Reviewerly/exHarmony huggingface dataset:
Towards a decentralized e-prescription system using smart contracts

Electronic prescription (e-Prescription) is a digital way to manage medical prescriptions and reduce inconsistencies in the communication between doctors, patients, and pharmacies. Smart contracts allow the automation of tasks and business rules in a decentralized architecture (i.e., without the need for an intermediary or central authority). Platforms such as Ethereum and Hyperledger Fabric support smart contracts development through a consensus mechanism such as Proof-of-Work or another criterion among the network's participating nodes. This paper explores Tendermint, a Byzantine Fault Tolerant (BFT) based consensus mechanism that has not yet been widely adopted for smart contracts platforms. We apply our devised model to the healthcare application domain, more precisely in the field of e-prescription, and our results demonstrate that smart contracts can be implemented on a BFT-based platform. To the best of our knowledge, this is the first study investigating the implementation of smart contracts in a BFT-based platform such as Tendermint. We exploit this domain as there can exist some conflicting interests of profit-taking. For example, pharmacists can increase the medication dosage above the one prescribed by doctors for profit-taking. Such a scenario can occur particularly in countries where healthcare is free of charge and offered as a public service (e.g., Brazil). We show that smart contracts in BFT-based blockchain can help in solving problems in these application scenarios. Finally, our two key contributions in this paper are two-fold: (i) exploit smart-contracts in BFT-based platforms where they (smart contracts) are not very established yet in the blockchain domain; (ii) provide a smart-contract-based e-prescription solution to reduce the costs (no need for a central authority) and scams particularly in countries where the medical service is free and public.
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation

Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. Efforts to alleviate this look at varying degrees and quality of supervision. Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples for novel classes. This taxonomy has largely siloed algorithmic designs. In this work, we aim to bridge this divide by proposing an intuitive and unified semi-supervised model that is applicable to a range of supervision: from zero to a few instance-level samples per novel class. For base classes, our model learns a mapping from weakly-supervised to fully-supervised detectors/segmentors. By learning and leveraging visual and lingual similarities between the novel and base classes, we transfer those mappings to obtain detectors/segmentors for novel classes: refining them with a few novel class instance-level annotated samples, if available. The overall model is end-to-end trainable and highly flexible <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Through extensive experiments on MS- COCO [32] and Pascal VOC [14] benchmark datasets we show improved performance in a variety of settings.
Multi-level Thresholding Image Segmentation Based on Nature-Inspired Optimization Algorithms: A Comprehensive Review

Image segmentation is an essential step involved in most computer vision systems used in several areas such as engineering, communications, transportation, business, and social sciences. Besides, several techniques are employed to accomplish the task of segmentation for example (Threshold Based, Region Based, Edge Based, Clustering Based, Neural Network, and Partial Differential Equation). However, thresholding based techniques are the most frequently used techniques for segmentation due to its simplicity, easy to understand and implement. Thresholding in simple words can be defined as the process of dividing an image into multiple homogeneous classes. Mainly, thresholding techniques can be classified into two main categories namely Bi-level Thresholding (BT) and Multi-level Thresholding (MT) based on the number of thresholds selected. BT methods aim to find a single optimal threshold value that separates an image based on each pixel intensity into two regions namely foreground and background. While the MT methods are used to divide an image into two or more classes using two or more thresholds. However, to accomplish the selection task of the optimal thresholds traditional thresholding methods are exhaustive and time-consuming. On the other hand, optimization algorithms proved its efficiency in solving several real-world issues, which make the use of optimization algorithms a promising way of solving thresholding problems. This study presents a literature review of nature inspired optimization algorithms (NIOAs) and its applications in thresholding to optimize the selection of optimal thresholds process based image segmentation domain, also covers a large-scale of the researches published in this area of image segmentation based NIOAs over the last ten years.
Improved Language Identification Through Cross-Lingual Self-Supervised Learning

Language identification greatly impacts the success of downstream tasks such as automatic speech recognition. Recently, self-supervised speech representations learned by wav2vec 2.0 have been shown to be very effective for a range of speech tasks. We extend previous self-supervised work on language identification by experimenting with pre-trained models which were learned on real-world unconstrained speech in multiple languages and not just on English. We show that models pre-trained on many languages perform better and enable language identification systems that require very little labeled data to perform well. Results on a 26 languages setup show that with only 10 minutes of labeled data per language, a cross-lingually pre-trained model can achieve over 89.2% accuracy.
State of the art on immersive virtual reality and its use in developing meaningful empathy

Empathy seems to have been one of the most relevant factors in examining the use of Virtual Reality (VR) in recent years. This technology allows users to embody someone else through virtual experiences, taking another's perspective and perceive their avatar's actions as their own. Thus, Perspective-Taking with Virtual Reality (PTVR) looks promising as an entry portal to promote or develop empathic responses. This article presents a state-of-the-art from the academic and scientific literature published between 2015 and 2020 on the use of VR to promote empathic capacity development, with attention to implementations in educational contexts (although not exclusively). Findings include trends about the use of VR to promote empathic responses and how previous studies have carried out experimentations. We discuss empathy measurement and the lack of rigorous, empirical, scientific evidence to ensure that VR could be the vehicle for developing empathy, as many would wish.
Testing the Feasibility of Virtual Reality With Older Adults With Cognitive Impairments and Their Family Members Who Live at a Distance

