Reviewerly/exHarmony huggingface dataset:
Controlling Neural Level Sets

The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surfaces. Thus, methods for controlling the neural level sets could find many applications in machine learning. In this paper we present a simple and scalable approach to directly control level sets of a deep neural network. Our method consists of two parts: (i) sampling of the neural level sets, and (ii) relating the samples' positions to the network parameters. The latter is achieved by a sample network that is constructed by adding a single fixed linear layer to the original network. In turn, the sample network can be used to incorporate the level set samples into a loss function of interest. We have tested our method on three different learning tasks: improving generalization to unseen data, training networks robust to adversarial attacks, and curve and surface reconstruction from point clouds. For surface reconstruction, we produce high fidelity surfaces directly from raw 3D point clouds. When training small to medium networks to be robust to adversarial attacks we obtain robust accuracy comparable to state-of-the-art methods.
End-to-End Losses Based on Speaker Basis Vectors and All-Speaker Hard Negative Mining for Speaker Verification

In recent years, speaker verification has primarily performed using deep neural networks that are trained to output embeddings from input features such as spectrograms or Mel-filterbank energies. Studies that design various loss functions, including metric learning have been widely explored. In this study, we propose two end-to-end loss functions for speaker verification using the concept of speaker bases, which are trainable parameters. One loss function is designed to further increase the inter-speaker variation, and the other is designed to conduct the identical concept with hard negative mining. Each speaker basis is designed to represent the corresponding speaker in the process of training deep neural networks. In contrast to the conventional loss functions that can consider only a limited number of speakers included in a mini-batch, the proposed loss functions can consider all the speakers in the training set regardless of the mini-batch composition. In particular, the proposed loss functions enable hard negative mining and calculations of between-speaker variations with consideration of all speakers. Through experiments on VoxCeleb1 and VoxCeleb2 datasets, we confirmed that the proposed loss functions could supplement conventional softmax and center loss functions.
Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery

In the treatment of head and neck cancer, physicians can benefit from augmented reality in preparing and executing treatment. We present a system allowing a physician wearing an untethered augmented reality headset to see medical visualizations precisely overlaid onto the patient. Our main contribution is a strategy for markerless registration of 3D imaging to the patient's face. We use a neural network to detect the face using the headset's depth sensor and register it to computed tomography data. The face registration is seamlessly combined with the headset's continuous self-localization. We report on registration error and compare our approach to an external, high-precision tracking system.
A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight

Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis.
Augmented Reality for the enhancement of space product assurance and safety

A growing number of companies in the aerospace industry are already leading projects to deploy Augmented Reality (AR) to improve their workplace performance, knowledge transfer, as well as workforce productivity. In parallel, national and international agencies in aerospace, such as the European Space Agency (ESA), are running studies to evaluate the application of AR to enhance the quality and cost effectiveness of space missions. We hereby present the results of a study performed by ESA to assess the maturity and potential business value of AR for application to space product assurance and safety activities. For this purpose, we conduct an on-line survey and interviews with product assurance and safety professionals. Moreover, we provide a detailed review of space industry use case requirements and readiness levels of potentially involved technology components. Findings indicate that maturity of many components enabling AR may not fully satisfy the space industry's requirements, while, at the same time, there is great potential for impact and long-term benefits from AR introduction.
Object Detection with Convolutional Neural Networks

This chapter presents a brief overview of the recent developments in object detection using convolutional neural networks (CNNs) and describes several classical CNN-based detectors. It also presents some performance comparison results of different models on several benchmark datasets. With more and more powerful computing ability available, CNN layers are becoming deeper and deeper. The chapter introduces the region-based CNN (R-CNN), fast R-CNN, and Faster R-CNN models, and discusses the improvements of each model. The R-CNN model utilizes selective searc, which takes the entire image as input and generates around 2,000 class-independent region proposals. In theory, R-CNN is able to work with any region proposal methods. Selective search is chosen because it performs well and has been employed by other detection models. Before fast R-CNN, the time taken at the second stage is significantly more than that at the region proposal stage.
Understanding and Improving Information Transfer in Multi-Task Learning

We investigate multi-task learning approaches which use a shared feature representation for all tasks. To better understand the transfer of task information, we study an architecture with a shared module for all tasks and a separate output module for each task. We study the theory of this setting on linear and ReLU-activated models. Our key observation is that whether or not tasks' data are well-aligned can significantly affect the performance of multi-task learning. We show that misalignment between task data can cause negative transfer (or hurt performance) and provide sufficient conditions for positive transfer. Inspired by the theoretical insights, we show that aligning tasks' embedding layers leads to performance gains for multi-task training and transfer learning on the GLUE benchmark and sentiment analysis tasks; for example, we obtained a 2.35% GLUE score average improvement on 5 GLUE tasks over BERT LARGE using our alignment method. We also design an SVD-based task re-weighting scheme and show that it improves the robustness of multi-task training on a multi-label image dataset.
User preferences related to virtual reference services in an academic library

