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The German Institute for Quality and Efficiency in Health Care (IQWiG) has updated its methods paper to Version 7.0, which is now available in English. The General Methods provide criteria for assessing the benefits and harms of medical interventions. The update includes a revision of the chapter on health economic evaluation, making the evaluation process less complex. Additionally, IQWiG has included a new section on routine practice data collection, which involves developing a concept for data collection and searching for suitable disease registries. The methods paper was revised based on feedback received during the commenting procedure. [Extracted from the article]

This article discusses the need for more research on the biological factors contributing to mental health issues in adolescents. While there is a considerable understanding of environmental and psychosocial risk factors, there is a lack of representative biological evidence, particularly in relation to economic, social, and ethnic diversity. The objective of this scoping review is to identify and understand the barriers and facilitators to collecting biological data in adolescent mental health research. The review will involve a systematic search of various databases and publications to identify relevant studies. The findings will be disseminated through peer-reviewed journals, academic presentations, and the project website. [Extracted from the article]

The article offers the author's insights regarding her experience of participating in an academic writing retreat in Queensland, Australia. Topics discussed include the process of writing a conference paper about Scholarship of Teaching and Learning (SoTL), the use of the new methods of data gathering and analysis, and the application of traditional and non-traditional headings.

The article discusses the benefits of using digital pen and paper technology for data collection during clinical trial processes. It states that the use of digital pen and paper reduces delays and cuts back costs for collection and processing of clinical trial data. It cites that most pharmaceutical companies have adopted this technology since it provides faster access to information on drug development.

Globally, cyber threats continue to increase in severity, complexity and volume. Cyber intelligence tradecraft requires a deep understanding of the cyber threat landscape and the application of analytical rigour to identify patterns and draw conclusions from a varied and evolving attack surface. To achieve this, adopting tools and frameworks synergises the benefits between the creative human mind with the power of technology under a standardised structure. This improves cyber intelligence practice by enabling analysts to evaluate and compare local trends with global trends; produce meaningful insights; and apply models and solutions to address cyber-specific challenges. Originally heavily rooted in the notion that pieces of data and information equate to intelligence, the practice of cyber intelligence has notably improved. The right tools and frameworks enable intelligence analysts to work more efficiently and effectively, moving beyond the data to achieve a thorough understanding of the threat environment. This enables them to provide more timely, accurate, relevant and actionable intelligence to decision makers. This paper describes how to enhance human-driven intelligence tradecraft through the adoption of technology to improve the efficiency and accuracy of data collection in addition to enriching data analysis and providing actionable insights in the everchanging cyber threat landscape. [ABSTRACT FROM AUTHOR]

The aticle presents an extended example of visualizing the digital collection of papers of the astronomer Clyde W. Tombaugh at New Mexico State University Library Archives and Special Collections. Topics include the discovery of Pluto has the beginning of an exciting career; and awarded the Edwin Emory Slosson Scholarship to the University of Kansas, US.

Keywords: Brisbane; Australia; Australia and New Zealand; Managed Care; Health Policy; Health and Medicine; Legislation; Quality of Life EN Brisbane Australia Australia and New Zealand Managed Care Health Policy Health and Medicine Legislation Quality of Life 2417 2417 1 09/11/23 20230915 NES 230915 2023 SEP 15 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Current study results on Managed Care have been published. Brisbane, Australia, Australia and New Zealand, Managed Care, Health Policy, Health and Medicine, Legislation, Quality of Life The EQ-5D is a validated instrument widely used for health economic evaluation and is useful for informing health policy.". [Extracted from the article]

Copyright of Nuova Secondaria is the property of Edizioni Studium S.r.l and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses various aspects of computer and information security practices, and it mentions computer user behavior in relation to information privacy and security-related problems. Computer user behavior-based data collection is examined, along with analyses of how individuals react to advice regarding security measures and procedures. Exposure to computer-based vulnerabilities is assessed, as well as user interviews and passive measurements of user behavior.