This study tests the feasibility of using virtual reality (VR) with older adults with mild cognitive impairment (MCI) or mild-to-moderate dementia with a family member who lives at a distance.21 residents in a senior living community and a family member (who participated in the VR with the older adult from a distance) engaged in a baseline telephone call, followed by 3 weekly VR sessions.Residents and family members alike found the VR safe, extremely enjoyable, and easy to use. The VR was also acceptable and highly satisfying for residents with MCI and dementia. Human and automated coding revealed that residents were more conversationally and behaviorally engaged with their family member in the VR sessions compared to the baseline telephone call and in the VR sessions that used reminiscence therapy. The results also illustrate the importance of using multiple methods to assess engagement. Residents with dementia reported greater immersion in the VR than residents with MCI. However, the automated coding indicated that residents with MCI were more kinesically engaged while using the VR than residents with dementia.Combining networking and livestreaming features in a single VR platform can allow older adults in senior living communities to still travel, relive their past, and engage fully with life with their family members, despite geographical separation and physical and cognitive challenges.
Evaluation of locomotion methods in virtual reality navigation environments: An involuntary position shift and task performance

Navigation is a representative task in a virtual environment (VE). The locomotion methods affect the navigation performance and can cause involuntary movements by the users, which may cause critical safety problems. Thus, this study examined the effects of different locomotion methods on the users’ involuntary position shift (IPS) and navigation performance in a VE. Twenty-one healthy participants performed the navigation task with four locomotion methods: walking in place (WIP), running in place (RIP), joystick, and teleportation. The results showed that a larger amount of IPS occurred with VE navigation under WIP and RIP conditions than joystick and teleportation conditions, which may cause critical safety problems, such as collisions with walls. Moreover, teleportation took the shortest task completion time, but its error time ratio was the largest. These results are expected to help develop basic guidelines for applying locomotion methods to specific situations and establishing safety precautions for virtual reality experiences.
What Early User Involvement Could Look Like—Developing Technology Applications for Piano Teaching and Learning

Numerous technological solutions have been proposed to promote piano learning and teaching, but very few with market success. We are convinced that users’ needs should be the starting point for an effective and transdisciplinary development process of piano-related Tactile Internet with Human-in-the-Loop (TaHIL) applications. Thus, we propose to include end users in the initial stage of technology development. We gathered insights from adult piano teachers and students through an online survey and digital interviews. Three potential literature-based solutions have been visualized as scenarios to inspire participants throughout the interviews. Our main findings indicate that potential end users consider posture and body movements, teacher–student communication, and self-practice as crucial aspects of piano education. Further insights resulted in so-called acceptance requirements for each scenario, such as enabling meaningful communication in distance teaching, providing advanced data on a performer’s body movement for increased well-being, and improving students’ motivation for self-practice, all while allowing or even promoting artistic freedom of expression and having an assisting instead of judging character. By putting the users in the center of the fuzzy front end of technology development, we have gone a step further toward concretizing TaHIL applications that may contribute to the routines of piano teaching and learning.
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation

Abstract Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.
Routing for unmanned aerial vehicles: Touring dimensional sets

In this paper we deal with an extension of the crossing postman problem to design routes that have to visit different shapes of dimensional elements rather than edges. This problem models the design of routes of drones or other vehicles that must visit a number of geographical elements to deliver some good or service and then move directly to the next using straight line displacements. We present two families of mathematical programming formulations. The first one is time-dependent and captures a number of characteristics of real applications at the price of using three indexes variables. The second family of formulations is not time-dependent, instead it uses connectivity properties to ensure the proper definition of routes. We compare them on a testbed of instances with different shapes of elements: second order cone (SOC) representable and polyhedral neighborhoods and polygonal chains. The computational results reported in this paper show that our models are useful and our formulations can solve to optimality medium size instances of sizes similar to other combinatorial problems including neighborhoods that have already been studied in the literature. To address larger instances we also present a heuristic algorithm that runs in two phases: clustering and Variable Neighborhood Search. This algorithm performs very well since it provides promising feasible solutions and, in addition, it can be used to initialize the solvers with feasible solutions.
A Systems Theory of Transfer Learning

Existing frameworks for transfer learning are incomplete from a systems theoretic perspective. They place emphasis on notions of domain and task, and neglect notions of structure and behavior. In doing so, they limit the extent to which formalism can be carried through into the elaboration of their frameworks. Herein, we use the Mesarovician systems theory to define transfer learning as a relation on sets, and subsequently, characterize the general nature of transfer learning as a mathematical construct. We interpret existing frameworks in terms of ours and go beyond existing frameworks to define notions of transferability, transfer roughness, and transfer distance. Importantly, despite its formalism, our framework avoids the detailed mathematics of the learning theory or machine learning solution methods without excluding their consideration. As such, we provide a formal, general systems framework for modeling transfer learning that offers a rigorous foundation for system design and analysis.
A self-adaptive approach for white blood cell classification towards point-of-care testing