Library users have a wide variety of methods at their disposal for interacting virtually with libraries. This exploratory study examines user preferences with regard to virtual reference services and factors that account for these preferences from a different vantage point than previous literature by relying on semi-structured interviews with users. Using NVivo qualitative data analysis software, I coded interview transcripts and applied grounded theory to identify preferences from among email to the library, email to a liaison librarian, chat and texting. In terms of virtual reference methods currently offered by the library, participants indicated a general preference for chat, highlighting the importance of this service. However, participants were reluctant to use chat on mobile phones, their most used communication technology. Findings also show that relational aspects are major factors influencing participants' choice of communication. Specifically, participants expressed a preference for modes of communication that are personal, informal, perceived as safe and secure and conversational. Participants expressed reservations for texting due to ambiguity about response times, the perception of the method as being too personal and safety and security concerns. Participants were reluctant to use email in general due to response times and its level of formality, but valued email with their liaison librarian for its level of personalness and the level of expertise they felt that the liaison librarian could offer. Understanding these preferences and the factors that account for them is important because it can influence which virtual reference services librarians choose to offer. It can also help to determine how well virtual reference provision is currently meeting user needs and identify ways service delivery and promotion can be improved.
Secure and Trust-Oriented Edge Storage for Internet of Things

The edge storage is a promising paradigm to support the Internet of Things (IoT) data storage, and is more efficient than the cloud storage in terms of the bandwidth overhead, the response latency, and so on. However, existing edge storage models cannot offer the security-aware data robustness and the adaptable data sharing among many uncertain users, due to the limitations of the utilized fault-tolerant storage technologies and the access control methods. In this article, we propose a secure and trust-oriented edge storage model, which would efficiently tackle the aforementioned two challenging issues in the IoT environment. More precisely, we first propose a robust and secure edge storage (RoSES) model using the totally local reconstruction code (TLRC) method presented in this article. It can achieve data robustness, high security, and lightweight computation at end devices. We further propose a trust-oriented data access (TODA) strategy for our RoSES model, which supports a wide and adaptable range of legitimate data accesses from uncertain requesters for IoT data sharing. We conduct extensive comparison and simulations to evaluate the performance of our new edge storage models. The results show that our model can efficiently realize the data storage, data recovery, and data sharing at the network edge, saving about 35% of storage cost and 76% degraded read latency. Besides, the data leakage probability is significantly reduced during the data storage and sharing processes.
Improved 1D-CNNs for behavior recognition using wearable sensor network

Wireless Body Sensor Network (BSNs) are wearable sensors with varying sensing, storage, computation, and transmission capabilities. When data is obtained from multiple devices, multi-sensor fusion is desirable to transform potentially erroneous sensor data into high quality fused data. By analyzing and processing the perceived physical activity data of users, they can be provided with services that may be needed. Wearable sensors transmit human acceleration information to the server through 4G network. In this way, online analysis and recognition of human behavior is realized. In this paper, we study on efficiently real-time behavior recognition algorithm using acceleration sensor. We propose a human behavior recognition method based on improved One-Dimensional Convolutional Neural Networks(1D-CNNs). According to the motion characteristics, the eigenvalues are extract which can distinguish the types of activities. At the same time, we propose a sample autonomous learning method, which aims to find the optimal sample training set and avoid over-fitting problems in traditional CNNs. In the recognition of 11 human activities, our method can reach the average accuracy of 98.7%. Compared with other behavior recognition methods in the same dataset, better classification is achieved by this method.
FileShare: A Blockchain and IPFS Framework for Secure File Sharing and Data Provenance

In this paper, we introduce FileShare—a secure decentralized application framework for sharing files and data provenance. It overcomes the integrity and ownership issues in the existing solutions for file sharing and data provenance. In the proposed framework, a decentralized application (DApp) on top of Ethereum is responsible for user registration and for provenance purposes. Ethereum smart contract is used to govern, manage, and provide traceability and visibility into the history of the shared content from its origin to the latest version. It employs IPFS, a distributed file system, as its data storage layer, avoiding the pitfalls of centralized storage solutions. The proposed framework utilizes an inbuilt editor to view and modify files. The files will be stored in an encrypted form on IPFS and can only be accessed in the FileShare text editor. Modify and share operations performed on shared files are recorded separately to the blockchain, ensuring high integrity, resiliency, and transparency.
Feature-Improving Generative Adversarial Network for Face Frontalization

