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Internet of things (IoT) is a system of interrelated computing devices with unique identifiers that has the ability to transfer data over a network without requiring human interaction. In this paper, an IoT based system is proposed for monitoring surface temperature (humidity and temperature) and moisture content inside a low-cost urban greenhouse. The proposed system consists of the integration of hardware and software parts, in which the hardware part includes a microcontroller and multiple sensors. The performance of three various soil moisture sensors when detecting moisture content of various growing media inside a low-cost urban greenhouse is presented as well. The network setup and web page development using the user-friendly Hostinger platform are discussed as well. The proposed system will enable reliable remote monitoring of the surface temperature and moisture content of growing media inside a low-cost urban greenhouse as well as eliminating the need for human intervention. The data collected by sensors can be used for predictive analysis to track the state of the system in general as well as equipment efficiency.

This paper studies the technical characteristics of a Unique Health Identifier (UHID) in Indian and global context while also dwells upon the usage of Aadhaar has a Unique Health Identifier in India. Specifically, we highlight the major properties of a Unique Identifier (UID) transitioning into a Unique Health Identifier and study the advantages and disadvantages of using them and limit our scope to the technical aspects of these identifiers. Further, we study and discuss National Identification Numbers/UIDs of various countries along with an in-depth policy level and feature level study of UHID in India and applicability of Aadhaar for the same.

IntroductionThere are few examples of functional health information exchange environments or data linkage centres in the African settings. New opportunities are emerging as unique health identifiers and patient registration systems are being established nationally in many countries, and increasingly individuated health data are available for linkage, often linked to priority global health initiatives such as to support HIV and tuberculosis services. Objectives and ApproachWe sought to establish a province-wide health information exchange and data centre for individuated health data, leveraging a unique identifier and available individuated data. The intention with the Provincial Health Data Centre (PHDC) was to create a single well-governed environment which could simultaneous fulfil the functions of an exchange directly supporting care, as well as support research requests. Often the demands of academics, funders and global agencies for reporting result in data consolidation for research and reporting taking precedence over service delivery. ResultsThrough pragmatic use of data from all sources, the PHDC is able to usefully enumerate many health conditions of interest with sufficient fidelity for both service and research purposes. For research data requests, there has been a huge improvement in data governance alongside increased data availability as a result of the single environment with clear procedures for patient protection, and the benefits of data linkage prior to anonymisation. Many of the inference approaches have benefited hugely from interactions with researchers, which has in turn improved the quality of outputs for routine care. Conclusion / ImplicationsIn maturing digital health environments which are establishing consolidated data environments for the first time, aspiring from the start to a single well-governed environment for both patient care and research, is a virtuous model with many benefits over fragmented data linkage efforts

IntroductionAs there is no single unique identifier for Australians across government datasets, commonwealth and state data must be brought together using data integration methods. The ABS has developed the Person Linkage Spine (the 'Spine') as an enduring piece of data linking infrastructure that is the core of the Multi-Agency Data Integration Project (MADIP) data asset, and is used to combine person-centred datasets. Objectives and ApproachThe Spine was created using a staged deterministic linkage process combining three core datasets: Medicare Consumer Directory, Social Security and Related Information, and Personal Income Tax. The Spine aims to broadly cover all people resident in Australia at any point during the reference period of 2006 to 2019. The Spine is used in combination with anonymised name and address history information from the core datasets to facilitate linkage of datasets to the Spine from any time period within its scope. The Spine was recently updated with new data from the core datasets in a 'Spine Maintenance' process, a key part of which involves the use of graph based methods to identify spine elements requiring updating in the new version. Spine Maintenance was conducted in such a way as to ensure consistency between Spine versions, enabling linkages to be conducted and retained across different versions of the Spine, whilst still allowing for improvements to the Spine based on updated information. This talk will focus on the methodological challenges associated with creating, using and maintaining the Person Linkage Spine. ResultsThe Spine has been successfully utilised as the core of the MADIP data asset, enabling valuable longitudinal person centred analysis. Conclusion / ImplicationsThe Spine has created a foundational linking infrastructure which is a sound basis for extending across time (longitudinally) and expanding to include datasets from across a broad range of sectors and jurisdictions.

International audience; The increase of intelligent environments suggests the interconnectivity of applications and the use of the Internet. For this reason, arise what is known as the Internet of Things (IoT). The expansion of the IoT concept gives access to the Internet of Nano Things (IoNT). A new communication networks paradigm based on nano technology and IoT, in other words, a paradigm with the capacity to interconnect nano-scale devices through existing networks. From the interconnection of these nano machines with the Internet emerged the concept of Internet of Nano Things (IoNT). The Internet of Nano-Things (IoNT) is a system of nano connected devices, objects, or organisms that have unique identifiers to transfer data over a computer or cellular network wirelessly to the Cloud. The data delivery, caching, and energy consumption are among the most significant topics in the IoNT nowadays. The nano-networks paradigm can empower the consumers to make a difference to their well-being by connecting data to personalized analysis within timely insights. The real-time data can be used in a diversification of nano-applications in the Internet of Nano-Things (IoNT), from preventive treatment to diagnostics and rehabilitation. In this paper intelligibly explains the Internet of Nano Things (IoNT), its architecture, challenges, explains the role of IoNT in global market, IoNT applications in various domains. Internet of things has provided countless new opportunity to create a powerful industrialized structure and many more. The key applications for IoNT communication including healthcare, transportation and logistics, defense and aerospace, media and entertainment, manufacturing, oil and gas, high speed data transfer & cellular, multimedia, immune system support and others services. In the end, since security is considered to be one of the main issues of the IoNT system, we provide an in-depth discussion on security, communication network and Internet of Nano Things (IoNT) market trends.