The reports on the invitation of the Pipeline and Hazardous Materials Safety Administration (PHMSA) to tank truck carriers to participate in a voluntary online data collection effort to aid in the assessment of potential impacts, such as cost-benefit information, associated with using paperless communication systems (e-systems) to transfer hazardous materials (HM) shipping paper information. It features the Paperless Hazard Communications Pilot Program of the administration.

Copyright of Nuova Secondaria is the property of Edizioni Studium S.r.l and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Nuova Secondaria is the property of Edizioni Studium S.r.l and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. Biological retinas, and their artificial counterparts, are totally asynchronous and data-driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost. [ABSTRACT FROM PUBLISHER]

The article discusses the Reproducibility Project: Cancer Biology study which aimed to replicate 193 experiments from 53 top cancer papers published from 2010 to 2012. Topics include the replication problem with cancer research identified in the study, the reasons researchers could not complete the majority of experiments such as not getting enough information from the original papers, and the criteria used by the research team to determine if a replication was successful such as effect size.

Once called the "fourth estate" for its power, crucial to democracy, to check the three official branches of government - legislative, executive, and judicial - journalism has suffered a hemorrhage of resources since the advent of the digital era. While social media became a vaster and faster channel for news, papers' print circulations and advertising revenues dwindled, forcing major newspapers to go online and many smaller, local ones to shut down entirely. Most people think that privacy laws are in place to do this, but existing privacy laws, including the European privacy law, have done little to actually slow down the collection of data. [Extracted from the article]

A preprint abstract from medrxiv.org discusses the development of a multi-scanner facility called AI FORGE by the National Pathology Imagine Cooperative (NPIC). This facility aims to compare scanner performance and replicate digital pathology image datasets across whole slide imaging (WSI) systems. The facility currently has 15 scanners from 9 manufacturers and can generate approximately 4000 WSI images per day. The paper describes the planning and construction process of the facility, highlighting its potential to improve the robustness and generalizability of artificial intelligence (AI) algorithms in pathology. [Extracted from the article]

Keyword spotting (KWS) is a crucial function enabling the interaction with the many ubiquitous smart devices in our surroundings, either activating them through wake-word or directly as a human-computer interface. For many applications, KWS is the entry point for our interactions with the device and, thus, an always-on workload. Many smart devices are mobile and their battery lifetime is heavily impacted by continuously running services. KWS and similar always-on services are thus the focus when optimizing the overall power consumption. This work addresses KWS energy-efficiency on low-cost microcontroller units (MCUs). We combine analog binary feature extraction with binary neural networks. By replacing the digital preprocessing with the proposed analog front-end, we show that the energy required for data acquisition and preprocessing can be reduced by $29\times $ , cutting its share from a dominating 85% to a mere 16% of the overall energy consumption for our reference KWS application. Experimental evaluations on the Speech Commands Dataset show that the proposed system outperforms state-of-the-art accuracy and energy efficiency, respectively, by 1% and $4.3\times $ on a 10-class dataset while providing a compelling accuracy-energy trade-off including a 2% accuracy drop for a $71\times $ energy reduction. [ABSTRACT FROM AUTHOR]

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings.1

We would like to thank the Institut National de l'Energie Solaire (INES) and the CEA, LITEN, Laboratoire Energétique du Bâtiment for providing the data. [ABSTRACT FROM AUTHOR]

The article discusses the study by Institute of Medicine (IOM) which states that information collected from an individual patient during their visit to the physician's office and hospital can improve their health care and could also reduce the research costs on a national scale.

Cosmic voids, which are large empty spaces in the universe, are gaining attention from scientists as they could provide important clues about dark matter, dark energy, and neutrinos. These voids challenge the assumption that the universe is homogeneous and have shown that Einstein's theory of relativity likely operates on large scales as well. Recent advancements in technology and data collection have allowed researchers to identify and study thousands of voids, with plans to rapidly increase this number in the coming years. By studying voids, scientists hope to gain insights into the forces that shaped the structure of the universe and the nature of dark energy and dark matter. [Extracted from the article]

The article focuses on the concern of Internet corporation Yahoo! Inc. regarding the release of U.S. Foreign Intelligence Surveillance Court (FISC) documents about a dispute over data collection between the government and the company. Topics discussed include the public release of a secret order in a surveillance dispute in 2008, the Foreign Intelligence Surveillance Act (FISA), and the surveillance program called Prism.

