wired HMD solutions.
Patient Profile Using Ontologies in an Older Adults Monitoring IoT-Based Platform

The vINCI project proposes an approach for providing personalized assistance services for patients in an IoT-based ecosystem. In this context, the challenges remain to develop technologies that meet the needs of older adults, accommodate their cognitive and perceptual declines. One of the challenges to the effective design of such technologies is the understanding of the conceptual model of the older adult. vINCI offers a feasible opportunity for seniors to independently evaluate their quality of life and health status and receive a direct feedback which would enable them to take appropriate measures to improve their health status and prevent future negative events. In this paper, we present the design considerations for a profile of an elderly patient, as modeled in vINCI using ontologies. For vINCI care, the patient profile will be the input to provide personalized support for daily / medical activities. The profile will be used as evidence to evaluate the impact of vINCI on the perceived Quality of Life (QoL) level, allowing a proper adjustment (if needed) of the intervention support provided by caregivers.
Automatic recognition of anomalous patterns in discharges by recurrent neural networks

Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These off-normal patterns are often referred to as anomalies, outliers, discordant observations, or exceptions in different application domains. The importance of anomaly detection is due to the fact that anomalies in data frequently involve significant and critical information in many application domains. In the particular case of nuclear fusion, there are a wide variety of anomalies that could be related to particular plasma behaviors, such as disruptions or L-H transitions. In the case of unknown anomalies, they probably represent the major proportion with respect to the total anomalies that can be found in fusion. Whether the anomaly is known or not, all the anomalies in a nuclear fusion device should be detected by using the same approach, i.e. the physical state of the plasma during a shot should be reflected in some of the thousands acquired signals. In this article, we study the application of Deep Learning and a particular recurrent neural network called LSTM to detect anomalies by forecasting in a discharge.
Above Surface Interaction for Multiscale Navigation in Mobile Virtual Reality

Virtual Reality enables the exploration of large information spaces. In physically constrained spaces such as airplanes or buses, controller-based or mid-air interaction in mobile Virtual Reality can be challenging. Instead, the input space on and above touchscreen enabled devices such as smartphones or tablets could be employed for Virtual Reality interaction in those spaces.In this context, we compared an above surface interaction technique with traditional 2D on-surface input for navigating large planar information spaces such as maps in a controlled user study (n = 20). We find that our proposed above surface interaction technique results in significantly better performance and user preference compared to pinch-to-zoom and drag-to-pan when navigating planar information spaces.
Synchronization properties and reservoir computing capability of hexagonal spintronic oscillator arrays

The influence of array geometry on synchronization properties of a 2-D oscillator array is investigated based on a comparison between a rectangular and a hexagonal grid. The Kuramoto model is solved for a nearest-neighbor case with periodic boundary conditions and for a small-scale, realistic coupling case with 1/r3 decay characteristic of spintronic oscillators. In both cases, it is found that the hexagonal grid choice leads to lower synchronization threshold and higher emission power than its rectangular counterpart, which results from increased connectivity, as well as, in the realistic-coupling case, from decreased contributions of the array edges. Additionally, a more general spin-torque oscillator model including both amplitude and phase as degrees of freedom is employed for reservoir computing simulations, showing that by using hexagonal grid one can increase the short-term memory capacity but not the parity-check capacity of the system.
Assessing the Impact of a Preprocessing Stage on Deep Learning Architectures for Breast Tumor Multi-class Classification with Histopathological Images

In this work, we assess the impact of the adaptive unsharp mask filter as a preprocessing stage for breast tumour multi-class classification with histopathological images, evaluating two state-of-the-art architectures, not tested so far for this problem to our knowledge: DenseNet, SqueezeNet and a 5-layer baseline deep learning architecture. SqueezeNet is an efficient architecture, which can be useful in environments with restrictive computational resources. According to the results, the filter improved the accuracy from 2% to 4% in the 5-layer baseline architecture, on the other hand, DenseNet and SqueezeNet show a negative impact, losing from 2% to 6% accuracy. Hence, simpler deep learning architectures can take more advantage of filters than complex architectures, which are able to learn the preprocessing filter implemented. Squeeze net yielded the highest per parameter accuracy, while DenseNet achieved a 96% accuracy, defeating previous state of the art architectures by 1% to 5%, making DenseNet a considerably more efficient architecture for breast tumour classification.
Using Certainty Factor Method to Handle Uncertain Condition in Hepatitis Diagnosis