Data integration is one of the key problems in the era of Big Data analytics. The key challenge of data integration is the identification of records representing the same entities (e.g. person). This task is referred to as Record Linkage. It is uncommon for different data sources to share a unique identifier hence the records must be matched by comparing their corresponding values. Most of the existing methods assume that records across different sources are structured and represented by the same set of attributes (e.g. name, date of birth). However, nowadays majority of the data comes without structure (e.g. social media sites). We propose a new approach to Record Linkage based on application of Siamese Neural Network. The model can be applied with structured, semi-structured and unstructured records and it does not assume a common format across different data sources. We demonstrate that the model performs on par with other approaches, which make constraining assumptions regarding the data.

Due to the accessibility of vast volume of security camera data, there is an opportunity for identifying people using gait profiling for social security and person identification. But the gait sampling process is highly dependent on the environmental conditions of the surroundings. In addition to that different condition of the person to be identified like different clothing conditions also adds significant complexity to the problem. In this paper, we have presented a solution in form of a deep learning network for dealing with the above issues while gait feature extraction and person identification. The proposed method uses Temporal Pyramid Pooling with 3D Convolutional Neural Network to extract the temporal features from the silhouettes of the gait sequence. Then a series of 2D Convolutional is applied on these spatio-temporal features to get an embedding for the gait sequence of the person. This embedding is then used as an unique identifier for each person. After obtaining the unique identifier each gait sequence is assigned an identifier which uniquely identifies the person. Our method has been tested on CASIA-B dataset consisting of 124 persons gait sequences from different viewpoint and in different conditions. The experimental results shows that our method has outperformed available gait identification approaches.

With the advancement of technology and the internet, almost every device is connected to internet. Internet of Things (IoT) can be explained as an extension of the internet in which smart devices are interconnected with each other. While each smart device has unique identifiers, which makes every device unique. Now, IoT has helped in developing smart architectures whether its home, healthcare, financial institutions or industries. There are smart devices everywhere. These smart devices can also communicate to each other in a network and they work together thus eliminating the need for human interaction. IoT is relatively new and is still developing rapidly. So, there are numerous privacy concerns in IoT. IoT can be divided into different architectural layers depending where the IoT is being used. The IoT model discussed in this research report is Service Oriented Architecture (SOA) which is divided into three layers, application, Network and perception layer. In 2020, during the COVID pandemic the reliability on IoT has increased as people are working from home and many of the tasks has been automated using IoT. The number of security attacks on IoT has also been increased in 2020 alone, which has affected many IoT devices. The objective of this research report is to discuss a number of security and privacy challenges in IoT based on the three SOA layers, the objective of this report also covers discussion on the above mentioned three layers, different technologies used in each layer for communication and different attacks and methods which target each specific layer also discussing different security attacks on IoT which occur ed in 2020 during the COVID pandemic phase. This topic is chosen because Internet of Things is becoming important and is impacting everything around us. It is expected that the total number of IoT devices will cross 20 Billion in 2020 and will have an impact of more than $11 Trillion by 2025. Thus, the security of IoT is to be discussed.

Modular robots are autonomous systems with variable morphology, composed of independent connected computational elements, called particles or modules. Due to critical resource constraints and limited capabilities, globally unique identifier (ID) assignment to each particle is a very challenging task in modular robots. However, having a unique ID in each one remains essential for various operations and applications in this domain. For instance, it is required to establish communications between nodes and implement routing protocols. It helps in saving energy consumption and enhancing the security mechanisms. In this paper, we propose a distributed unique ID assignment method for modular robots. It is a three phases based algorithm. The first phase consists in discovering the system while building a logical tree. The second phase finds the total size of particles in the system needed for several operations in modular robots, and the third one is dedicated to the unique ID assignment. After fully optimizing the distributed algorithm, the effects of various system shapes and leader positions on the energy and time complexity are studied, while proposing fitting solutions for different requirements.

Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. The standard approach creates an embedding table where each row represents a dedicated embedding vector for every unique feature value. However, this method fails to efficiently handle high-cardinality features and unseen feature values (e.g. new video ID) that are prevalent in real-world recommendation systems. In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE), replacing embedding tables by a deep embedding network to compute embeddings on the fly. DHE first encodes the feature value to a unique identifier vector with multiple hashing functions and transformations, and then applies a DNN to convert the identifier vector to an embedding. The encoding module is deterministic, non-learnable, and free of storage, while the embedding network is updated during the training time to learn embedding generation. Empirical results show that DHE achieves comparable AUC against the standard one-hot full embedding, with smaller model sizes. Our work sheds light on the design of DNN-based alternative embedding schemes for categorical features without using embedding table lookup., Accepted to KDD'21, Research Track

In this digital era, most of the documents such as Educational Certificates, Memorandum of Understanding (MoU)s/Agreements, Driving Licenses, Health Records, Employee Service Records, Sale Deeds and Property Registration Records, Birth and Death Certificates, Tax Returns, and so on are being generated and maintained as digital artifacts. But, people are misusing this facility by generating and submitting fake documents. The organization or person, whoever is receiving the documents, is unable to verify the authenticity and integrity of the submitted documents, so verification of these documents is a challenging task. To address this problem, Blockchain technology can be leveraged. Blockchain technology provides document authenticity, ownership, immutability, and tamper-proofness to the data recorded in it. Out of these digital documents, educational certificates are one of the most important entities to be secured as people keep hearing news about fake certificates. C-DAC has considered this as an important use case and implemented a Blockchain based solution for it. The organization, which is issuing the educational certificates, should issue certificates through Blockchain. Once a certificate is recorded on the Blockchain, it is persistent. Anyone can verify it by providing the unique identifier of the certificate such as student roll number, transaction ID, or cryptographic hash of the certificate. This will ease the verification process for the document receivers for verifying the authenticity and integrity of the document. And it also eliminates the role of third party verification agencies, which saves cost and increases the efficiency of verification.