The article presents the discussion on mobile data collection and cloud technology for managing food safety, process automation, and process control. Topics include Foods Connected being a transformative Supply Chain Management Software created by food professionals; and managing the track performance across the food business while ensuring the right supplier, the right product, and the right quality.

Purpose -- Although many cases where viable system model (VSM) was successfully applied exist, hardly any literature advises the novice VSM user regarding the accomplishment of a VSM diagnosis. The purpose of this paper is to show practitioners and researchers how to conduct a VSM diagnosis. The paper further seeks to encourage others to apply VSM and to share their experience with using VSM. Design/methodology/approach -- The paper provides detailed guidelines on how to conduct a VSM diagnosis in conjunction with qualitative research methods. It outlines the data collection, analysis and presentation of results. Findings -- VSM is an outstanding diagnostic tool. Qualitative research methods provide access to the essential information for the VSM diagnosis and should be used in iteration with VSM. They can enhance the VSM diagnosis by focusing on the soft aspects in the investigated system. The VSM language needs to be adapted to the specific context in which VSM is used and VSM can be applied in a participatory manner. Further research needs to explore possibilities to strengthen the handling of detected shortcomings that were revealed during the VSM diagnosis. Research limitations/implications -- This paper is based on one experience with the VSM application and other VSM users might provide different insights. Originality/value -- There is little practical advice in existing literature regarding the accomplishment of a VSM diagnosis. This paper addresses that gap. In addition, VSM has not been applied to a sugarcane production and supply system before. [ABSTRACT FROM AUTHOR]

This study introduces the concepts of translational distance and complexity distance to explain challenges to adoption of research methods in JIBS. We examine three analytical techniques and data collection approaches: (1) Heckman models, (2) ethnographic studies, and (3) data collection equivalence procedures in survey-based research. We note that progress has been made to reduce translational and complexity distance for analytical techniques. However, concerns remain for data collection equivalence and ethnography as IB scholars are using increasingly advanced analytical techniques on less credible data. [ABSTRACT FROM AUTHOR]

Truth discovery is an efficient technique for tackling data conflict problems in crowd sensing for distributed data collection. As the sensory data to be collected may include sensitive information about users, privacy-preserving truth discovery has attracted significant attention in recent years. Most existing studies apply a centralized architecture based on a cryptographic system, which may be vulnerable to single-point attacks and also has a very high computational cost. In this paper, we propose DPriTD, a decentralized privacy-preserving framework for truth discovery in crowd sensing. The proposed approach leverages the additively homomorphic property of Shamir's Secret Sharing scheme to protect user's privacy. DPriTD provides a strict privacy guarantee for crowd sensing applications. Because each sensitive data point, considered to be a secret, is split into a batch of shares, and the secret cannot be recovered unless a sufficient number of shares are aggregated, DPriTD achieves effective truth discovery while protecting sensitive data from collusion attacks. Furthermore, DPriTD is independent of a centralized server and can perform reliably when not all participants are online in real time. It thus enhances the robustness of a crowd sensing system. Extensive experiments conducted on real-world datasets demonstrate the high performance of our method compared with existing mechanisms. • DPriTD is a decentralized privacy-preserving framework for truth discovery in crowd sensing. • DPriTD leverages the additively homomorphic property of Shamir's Secret Sharing scheme to protect user's privacy. • DPriTD achieves effective truth discovery while protects sensitive data from collusion attacks. [ABSTRACT FROM AUTHOR]