The research aimed to develop a web-based application using the certainty factor. The use of this certainty factor method allowed processing the data based on the degree of confidence from the experts and the users. The users inputted their symptoms each with the level of confidence. The inference engine drew some conclusions based on the matching process between the input and the rules in the knowledge-based. For every matching pair, the system would calculate the certainty factor. The knowledge-based was developed through discussion with three specialist physicians and literature in some previous studies. The evaluation of the system involved three specialists for validation testing and 51 respondents for BlackBox testing. The final result is displayed in the form of a percentage for each hepatitis type, explanation of first aid for hepatitis, and referral hospital for hepatitis patients. The result shows that the error rate in the diagnosis process is under 36%. Most of the respondents think that the quality of the system is good overall.
Blockchain Technology Reshaping Education

This chapter aims to analyse the potentialities of blockchain in education. It is an exploratory study focused on blockchain technology applied to European Union education policy. To achieve this goal, a bibliometric analysis was done to identify studies already done on blockchain applied to education, and the main research items studied and how they can contribute to policy definition. The research question of this study is: What kind of contributions for policy emerge from the research that has been made on blockchain in education? The main findings are focused on getting a diploma, on the assessment, and the formative evaluation, but also related to the learning process in a distributive way to facilitate the educational management process.
Rancang Bangun Sistem Informasi Desa Berbasis Website (Studi Kasus Desa Netpala)

Abstract&#x0D; The development of the village government are now either in the presence of village funds from the central government that aim to advance, village the current rural development related to information technology support, almost luminance village need to improve information technology services to the.One example from the village, about the internet the internet is not currently only enjoyed by urban residents but comes to. village communityThe difficulty of access to information that is keen to develop research make village based information system website for the purpose of improving public services, village access to information can be obtained easily, for the villagers, promotion , potential of the village , population area and forth with, literature in a interview observation, field and drafting, system the website based information system able to improve services to the community so that information to the public can be accessed easily and accurately.
Personalized gestural interaction applied in a gesture interactive game-based approach for people with disabilities

Technology can support people with disabilities to participate in social and economic life. Using relevant Human-Computer Interaction, as obtained through Intelligent User Interfaces, people with motor and speech impairments may be able to communicate in different ways. Augmentative and Alternative Communication supported by Computer Vision systems can benefit from the recognition of users' remaining functional motions as an alternative interaction design approach. Based on a methodology in which gestures and their meanings are created and configured by users and their caregivers, we developed a Computer Vision system, named PGCA, that employs machine learning techniques to create personalized gestural interaction as an Assistive Technology resource for communication purposes. Using a low-cost approach, PGCA has been experienced with students with motor and speech impairments to create personalized gesture datasets and to identify improvements for the system. This paper presents an experiment carried with the target audience using a game-based approach where three students used PGCA to interact with communication boards and to play a game. The system was evaluated by special education professionals using the System Usability Scale and was considered suitable for its purpose. Results from the experiment suggest the technical feasibility for the methodology and for the system, also adding knowledge about the interaction process of disabled people with a game.
Secure Coded Cooperative Computation at the Heterogeneous Edge against Byzantine Attacks

Edge computing is emerging as a new paradigm to allow processing data at the edge of the network, where data is typically generated and collected by exploiting multiple devices at the edge collectively. However, offloading tasks to other devices leaves the edge computing applications at the complete mercy of an attacker. One of the attacks, which is the focus of this work, is Byzantine attacks, where one or more devices can corrupt the offloaded tasks. Furthermore, exploiting the potential of edge computing is challenging mainly due to the heterogeneous and time-varying nature of the devices at the edge. In this paper, we develop a secure coded cooperative computation mechanism (SC3) that provides both security and computation efficiency guarantees by gracefully combining homomorphic hash functions and coded cooperative computation. Homomorphic hash functions are used against Byzantine attacks and coded cooperative computation is used to improve computation efficiency when edge resources are heterogeneous and time-varying. Simulation results show that SC3 improves task completion delay significantly.
INTERPRETATION OF PHYSICAL CONDITIONS OF SCHOOLS WITH FUZZY MULTI CRITERIA DECISION MAKING