As important immune cells in the human body, white blood cells play a very significant role in the auxiliary diagnosis of many major diseases. Clinically, changes in the number and morphology of white blood cells and their subtypes are the prediction index for important, serious diseases, such as anaemia, malaria, infections, and tumours. The application of image recognition technology and cloud computing to assist in medical diagnosis is a hot topic in current research, which we believe have great potential to further improve real-time detection and improve medical diagnosis. This paper proposes a novel automatic classification framework for the recognition of five subtypes of white blood cells, in the hope of contributing to disease prediction. First, we present an adaptive threshold segmentation method to deal with blood smear images with nonuniform colour and uneven illumination. The method is designed based on colour space information and threshold segmentation. After successfully separating the white blood cell from the blood smear image, a large number of features, including geometrical, colour, and texture features are extracted. However, redundant features can affect the classification speed and efficiency, and in view of that, a feature selection algorithm based on classification and regression trees (CART) is designed to successfully remove irrelevant and redundant features from the initial features. The selected prominent features are fed into a particle swarm optimisation support vector machine (PSO-SVM) classifier to recognise the types of white blood cells. Finally, to evaluate the performance of the proposed white blood cell classification methodology, we build a white blood cell data set containing 500 blood smear images for experiments. The proposed methodology achieves 99.76% classification accuracy, which well demonstrates its effectiveness.
Effective Sparsification of Neural Networks with Global Sparsity Constraint

Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative importance of weights are very different for different layers of a neural network, existing methods rely on either manual tuning or handcrafted heuristic rules to find appropriate pruning rates individually for each layer. This approach generally leads to suboptimal performance. In this paper, by directly working on the probability space, we propose an effective network sparsification method called probabilistic masking (ProbMask), which solves a natural sparsification formulation under global sparsity constraint. The key idea is to use probability as a global criterion for all layers to measure the weight importance. An appealing feature of ProbMask is that the amounts of weight redundancy can be learned automatically via our constraint and thus we avoid the problem of tuning pruning rates individually for different layers in a network. Extensive experimental results on CIFAR-10/100 and ImageNet demonstrate that our method is highly effective, and can outperform previous state-of-the-art methods by a significant margin, especially in the high pruning rate situation. Notably, the gap of Top-1 accuracy between our ProbMask and existing methods can be up to 10%. As a by-product, we show ProbMask is also highly effective in identifying supermasks, which are sub-networks with high performance in a randomly weighted dense neural network.
Uniswap and the Rise of the Decentralized Exchange

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Fake News Detection on Reddit Utilising CountVectorizer and Term Frequency-Inverse Document Frequency with Logistic Regression, MultinominalNB and Support Vector Machine

The distribution of misleading information or fake news has become a problem for society in recent times. In the world of social media, where anyone can share their opinions, beliefs and make it sound like these are fact, fake news becomes a threat to the reputation of companies and to people. In 2016, the USA Presidential elections gathered more attention from the generation of fake news articles, leading to a huge number of researchers and scientists to explore this Natural Language Processing research area with a sense of urgency and keen interest. However, investigation regarding what people are consuming from social media is in early stages and efforts are in progress to explore how people can separate disinformation from truthful content. The primary challenge in fake news detection is determining how to detect it. Supervised learning methods help us to detect these stories using labelled data to determine if text is real or fake. This research aims to develop and compare supervised learning models using Logistic Regression, MultinominalNB, and Support Vector Machine with CountVectorizer and Term Frequency -Inverse Document Frequency methods on Reddit data. The research concludes that the CountVectorizer and MultinominalNB model achieved highest accuracy on the Reddit dataset.
Automation and optimization in crane lift planning: A critical review

Safe and efficient crane operations play a significant role in successful delivery of construction projects, and thus meticulous planning of crane lifts becomes increasingly critical. Crane lift planning involves a series of complex decisions to be made, while satisfying a wide range of criteria and constraints. Conventionally, making these decisions is time-consuming and to a large extent relies on the planner's experience. To make more informed and optimized planning decisions, past research works investigated various automated planning techniques and optimization algorithms. However, most studies focus on an individual planning decision or a particular lifting scenario, which makes the findings hard to be generalized. Thus, the knowledge in lift planning is rather fragmented and the state-of-the-art in lift planning is not explicitly presented. This study, therefore, aims to conduct a critical review and assessment on the literature on crane lift planning automation and optimization and to establish a solid foundation to inform future research. It first presents an overview of the literature in crane lift planning with respect to the planning decision and the type of cranes the studies focus on. Secondly, for each lift planning decision, the assumptions, objectives, decision variables, and constraints are formulated based on the literature and analyzed from the perspectives of problem formulation coherence. Furthermore, each problem-solving method is evaluated with regard to a tri-axial evaluation diagram to allow an in-depth discussion on the efficacy and practicality of planning results. Finally, based on the discussion and existing literature, a BIM-based lift planning framework is presented and future research directions are recommended to further improve the effectiveness and efficiency of lift planning practice.