Supervisory control and data acquisition (SCADA) systems are widely used in the national infrastructure. As more and more SCADA systems have been connected to the Internet, SCADA systems are facing great network security threats, especially attacks from industrial viruses. This paper aims to propose a novel mathematical model of industrial viruses propagation over SCADA systems. Then, the existence and stability of the equilibrium point of this model are studied. To better control the spread of this virus with limited resources, an optimal control system is established for the model. Then, the existence of optimal control is proved, and the corresponding optimal control system is derived. Numerical simulation results show that our proposed control method can effectively control the spread of industrial viruses. • A novel industrial virus propagation model based on SCADA system. • The local and global dynamic behaviors are fully studied. • The optimal control strategy to suppress malware propagation is obtained. • Numerical simulations are given to verify the main results. [ABSTRACT FROM AUTHOR]

Data quality is an important consideration in many engineering applications and projects. Data collection procedures do not always involve careful utilization of the most precise instruments and strictest protocols. As a consequence, data are invariably affected by imprecision and sometimes sharply varying levels of quality of the data. Different mathematical representations of imprecision have been suggested, including a classical approach to censored data which is considered optimal when the proposed error model is correct, and a weaker approach called interval statistics based on partial identification that makes fewer assumptions. Maximizing the quality of statistical results is often crucial to the success of many engineering projects, and a natural question that arises is whether data of differing qualities should be pooled together or we should include only precise measurements and disregard imprecise data. Some worry that combining precise and imprecise measurements can depreciate the overall quality of the pooled data. Some fear that excluding data of lesser precision can increase their overall uncertainty about results because lower sample size implies more sampling uncertainty. This paper explores these concerns and describes simulation results that show when it is advisable to combine fairly precise data with rather imprecise data by comparing analyses using different mathematical representations of imprecision. Pooling data sets is preferred when the low-quality data set does not exceed a certain level of uncertainty. However, so long as the data are random, it may be legitimate to reject the low-quality data if its reduction of sampling uncertainty does not counterbalance the effect of its imprecision on the overall uncertainty. [ABSTRACT FROM AUTHOR]

This article details best practices for open source ecosystems research to uphold the integrity of ecosystems. The article details nine best practices as a guide for researchers working with ecosystems with an emphasis on ethics and respect. Topics include understanding and adhering to information usage policies, data collection methods, and collaboration with the communities involved with these ecosystems.

Since the 1990s, libraries have invested in developing digital collections and online services to provide access to historical sources. One way to inspire users to actively engage with these materials is by creating visual contexts for the materials. These visuals provide an overview of a collection's content and inspire users to experiment with the collection's data for various purposes, including research. This issue of Library Technology Reports (vol. 57, no. 1) presents an approach that views digital collections as data that can be mined, analyzed, and visualized by means of the R programming language. R is open source, relatively easy to learn, and supported by an established community of coders. The selection of plots presented in the report includes R scripts, fragments of data tables, and some explanation of the R code used to create the plots. [ABSTRACT FROM AUTHOR]

• Emerging Mobile Health systems are examples of novel technologies. • Data are collected from sensor nodes and forwarded to local databases. • From cloud computing services, the data are collected for further analysis. • This paper presents a detailed overview of M-Health systems. • We propose a secure Android-based architecture to collect patient data. Nowadays, new highly-developed technologies are changing traditional processes related to medical and healthcare systems. Emerging Mobile Health (M-Health) systems are examples of novel technologies based on advanced data communication, deep learning, artificial intelligence, cloud computing, big data, and other machine learning methods. Data are collected from sensor nodes and forwarded to local databases through new technologies that enable cellular networks and then store the information in cloud storage systems. From cloud computing services or medical centres, the data are collected for further analysis. Furthermore, machine learning techniques are being used for accurate prediction of disease analysis and for purposes of classification. This paper presents a detailed overview of M-Health systems, their model and architecture, technologies and applications and also discusses statistical and machine learning approaches. We also propose a secure Android-based architecture to collect patient data, a reliable cloud-based model for data storage. Finally, a predictive model able to classify cardiovascular diseases according to their seriousness will be discussed. Moreover, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results show encouraging results in terms of the proposed predictive model for an M-Health system. Keywords: Machine Learning, Predictive, Models, M-Health, Classification, SVM, Decision Tree, Accuracy [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to develop an approach for investigating the impact of surveillance technologies used to facilitate security and its effect upon privacy. Design/methodology/approach – The authors develop a methodology by drawing on an isomorphy of concepts from the discipline of Macroeconomics. This proposal is achieved by considering security and privacy as economic goods, where surveillance is seen as security technologies serving identity (ID) management and privacy is considered as being supported by ID assurance solutions. Findings – Reflecting upon Ashby's Law of Requisite Variety, the authors conclude that surveillance policies will not meet espoused ends and investigate an alternative strategy for policy making. Practical implications – The result of this exercise suggests that the proposed methodology could be a valuable tool for decision making at a strategic and aggregate level. Originality/value – The paper extends the current literature on economics of privacy by incorporating methods from macroeconomics. [ABSTRACT FROM AUTHOR]

