Reviewerly/exHarmony huggingface dataset:
VRsneaky

While Virtual Reality continues to increase in fidelity, it remains an open question how to effectively reflect the user's movements and provide congruent feedback in virtual environments. We present VRsneaky, a system for producing auditory movement feedback, which helps participants orient themselves in a virtual environment by providing footstep sounds. The system reacts to the user's specific gait features and adjusts the audio accordingly. In a user study with 28 participants, we found that VRsneaky increases users' sense of presence as well as awareness of their own posture and gait. Additionally, we find that increasing auditory realism significantly influences certain characteristics of participants' gait. Our work shows that gait-aware audio feedback is a means to increase presence in virtual environments. We discuss opportunities and design requirements for future scenarios where users walk through immersive virtual worlds.
An application of constacyclic codes to entanglement-assisted quantum MDS codes

The constructions of entanglement-assisted quantum codes have been studied intensively by researchers. Nevertheless, it is hard to determine the number of shared pairs required for constructing entanglement-assisted quantum codes from linear codes. In this paper, by making use of the notion of decomposition for defining sets of constacyclic codes, we construct several new families of entanglement-assisted quantum MDS codes from constacyclic codes, some of which are of minimum distances greater than $$q+1$$ . Moreover, we tabulate their parameters to illustrate what we find in this paper.
Pose Anchor: A Single-Stage Hand Keypoint Detection Network

This paper presents an effective single network for hand keypoint detection, instead of relying on the frequently-used two-stage pipeline consisting of localizing the hand and detecting the key points. Our method trains a fully convolutional neural network in an end-to-end manner, based on a novelly proposed pose anchor network, which can be deemed as an extension of the region proposal network (RPN) in Faster Region-based convolutional network. Moreover, we generate our pose anchor in a data-driven way, i.e., a K-means cluster algorithm based on object keypoint similarity (OKS), instead of manually design. In this way, we can obtain multiple representative pose anchors with various gestures, angles, and scales. By introducing the pose anchor, we are capable of utilizing the prior knowledge of the hand structure, mitigating the problem of occlusion to some extent. We demonstrate the feasibility and effectiveness of our method with extensive experiments on the challenging large-scale multiview 3D hand pose dataset (LSM-HPD) and New Zealand Sign Language Dataset (NZSL).
An Efficient Data Security in Medical Report using Block Chain Technology

The health care services industry is always showing signs of change and supporting new advancements and advances. One of the predominant requirements in today's health care systems is to protect the patient's medical report against potential attackers. Hence, it is basic to have secure information that can just approve people can get to the patient's medical report. So, We have proposed Block chain technology as a disbursed approach to grant security in accessing the medical report of a patient. It's composed of three 1. Authentication, 2. Encryption and 3. Data Retrieval using Block Chain technology. For authentication - Quantum Cryptography, for Encryption - AES and for Data Retrieval - SHA algorithms are used to resist the frequent attacks. This proposed framework may likewise ensure the protection of the patients and moreover keeps up the security and trustworthiness of the health care system.
Your Period Rules

In this work, we address the challenges of designing interactive technologies that approach menstruation in a positive way. Building on a Research through Design approach, we underline the tensions emerging from first-hand experiences and reflections in a design workshop. In order to maintain a positive approach, rather than asking participants what problems they encountered while on their period, we asked them what desires they had, and what experiences might help them cope with it. The results of the workshop emphasized the need for reflecting critically on how we perceive menstruation when designing and how viewing menstruation as a problem might perpetuate taboos and distance women's experiences from their bodies. We aim to contribute to the ongoing discussion on designing for women's health in HCI by suggesting implications for researchers and practitioners.
Topographic gradients and lode gold sourcing recorded by placer gold morphology, geochemistry, and mineral inclusions in the east fork San Gabriel River, California, U.S.A

Topography plays a major role in the morphology, geochemistry, and mineral inclusion profiles of placer gold from the East Fork of the San Gabriel River. These trends are consistent with a significant change in topographic gradient over a short distance, and coincident with the break in slope along the range-front of the San Gabriel Mountains. Placer gold from above this topographic transition is more rounded and 3-dimensional, with elevated silver in the alloy of the native gold, little evidence of gold enrichment rims, and abundant inclusions of brittle and reactive minerals. Below the topographic transition, placer gold assumes a distinctly flattened form, with lower silver alloy content, thin gold enrichment rims, and rare inclusions of relatively inert minerals. This relationship is observed for placer gold shed from two inferred lode sources within the river catchment, with placer gold from each of these sources being traceable as a unique population over at least 8 km of the river. Significantly, these placer gold properties are distinctive enough to fingerprint gold from specific portions of the placer deposit. These identifying characteristics, with virtually identical morphology and chemical trends, are observed in the large nuggets of the Mel Fisher collection of purported San Gabriel gold. These similarities appear to confirm the provenance of the collection, including identification of the specific locations where each nugget originated.
Model for User Customization in wearable Virtual Reality Devices with IoT for “Low Vision”

