Reviewerly/exHarmony huggingface dataset:
A Comprehensive and Unified Approach to Web Service Trust Evaluation Based on Uncertainty Methodology

Web services have the advantage of being able to generate new value-added services based on existing services. To effectively compose Web services, the composition process necessitates that the services that will participate in a given composite service are more trustworthy than those that provide similar functionality. The trust mechanism appears to be a promising way for determining service selection and composition. Existing trust evaluation approaches do not take into account customer expectations. Based on fuzzy set theory and probability theory, this work proposes a unique Web service trust evaluation approach that is notable for its ability to provide personalized service selection based on customer expectations and preferences. The proposed approach defines trust as a fuzzy notion that is related to prior experiences and ratings, and expresses trust in two different forms. This work mainly solves two key issues in Web service trust architectures, bootstrapping trust for the newcomer services and deriving trust for composite services. The proposed approach combines the solutions to numerous issues in a natural way. The case study and approaches comparison demonstrate that the proposed approach is feasible.
Probability bounds analysis for Python

Probability bounds analysis (PBA) is a collection of mathematical methods generalising interval analysis and probability theory. PBA can be utilised for uncertainty quantification for both aleatory and epistemic uncertainty across a wide range of scientific fields. PBA is most useful when information about variables is only partially known and can be used without requiring untenable assumptions to be made about parameter values, distribution shapes or dependence between variables. This paper introduces a PBA library for the Python programming language.
Multiscale Balanced-Attention Interactive Network for Salient Object Detection

The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient object detection. In this paper, we propose a processing mechanism based on a balanced attention module and interactive residual module. The mechanism addressed the acquisition of the multiscale features by capturing shallow and deep context information. For effective information fusion, a modified bi-directional propagation strategy was adopted. Finally, we used the fused multiscale information to predict saliency features, which were combined to generate the final saliency maps. The experimental results on five benchmark datasets show that the method is on a par with the state of the art for image saliency datasets, especially on the PASCAL-S datasets, where the MAE reaches 0.092, and on the DUT-OMROM datasets, where the F-measure reaches 0.763.
Synthesis of Data Science Competency for Higher Education Students

The research aims to Data Science Performance Synthesis for Higher Education Students and Data Science Performance Suitability Assessment for Higher Education Students. The research instruments include 1) data science performance synthesis tables, 2) expert interviews in data science performance assessments, 3) expert questionnaires to assess the consistency of data science performance. Analytical methods include 1) analyzing the frequency obtained from the content analysis table, 2) synthesis of content from interviews, 3) analyzing performance consistency, and components of data science performance, from data science synthesis for higher education students, finding that data performance for higher education students consists of five performances: 1) programming skills, 2)elementary statistics, 3) fundamentals of data science, 4) data preparation, and 5) Big data analytics.
How the Brain Creates Emergent Information by the Development of Mental Models: An Analysis from the Perspective of Temporal Factorisation and Criterial Causation

Mental modelsMental model usually are considered to represent a form of information or knowledge about situations or processes in the world or about mental statesMental state and processes in humans. In this chapter it is explored how this informational view on mental modelsMental model relates to two more general principles that have been introduced in recent years: temporal factorisationTemporal factorisation and criterial causationCriterial causation. Propagated activation of neurons in the brainBrain through their networkNetwork co-occurs with adaptationAdaptation of characteristics of this networkNetwork such as connectionConnection strengths or excitability thresholdsExcitability threshold. According to the principle of criterial causationCriterial causation, these adaptive networkAdaptive network characteristics can be interpreted as informational criteria for future activation of a considered neuron. This then is viewed as a form of emergentEmergent information formation and use of this information: the activation of neurons as determined by such information is termed criterial causationCriterial causation, where the information defines the criteria. The criterial causationCriterial causation principle strongly relates to the principle of temporal factorisationTemporal factorisation for the dynamicsDynamics of the world in general, describing how the world represents information about its past in its present stateState, which then in turn determines the world's future. In the chapter, these processes are analysed in more detail and modeled by (adaptive) networkNetwork models showing how mental modelsMental model that are learnt can be interpreted as emerging information according to the principles of criterial causationCriterial causation and temporal factorisationTemporal factorisation.
Public Perception of the Use of Digital Contact-Tracing Tools After the COVID-19 Lockdown: Sentiment Analysis and Opinion Mining