In the last decade, mobile technology offered new opportunities and challenges in animal health surveillance. It began with the use of basic mobile phones and short message service (SMS) for disease reporting, and the development of smartphones and other mobile tools has expanded the possibilities for data collection. These tools assist in the collection of data as well as geo-referenced mapping of diseases, and mapping, visualization and identification of vectors such as ticks. In this article we share our findings about new technologies in the domain of animal health surveillance, based on several projects using a wide range of mobile tools, each with their specific applicability and limitations. For each of the tools used, a comprehensive overview is given about its applicability, limitations, technical requirements, cost and also the perception of the users.The evaluation of the tools clearly shows the importance of selecting the appropriate tool depending on the envisaged data to be collected. Accessibility, visualization and cost related to data collection differ significantly among the tools tested. This paper can thus be seen as a practical guide to the currently available tools. [ABSTRACT FROM PUBLISHER]

Purpose: The purpose of this study is to analyze citizen engagement and to explain the underlying mechanism that makes well-intended people to act as disinformation amplifiers in the online space. The study offers new insights to be used by knowledge management for improving society's potential to downsize the impact of disinformation that puts both knowledge system and social trust (ST) under high pressure. Design/methodology/approach: The study proposes an integrative research model to explain how ST and conspiracy mentality (CM) are influencing citizen engagement in public life through different forms of action that is specific to offline or online spaces. The research model and its nine hypotheses are tested based on a survey for data collection and partial least squares method for data analysis. Findings: The study finds that both online and offline actions are mediating the positive effect of ST on citizen engagement. Yet, CM has a high impact on online actions, and it exerts a significant indirect influence on citizen engagement in this manner. Originality/value: Revealing the mediator role of online actions in the relationship between CM and civic engagement, the paper brings novel insights on disinformation spreading. The study explains how citizen engagement can sometimes be turned against social well-being because those prone to belief in conspiracies are the perfect targets of deceivers seeking for disinformation amplifiers in the online environment. [ABSTRACT FROM AUTHOR]

Differential privacy is a state-of-the-art technology for privacy preserving, and Laplace mechanism is a simple and powerful tool to realize differential privacy. However, there is an obvious flaw in differential privacy, which is each query function can only be executed finite times for the reason that adversary can recover the real query result if he executes the same query function many times. Unfortunately, how to set the upper bound for the number of linear queries is still an issue. In this paper, we focus on the linear query function in Laplace-based mechanisms, and we propose a method to set the upper bound for the number of linear queries from the perspective of information theory. The main idea is, firstly we find the most aggressive linear query that leaks the maximum information about the dataset to adversaries, and we set the upper bound of the number of queries so that even if the most aggressive linear query cannot leak the whole self-information about any individual to the adversary. On the other hand, the number of queries is also influenced by the type of dataset (continuous and discrete). In this paper, we also discuss the different upper bound for the number of queries for continuous datasets and discrete datasets. Finally, we conduct the experiments on the continuous dataset and the discrete dataset to prove our result. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is expanding rapidly and so is the number of devices, sensors and actuators joining this world. IoT devices are an important part of the data collection process in Big Data systems, so by protecting them we support and improve the security of the whole system. ZigBee is a secure communication system for the underlying Internet of Things (IoT) infrastructure. Even though ZigBee has a strong security stack built on a variety of secret keys, ZigBee devices are vulnerable to the side-channel and key extraction attacks. Due to the low cost and limited resources, most ZigBee devices store their secret keys in plaintext. In this paper, we focus on protecting the storage of ZigBee secret keys and show how Physical Unclonable Functions (PUFs) can help the ZigBee devices to be robust tamper-resistant against the physical attacks. The proposed schemes include PUF-based key storage protection and key generation. The experiments in this paper were done using SRAM-PUF. Furthermore, two algorithms were proposed to overcome the defects in the randomness of keys generated using SRAM-PUF and, at the same time, to increase the reliability of these keys. We were able to significantly improve the hardware security of ZEDs by protecting their keying materials using costless, high secure, random, stable and volatile PUF-based secret keys. [ABSTRACT FROM AUTHOR]

