Reviewerly/exHarmony huggingface dataset:
Incidence and severity of Visually Induced motion Sickness during 3D laparoscopy In Operators who had No experience with it (VISION)

Aim:The aim of the study was to evaluate the incidence and severity of visually induced motion sickness (VIMS) during 3D laparoscopy, in operators without prior experience.Material and methods: Design: A retrospective comparative study (Canadian Task Force classification II-2).Setting: A university hospital.Intervention: Gynecologic surgery.Main outcome measure: This is a prospective observational study, which enrolled 9 surgeons as participants.None of these surgeons had any prior experience with 3D laparoscopy.Each participant performed 10 consecutive cases of 3D laparoscopy in patients with benign or premalignant gynecological diseases.The primary outcome measure was the incidence and severity of VIMS, which was evaluated using the validated Simulator Sickness Questionnaire.Personal preferences, discomfort, and ease of 3D laparoscopy were also evaluated.Results: Sixty-seven percent of surgeons experienced VIMS during their first 3D laparoscopy case.The incidence and severity of VIMS dramatically decreased from the second case onward.However, in some surgeons (22-44%), VIMS did not completely disappear until the tenth case.With respect to the discomfort using 3D laparoscopy, 84 self-reported responses after each surgery were "favor 3D laparoscopy," and "no" in 61 (72.6%) and 47 (55.9%) participants, respectively.Most participants found it easier to perform 3D laparoscopy than 2D laparoscopy.Conclusions: The occurrence of visually induced symptoms in susceptible individuals during 3D laparoscopy is high, particularly during their first case.This suggests the need for increasing surgeons' awareness regarding the possibility of discomfort.
Offer virtual reality tours to attract prospects who can't make it to campus

A rite of passage every high school student should get to experience, regardless of economic status — that's how Carol Cutler White feels about campus tours. And that was one of the driving forces behind the virtual reality campus tour app White helped create for the University of North Carolina System.
Blockchain and Charities: A Systemic Opportunity to Create Social Value

Countless media reports have exposed scandals concerning charities and non-profit organisations, involving mismanagement of funds, or even outright theft and misappropriation of funds earmarked for social works and projects. The aim of this paper is to show how Blockchain technology can be reconciled with the operating mechanisms of charities.
Integrating Blockchain Technology in Healthcare via Active Learning

As the healthcare industries move into the digitalization of health records, blockchain application could be a significant game-changer in the healthcare sector, creating a secure and flexible ecosystem for the exchange of records. Making healthcare clearer by creating a blockchain system to eliminate fraud and improve data accuracy is vital. Blockchain can securely interoperable record data such as organs, blood, critical drugs, and keeping medical licenses and certificates. With the emergence of Bitcoin, the first popular blockchain decentralized application, blockchain has become a viable solution for recording transactions in a growing list called blocks which are linked and protected using cryptography. However, with the ever-increasing demand for blockchain professionals and developers, there are few hands-on labs/modules available for training current students, the future developer professionals. The objective is to develop a framework including a series of hands-on labs that would fit individual student's needs for blockchain in public health.
The Covid-19 explosion in the state of Amapá: how is the most preserved region in the Brazilian Amazon currently fighting the SARS-COV 2 pandemic?

The state of Amapá is located in the extreme north of Brazil, within the Amazon rainforest and is crossed by the Equator. It has a hot and humid climate with rains that last 8 months a year and 4 months of unrelenting sun that melts rubber from car seals, fries eggs on the floor and even cooks a whole egg tub, in case you forget in a car exposed to the sun . It was believed that with this potent solar incidence, the Sars-COV 2 virus would not have so much impact in this region, a terrible mistake! Today Amapá has the highest incidence of Covid-19 in the whole of Brazil, with a maid of 600 cases per hundred thousand inhabitants and in the Amazon it is the 3rd in deaths and loses in this item only to the state of Amazonas and Pará.
MAME : Model-Agnostic Meta-Exploration

Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings. Existing approaches towards finding efficient exploration strategies add auxiliary objectives to promote exploration by the pre-update policy, however, this makes the adaptation using a few gradient steps difficult as the pre-update (exploration) and post-update (exploitation) policies are often quite different. Instead, we propose to explicitly model a separate exploration policy for the task distribution. Having two different policies gives more flexibility in training the exploration policy and also makes adaptation to any specific task easier. We show that using self-supervised or supervised learning objectives for adaptation allows for more efficient inner-loop updates and also demonstrate the superior performance of our model compared to prior works in this domain.
OmniGlobeVR: A Collaborative 360° Communication System for VR