Face frontalization can boost the performance of face recognition methods and has made significant progress with the development of Generative Adversarial Networks (GANs).However, many GAN-based face frontalization methods still perform relatively weak on face recognition tasks under large face poses.In this paper, we propose Feature-Improving GAN (FI-GAN) for face frontalization, which aims to improve the recognition performance under large face poses.We assume that there is an inherent mapping between the frontal face and profile face, and their discrepancy in deep representation space can be estimated.The generation module of FI-GAN has a compact module named Feature-Mapping Block that helps to map the features of profile face images to the frontal space.Moreover, we produce a feature discriminator that can distinguish the features of profile face images from those of ground true frontal face images, which guide the generation module to provide high-quality features of profile faces.We conduct experiments on the MultiPIE, Labeled Faces in the Wild (LFW), and Celebrities in Frontal-Profile (CFP) databases.Our method is comparable to state-of-the-art methods under small poses and outperforms them on large pose face recognition.
Leukemia Disease Detection and Classification Using Machine Learning Approaches: A Review

Leukemia is a type of blood cancer which occurs due to abnormal increase in WBCs (white blood cells) in bone marrow of human body. Leukemia can be classified as acute leukemia and chronic leukemia, in which acute leukemia grows very fast whereas chronic leukemia grows slowly. Further both the types have two sub categories lymphocytic and myeloid. In this paper, we are going to analyze different image processing and machine learning techniques used for classification of leukemia detection and try to focus on merits and limitations of different similar researches to summarize a result which will be helpful for other researchers.
Digital watermarking secure scheme for remote sensing image protection

As a means of copyright protection for multimedia data, digital watermarking technology has attracted more and more attention in various research fields. Researchers have begun to explore the feasibility of applying it to remote sensing data recently. Because of the particularity of remote sensing image, higher requirements are put forward for its security and management, especially for the copyright protection, illegal use and authenticity identification of remote sensing image data. Therefore, this paper proposes to use image watermarking technology to achieve comprehensive security protection of remote sensing image data, while the use of cryptography technology increases the applicability and security of watermarking technology. The experimental results show that the scheme of remote sensing image digital watermarking technology has good performance in the imperceptibility and robustness of watermarking.
Intra-day solar probabilistic forecasts including local short-term variability and satellite information

In this work, three models are built to produce intra-day probabilistic solar forecasts with lead times ranging from 10 min to 3 h with a granularity of 10 min. The first model makes only use of past ground measurements. The second model upgrades the first one by adding a variability metric obtained also from the past ground measurements. The third model takes as additional input the satellite albedo. A non parametric approach based on the linear quantile regression technique is used to generate the set of quantiles that summarize the predictive distributions of the global solar irradiance at a horizontal plane (GHI). The probabilistic models are evaluated on several sites that experience very different climatic conditions. It is shown that incorporating variability significantly reduces the width of interval predictions. The addition of satellite information further improves the quality of the probabilistic forecasts.
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation. The full hierarchical policy is then transferred to the real world in a zero-shot fashion. The application of domain randomization during training enables the learned behaviors to generalize to real-world settings, while the use of hierarchy provides a modular paradigm for learning and transferring increasingly complex behaviors. We evaluate our method on a number of real-world tasks, including coordinated object manipulation in a multi-agent setting. See videos at this https URL
Virtual and augmented reality applications for environmental science education and training

Recent advancements in positioning and depth sensing using sensor technologies, graphic processor units and artificial intelligence led to the development of generalized and affordable virtual and augmented reality devices. These developments make it possible for individuals to immerse themselves in state-of-the-art simulations with real-world physics and scenarios. Public, scientists, decision-makers and professionals can benefit from virtual and augmented reality applications to simulate various environmental scenarios for a realistic and safe workspace that allows repetition and precise measurements. The primary focus of this chapter is to review the available literature on the effectiveness and efficiency of using virtual and augmented reality approaches in the environmental domain. The chapter presents seven case studies to demonstrate potential benefits of mixed reality (XR) as an educational tool in environmental sciences for K-12 and college-level students, an exhibit for community outreach to increase awareness and to present data resources in an engaging way, a decision support system for environmental planning and disaster management and a training platform for technical staff and first responders. Finally, future directions are provided for augmenting conventional data communication tools with augmented reality (AR) and virtual reality (VR) for next-generation environmental information systems.
SEEK: Segmented Embedding of Knowledge Graphs

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory.To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity.Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations.It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases.Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.
Effectiveness of Virtual Versus Physical Training: The Case of Assembly Tasks, Trainer's Verbal Assistance, and Task Complexity

Virtual immersive training (VIT) systems based on gamification of tasks are increasingly employed to train assembly workers. In this article, we present a study that compares the effectiveness of virtual and physical training for teaching a bimanual assembly task and in a novel approach, we introduce task complexity (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CXB</sub> ) as an indicator of assembly errors during final assembly. In a between-subjects experiment, 100 participants were trained to assemble a 3-D cube in one of the four conditions (physical, virtual and with trainer's verbal assistance or not). The results demonstrate that the best-performing conditions, both in terms of successful assemblies and time performance, are the ones that the physical objects are included in the training, whereas no significant difference is found when the trainer's verbal assistance is present or absent during training. Additionally, we address the validity of a practical T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CXB</sub> CXB list as a tool for supporting the design of VIT systems.