Multithreaded programs are notoriously prone to race conditions. Prior work developed precise dynamic race detectors that never report false alarms. However, these checkers employ expensive data structures, such as vector clocks (VCs), that result in significant performance overhead. This paper exploits the insight that the full generality of VCs is not necessary in most cases. That is, we can replace VCs with an adaptive lightweight representation that, for almost all operations of the target program, requires constant space and supports constant-time operations. Experimental results show that the resulting race detection algorithm is over twice as fast as prior precise race detectors, with no loss of precision. [ABSTRACT FROM AUTHOR]

Stochastic configuration networks (SCNs), as a class of randomized learning models, are incrementally built under a supervisory mechanism, and theoretically ensure error-free learning for training sets. This paper proposes a federated version of SCNs (FSCNs) for large-scale data, which are geographically distributed among different end-user clients with non-shareable data due to privacy and security concerns. Unlike centralized learning that needs to collect data from clients and store them collectively on a cloud server, FSCNs enable distributed analytics in a collaborative learning paradigm without centrally aggregating new data, thereby preventing the leakage of private information. Considering different supervisory and aggregate schemes of model parameters, two FSC algorithms with two aggregate strategies are presented. The experiment results on both data regression and classification show the effectiveness and feasibility of our proposed federated learning scheme. [ABSTRACT FROM AUTHOR]

With the development of data acquisition technology, high-dimensional data clustering is an important yet challenging task in data mining. Despite advances achieved by current clustering methods, they can be further improved. First, many of them usually unfold the high-dimensional data into a large matrix, consequently resulting in destroying the intrinsic structural property. Second, some methods assume that the noise in the dataset conforms to a predefined distribution (e.g., the Gaussian or Laplacian distribution), which violates real-world applications and eventually decreases the clustering performance. To address these issues, in this paper, we propose a novel tensor dictionary learning method for clustering high-dimensional data with the coexistence of structure noise. We adopt tensors, the natural and powerful tools for the generalizations of vectors and matrices, to characterize high-dimensional data. Meanwhile, to depict the noise accurately, we decompose the observed data into clean data, structure noise, and Gaussian noise. Furthermore, we use low-rank tensor modeling to characterize the inherent correlations of clean data and adopt tensor dictionary learning to adaptively and accurately describe the structure noise instead of using the predefined distribution. We design the proximal alternating minimization algorithm to solve the proposed model with the theoretical convergence guarantee. Experimental results on both simulated and real datasets show that the proposed method outperforms the compared methods for high-dimensional data clustering. [ABSTRACT FROM AUTHOR]

Among a number of ensemble learning techniques, boosting and bagging are the most popular sampling-based ensemble approaches for classification problems. Boosting is considered stronger than bagging on noise-free data set with complex class structures, whereas bagging is more robust than boosting in cases where noise data are present. In this paper, we extend both ensemble approaches to clustering tasks, and propose a novel hybrid sampling-based clustering ensemble by combining the strengths of boosting and bagging. In our approach, the input partitions are iteratively generated via a hybrid process inspired by both boosting and bagging. Then, a novel consensus function is proposed to encode the local and global cluster structure of input partitions into a single representation, and applies a single clustering algorithm to such representation to obtain the consolidated consensus partition. Our approach has been evaluated on 2-D-synthetic data, collection of benchmarks, and real-world facial recognition data sets, which show that the proposed technique outperforms the existing benchmarks for a variety of clustering tasks. [ABSTRACT FROM AUTHOR]