Background Acute Care Surgery (ACS) was developed as a structured, team-based approach to providing round-the-clock emergency general surgery (EGS) care for adult patients needing treatment for diseases such as cholecystitis, gastrointestinal perforation, and necrotizing fasciitis. Lacking any prior evidence on optimizing outcomes for EGS patients, current implementation of ACS models has been idiosyncratic. We sought to use a Donabedian approach to elucidate potential EGS structures and processes that might be associated with improved outcomes as an initial step in designing the optimal model of ACS care for EGS patients. Methods We developed and implemented a national survey of hospital-level EGS structures and processes by surveying surgeons or chief medical officers regarding hospital-level structures and processes that directly or indirectly impacted EGS care delivery in 2015. These responses were then anonymously linked to 2015 data from the American Hospital Association (AHA) annual survey, Medicare Provider Analysis and Review claims (MedPAR), 17 State Inpatient Databases (SIDs) using AHA unique identifiers (AHAID). This allowed us to combine hospital-level data, as reported in our survey or to the AHA, to patient-level data in an effort to further examine the role of EGS structures and processes on EGS outcomes. We describe the multi-step, iterative process utilizing the Donabedian framework for quality measurement that serves as a foundation for later work in this project. Results Hospitals that responded to the survey were primarily non-governmental and located in urban settings. A plurality of respondent hospitals had fewer than 100 inpatient beds. A minority of the hospitals had medical school affiliations. Discussion Our results will enable us to develop a measure of preparedness for delivering EGS care in the US, provide guidance for regionalized care models for EGS care, tiering of ACS programs based on the robustness of their EGS structures and processes and the quality of their outcomes, and formulate triage guidelines based on patient risk factors and severity of EGS disease. Conclusions Our work provides a template for team science applicable to research efforts combining primary data collection (i.e., that derived from our survey) with existing national data sources (i.e., SIDs and MedPAR).

Internet of Things (IoT) is defined as an interconnection among several devices which share data and all other relevant information across the network using unique identifiers without the involvement of humans. Any physical device can be easily operatable by using sensing devices, which reduces human workforce. The applications of IoT have been used in each and every field. With its ease of use of applicability in every sector, it is nowadays used in Industrial Internet of Things (IIoTs). In this paper, we present an overview of different emerging applications of IoT such as blockchain applications in Industry 4.0 and IIoT settings. As security is considered main aspect in any model; therefore, security metrics have been highlighted along with IoT-based architecture. Later section of paper describes the evolution of IIoT, revolution in industrial sector, collaboration of Industry 4.0 and IIoT, industrial automation, cybersecurity and data analytics, blockchain enrolled with IIoT, applications in public domain, etc.

The main objective of this paper is to reduce human intervention and increase the irrigation efficiency by controlling and monitoring of irrigation system using IoT. Interconnection of number of devices through Internet describes the Internet of things (IoT). Every object is connected with each other through unique identifier so that data can be transferred without human to human interaction. The objective of project is to control the agriculture water pump motor by using IOT-based controller, by sensing the water flow and running time of motor and by analyzing statues of motor. The time of situation water problem occurs in the agriculture area. Then motor running time is reducing due to the waste of water in the irrigation system. Our concept is to monitor and control the motor and also reduce some difficulty to ON/OFF.

The bookkeeping accounting method recognized all over the world is based on the so-called double entry, with which in each account (defined T-Account for its shape) there are two sections, Debit and Credit. These two sections take on different meanings depending on the nature of each account. The double entry is an exceptional consolidated accounting method that dates back to the fifteenth century, however it has highlighted limits in terms of auditing. The single entry did not take into account the cross relations between the accounts, reporting only on the journal. The double entry has overcome this limit by introducing the ledger, but a cross-check of the mirrored transactions recorded by the other companies is still missing. The introduction of blockchain technology could now offer a new opportunity to ensure further auditing control. The transition from double entry to triple entry is therefore only a matter of time. The blockchain itself is borrowing the word "ledger" from accounting and now it is time for accounting to introduce a third leg (another axis) to the T-Accounts, that can be turned into a new X-shaped version of accounts. This third axis can be used to include the unique identifier of each transaction represented by the Hash.

The attendance machine is a machine that can record a person's attendance data at an institution or office that applies it. The current attendance system is considered to be less effective because it still implements a manual system that has weaknesses in its use, such as many paper usage and opening gaps to falsify data. One effort to solve this problem is to use fingerprint and RFID attendance machine. In this research, the fingerprint grouping is performed so the counterfeit presence data can be minimized due to the unique and identical fingerprint pattern. The process of grouping fingerprint images requires an approach that uses the convolutional Neural Network (CNN) algorithm because of the difficulty of distinguishing fingerprint patterns. Based on the results of the implementation, it has managed to obtain an accuracy rate of 95.64%, validation accuracy of 98.76%, and a loss of 0.001% against 2 image classes. The attendance machine also uses RFID technology as an alternative if the fingerprint system is experiencing interference. In this research, the RFID authentication process uses the RC4 (Rivest Code4) cryptographic algorithm to encrypt the Unique Identifier (UID) of the student card. Attendance machine built using Raspberry Pi 3 microcontroller integrated with the GT-521F52 type fingerprint sensor and RFID RC522. The system saves the recorded data in the database and connects to server so that it can be accessed by the user via the website.

The Internet of Things (IoT) is a system of interrelated computing devices with unique identifiers and the ability to transfer data over a network without human-to-human or human-to-computer intera...

The Internet of Things (IoT) is a process of interconnected, mechanical, and digital computing devices with unique identifiers (UIDs) that can transmit data through a network without the need for interaction between humans directly .The Internet of Things is the future, where we will perform our daily tasks and transactions as almost everything is connected to the Internet. And if there are problems in providing services, this may lead to insecurity and lack of confidence in the integrity of the personal information of users, as there is a possibility of theft, modification and dissemination by hackers.Therefore, this research discusses the research work for a set of methods to protect the identity management technology (IDM) in the Internet of Things environment, which shares the same goals and approaches and examines the main differences between them and the proposed solution.

