Reviewerly/exHarmony huggingface dataset:
Entropy Maximization Problem in Network using Dijkstra’s-Floyd Warshall Algorithm

The shortest path problem is to find a path between two vertices on a given graph, such that the sum of the weights on its constituent edges is minimized.The classic Dijkstra's algorithm was designed to solve the single source shortest path problem for a static graph.It works starting from the source node and calculating the shortest path on the whole network.This work aims to develop a Hybrid algorithm Dijkstra's -Floyd Warshall algorithm to solve entropy maximization routing protocol problem.The algorithm has to find the shortest path between the source and destination nodes.Route guidance algorithm is use to find best shortest path in routing network, this is poised to minimize costs between the origin and destination nodes.The proposed algorithm is compared with the existing in order to find the best and shortest paths.
Performance Metric Elicitation from Pairwise Classifier Comparisons

Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of metric elicitation is to discover the performance metric of a practitioner, which reflects her innate rewards (costs) for correct (incorrect) classification. In particular, we focus on eliciting binary classification performance metrics from pairwise feedback, where a practitioner is queried to provide relative preference between two classifiers. By exploiting key geometric properties of the space of confusion matrices, we obtain provably query efficient algorithms for eliciting linear and linear-fractional performance metrics. We further show that our method is robust to feedback and finite sample noise.
Establishing the Impact of Area Studies Collections and Exploring Opportunities for Collaborative Collecting

This study examines the use of area studies materials by assessing five years of Interlibrary Loan (ILL) lending data and local circulation data from a single research library. It seeks to lay groundwork for future explorations into the implementation of a robust cooperative collection development model for area studies at the national level, with analysis demonstrating that existing ILL programs support scholars from research institutions far beyond their owning institution. They can do so with minimal adverse impact on the local community of scholars at a typical top-tier research library. This case study also investigates the similarities and differences between lending patterns of Less Commonly Taught Language (LCTL) materials and non-domestic area studies titles that are authored in commonly taught languages. The authors conclude with an argument that communities of institutions could develop highly structured cooperative collection building efforts in the area studies that would permit them to redirect resources strategically, collecting area studies materials both more deeply across the community and with a greater emphasis on primary source materials.
Electronic Resources in a Consortial Implementation of Alma and Primo

The California State University system implemented Ex Libris’s Alma and Primo as a consortium in 2017. Consortial management of electronic resources in these systems has offered significant benefits as well as surprises and difficulties. While Alma is able to support a consortium with multiple ways of acquiring and accessing electronic resources, the result is an architecture that is more complex than the setup for print resources. Similarly, Primo is able to manage the complexity of bibliographic records from twenty-three libraries as well as article-level discovery records, but its mechanisms for this may be counterintuitive at times. This article discusses some of those details and also addresses the interlibrary communication that has developed to meet the demands of this shared implementation.
Managing Initial Coin Offerings: Towards a Taxonomy of ICO Processes

Initial Coin Offerings are a new type of crowdbased fundraising mechanism that uses the blockchain to issue tokens to a crowd of people in exchange for funds that blockchain start-ups use to develop their business.Unfortunately, due to the recency of this new phenomenon, there is no systematic understanding of the ICO process and its underlying process characteristics.However, companies engaging in ICOs should be able to evaluate and choose the right process steps to best achieve their goal.Against this background, we develop a taxonomy for ICO processes.In contrast to previous work, this classification scheme focuses exclusively on the processual nature of ICOs and its underlying mechanisms.
Encrypted Big Data Deduplication in Cloud Storage

Encrypted data deduplication is an efficient way to solve the waste of resources caused by duplicated data in cloud storage services without the loss of security. In this chapter, we proposed three data deduplication schemes on encrypted big data with flexible data access control, data deletion, and data update. We integrate data deduplication with secure data access control by applying ownership challenge, proxy re-encryption and attribute-based encryption in deduplication. The proposed three schemes cannot only guarantee the data security and privacy but also be applicable to different cloud data deduplication scenarios. We evaluate their performances through comprehensive analysis on security, computation complexity, and operation time, along with some computer simulations. In addition, we summarize the usage scenarios and limitations of our schemes when being deployed in practice for the purpose of proposing some future research directions.
Persian Text Classification Enhancement by Latent Semantic Space

Heterogeneous data in all groups are growing on the web nowadays. Because of the variety of data types in the web search results, it is common to classify the results in order to find the preferred data. Many machine learning methods are used to classify textual data. The main challenges in data classification are the cost of classifier and performance of classification. A traditional model in IR and text data representation is the vector space model. In this representation cost of computations are dependent upon the dimension of the vector. Another problem is to select effective features and prune unwanted terms. Latent semantic indexing is used to transform VSM to orthogonal semantic space with term relation consideration. Experimental results showed that LSI semantic space can achieve better performance in computation time and classification accuracy. This result showed that semantic topic space has less noise so the accuracy will increase. Less vector dimension also reduces the computational complexity.
Survey on Multiple Objects Tracking in Video Analytics