Tensor data, such as hyperspectral images and multi-frame videos, have gained significant attention in practical applications. However, the inherent degradation phenomena during data acquisition, including noise and missing pixels, give rise to a series of ill-posed inverse problems that need to be addressed. Currently, the rational exploration of prior knowledge for tensor recovery, including global low-rankness and local smoothness, has emerged as a common concern. Inspired by recent notable works, this paper proposes a novel tensor non-convex unified prior term, which employs weighted tensor Schatten p -norm as a rank surrogate function in the gradient domain. The new prior can yield a regularizer that effectively captures low-rankness and smoothness, and is applied to tensor completion and tensor robust principal component analysis models. An efficient algorithm is developed by using the alternating direction method of multipliers and its convergence analysis is also provided. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods, particularly in cases of high missing rates and strong noise levels. [ABSTRACT FROM AUTHOR]

Taking a food systems approach is a promising strategy for improving diets. Implementing such an approach would require the use of a comprehensive set of metrics to characterize food systems, set meaningful goals, track food systems performance, and evaluate the impacts of food systems interventions. Food systems metrics are also useful to structure debates and communicate to policy makers and the general public. This paper provides an updated analytical framework of food systems and uses this to systematically identify relevant metrics and indicators based on data availability in low- and middle-income countries (LMICs). The list of indicators partly overlaps with the Sustainable Development Goals (SDG) indicators, but these do not cover all aspects of the food system. We conclude that public data are relatively available on food systems drivers and outcomes, and on some, but not all, of the activities. With only minor additional investments, existing surveys could be extended to cover a large part of the required additional data. For some indicators, targeted data collection efforts are needed. Because of the overlap with the SDG indicators, part of the collected data could serve not only to describe and monitor food systems, but to track progress towards attaining the SDGs. [ABSTRACT FROM AUTHOR]

Cluster analysis of unlabeled data sets has been recognized as a key research topic in varieties of fields. In many practical cases, no a priori knowledge is specified, for example, the number of clusters is unknown. In this paper, grid clustering based on the polar self-organizing map (PolSOM) is developed to automatically identify the optimal number of partitions. The data topology consisting of both the distance and density is exploited in the grid clustering. The proposed clustering method also provides a visual representation as PolSOM allows the characteristics of clusters to be presented as a 2-D polar map in terms of the data feature and value. Experimental studies on synthetic and real data sets demonstrate that the proposed algorithm provides higher clustering accuracy and lower computational cost compared with six conventional methods. [ABSTRACT FROM PUBLISHER]

Purpose -- The purpose of this paper is to develop some weight determining methods for hesitant fuzzy multi-criterion decision making (MCDM) in which the preference information on attributes is collected over different periods. Design/methodology/approach -- Based on the proposed weight determining methods and dynamic hesitant fuzzy aggregation operators, an approach is developed to solve the hesitant fuzzy multi-stage multi-attribute decision-making problem where all the preference information of attributes over different periods is represented in hesitant fuzzy values. Findings -- In order to determine the weights associated with dynamic hesitant fuzzy operators, the authors propose the improved maximum entropy method and the minimum average deviation method. Research limitations/implications -- This paper does not consider the multi-stage multi-criteria group decision-making problem. Practical implications -- An example concerning the evaluation of rangelands is given to illustrate the validation and efficiency of the proposed approach. It should be stated that the proposed approach can also be implemented into other multi-stage MCDM problems. Originality/value -- The concept of hesitant fuzzy variable (HFV) is defined. Some operational laws and properties of the HFVs are given. Moreover, to fuse the multi-stage hesitant fuzzy information, the aggregation operators of hesitant fuzzy sets are extended to that of the HFVs. [ABSTRACT FROM AUTHOR]