In this paper, we propose OmniGlobeVR, a novel collaboration tool based on an asymmetric cooperation system that supports communication and cooperation between a VR user (occupant) and multiple non-VR users (designers) across the virtual and physical platform. The OmniGlobeVR allows designer(s) to access the content of a VR space from any point of view using two view modes: 360° first-person mode and third-person mode. Furthermore, a proper interface of a shared gaze awareness cue is designed to enhance communication between the occupant and the designer(s). The system also has a face window feature that allows designer(s) to share their facial expressions and upper body gesture with the occupant in order to exchange and express information in a nonverbal context. Combined together, the OmniGlobeVR allows collaborators between the VR and non-VR platforms to cooperate while allowing designer(s) to easily access physical assets while working synchronously with the occupant in the VR space.
Secure Decentralized Machine Identifiers for Internet of Things

The digital identities of Internet of Things (IoT) devices are vital to the security of IoT system. However, current centralized identity management systems have many drawbacks such as the single point of failure, suffering DDoS attacks and privacy issues. In this paper, to solve the above problems, we combine with the scenario of electrical vehicles charging to present our decentralized machine identifier (DMID) and identity management scheme. The scheme is based on blockchain and InterPlanetary File System (IPFS) technologies. Moreover, to improve the security of DMID, we design a secure hardware module by employing Physical Unclonable Function (PUF), True Random Number Generation (TRNG) and Trustzone. At last, we design a simulation system, and the result demonstrates the feasibility and effectiveness of our proposed scheme.
Development and Usability Evaluation on Individualized Reading Enhancing Application for Dyslexia (IREAD): A Mobile Assistive Application

Abstract Mobile Applications Development has been the trend for the previous year to revolutionize the teaching and learning process. These technologies were designed to improve the quality of education for all types of learners, more specifically for learners who have learning difficulties like dyslexia. Learners having this learning disorder oftentimes have difficulty in reading and writing. The use of conventional instruction is challenged for this learner. With the advent of technology, specifically mobile assistive application development helps implement more dynamic, effective and efficient teaching strategies, thus provides an opportunity to improve the learning of the students. This study aimed to develop a mobile assistive application name Individualized Reading Enhancing Application for Dyslexia (IREAD) which primarily implements text-to-speech technology to enhance the reading capability of learners with dyslexia. Unity 3D is utilized in the development of the application and web support is present to manage learning resources of the application. Moreover, usability evaluation is performed to determine the overall usability of the application, with the following dimensions effectiveness, efficiency, and quality of support, ease of learning and satisfaction. The 10 participants who were teachers teaching in specialized education answered the usability questionnaire. Results show that the overall usability of the IREAD application is immensely positive.
Column Partition and Permutation for Run Length Encoding in Columnar Databases

Effective compression is essential when databases are used in Big Data applications. For in-memory columnar databases, compression can help to load the columns faster and speed up query evaluation. In this paper, we consider compressing columns using the Run Length Encoding (RLE). This algorithm encodes each region with identical value using a single run. The question we study in this paper is 'how to rearrange table columns for better compression?' We observe that not every column of a table benefits from column compression in an ideal column arrangement. Because finding the optimal column arrangement is NP-hard, we propose an incremental heuristic that identifies the set of columns to be compressed and the order of rows that offer a better compression ratio. Our preliminary experiments confirm that our algorithm improves the compression rate by up to 25% on test data, compared with compressing all columns of a table.
Genetic Algorithm with Comprehensive Sequential Constructive Crossover for the Travelling Salesman Problem

The travelling salesman problem (TSP) is a very famous NP-hard problem in operations research as well as in computer science.To solve the problem several genetic algorithms (GAs) are developed which depend primarily on crossover operator.The crossover operators are classified as distance-based crossover operators and blind crossover operators.The distance-based crossover operators use distances between nodes to generate the offspring(s), whereas blind crossover operators are independent of any kind of information of the problem, except follow the problem's constraints.Selecting better crossover operator can lead to successful GA.Several crossover operators are available in the literature for the TSP, but most of them are not leading good GA.In this study, we propose reverse greedy sequential constructive crossover (RGSCX) and then comprehensive sequential constructive crossover (CSCX) for developing better GAs for solving the TSP.The usefulness of our proposed crossover operators is shown by comparing with some distance-based crossover operators on some TSPLIB instances.It can be concluded from the comparative study that our proposed operator CSCX is the best crossover in this study for the TSP.
RETRACTED: Mobile-based education design for teaching and learning platform based on virtual reality