The technological advancements and innovations have been revolutionary and lead the markets to adapt for convenience, ease, and profitability for decades. Tourism and travel industry has been one of the forerunners in adapting the innovations. Today the virtual reality, IOT, and big data are some examples booming the industries. The virtual reality is affecting the experience of the prospective buyer and end consumer substantially in the tourism industry. This paper is an attempt to identify the VR innovation for a Low vision visitor and to help with customization in VR technology, which will enhance the experience of the visitor through a vital combination of innovation for indoor obstacle identification and image reader. The methodology of the paper talks about the possibility to achieve customization at the device level. As the visitor, who can be in any defined category of Low vision, so accordingly, the device which will help in navigation and object identification can help him if the device is customizable as per the visitor selected parameter. This will enhance the visitor experience and will also motivate to another visitor with low vision problem to explore the world with VR facility. This will be a win-win situation for the visitor and also for the service provider. In result, this will help for enhancing the visitor experience and expanding business too.
Conceptualization and Non-Relational Implementation of Ontological and Epistemic Vagueness of Information in Digital Humanities

Research in the digital humanities often involves vague information, either because our objects of study lack clearly defined boundaries, or because our knowledge about them is incomplete or hypothetical, which is especially true in disciplines about our past (such as history, archaeology, and classical studies). Most techniques used to represent data vagueness emerged from natural sciences, and lack the expressiveness that would be ideal for humanistic contexts. Building on previous work, we present here a conceptual framework based on the ConML modelling language for the expression of information vagueness in digital humanities. In addition, we propose an implementation on non-relational data stores, which are becoming popular within the digital humanities. Having clear implementation guidelines allow us to employ search engines or big data systems (commonly implemented using non-relational approaches) to handle the vague aspects of information. The proposed implementation guidelines have been validated in practice, and show how we can query a vagueness-aware system without a large penalty in analytical and processing power.
A fully dynamic secret sharing scheme

Based on the homogeneous linear recursive equation and the Elliptic curve cryptography (ECC), a fully dynamic secret sharing scheme is proposed for the general access structure of the participants. The participants choose their own shadows by themselves. When the shared secrets are updated, the access structure is changed, and other participants are added to or deleted from the existed group, but the shadow of each participant remains unchanged. Each participant should only maintain a shadow to share the multiple secrets. Compared with the existing popular dynamic schemes, the key construction of the proposed scheme is simpler and the proposed scheme has a completely dynamic characteristic, so that it has the potential to extend its practical application scenarios. That is, the proposed scheme can improve the performances of the key management and the distributed system. The security of the scheme is based on the Shamir threshold scheme, the computational Diffie–Hellman problem (CDHP) and the discrete logarithm problem (DLP).
Architectural Security Weaknesses in Industrial Control Systems (ICS) an Empirical Study Based on Disclosed Software Vulnerabilities

Industrial control systems (ICS) are systems used in critical infrastructures for supervisory control, data acquisition, and industrial automation. ICS systems have complex, component-based architectures with many different hardware, software, and human factors interacting in real time. Despite the importance of security concerns in industrial control systems, there has not been a comprehensive study that examined common security architectural weaknesses in this domain. Therefore, this paper presents the first in-depth analysis of 988 vulnerability advisory reports for Industrial Control Systems developed by 277 vendors. We performed a detailed analysis of the vulnerability reports to measure which components of ICS have been affected the most by known vulnerabilities, which security tactics were affected most often in ICS and what are the common architectural security weaknesses in these systems. Our key findings were: (1) Human-Machine Interfaces, SCADA configurations, and PLCs were the most affected components, (2) 62.86% of vulnerability disclosures in ICS had an architectural root cause, (3) the most common architectural weaknesses were “Improper Input Validation”, followed by “Im-proper Neutralization of Input During Web Page Generation” and “Improper Authentication”, and (4) most tactic-related vulnerabilities were related to the tactics “Validate Inputs”, “Authenticate Actors” and “Authorize Actors”.
Modelling Chlorophyll-a Concentration using Deep Neural Networks considering Extreme Data Imbalance and Skewness