The disease produced by the new coronavirus known as SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2), named COVID-19 (Coronavirus Disease-2019) has recently been classified as a pandemic by the World Health Organization (WHO). However, scarce clinical data is available and generally limited to the Chinese population due to the first cases were identified in Wuhan (Hubei, China). This article describes the rationale and design of the HOPE COVID-19 (Health Outcome Predictive Evaluation for COVID 19) registry ( ClinicalTrials.gov Identifier: NCT04334291). With an ambispective cohort design, eligible patients are those discharged, deceased or alive, from any hospital center with a confirmed diagnosis or a COVID-19 high suspicion. With a current recruitment of more than 7000 cases, in 46 hospitals in 8 countries, since it is not possible to estimate the sample size based on literature reports, the investigators will try to get the maximum numbers of patients possible. The study primary objective is all cause mortality and aims to characterize the clinical profile of patients infected in order to develop a prognostic clinical score allowing, rapid logistic decision making. As secondary objectives, the analysis of other clinical events, the risk-adjusted influence of treatments and previous comorbidities of patients infected with the disease will be performed. The results of HOPE COVID-19 will contribute to a better understanding of this condition. We aim to describe the management of this condition as well as the outcomes in relation to the therapy chosen, in order to gain insight into improving patient care in the coming months. Clinical Trial registration ClinicalTrials.gov. Unique identifier: NCT04334291.

In recent Android versions, access to various (unique) identifiers has been restricted or completely removed for third-party applications. However, many information sources can still be combined to create a fingerprint, effectively substituting the need for these unique identifiers. Until now, finding these fingerprintable sources required manually sifting through the API documentation to identify each information source individually. This paper presents AndroPRINT, a framework that automatically recognizes fingerprintable information sources on Android devices. For this purpose it automatically invokes methods, queries fields, and retrieves data from content providers. We show that this framework allows automating the elaborate task of finding such fingerprintable information sources in different experiments. In these experiments, a variety of information sources could be identified, which provide a vast amount of unique features for fingerprinting. Furthermore, AndroPRINT detected undocumented unique device identification features, which are a result of manufacturer adaptations. These vendor customisations even revealed personal data, such as the user's email address and cryptographic keys used for cross-device communication. The fact that this information can be retrieved without the user noticing means that vendor customisations can effectively defeat the tight permission system of modern smartphone operating systems.

Objectives To explore how interRAI assessments could be used to identify opportunities to integrate palliative care into a plan of care. Methods A population-based, cross-sectional design using unique identifiers to link deaths with a national interRAI database. Data were analysed using logistic regression models and chi-square tests. Results A total of 4869 people died over a 12-month period in one district health board area; 50.9% (n = 2478) received one or more interRAI assessments in the year before death. Diagnosis impacted on the type and timing of interRAI assessments and the recognition of end-stage disease. Conclusion People in the last year of life experience frequent interRAI assessments. There are opportunities to identify people who might benefit from a palliative care approach. Future research is needed to understand how interRAI assessors can be supported in the application of assessment items related to palliative care.

Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user&rsquo, s device, which can cause the leakage of user&rsquo, s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

IntroductionDegenerative cervical myelopathy (DCM) is a common, disabling and progressive neurological condition triggered by chronic compression of the cervical spinal cord by surrounding degenerative changes. Early diagnosis and specialist management are essential to reduce disability, yet time to diagnosis is typically prolonged. Lack of sufficient representation of DCM in undergraduate and postgraduate medical curricula may contribute to the poor recognition of DCM by non-specialist doctors in clinical practice.In this study, our objective, therefore, is to assess DCM teaching provision in medical schools throughout the UK and to assess the impact of teaching on the DCM knowledge of UK medical students.Methods and analysisA 19-item questionnaire capturing data on medical student demographics, myelopathy teaching and myelopathy knowledge was designed. Ethical approval was granted by the Psychology Research Ethics Committee, University of Cambridge. An online survey was hosted on Myelopathy.org, an international myelopathy charity. Students studying at a UK medical school are eligible for inclusion. The survey is advertised nationally through university social media pages, university email bulletins and the national student network of Myelopathy.org. Advertisements are scheduled monthly over a 12-month recruitment period. Participation is incentivised by entering consenting participants of completed surveys to an Amazon voucher prize draw. Responses are anonymised using participant-chosen unique identifier codes. A participant information sheet followed by an explicit survey question captures participant informed consent. Regular updates on the progress of the study will be published on Myelopathy.org.Ethics and DisseminationEthical approval for the study was granted by the Psychology Research Ethics Committee, University of Cambridge (PRE.2018.099). The findings of the study described in this protocol, and all other related work, will be submitted for publication in a peer-reviewed journal and will be presented at scientific conferences.

Background Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of cases of coronavirus disease (COVID-19) in the United States has exponentially increased. Identifying and monitoring individuals with COVID-19 and individuals who have been exposed to the disease is critical to prevent transmission. Traditional contact tracing mechanisms are not structured on the scale needed to address this pandemic. As businesses reopen, institutions and agencies not traditionally engaged in disease prevention are being tasked with ensuring public safety. Systems to support organizations facing these new challenges are critically needed. Most currently available symptom trackers use a direct-to-consumer approach and use personal identifiers, which raises privacy concerns. Objective Our aim was to develop a monitoring and reporting system for COVID-19 to support institutions conducting monitoring activities without compromising privacy. Methods Our multidisciplinary team designed a symptom tracking system after consultation with experts. The system was designed in the Georgetown University AvesTerra knowledge management environment, which supports data integration and synthesis to identify actionable events and maintain privacy. We conducted a beta test for functionality among consenting Georgetown University medical students. Results The symptom tracker system was designed based on guiding principles developed during peer consultations. Institutions are provided access to the system through an efficient onboarding process that uses clickwrap technology to document agreement to limited terms of use to rapidly enable free access. Institutions provide their constituents with a unique identifier to enter data through a web-based user interface to collect vetted symptoms as well as clinical and epidemiologic data. The website also provides individuals with educational information through links to the COVID-19 prevention recommendations from the US Centers for Disease Control and Prevention. Safety features include instructions for people with new or worsening symptoms to seek care. No personal identifiers are collected in the system. The reporter mechanism safeguards data access so that institutions can only access their own data, and it provides institutions with on-demand access to the data entered by their constituents, organized in summary reports that highlight actionable data. Development of the system began on March 15, 2020, and it was launched on March 20, 2020. In the beta test, 48 Georgetown University School of Medicine students or their social contacts entered data into the system from March 31 to April 5, 2020. One of the 48 users (2%) reported active COVID-19 infection and had no symptoms by the end of the monitoring period. No other participants reported symptoms. Only data with the unique entity identifier for our beta test were generated in our summary reports. Conclusions This system harnesses insights into privacy and data sharing to avoid regulatory and legal hurdles to rapid adaption by entities tasked with maintaining public safety. Our pilot study demonstrated feasibility and ease of use. Refinements based on feedback from early adapters included release of a Spanish language version. These systems provide technological advances to complement the traditional contact tracing and digital tracing applications being implemented to limit SARS-CoV-2 transmission during reopening.