As a new teaching medium, virtual reality technology has been widely used in the field of education due to the characteristics of immersiveness, imagination, and interactivity, thereby changing the previous teaching concepts and teaching models. Mobile learning has developed rapidly due to its characteristics of mobility, personalization, fragmentation, and hypermedia. Based on the characteristics of the two, we innovatively propose the application of virtual reality technology to the development and construction of mobile learning platforms. This article analyzes the application advantages of virtual reality technology in mobile learning first; proposes design ideas, development tools, architecture design, functional design, and development process of the virtual reality-based mobile learning platform second; displays some of the virtual reality-based mobile learning platforms that have been completed subsequently. Various teaching practice activities have been carried out based on these platforms and achieved good results.
An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing (CAIR) is an excellent technology used widely for image retarget. It can also be used to tamper with images and bring the trust crisis of image content to the public. Once an image is processed by CAIR, the correlation of local neighborhood pixels will be destructive. Although local binary patterns (LBP) can effectively describe the local texture, it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise. Therefore, to deal with the detection of CAIR, a novel forensic method based on improved local ternary patterns (ILTP) feature and gradient energy feature (GEF) is proposed in this paper. Firstly, the adaptive threshold of the original local ternary patterns (LTP) operator is improved, and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR. Secondly, the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection. Then, the ILTP features and the gradient energy features are concatenated into the combined features, and the combined features are used to train classifier. Finally support vector machine (SVM) is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not. The candidate images are extracted from uncompressed color image database (UCID), then the training and testing sets are created. The experimental results with many test images show that the proposed method can detect CAIR tampering effectively, and that its performance is improved compared with other methods. It can achieve a better performance than the state-of-the-art approaches.
NorMAS-ML: Supporting the Modeling of Normative Multi-agent Systems

Context: A normative multi-agent system (NMAS) is composed of agents that their behavior is regulated by norms. The modeling of those elements (agents and norms) together at design time can be a good way for a complete understanding of their structure and behavior. Multi-agent system modeling language (MAS-ML) supports the representation of NMAS entities, but the support for concepts related to norms is somewhat limited. MAS-ML is founded in taming agents and objects (TAO) framework and has a support tool called the MAS-ML tool. Goal: The present work aims to present a UML-based modeling language called normative multi-agent system (NorMAS-ML) able to model the MAS main entities along with the static normative elements. Method: We extend the TAO adding normative concepts and spread out those concepts in two syntaxes of MAS-ML. Either abstract, adding or updating metaclasses and stereotypes or concrete, defining new graphic elements for representing the elements defined in the abstract syntax. Besides, we evolve the MAS-ML tool, considering the extension of MAS-ML by the model-driven approach. Results: NorMAS-ML, the new version of MAS-ML, allows a complete view of NMAS entities and has a support tool called NorMAS-ML tool. Beyond the definition of NorMAS-ML and its tool, we generate a new static diagram called “norm diagram” supported by the NorMAS-ML tool. In order to illustrate the syntax of NorMAS-ML, the entities of a conference management system and its norms are modeled jointly. Conclusion: NorMAS-ML can help software designers (i) to understand the properties and behavior of NMAS entities and (ii) to provide a software modeling following the stakeholders’ need and less complex for the development phase.
Sparse Layered Graphs for Multi-Object Segmentation

We introduce the novel concept of a Sparse Layered Graph (SLG) for s-t graph cut segmentation of image data. The concept is based on the widely used Ishikawa layered technique for multi-object segmentation, which allows explicit object interactions, such as containment and exclusion with margins. However, the spatial complexity of the Ishikawa technique limits its use for many segmentation problems. To solve this issue, we formulate a general method for adding containment and exclusion interaction constraints to layered graphs. Given some prior knowledge, we can create a SLG, which is often orders of magnitude smaller than traditional Ishikawa graphs, with identical segmentation results. This allows us to solve many problems that could previously not be solved using general graph cut algorithms. We then propose three algorithms for further reducing the spatial complexity of SLGs, by using ordered multi-column graphs. In our experiments, we show that SLGs, and in particular ordered multi-column SLGs, can produce high-quality segmentation results using extremely simple data terms. We also show the scalability of ordered multi-column SLGs, by segmenting a high-resolution volume with several hundred interacting objects.
Population-Based Black-Box Optimization for Biological Sequence Design