Algal bloom has been a serious problem, as some of algae such as cyanobacteria produce toxic wastes. Chlorophyll-a has been one of the primary indicator of algal bloom; however, it is difficult to model to forecast due to scarceness of the events. Since canonical machine learning algorithms assume balanced datasets, data imbalance of the Chlorophyll-a concentration must be visited for accurate prediction. In this paper, we present a convolutional neural network model to predict Chlorophyll-a concentration, handling its data imbalance and skewness. The experiment results show that proper data transformation and oversampling can improve prediction accuracy, especially in rare-event regions.
Gesture recognition algorithm based on image information fusion in virtual reality

Combining image information fusion theory with machine learning for biometric recognition is an important field in computer vision research in recent years. Based on this, a gesture recognition algorithm based on image information fusion in virtual reality is proposed. Firstly, it introduces the basic concepts and principles of virtual reality and information fusion technology, analyzes the characteristics and basic components of virtual environment system, points out the relationship between human and virtual environment and the impact of virtual environment on people, and gives a virtual reality. Then, the multi-sensor information fusion model of the virtual environment for gesture recognition is proposed. The membership degree and template matching algorithm are further selected for data correlation and gesture recognition in the fusion model. Finally, the design comparison experiment verifies the proposed method. The results show that the proposed multi-sensor information fusion model in the interactive virtual environment achieves the highest recognition success rate of 96.17% and is better than several comparison machine learning methods in recognition time.
Multi-Domain Task-Completion Dialog Challenge

This challenge intends to foster progress in two important aspects of dialog systems: dialog complexity and scaling to new domains. First, there is an increasing interest in building complex bots that span over multiple sub-domains to accomplish a complex user goal such as travel planning which may include hotel, restaurant, attraction and so on. To advance state-of-the-art technologies for handling complex dialogs, we over a timely task focusing on multi-domain end-to-end task completion dialog. Second, neural dialog systems require very large datasets to learn to output consistent and grammatically-correct sentences. This makes it extremely hard to scale out the system to new domains with limited in-domain data. With this challenge, our goal is to investigate whether sample complexity can decrease with time, i.e., if a dialog system that was trained on a large corpus can learn to converse about a new domain given a much smaller in-domain corpus.
Improving Dataset Volumes and Model Accuracy With Semi-Supervised Iterative Self-Learning

Within this paper, a novel semi-supervised learning technique is introduced based on a simple iterative learning cycle together with learned thresholding techniques and an ensemble decision support system. The state-of-the-art model performance and increased training data volume are demonstrated through the use of unlabeled data when training deeply learned classification models. The methods presented work independently from the model architectures or loss functions, making this approach applicable to a wide range of machine learning and classification tasks. Evaluation of the proposed approach is performed on commonly used datasets when evaluating semi-supervised learning techniques and a number of more challenging image classification datasets (CIFAR-100 and a 200 class subset of ImageNet).
VetTag: improving automated veterinary diagnosis coding via large-scale language modeling

Unlike human medical records, most of the veterinary records are free text without standard diagnosis coding. The lack of systematic coding is a major barrier to the growing interest in leveraging veterinary records for public health and translational research. Recent machine learning effort is limited to predicting 42 top-level diagnosis categories from veterinary notes. Here we develop a large-scale algorithm to automatically predict all 4577 standard veterinary diagnosis codes from free text. We train our algorithm on a curated dataset of over 100 K expert labeled veterinary notes and over one million unlabeled notes. Our algorithm is based on the adapted Transformer architecture and we demonstrate that large-scale language modeling on the unlabeled notes via pretraining and as an auxiliary objective during supervised learning greatly improves performance. We systematically evaluate the performance of the model and several baselines in challenging settings where algorithms trained on one hospital are evaluated in a different hospital with substantial domain shift. In addition, we show that hierarchical training can address severe data imbalances for fine-grained diagnosis with a few training cases, and we provide interpretation for what is learned by the deep network. Our algorithm addresses an important challenge in veterinary medicine, and our model and experiments add insights into the power of unsupervised learning for clinical natural language processing.
Prototyping of Artificial Respiration Machine Using AMBU Bag Compression