Singapore's national digital contact-tracing (DCT) tool-TraceTogether-attained an above 70% uptake by December 2020 after a slew of measures. Sentiment analysis can help policymakers to assess public sentiments on the implementation of new policy measures in a short time, but there is a paucity of sentiment analysis studies on the usage of DCT tools.We sought to understand the public's knowledge of, concerns with, and sentiments on the use of TraceTogether over time and their preferences for the type of TraceTogether tool.We conducted a cross-sectional survey at a large public hospital in Singapore after the COVID-19 lockdown, from July 2020 through February 2021. In total, 4097 respondents aged 21-80 years were sampled proportionately by sex and 4 age groups. The open-ended responses were processed and analyzed using natural language processing tools. We manually corrected the language and logic errors and replaced phrases with words available in the syuzhet sentiment library without altering the original meaning of the phrases. The sentiment scores were computed by summing the scores of all the tokens (phrases split into smaller units) in the phrase. Stopwords (prepositions and connectors) were removed, followed by implementing the bag-of-words model to calculate the bigram and trigram occurrence in the data set. Demographic and time filters were applied to segment the responses.Respondents' knowledge of and concerns with TraceTogether changed from a focus on contact tracing and Bluetooth activation in July-August 2020 to QR code scanning and location check-ins in January-February 2021. Younger males had the highest TraceTogether uptake (24/40, 60%), while older females had the lowest uptake (8/34, 24%) in the first half of July 2020. This trend was reversed in mid-October after the announcement on mandatory TraceTogether check-ins at public venues. Although their TraceTogether uptake increased over time, older females continued to have lower sentiment scores. The mean sentiment scores were the lowest in January 2021 when the media reported that data collected by TraceTogether were used for criminal investigations. Smartphone apps were initially preferred over tokens, but the preference for the type of TraceTogether tool equalized over time as tokens became accessible to the whole population. The sentiments on token-related comments became more positive as the preference for tokens increased.The public's knowledge of and concerns with the use of a mandatory DCT tool varied with the national regulations and public communications over time with the evolution of the COVID-19 pandemic. Effective communications tailored to subpopulations and greater transparency in data handling will help allay public concerns with data misuse and improve trust in the authorities. Having alternative forms of the DCT tool can increase the uptake of and positive sentiments on DCT.
A Secure Cloud-Edge Collaborative Logistic Regression Model

Outsourcing logical regression model to the cloud is highly beneficial, yet it will not only cause privacy issues due to the confidentiality of the data, but also cause bandwidth and computing burden for the cloud. In this paper, we design, implement and evaluate a new system that allows secure cloud-edge collaborative logistic regression model (SC-CLRM) based on multi-key fully homomorphic encryption (multi-key FHE). Our system significantly shifts the processing of the cloud to the edge and securely trains logistic regression model among multiple parties. We first propose a new scheme which uses the cloud server and edge node to jointly train logistic regression model over the ciphertext to ensure the security of data and classification model against the cloud server, and data source can go offline after providing the data. We further design security protocols based on multi-key FHE which supports multiple data sources and satisfies the semi-honest model security under outsourcing cloud computing. Performance evaluation demonstrates our new system can securely train the logistic regression model of multiple data sources, as well as share 41.72&#x0025;&#x007E;44.92&#x0025; communication and 53.13&#x0025;&#x007E;60.99&#x0025; computation overhead of the cloud.
Peer-to-Peer Trade Registration Process with Blockchain in Small And Medium Enterprises (SMEs) in E-Governance

The trade registration process is a big part of starting economic development for any region of the country. It provides employment, economic growth for the region, production of goods and product of the whole community. In the traditional trade registration system, challenges like corruption, delay in time, non-availability of authorities and critical registration process. Blockchain is a transparent, distributed, decentralized and reliable technology for the fast and trusted working process. Using blockchain in the trade registration process can solve the problems mentioned above in the industrial scenario. The consensus algorithm in blockchain provides the decentralized authority approval process. Any authority can approve the application process based on the digital signature availability in the transactions. In this paper, we use proof-of-authentication for transaction approval and a novel miner selection algorithm to reduce the biasing and centralization of authorization in e-governance. For key generation, we are using Ed25519, and for the implementation of blockchain, a custom PHP-based blockchain is used.
Enhancing Autotuning Capability with a History Database

Autotuning is gaining importance to achieve the best possible performance for exascale applications. The performance of an autotuner usually depends on the amount of performance data collected for the application, however, collecting performance data for large-scale applications is oftentimes an expensive and daunting task. This paper presents an autotuner database, which we call a history database, for enhancing the reusability and reproducibility of performance data. The history database is built into a publicly available autotuner called GPTune, and allows users to store performance data obtained from autotuning and download historical performance data provided by the same or other users. The database not only allows reuse of the best available tuning results for widely used codes but also enables transfer learning that can leverage knowledge of pre-trained performance models. An evaluation shows that, for ScaLAPACK&#x0027;s PDGEQRF routine, a transfer learning approach using the history database can attain up to 33% better tuning results compared to single task learning without using prior knowledge, on 2,048 cores of NERSC&#x0027;s Cori supercomputer.
Multiview from micro-lens image of multi-focused plenoptic camera