A set of participants that want to agree on a common opinion despite the presence of malicious or Byzantine participants need to solve an instance of a Byzantine agreement problem. This classic problem has been well studied but most of the existing solutions assume that the participants are aware of n --- the total number of participants in the system --- and f --- the upper bound on the number of Byzantine participants. In this paper, we examine a synchronous system with Byzantine faults where the participants are neither aware of n nor f. The participants have unique identifiers, which are not necessarily consecutive. For such a system, we give algorithms for rotor-coordinator and consensus, both with the resiliency of n > 3f, which is also the optimal resiliency for solving consensus when the participants know n and f. Thus, resiliency is unaffected even if the Byzantine participants can lie about n and f.

The things with unique identifiers are added in the cloud architecture as the end devices for data collection and manipulation. The distance between the clients and the cloud is too far, so there may be a flaw in their collection. This flaw is prone to data theft or loss that can be handled by strengthening the cloud architecture with Fog computing. The recent approach of gluing the cloud and IoT with Fog upgrades the security in cloud services to a remarkable level. In this paper, the three-layer cloud architecture with Fog as the interface between the end devices and cloud is overviewed, and registration process of IoT register on cloud through Fog is demonstrated. Particularly, the robust face biometric ID registration on cloud with Fog is visited, and a new face registration model with principal components is proposed for face verification to overcome the security issues like integrity.

IoT Security is the area concerned with safeguarding connected systems. IoT involves the set-up of various integrated devices. Devices are identified with a unique identifier, and provided with the ability to transfer data over the network opens them up to several serious vulnerabilities, if not appropriately protected. An Intrusion Detection and Prevention System (IDPS) plays a crucial part in discovering and preventing numerous attacks entering the network and provide an uncompromised secure system. The sheer volume of the sensors in a system comes with limitations such as interoperability, scalability, and storage, where security algorithms like IDS couldn’t perform well as it requires a huge amount of labelled data for training, to detect intrusions and ascertain new attacks. Fog computing plays a major role with a decentralized architecture allows IoT devices to compute, make decisions, take actions, and push only relevant information to the cloud. Data availability is closer and can act immediately for the sensitive information, which in turn helps the IDS to perform well using Artificial Intelligence algorithm to detect and prevent various attacks. This paper categorizes the existing recent researches in IoT Intrusion Detection systems using artificial intelligence and fog computing architecture in terms of technical constraints.

Internet of things (IoT) is a system of interconnected computing devices, mechanical, digital devices, objects with unique identifiers, and able to communicate over a network without any intervention. Congestion control is one of the critical challenges in IoT networks. The reasons which cause congestion in the IoT networks are unnecessary retransmissions, packet collisions, lossy links, and higher packet error rates. The Internet Engineering Task Force (IETF) has standardized CoCoA+ protocol for CoAP. CoCoA+ derived from LinuxRTO, performs better than basic CoAP in many scenarios. But performs significantly worse in light traffic and bursty traffic environment. In this paper, we present an adaptive congestion control protocol for CoAP derived from CoCoA+, named Adaptive Congestion Control Algorithm (AdCoCoA). The proposed algorithm estimates RTO using dynamic factors instead of fixed values. This dynamic adaptation helps to improve CoAP performance and reduce retransmissions. The results show AdCoCoA has lower RTO values, retransmission index (11.6%); higher goodput (71.3%), packet delivery ratio (36.4%), and transmission rate (55.6%) compare to CoCoA+.

Internet of Things (IoT) is a buzzword in the area of information technology. The Internet of Things is an interconnected computer system with unique identifiers (UIDs) that are capable of transmitting information over a network. This review article highlights the potential applications of IoT in pharmaceutical manufacturing, warehousing, and supply chain management to enhance product quality, increase productivity, and reduction in errors during different stages of a pharmaceutical product. During the manufacturing of the pharmaceutical product, IoT may be useful in supervising and optimizing different unit operations for real-time monitoring and control to enhance production efficiency. In warehousing and supply chain management of pharmaceutical products, IoT is applicable in monitoring the real-time storage conditions of the drug product and improving visibility to enhance operational effectiveness.