Ventilators are crucial medical equipment. Mostly, they are used to support critically ill patients. Nowadays, more and more people are adopting sedentary lifestyles, this along with the severe pollution problem has caused a surge in the number of patients with pulmonary disorders. Hence, ventilators hold a prominent position in any healthcare system in the world. However, not every medical facility can afford a ventilator due to resource constraints. Keeping all this in mind, a low-cost ventilator has been prototyped by using the AMBU (Artificial Manual Breathing Unit) bag. This device is fully capable to perform all the functions of a normal ventilator and is much more cost effective than its contemporaries. Moreover, it is capable of adjusting the BPM. (Breaths Per Minute) of patients at the physicians desired level. In addition to this, the volume of the air delivered by this ventilator is adjustable. Similarly, the inspiration to expiration ratio and Peep rate can also be regulated. Hence, it makes the ventilator viable for patients of every age and category. This system comes with two modes. The first one involves mandatory ventilation while the second one is characterized by assisted ventilation. The physician can rely on either ventilator's inbuilt triggering mechanism which alters the respiration pattern once it detects a change in air pressure or can set a time interval for this alteration of the respiration pattern
Data protectors, benefit maximizers, or facts enthusiasts: Identifying user profiles for life-logging technologies

Sedentary behavior and lack of exercise pose a threat to both individual health and the viability of health-care systems and societies. Portable fitness trackers as prominent persuasive technologies are seen as a way to increase the level of physical activity. Yet, despite their technical capabilities, their affordability, and their advantages in regard to increased physical activity, they are neither used across the population, nor for long periods of time. To understand if and how product design influences acceptance and projected use, we evaluated users’ preferences of using wearables, using a conjoint analysis approach with 412 participants of a wide age spectrum. Besides different relative importances of product properties (privacy design, perceived utility, accuracy, motivational design are rated from most to least important), three user segments with distinct technical requirements were identified (data protectors, benefit maximizers, facts enthusiasts). The three segments differ not only in product preference but also regarding other user factors. We presume that a broader and more sustainable use of wearables can be achieved when tailoring information and communication strategies alongside with the requirements of these user segments.
Capturing source code semantics via tree-based convolution over API-enhanced AST

When deep learning meets big code, a key question is how to efficiently learn a distributed representation for source code that can capture its semantics effectively. We propose to use tree-based convolution over API-enhanced AST. To demonstrate the effectiveness of our approach, we apply it to detect semantic clones---code fragments with similar semantics but dissimilar syntax. Experiment results show that our approach outperforms an existing state-of-the-art approach that uses tree-based LSTM, with an increase of 0.39 and 0.12 in F1-score on OJClone and BigCloneBench respectively. We further propose architectures that incorporate our approach for code search and code summarization.
Toward Universal Word Sense Disambiguation Using Deep Neural Networks

Traditionally, approaches based on neural networks to solve the problem of disambiguation of the meaning of words (WSD) use a set of classifiers at the end, which results in a specialization in a single set of words-those for which they were trained. This makes impossible to apply the learned models to words not previously seen in the training corpus. This paper seeks to address a generalization of the problem of WSD in order to solve it through deep neural networks without limiting the method to a fixed set of words, with a performance close to the state-of-the-art, and an acceptable computational cost. We explore different architectures based on multilayer perceptrons, recurrent cells (Long Short-Term Memory-LSTM and Gated Recurrent Units-GRU), and a classifier model. Different sources and dimensions of embeddings were tested as well. The main evaluation was performed on the Senseval 3 English Lexical Sample. To evaluate the application to an unseen set of words, learned models are evaluated in the completely unseen words of a different corpus (Senseval 2 English Lexical Sample), overcoming the random baseline.
Robust Long-Term Tracking via Instance-Specific Proposals

Correlation filter-based trackers are able to achieve long-term tracking when an additional detector is available. However, it is still challenging to achieve robust and accurate tracking due to several complicated situations, including occlusion and severe deformation. This is because a simple model is difficult to adapt to dramatic appearance changes of the object. Furthermore, redetection results are sometimes unreliable as the detector is trained on only a limited number of samples. In this paper, we propose a tracker named Complementary Learners with Instance-specific Proposals (CLIP). This tracker consists of three major components, including a translation filter, a scale filter, and an error correction (EC) module. The translation filter incorporates complementary features to handle severe target appearance variations. It is further combined with the scale filter to predict the state of the target. Finally, an adaptive updating mechanism is proposed to balance the stability and flexibility of the tracking model. Moreover, an instance-specific proposal generator is embedded into the EC module to recover the lost target from tracking failures. The experimental results on OTB2015, VOT2016, Temple-Color 128, and UAV20L demonstrate that the proposed CLIP tracker achieves comparable performance with the state-of-the-art trackers on several scenarios, including occlusion, deformation, and out-of-plane rotation. Moreover, the CLIP tracker is able to run at a speed of 35 frames/s on OTB2015, which makes it highly suitable for many real-time applications.