Reviewerly/exHarmony huggingface dataset:
Few-Shot Semantic Segmentation via Mask Aggregation

Authors:
raw_author_name: Wei Ao
raw_affiliation_string: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
raw_author_name: Shunyi Zheng
raw_affiliation_string: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
raw_author_name: Yan Meng
raw_affiliation_string: School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, China
raw_author_name: Yang Yang
raw_affiliation_string: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China

Abstract: Abstract Few-shot semantic segmentation aims to recognize novel classes with only very few labelled data. This challenging task requires mining of the correlation between the query image and the support images. Previous works have typically regarded it as a pixel-wise classification problem. Therefore, various models have been designed to explore the correlation of pixels between the query image and the support images. However, they focus only on pixel-wise correspondence and ignore the overall correlation of objects. In this paper, we introduce a mask-based classification method for addressing this problem. The mask aggregation network, which is a simple mask classification model, is proposed to simultaneously generate a fixed number of masks and their probabilities of being targets. Then, the final segmentation result is obtained by aggregating all the masks according to their locations. Experiments on both the PASCAL- $$5^i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> and COCO- $$20^i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>20</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> datasets show that our method performs comparably to the state-of-the-art pixel-based methods. This competitive performance demonstrates the potential of mask classification as an alternative baseline method for few-shot semantic segmentation.
Service Level Agreement in cloud computing: Taxonomy, prospects, and challenges

Authors:
raw_author_name: Daehan Kwak
raw_affiliation_string: Department of Computer Science and Technology, Kean University, NJ, 07083, USA
raw_author_name: Fiaz Gul Khan
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abboottabad, 22060, Pakistan
raw_author_name: Farman Ali
raw_affiliation_string: Department of Applied AI, School of Convergence, Sungkyunkwan University, Seoul, 03063, South Korea
raw_author_name: Sami Ullah Khan
raw_affiliation_string: Department of Computer Science and Information Technology, Hazara University Mansehra, Mansehra, 21300, Pakistan

Abstract: Cloud computing represents a distributed computing paradigm, offering hosted services via the internet on a pay-as-you-go basis. The Service Level Agreement (SLA) serves as a pivotal element of communication between Cloud Service Providers (CSPs) and Cloud Service Users (CSUs), detailing Service Level Objectives (SLOs) pertinent to service functionalities and Quality of Service (QoS). Within an SLA, service terms are formally documented in a contract between the CSP and CSU, ensuring that outsourcing customers establish an SLA with their vendors, thereby holding vendors accountable for financial consequences or payments should the set objectives not be achieved. In recent years, SLA methodologies in cloud computing have attracted considerable attention from the research community, with numerous strategies being developed to tackle challenges that may impede the efficient provisioning and management of QoS. This survey paper offers a comprehensive review of SLA techniques, presenting a detailed taxonomy based on their distinctive attributes. It discusses the evaluation parameters and platforms utilized in analyzing SLA approaches. Furthermore, the paper outlines design objectives and highlights open research issues that should be addressed when proposing new SLA techniques.
Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals

Authors:
raw_author_name: Guillermo Granados-Garcia
raw_affiliation_string: King Abdullah University of Science and Technology (KAUST) Saudi Arabia
raw_author_name: Hernando Ombao
raw_affiliation_string: King Abdullah University of Science and Technology (KAUST) Saudi Arabia

Abstract: One of neuroscience's goals is to study the interactions between different brain regions during rest and while performing specific cognitive tasks. Multivariate Bayesian autoregressive decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariate uncorrelated latent oscillations. Each latent oscillation captures a specific underlying oscillatory activity and, hence, is modeled as a unique second-order autoregressive process due to a compelling property, namely, that its spectral density's shape is characterized by a unique frequency peak and bandwidth, parameterized by a location and a scale parameter. The posterior distributions of the latent oscillation parameters are computed using a Metropolis-within-Gibbs algorithm. One of the advantages of the MBMARD model is its higher robustness against misspecification, compared with standard models. The main scientific questions addressed by the MBMARD model relate to the effects of long-term alcohol abuse on memory. These effects were examined by analyzing the electroencephalogram records of alcoholic and non-alcoholic subjects performing a visual recognition experiment. The MBMARD model yielded novel and interesting findings, including the identification of subject-specific clusters of low- and high-frequency oscillations in different brain regions.
Average consensus of whole-process privacy protection: A scale parameter method

Authors:
raw_author_name: Jing Zhang
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
raw_author_name: Jianquan Lu
raw_affiliation_string: School of Mathematics, Southeast University, Nanjing 210096, China
raw_author_name: Jinling Liang
raw_affiliation_string: School of Mathematics, Southeast University, Nanjing 210096, China
raw_author_name: Jie Zhong
raw_affiliation_string: School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China