The education system has 4 basic elements. These; education system, educator, student (education student) and physical facility. In this study, the physical condition is researched. The aim of this study is to evaluate and interpret the suitability of the physical structures of schools in terms of education and training according to teachers by the multi-criteria decision making method in fuzzy logic. One of the factors affecting success is the physical structure. This study has been conducted with the help of teachers' ideas to determine whether physical structure has an impact on student achievement. This study is an application of a multi-criteria decision-making method, as there are many factors affecting physical structure. In this study 'individual interview' technique has been applied to interpret physical conditions of schools. Scores obtained as a result of individual interviews have been interpreted using multi criteria decision making method. Teachers' ideas aecting success have been taken into consideration. The results have been compared with the official data.
DECCNet: Depth Enhanced Crowd Counting

Crowd counting which aims to calculate the number of total instances on an image is a classic but crucial task that supports many applications. Most of the prior works are based on the RGB channels on the images and achieve satisfied performance. However, previous approaches suffer from counting highly congested region due to the incomplete and blurry shapes. In this paper, we present an effective crowd counting method, Depth Enhanced Crowd Counting Network (DECCNet), which leverages the estimated depth information with our novel Bidirectional Cross-modal Attention (BCA) mechanism. Utilizing the depth information enables our model to explicitly learn to pay attention to those congested regions on the basis of the depth information. Our BCA mechanism interactively fuses two different input modalities by learning to focus on the informative parts according to each other. In our experiments, we demonstrate that DECCNet outperforms the state-of-the-art on the two largest crowd counting datasets available, including UCF-QNRF, which has the highest crowd density. The visualized result shows that our method can accurately regress dense regions through leveraging depth information. Ablation studies also indicate that each component of our method is beneficial to final prediction.
A Novel Approach Based on Modified Cycle Generative Adversarial Networks for Image Steganography

Image steganography aims at hiding information in a cover medium in an imperceptible way. While traditionalsteganography methods used invisible inks and microdots, digital world started using images and video files for hiding the secret content in it. Steganalysis is a closely related field for detecting hidden information in these multimedia files. There are many steganography algorithms implemented and tested but most of them fail during Steganalysis. To overcome this issue, in this paper, we are proposing to use generative adversarial networks for image steganography which include discriminative models to identify steganography image during training stage and that helps us to reduce the error rate later during Steganalysis. The proposed modified cycle Generative Adversarial Networks (Mod Cycle GAN) algorithm is tested using the USC-SIPI database and the experimentation results were better when compared with the algorithms in the literature. Because the discriminator block evaluates the image authenticity, we could modify the embedding algorithm until the discriminator could not identify the change made and thereby increasing the robustness.
Generalized Hong-Ou-Mandel quantum interference with phase-randomized weak coherent states

In the context of Fock states, demonstrating multiphoton quantum interference, e.g., the generalized Hong-Ou-Mandel (HOM) interference, is at the core of many applications of quantum technologies. However, it is difficult to demonstrate high-visibility quantum interference for a large number of photons. Here, we introduce an experimentally feasible proposal to achieve this goal using phase-randomized weak coherent states and practical photon-number-resolving detectors. Our method can be used to obtain analytically tight bounds of the generalized HOM dip in a beam splitter, and it is robust against practical errors. The simulation shows that for an ideal detector that can resolve any number of photons without an upper bound, the proposed method can bound the generalize HOM interference for a large number of photons, e.g., 100 photons; for a practical detector that can resolve up to 29 photons, the proposed method can bound the generalized HOM interference for 56 photons with visibility $99.4%$. Our proposal is a general approach for multiphoton quantum interference in optical networks with multiple inputs and outputs.