IntroductionHigh rates of pre-treatment loss to care among persons diagnosed with HIV persist. Linkage to care can be improved through active digitally-based surveillance. Currently, record-keeping for HIV diagnoses in South Africa is paper-based. Aggregated testing data are reported routinely, and only discordant findings result in a specimen being tested at a laboratory and digitised. The Western Cape Province in South Africa has a Provincial Health Data Centre (PHDC) where person-level routine electronic data are consolidated in a single database, leveraging the existence of a unique patient identifier. To facilitate improved HIV surveillance, a pre-carbonated point-of-care test (PoCT) form was piloted, where one copy was routed to a central point and digitised for PHDC inclusion.MethodsWe evaluated the utility of the intervention using cross-sectional and retrospective cohort analyses, as well as comparisons with aggregate data. Data were linked to the Patient Master Index of the PHDC using unique identifiers. Prior evidences of HIV within the PHDC were used to differentiate newly diagnosed patients and those retesting, as well as linkage to care and treatment.ResultsFrom May 2017 to June 2018, 11337 digitised point-of-care HIV testing records were linked to the PHDC. Overall, 96% of records in the aggregate dataset were digitised, with 97% linked to the PHDC. Of those tested, 79% were female (median age 27 years). Linkage demonstrated that 51.3% (95% CI 48.4-54.1%) of patients testing HIV-positive were retesting. Of those newly diagnosed, 81% (95% CI 77.9-84.3%) were linked to HIV care and 25% (95% CI 21.6-28.7%) were initiated on antiretroviral therapy immediately.ConclusionDigitisation of PoCT results provides individuated HIV testing data to assist in linkage to care and in differentiating newly diagnosed patients from positive patients retesting. Actionable and accurate data can improve the measurement of performance towards the UNAIDS 90-90-90 targets.

The last few years has seen a substantial push toward "Open Data" by policy makers, researchers, archivists, and even the public. This article postulates that the value of data is not intrinsic but instead derives from its ability to produce knowledge; the extraction of which from data is not deterministic. The value of data is realized through a focus on the reproducibility of the findings from the data, which acknowledges the complexity of the leap from data to knowledge, and the inextricable interrelationships between data, software, computational environments and cyberinfrastructure, and knowledge. Modern information archiving practices have a long history and were shaped in a pre-digital world comprised of physical objects such as books, monographs, film, paper, and other physical artifacts. This article argues that "data," the modern collection of digital bits representing empirical measurements, is a wholly new entity and not a digital analog to any physical object. It further argues that a focus on the interrelationships between digital artifacts and their unique properties, instead of Open Data alone, will instead produce an augmented and more useful understanding of knowledge when it is derived from digital data. Data-derived knowledge, represented by claims in the scholarly record, must persistently link to immutable versions of the digital artifacts from which it was derived, including 1) any data, 2) software that allows access to the data and the regeneration of those claims that rely on the version of the data, and 3) computational environment information including input parameters, function invocation sequences, and resource details. In this sense the epistemological gap between data and extracted knowledge can be closed. Datasets and software are often subject to change and revision, sometimes even with high velocity, and such changes imply new versions with new unique identifiers. We propose considering knowledge, rather than data in isolation, with a schematic model representing the interconnectedness of datasets, software, and computational information upon which its derivation depends. Capturing the interconnectedness of these digital artifacts, and their relationship to the knowledge they generate, is essential for supporting the reproducibility, transparency, and cognitive tractability of scientific claims derived from digital data.

Security in Internet of Things (IoT) deals with safeguarding connected objects and networks. IoT involves the growing collection of objects known as things with unique identifiers. These objects have the ability to automatically transfer data over a network. Most of the increase in IoT communication comes from computing devices and embedded sensor systems which are used in industrial machine-to-machine (M2M) communication, home and building automation, vehicle to vehicle communication, smart energy grids, and wearable devices. Because the idea is to connect all physical objects to Internet, security has not always been measured in product design. The IoT products fail to select sufficiently strong security. Therefore, security is one of the important factors in IoT that should be taken care on priority basis. This study proposes IoT architecture which deals with security of devices. The main aim of this study is to make sure that an authentic device uploads the data and restrict false data being uploaded by devices trying to be an authentic device. The study presents a strong authentication technique based on secret keys which protects the identity of IoT devices in the network. The authentication and authorization techniques presented in this paper ensure that the right data is sent and received by right devices at right places and for right purpose.

Background/Aims The objective of this manuscript is to present challenges and solutions that arose during a mid-sized single-site RCT of a rehabilitation intervention performed in an inpatient stroke rehabilitation setting. Methods Seventy-six participants from an inpatient stroke rehabilitation unit were randomized to experimental and control groups. All participants did 30–45 min of virtual reality (VR) daily for 10–12 sessions. The experimental group did VR targeting sitting balance while the control group did VR with limited arm movement. Challenges during the implementation of the RCT were documented and strategies to mitigate them were applied. Results Challenges were placed into five categories: 1. Recruitment. Our recruitment procedures required multiple steps prior to initiating direct patient contact; one solution would be to have patients consent to be approached about research upon admission to the inpatient unit. 2. Patient-specific Issues. Fatigue, pain, vision problems and engagement were managed through scheduling, increasing the workload slowly and personalized modifications to the VR. 3./4. Scheduling and Staffing. Recruitment and attendance at VR sessions were maximized through good communication, flexibility and cooperation, between research staff, clinical staff, volunteers, students and participants. 5. Technology. Because hospital internet service was poor, a mobile internet data plan was purchased to ensure the system's reliability. Conclusions We have identified challenges in delivering a rehabilitation intervention on an inpatient stroke rehabilitation unit and some of the measures taken to surmount these challenges. Through good planning, flexibility and collaboration, almost all of the challenges were successfully addressed. Clinical trial registration number URL: http://www.clinicaltrials.gov . Unique identifier: NCT02285933.

The Rx (prescription) for Addiction and Medication Safety (RAMS) program was developed during the 2017 through 2018 academic year to educate students from 6 selected Rhode Island public high schools about opioid misuse, overdose, and recovery. During 2016, 3 schools participated in the RAMS program and returned for RAMS-PEER in 2017; 3 schools were newly recruited in 2016. Tenth graders returned from schools that participated during RAMS in 2016, and all ninth graders were new. Our study's aim was to evaluate the overall effect and spillover benefit of the RAMS-PEER intervention from tenth to ninth graders by surveying students both before and after the education program. Survey questions were modified from the 2015 Youth Risk Behavior Survey and the 2015 Ontario Study Survey. Student responses were matched for preintervention and postintervention analysis using a unique identifier. We observed an improvement in knowledge of opioid misuse; however, we found no evidence of a significant spillover benefit.