Abstract: Privacy-preserving average consensus is a distributed control/estimation framework where a group of agents cooperatively compute the average of initial state. Meanwhile, the initial state and real-time state (such as initial opinion, initial location, real-time location, real-time active power ratio) in the communication and computation process may contain some sensitive information, which do not want other nodes of the network to obtain. However, existing privacy protection results mainly focused on protecting the initial state, and rarely took into account the privacy effect from the beginning to the end. To deal with this problem, a novel and interesting scale parameter method is proposed. First, a group of privacy coefficients is designed and assigned in advance for each node, which ensures that only their own parameters can be known and has no knowledge of others. Correspondingly, the initial state and real-time state can be hidden in the false value after multiplying these scale parameters. Then, the correctness analysis and privacy analysis of this method are provided respectively. Later, the generalization on directed networks is discussed. Finally, numerical simulations are conducted to show the effectiveness of obtained theoretical results.
Network traffic classification based on federated semi-supervised learning

Authors:
raw_author_name: ZiXuan Wang
raw_affiliation_string: School of Modern Posts, Nanjing University of Posts & Telecommunications, Nanjing, China
raw_author_name: ZeYi Li
raw_affiliation_string: School of Computer Science, Nanjing University of Posts & Telecommunications, Nanjing, China
raw_author_name: Pan Wang
raw_affiliation_string: School of Modern Posts, Nanjing University of Posts & Telecommunications, Nanjing, China

Abstract: Traffic Classification (TC) has been applied to a wide range of applications, from security monitoring to quality of service (QoS) provisioning in network Internet Service Providers (ISPs). In recent years, many researchers have applied Machine Learning (ML) or Deep Learning (DL) to TC, namely AI-TC. However, AI-TC methods face significant challenges, including high data dependency, exhaustively costly traffic labeling, and network subscribers’ privacy. This paper proposes a TC framework for smart home networks using Federated Learning (FL) that protects traffic data privacy by performing local training and inference of TC models. Firstly, we design a DPI-based traffic labeling method on edge home gateways as FL nodes, which enables these nodes to have data labeling capability while protecting data privacy. Then, a semi-supervised TC model based on an autoencoder (AE) is proposed to reduce the dependence of the model on labeled traffic samples. Finally, an XAI-based method is utilized to interpret the model to ensure its explainability. We validate the proposed method on public and real datasets using benchmarking methods. The experimental results show that the method can achieve high performance using a small number of samples while protecting data privacy and improving the model’s credibility. Experimental code can be found in the following url: https://github.com/PrinceXuan12138/HGW-TC-Experimental-code.
Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence

Authors:
raw_author_name: Jesse Thaler
raw_affiliation_string: Massachusetts Institute of Technology Cambridge Massachusetts USA
raw_author_name: Mike Williams
raw_affiliation_string: Massachusetts Institute of Technology Cambridge Massachusetts USA

Abstract: Abstract The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aI‐faI/) is one of the inaugural NSF AI research institutes ( https://iaifi.org ). The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state‐of‐the‐art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discovery as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.
Toward Useful Quantum Kernels

Authors:
raw_author_name: Massimiliano Incudini
raw_affiliation_string: Department of Computer Science University of Verona Strada Le Grazie, 15 Verona 37134 Italy
raw_author_name: Francesco Martini
raw_affiliation_string: Department of Computer Science University of Verona Strada Le Grazie, 15 Verona 37134 Italy
raw_author_name: Alessandra Di Pierro
raw_affiliation_string: Department of Computer Science University of Verona Strada Le Grazie, 15 Verona 37134 Italy

Abstract: Abstract Supervised machine learning is a popular approach to the solution of many real‐life problems. This approach is characterized by the use of labeled datasets to train algorithms for classifying data or predicting outcomes accurately. The question of the extent to which quantum computation can help improve existing classical supervised learning methods is the subject of intense research in the area of quantum machine learning. The debate centers on whether an advantage can be achieved already with current noisy quantum computer prototypes or it is strictly dependent on the full power of a fault‐tolerant quantum computer. The current proposals can be classified into methods that can be suitably implemented on near‐term quantum computers but are essentially empirical, and methods that use quantum algorithms with a provable advantage over their classical counterparts but only when implemented on the still unavailable fault‐tolerant quantum computer. It turns out that, for the latter class, the benefit offered by quantum computation can be shown rigorously using quantum kernels, whereas the approach based on near‐term quantum computers is very unlikely to bring any advantage if implemented in the form of hybrid algorithms that delegate the hard part (optimization) to the far more powerful classical computers.
Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Authors:
raw_author_name: Fumiyoshi Yamashita
raw_affiliation_string: Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501, Japan