Multiple object tracking is being used for many applications nowdays such as automated surveillance, Robotics,self driving cars,medical and many more.There have been continuous improvements in existing state of art MOT(multiple object tracking) methods through many methods and global optimization techniques.This paper focuses on various MOT techniques and how to achieve speedup and efficiency using MOT methods.
Quantum supremacy using a programmable superconducting processor

The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2–7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8–14 for this specific computational task, heralding a much-anticipated computing paradigm. Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
Transformers: State-of-the-Art Natural Language Processing

Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining.Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community.The library consists of carefully engineered stateof-the art Transformer architectures under a unified API.Backing this library is a curated collection of pretrained models made by and available for the community.Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments.The library is available at https://github.com/huggingface/transformers.
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.
Quantum computational advantage using photons

Gaussian boson sampling exploits squeezed states to provide a highly efficient way to demonstrate quantum computational advantage. We perform experiments with 50 input single-mode squeezed states with high indistinguishability and squeezing parameters, which are fed into a 100-mode ultralow-loss interferometer with full connectivity and random transformation, and sampled using 100 high-efficiency single-photon detectors. The whole optical set-up is phase-locked to maintain a high coherence between the superposition of all photon number states. We observe up to 76 output photon-clicks, which yield an output state space dimension of $10^{30}$ and a sampling rate that is $10^{14}$ faster than using the state-of-the-art simulation strategy and supercomputers. The obtained samples are validated against various hypotheses including using thermal states, distinguishable photons, and uniform distribution.
U2-Net: Going deeper with nested U-structure for salient object detection

In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD). The architecture of our U$^2$-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U$^2$-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U$^2$-Net$^{\dagger}$ (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net.
Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks.Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fillin-the-blank" cloze statements.Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train.We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-theart pretrained language models.We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches.The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems.The code to reproduce our analysis is available at https: //github.com/facebookresearch/LAMA.
Blockchain for Internet of Things: A Survey

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges such as decentralization, poor interoperability, privacy and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as Blockchain of Things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.
Digital technologies in the public-health response to COVID-19

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases. The COVID-19 pandemic has resulted in an accelerated development of applications for digital health, including symptom monitoring and contact tracing. Their potential is wide ranging and must be integrated into conventional approaches to public health for best effect.
Time series forecasting of COVID-19 transmission in Canada using LSTM networks

On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.
Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT

The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. A dataset with the details of the surveyed papers is available via https://figshare.com/articles/Database_of_surveyed_literature_in_Not-so-supervised_a_survey_of_semi-supervised_multi-instance_and_transfer_learning_in_medical_image_analysis_/7479416.
Online Teaching and Learning in Higher Education during the Coronavirus Pandemic: Students’ Perspective

The research focuses on identifying the way in which Romanian universities managed to provide knowledge during the Coronavirus pandemic, when, in a very short time, universities had to adapt the educational process for exclusively online teaching and learning. In this regard, we analyzed students’ perception regarding online learning, their capacity to assimilate information, and the use of E-learning platforms. An online survey based on a semi-structured questionnaire was conducted. Data was collected from 762 students from two of the largest Romanian universities. The results of the research revealed that higher education institutions in Romania were not prepared for exclusively online learning. Thus, the advantages of online learning identified in other studies seem to diminish in value, while disadvantages become more prominent. The hierarchy of problems that arise in online learning changes in the context of the crisis caused by the pandemic. Technical issues are the most important, followed by teachers’ lack of technical skills and their teaching style improperly adapted to the online environment. However, the last place was assigned by students to the lack of interaction with teachers or poor communication with them. Based on these findings, research implications for universities and researchers are discussed.
Blockchain Technology in Healthcare: A Systematic Review

Since blockchain was introduced through Bitcoin, research has been ongoing to extend its applications to non-financial use cases. Healthcare is one industry in which blockchain is expected to have significant impacts. Research in this area is relatively new but growing rapidly; so, health informatics researchers and practitioners are always struggling to keep pace with research progress in this area. This paper reports on a systematic review of the ongoing research in the application of blockchain technology in healthcare. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and a systematic mapping study process, in which a well-designed search protocol is used to search four scientific databases, to identify, extract and analyze all relevant publications. The review shows that a number of studies have proposed different use cases for the application of blockchain in healthcare; however, there is a lack of adequate prototype implementations and studies to characterize the effectiveness of these proposed use cases. The review further highlights the state-of-the-art in the development of blockchain applications for healthcare, their limitations and the areas for future research. To this end, therefore, there is still the need for more research to better understand, characterize and evaluate the utility of blockchain in healthcare.