Keywords: Cardiology; Data Acquisition; Data Management; Health and Medicine; Information Technology; Information and Data Management EN Cardiology Data Acquisition Data Management Health and Medicine Information Technology Information and Data Management 773 773 1 08/14/23 20230814 NES 230814 2023 AUG 14 (NewsRx) -- By a News Reporter-Staff News Editor at Cardiovascular Week -- Research findings on data acquisition are discussed in a new report. Cardiology, Data Acquisition, Data Management, Health and Medicine, Information Technology, Information and Data Management. [Extracted from the article]

The data collection unit according to claim 1, further comprising: a processing unit configured to process the inbound data, wherein the transmitting unit is configured to transmit the processed inbound data as the outbound data. The data collection and monitoring system according to claim 11, wherein the data collection unit is further configured to process the inbound data, and wherein the data collection unit is configured to transmit the processed inbound as the outbound data. The data collection unit is configured to transmit the outbound data directly to the terminal via device-to-device, D2D, communication, wherein the outbound data is based on the inbound data. "According to another aspect of the present invention, there is provided a data collection and monitoring system for monitoring a hygiene equipment status, the data collection and monitoring system comprising a data collection unit configured to receive, from hygiene equipment, inbound data indicating the hygiene equipment status and to transmit outbound data; and a terminal configured to receive the outbound data from the data collection unit. [Extracted from the article]

Purpose – The purpose of this paper is to explore small hotels guests' perception of social responsibility (SR), to relate their SR perceptions with their motivation for choosing small hotels instead of large ones, and to check whether motivation is further related to guests' satisfaction. System thinking is used for better attainment of SR through linking the environmental and social dimension of hotel guests' SR perceptions. Design/methodology/approach – The paper starts with a theoretical background for the conceptual model. The empirical quantitative research was conducted in 2013. Data were collected through a structured questionnaire. The hypotheses were analysed with structural equation modelling. Findings – The findings show that guests of small hotels perceive SR predominantly through its environmental and socio-local perspective. The guests with stronger perception of SR are more motivated to choose small hotels for their vacations, and at the same time, demonstrate a higher level of satisfaction with their choice. Research limitations/implications – The research is limited to guests of small hotels only and to two dimensions of SR: environmental and socio-local. Originality/value – Results of the study should encourage the small hotel operators to implement system thinking when reviewing their existing SR actions and adding some new ones. Relevant SR actions in small hotels, based on managers' system thinking, should become an important part of strategic, managerial and operational decisions. [ABSTRACT FROM AUTHOR]

This paper regards the coordination of networked systems, studied in the framework of hybrid dynamical systems. We design a coordination scheme which combines the use of ternary controllers with a self-triggered communication policy. The communication policy requires the agents to measure, at each sampling time, the difference between their states and those of their neighbors. The collected information is then used to update the control and determine the following sampling time. We show that the proposed scheme ensures finite-time convergence to a neighborhood of a consensus state: the coordination scheme does not require the agents to share a global clock, but allows them to rely on local clocks. We then study the robustness of the proposed self-triggered coordination system with respect to skews in the agents' local clocks, to delays, and to limited precision in communication. Furthermore, we present two significant variations of our scheme. First, assuming a global clock to be available, we design a time-varying controller which asymptotically drives the system to consensus. The assumption of a global clock is then discussed, and relaxed to a certain extent. Second, we adapt our framework to a communication model in which each agent polls its neighbors separately, instead of polling all of them simultaneously. This communication policy actually leads to a self-triggered “gossip” coordination system. [ABSTRACT FROM PUBLISHER]

The article offers insights on how to overcome the "data bottleneck" of obtaining data for machine-learning (ML) applications. Particular focus is given to a comprehensive taxonomy of ways to tackle this "data bottleneck." Methods discussed include dataset repurposing (using a preexisting dataset for a different task than it was originally constructed for), data augmentation (artificial inflation of the training set through the application of modifications) and multimodal learning (attempts to enrich the input to the learning algorithm).