The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle. We find that the performance of existing methods varies drastically across optimization tasks, posing a significant obstacle to real-world applications. To improve robustness, we propose Population-Based Black-Box Optimization (P3BO), which generates batches of sequences by sampling from an ensemble of methods. The number of sequences sampled from any method is proportional to the quality of sequences it previously proposed, allowing P3BO to combine the strengths of individual methods while hedging against their innate brittleness. Adapting the hyper-parameters of each of the methods online using evolutionary optimization further improves performance. Through extensive experiments on in-silico optimization tasks, we show that P3BO outperforms any single method in its population, proposing higher quality sequences as well as more diverse batches. As such, P3BO and Adaptive-P3BO are a crucial step towards deploying ML to real-world sequence design.
Quality Difference Ranking Model For Smartphone Camera Photo Quality Assessment

Image quality assessment (IQA) for smartphone cameras is important in mobile phone performance evaluation, but existing image quality assessment methods are not suitable for smartphone camera photos. In this work, we propose a quality difference ranking model for smartphone camera photo quality assessment, which compares and ranks image quality from four distortion types. First, we select some image patches in a pair of distorted images according to the image content and distortion types. Then we input these image patches into the quality difference ranking model to predict the quality order of the two images. Finally, we use the bubble sort to rank the quality of all images. Experimental results show that our model outperforms previous IQA methods in smartphone cameras photo quality database.
Optimizing for the Future in Non-Stationary MDPs

Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process is stationary. However, in many real-world applications, this assumption is violated, and using existing algorithms may result in a performance lag. To proactively search for a good future policy, we present a policy gradient algorithm that maximizes a forecast of future performance. This forecast is obtained by fitting a curve to the counter-factual estimates of policy performance over time, without explicitly modeling the underlying non-stationarity. The resulting algorithm amounts to a non-uniform reweighting of past data, and we observe that minimizing performance over some of the data from past episodes can be beneficial when searching for a policy that maximizes future performance. We show that our algorithm, called Prognosticator, is more robust to non-stationarity than two online adaptation techniques, on three simulated problems motivated by real-world applications.
TailX: Scheduling Heterogeneous Multiget Queries to Improve Tail Latencies in Key-Value Stores

Users of interactive services such as e-commerce platforms have high expectations for the performance and responsiveness of these services. Tail latency, denoting the worst service times, contributes greatly to user dissatisfaction and should be minimized. Maintaining low tail latency for interactive services is challenging because a request is not complete until all its operations are completed. The challenge is to identify bottleneck operations and schedule them on uncoordinated backend servers with minimal overhead, when the duration of these operations are heterogeneous and unpredictable. In this paper, we focus on improving the latency of multiget operations in cloud data stores. We present TailX, a task-aware multiget scheduling algorithm that improves tail latencies under heterogeneous workloads. TailX schedules operations according to an estimation of the size of the corresponding data, and allows itself to procrastinate some operations to give way to higher priority ones. We implement TailX in Cassandra, a widely used key-value store. The result is an improved overall performance of the cloud data stores for a wide variety of heterogeneous workloads. Specifically, our experiments under heterogeneous YCSB workloads show that TailX outperforms state-of-the-art solutions and reduces tail latencies by up to 70% and median latencies by up to 75%.
DeepClass: edge based class occupancy detection aided by deep learning and image cropping

Detecting people's presence, monitoring their flows, and their activities, counting how many persons are in a specific place can be strategic goals in different contexts, providing useful insights for different purposes, including those ones related to the management of staying quality in indoor environments. In particular, having information about the actual and current occupancy of a specific room, in specific hours, could be strategic in providing interesting and helpful information for smart building management. In fact, this information could be needed to adequately set the Heat, Ventilation and Air Conditioning (HVAC), the alarm, the lighting systems, and other management issues also. In this context, the Internet of Things paradigm, together with the diffusion of the availability of sensors and smart objects, can provide significant support in monitoring and detecting daily life activities in various situations. Moreover, advancements and specific analysis in image processing can play a strategic role in guaranteeing and improving accuracy, whenever cameras are involved in these situations, to get pictures from the monitored environments. In this paper, we present a people counting approach we have defined and adopted to monitor persons' presence in smart campus classrooms, which is based on the use of cameras and Raspberry Pi platforms. Such an approach has been improved thanks to specific image processing strategies, to be generalized and adopted in different indoor environments, without the need for a specific training phase. The paper presents some evaluation tests we have conducted, showing the accuracy of our approach.