This paper presents a security strategy to protect smart meters against attacks that compromise critical software and hardware components. Many of these attacks consist of replacing or tampering memory components that store measuring procedures and sensitive configuration parameters. Our strategy makes use of physical properties from these components to create a secure identity for the meter. Firstly we explore concepts related to Physical Unclonable Functions to extract unique identifiers from SRAM components. Then we combine these identifiers to create a strong identity. Since this identity depends on intrinsic physical features from the meter components, we can implement a mechanism to detect the attacks described before. We also validate our approach in an experiment using a meter prototype built in an Arduino device and connected to SRAM memories. The results show that our idea is suitable for implementation in smart meters as so as in similar embedded devices.

Physical unclonable function (PUF) represents modern element of cryptographic systems. Its advantage resides in generation of a unique identifier based on the physical structure of the device - hard to predict, difficult to clone. PUF usually require application of various constructions, in order to ensure security and reliability of the generated response. This paper describes an improved Code Word Masking method for reconstruction of a PUF response. Improved version is used for maximizing of a PUF response size and thus minimize requirements for an error correction code. The modification resides in repeated encoding of one response. The construction is performed two times, using two simple error correction codes. Such codes minimize complexity of the implementation and maximize utilization of the cells. The method is evaluated using implementation of the PUF in a 32-bit microcontroller. It is based on uninitialized values of static random access memory cells. The stability of the cells is analysed and just the most reliable cells are picked out. The implementation is more secure and effective, when compared to existing solutions. It was able to generate 140 bits using 512 PUF response bits without error, across various temperature and voltage levels. The security of the helper data does not depend on the statistical properties of the generated response.

Cybersecurity is a paramount concern in automobiles since deficiencies in security controls put human lives at risk. Some security vulnerabilities are more critical than others and demand immediate attention. Therefore, it is imperative to quantify associated risks by means of rating security vulnerabilities on a scale of severity which has proven to be a useful tool for traditional IT security in comprehending the real risk associated with a vulnerability. In this paper, we present a methodology for adapting the proven CVSS scoring system to automobiles and illustrate the notion with several examples of real-world automotive security vulnerabilities. We also propose a CVV naming system, that is based on the existing CVE system by MITRE, to assign unique identifiers to these vulnerabilities which permits efficient tracking and analysis of automotive vulnerabilities.

Background and importance To prevent the introduction of falsified medical products into the supply chain, on 9 February 2019, the directive 2011/62/EU was applied. This legislation has allowed the implementation of measures to ensure authenticity and a high level of traceability, providing greater patient safety. Aim and objectives To assess the impact of the implementation of the falsified medicines directive 6 months after introducing the new legislation. Material and methods Elaboration of a form (MS-Excel) with the purpose of systematising the data was performed. Of all products prescribed between 18 and 27 September 2019, products not covered by the requirement of a unique identifier code were excluded. The following parameters were analysed: presence of the unique identifier code, start time and end of code scan, and appearance of problems with the scanning procedure. Results A total of 201 products were analysed. About 69% of the products had a unique identifier code. Of the products intended to be dispensed for outpatients, only 70% had a unique identifier. After reading 10 935 packages, it was found that, on average, reading of 12.9% of the products with a unique identifier code had at least one scanning issue. The average time for reading a unique identifier code was 9.5 s (includes connecting the software, verifying the safety device, positioning the packaging for the scan read and waiting for scan read confirmation). Conclusion and relevance Six months after introducing the counterfeit medicines directive, about 31% of the products received in the hospital pharmacy did not have a unique identifier code. This includes products for outpatients where scanning at dispensing could be a relevant added value. Reading time of the unique identifier code represents around 29 working hours in 8 working days, or 0.5 ETC (7 hour working day). Implementation of this directive required investments in software, material and human resources, and the internal work procedures were also reorganised. Direct advantages for patient care are not yet evident as the unique identifier is still not fully implemented. References and/or acknowledgements Directive2011/61/EU of the European Parliament and of the Council from June 8, 2011, Official Journal of The EU. No conflict of interest.

Traceability has been acknowledged as an important part of the software development process and is considered relevant when performing tasks such as change impact and coverage analysis. With the growing popularity of issue tracking systems and version control systems developers began including the unique identifiers of issues to commit messages. The goal of this message tagging is to trace related artifacts and eventually establish project-wide traceability. However, the trace creation process is still performed manually and not free of errors, i. e. developers may forget to tag their commit with an issue id. The prototype spojitR is designed to assist developers in tagging commit messages and thus (semi-) automatically creating trace links between commits and an issue they are working on. When no tag is present in a commit message, spojitR offers the developer a short recommendation list of potential issue ids to tag the commit message. We evaluated our tool using an open-source project hosted by the Apache Software Foundation. The source code, a demonstration, and a video about spojitR is available online: https://github.com/SECSY-Group/spojitr.

World population is increasing rapidly. Extensive industrialization, higher economic activity, better education and healthcare in cities have attracted people to flux into urban areas thereby causing rapid urbanization across the globe. This has impacted the environment and lifestyle of people. Therefore, it has become important to manage our cities more effectively and in efficient manner. This requires major changes in the way urban operations and infra structure are managed today. This paper attempts to provide fundamentals of smart city (SC) concept, briefly describes its main characteristics and worldwide implementation. Finally, a case study on Smart Dubai initiative is provided. SC is based on internet of things (IoT) that comprises of a system of interrelated computing devices, machines, objects, and living creatures that are provided with unique identifiers. The provision of enough funding and technology development are two major hurdles that are causing delay in integration of smart cities concept into urban management.