Abstract: Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.
Question answering systems for health professionals at the point of care—a systematic review

Authors:
raw_author_name: Gregory Kell
raw_affiliation_string: Department of Population Health Sciences, King’s College London , London, Greater London, SE1 1UL, United Kingdom
raw_author_name: Angus Roberts
raw_affiliation_string: Department of Biostatistics and Health Informatics, King’s College London , London, Greater London, SE5 8AB, United Kingdom
raw_author_name: Linglong Qian
raw_affiliation_string: Department of Biostatistics and Health Informatics, King’s College London , London, Greater London, SE5 8AB, United Kingdom
raw_author_name: Davide Ferrari
raw_affiliation_string: Department of Population Health Sciences, King’s College London , London, Greater London, SE1 1UL, United Kingdom
raw_author_name: Frank Soboczenski
raw_affiliation_string: Department of Population Health Sciences, King’s College London , London, Greater London, SE1 1UL, United Kingdom
raw_author_name: Byron C Wallace
raw_affiliation_string: Khoury College of Computer Sciences, Northeastern University , Boston, MA 02115, United States
raw_author_name: Iain J Marshall
raw_affiliation_string: Department of Population Health Sciences, King’s College London , London, Greater London, SE1 1UL, United Kingdom

Abstract: Abstract Objectives Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. Materials and methods We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. Results We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians’ questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. Discussion While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.
BLOCKBOX: Blockchain based black box designing and modeling

Authors:
raw_author_name: Cagatay Korkuc
raw_affiliation_string: Electrical and Electronics Engineering Gazi University  Ankara Turkey
raw_author_name: Yasin Genc
raw_affiliation_string: Electrical and Electronics Engineering Gazi University  Ankara Turkey
raw_author_name: Erkan Afacan
raw_affiliation_string: Electrical and Electronics Engineering Gazi University  Ankara Turkey

Abstract: Summary With the development of technology, data has become more accessible. The storage of critical and valuable information is getting harder and harder with the increase of cyber attacks and vulnerabilities of network and internet. The centralized storage of data causes security and privacy problems. As a remedy to these problems, blockchain has occurred. The reliability, transparency, integrity and confidentiality of the data have been increased with the blockchain technology. Blockchain maintains the immutability of the data with cryptographic hash algorithms. The data is kept in distributed structures with the consensus algorithms so that it is not lost. Projects and studies using blockchain technology have shown that data stored in centralized structures is not as reliable as data that is distributed and secured by cryptographic algorithms, smart contracts, and consensus. The data of flights, which is a critical data, has a great place in world transportation. The parameters inside the black boxes guide us on why the aircrafts have crashed: whether the responsibility belongs to the manufacturer, to the pilot, or to the aircraft technicians. In this way, the real reasons of the accident may be determined and measures can be taken for the accidents that may occur afterwards. In addition, the safe delivery of all black box data to the interested parties and its storage in a way that is immutable will not only benefit to the manufacturer's self‐development by processing the data, but also help flight companies to find optimum flight data. Both the flight companies and the manufacturer will be able to read the black box data, thus the suspicion of data manipulation will be eliminated and the data will become more transparent. In this study, a system is proposed in which an aircraft's black box data can be saved safely and securely with the help of blockchain technology.
Angell and McCall Meet Wansing

Authors:
raw_author_name: Hitoshi Omori
raw_affiliation_string: Graduate School of Information Sciences, Tohoku University, Sendai, Japan
raw_author_name: Andreas Kapsner
raw_affiliation_string: Munich Center for Mathematical Philosophy, LMU,  Munich, Germany

Abstract: Abstract In this paper, we introduce a new logic, which we call AM3 . It is a connexive logic that has several interesting properties, among them being strongly connexive and validating the Converse Boethius Thesis . These two properties are rather characteristic of the difference between, on the one hand, Angell and McCall’s CC1 and, on the other, Wansing’s C . We will show that in other aspects, as well, AM3 combines what are, arguably, the strengths of both CC1 and C . It also allows us an interesting look at how connexivity and the intuitionistic understanding of negation relate to each other. However, some problems remain, and we end by pointing to a large family of weaker logics that AM3 invites us to further explore.
What characterizes LIS as a fragmenting discipline?

Authors:
raw_author_name: Pertti Vakkari

Abstract: Purpose The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley’s (1984) theory about the organization of sciences and Fuchs’ (1993) theory about scientific change. Design/methodology/approach The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&amp;I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Findings The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&amp;I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Originality/value The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley’s (1984) theory.