We study the gathering problem requiring a team of mobile agents to gather at a single node in arbitrary networks. The team consists of $k$ agents with unique identifiers (IDs), and $f$ of them are weakly Byzantine agents, which behave arbitrarily except falsifying their identifiers. The agents move in synchronous rounds and cannot leave any information on nodes. If the number of nodes $n$ is given to agents, the existing fastest algorithm tolerates any number of weakly Byzantine agents and achieves gathering with simultaneous termination in $O(n^4\cdot|\Lambda_{good}|\cdot X(n))$ rounds, where $|\Lambda_{good}|$ is the length of the maximum ID of non-Byzantine agents and $X(n)$ is the number of rounds required to explore any network composed of $n$ nodes. In this paper, we ask the question of whether we can reduce the time complexity if we have a strong team, i.e., a team with a few Byzantine agents, because not so many agents are subject to faults in practice. We give a positive answer to this question by proposing two algorithms in the case where at least $4f^2+9f+4$ agents exist. Both the algorithms take the upper bound $N$ of $n$ as input. The first algorithm achieves gathering with non-simultaneous termination in $O((f+|\Lambda_{good}|)\cdot X(N))$ rounds. The second algorithm achieves gathering with simultaneous termination in $O((f+|\Lambda_{all}|)\cdot X(N))$ rounds, where $|\Lambda_{all}|$ is the length of the maximum ID of all agents. The second algorithm significantly reduces the time complexity compared to the existing one if $n$ is given to agents and $|\Lambda_{all}|=O(|\Lambda_{good}|)$ holds., Comment: 20 pages, 1 figure, 2 tables, 5 pseudo-codes

Background: In Tanzania, HIV testing data are reported aggregately for national surveillance, making it difficult to accurately measure the extent to which newly diagnosed persons are entering care, which is a critical step of the HIV care cascade. We assess, at the individual level, linkage of newly diagnosed persons to HIV care. Methods: An expanded two-part referral form was developed to include additional variables and unique identifiers. The expanded form contained a corresponding number for matching the two-parts between testing and care. Data were prospectively collected at 16 health facilities in the Magu District of Tanzania. Results: The records of 1,275 unique people testing HIV positive were identified and included in our analysis. Of these, 1,200 (94.1%) responded on previous testing history, with 184 (15.3%) testing twice or more during the pilot, or having had a previous HIV positive test. Three-quarters (932; 73.1%) of persons were linked to care during the pilot timeframe. Health service provision in the facility carrying out the HIV test was the most important factor for linkage to care; poor linkage occurred in facilities where HIV care was not immediately available. Conclusions: It is critical for persons newly diagnosed with HIV to be linked to care in a timely manner to maximize treatment effectiveness. Our findings show it is feasible to measure linkage to care using routinely collected data arising from an amended national HIV referral form. Our results illustrate the importance of utilizing individual-level data for measuring linkage to care, as repeat testing is common.

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. This paper describes the problems of integration of computing networks of the Internet of Things type in Russia, which slow down the development of the industry. The problems are divided into 2 groups according to the type of end user - B2B and B2C segments.

Wearable devices (WDs) are a specific part of the general concept of the “Internet of Things”. This concept refers to infrastructure in which many sensors are designed to record, process, and store data locally, or interact with each other both in the medium range, through the use of radio frequency technologies (e.g. RFID, bluetooth, etc.), or through an electronic communications network. The devices involved are not only traditional computers or smartphones, but also daily life objects ("things"), such as wearable devices, home automation geo-referencing, and assisted navigation objects. Indeed, the “Internet of Things” refers to a further development of the Internet resulting from physical objects networking. These objects may be equipped with a unique identifier (for example, a serial number), recognizable even by radio frequency. However, the identification of these objects could also be made without resorting to radio tags, simply by combining sensors and automatic recognition procedures (for example, the recognition of a barcode carried out with a mobile phone connected to the Internet). The current state of research on this issue concerns mainly the use of WDs in daily life, and the enforcement of privacy rules, due to the capacity of WDs for the massive collection of personal data, such as in the health care and labour contexts, both sensitive areas for discrimination.

The Internet of Things (IoT) is an arrangement of connected numerous digital devices usually contained Unique Identifiers (UIDs) and have the capability to exchange data over a network without any human interaction. Another new paradigm Software-Defined Networking (SDN) comes in for the organization and control of the large amount of data produced by IoT devices. It separates the data plane from the control plane of network devices which enables easy configuration and management of those devices. Furthermore, Network Function Virtualization (NFV) is emerged to optimize and secure the SDN-IoT network. It enables network devices to be deployed as virtualized components via software. In this research, the authors have proposed an SDN based distributed IoT network with NFV implementation for smart cities. Where smart city is a residential area which utilizes Information and Communication Technology (ICT) as well as IoT network to develop the standard of living of its residents. The integration of NFV in the SDN-IoT network improves the network performance by increasing throughput, and time sequence while mitigating the round trip time as well. Moreover, the authors have used multiple distributed controllers and a clustering scheme to improve load balancing, scalability, availability, integrity, and security of the whole network.

In nowadays’ Internet, websites rely more and more on obtaining users’ geolocation to provide customized services. However, besides Internet giants such as Google, who retains a large amount of detailed user information, most websites still rely on IP addresses for user geolocation, which is proven inaccurate and misleading by existing studies. In this paper, we propose a novel approach, namely CacheLoc, for coarse-grained user geolocation leveraging widely-deployed content delivery networks (CDNs). This work is motivated by the fact that CDN providers deploy a number of edge servers that are geographically distributed across the world. Many of these edge servers are assigned with unique identifiers that are tied to their location, which can be easily retrieved by inspecting HTTP responses headers served by these edge servers. As a result, a website can infer coarse-grained user location by asking a user to send an HTTP request to an arbitrary domain that is known being served by a CDN, and inspecting the corresponding responses. To evaluate the usability and accuracy of the cache-based user geolocation, we conducted practical experiments based on a commercial VPN with over 160 endpoints distributed in 94 countries. Our experiments demonstrate that cache-based geolocation can achieve at least accurate country-level granularity in the regions where CDN edge servers are densely deployed. Our work sheds light on a novel light-weight and self-contained user geolocation solution.