Multi-focused Plenoptic cameras (Plenoptic 2.0) allow the acquisition of the Light-Field of a scene. However, extracting a novel view from the resulting Micro-Lens Array (MLA) image poses several challenges: micro-lenses calibration, noise reduction, patch size (depth) estimation to convert micro-lens image to multi-view images. We propose a novel method to easily find important micro-lenses parameters, avoid the unreliable luminance area, estimate the depth map, and extract sub-aperture images (multiview) for the single- and multi-focused Plenoptic 2.0 camera. Our results demonstrate significant improvement in quality and reduction in computational time compared to the state-of-the-art conversion tool Reference Lenslet content Convertor from MLA image to multiview images.
QuakeLabeler: A Fast Seismic Data Set Creation and Annotation Toolbox for AI Applications

Abstract The production and preparation of data sets are essential steps in machine learning (ML) applications. With the increasing volume and scale of available ML techniques in seismology, annotating seismograms or seismic features has become time consuming and tedious for many researchers. Furthermore, most methods train and validate on unique data subsets, which hampers independent performance evaluation and comparison. To address this problem, we have developed the software QuakeLabeler, an open-source Python package to customize, build, and manage earthquake training data sets, including processing and visualization. QuakeLabeler has tight pipeline functions, which include retrieving seismograms from multiple online data centers, querying online human-reviewed catalogs, signal processing, annotating (labeling), and analyzing data distribution. In addition, relevant statistical graphics and human-readable output files can be generated. Various file export formats are supported, such as Seismic Analysis Code (*.sac), mini Standard for Exchange of Earthquake Data (*.mseed), NumPy (*.npz), MATLAB (*.mat), and the Hierarchical Data Format version 5 (*.hdf5). This toolbox is packaged with an interactive command-line interface. Three alternative running modes (beginner, advanced, and benchmark) are implemented, intended to offer specific data set solutions for different types of applications, that is, quick-start recipes for simple ML solutions, advanced design for customized project training, and benchmark bulletins for model comparison.
Multiclass Discriminant Analysis using Ensemble Technique: Case Illustration from the Banking Industry

Linear discriminant analysis (LDA) has found extensive application in predicting bankruptcy. In this article, we elucidate a novel modelling approach for LDA that can also aid in gaining useful insights regarding the relative importance and ranking of factors in the banking industry. The model steers away from the traditional computation of the variance/covariance matrix and employs an ensemble technique to assign records to classes. The efficacy of our model is tested using two datasets. Specifically, a large dataset from the banking industry was partitioned into the testing and training datasets, and an accuracy of 87.9% was achieved JEL Codes: C38, G33
The unwanted effects of imprecise language in forensic science standards

The author has previously contributed to work on requirements definitions in digital forensic methods, and identified a potential gap which could not be fully explained at the time (Marshall and Paige, 2018). The former Forensic Science Regulator (FSR), with others, commented on this and challenged the finding (Tully et al., 2020). This paper re-addresses this issue and explores the issue of language used in the various standards from ISO/IEC 17 025 (2017) through to the FSR's own guidance on digital forensic method (Forensic Science Regulator, 2020), comparing it with language used in other related standards and in software engineering standards. From this, the author proposes that the language used by the FSR may cause an over-emphasis on establishing requirements for the ultimate end-user, to the detriment of requirements for purely internal use of processes. This can also result in overly complex methods, which are inherently difficult to fully validate, being produced. Furthermore, the use of overloaded terminology may also lead to confusion about some key concepts in the various stages of method validation and re-validation.
Overview of Blockchain-Based Privacy Preserving Machine Learning for IoMT

The rapid growth of the internet of medical things (IoMT) in practical life causes privacy concerns for data owners at the time of data analysis. There are many methods available to examine the IoMT data in a privacy-preserving manner but a study is needed to understand the best practical method for real life. So, this study will not propose a novel method but will explore the state-of-the-art method to address the most realistic method for privacy-preserving data analysis. This considered two widely used technologies are Cryptography and Differential privacy. Performances are considered in terms of accuracy and time consumptions for the latest published research. Based on the analysis it is clear that both methods have flaws and can be used depending on the constraints of domains. Moreover, Differential privacy is more practical as it has almost similar time consumptions just like standard unsecured methods.
Optimal quantum detector tomography via linear regression estimation

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we design the optimal probe states for quantum detector tomography via linear regression estimation. In particular, two optimal criteria are adopted where one is related to upper bound of the mean squared error and the other concerns how robust the estimation result is with respect to measurement noise. We prove the optimal value based on the two criteria and provide two illustrative examples of optimal probe states.
ATTL: An Automated Targeted Transfer Learning with Deep Neural Networks

Success of machine learning algorithms hinges on access to labeled dataset. Obtaining a labeled dataset is an expensive, challenging and time-consuming process, leading to the development of transfer learning (TL) methodology. TL incorporates gained knowledge from a previously trained source model into specific yet similar task models with limited data domain coverage. In this paper, we propose an automated targeted transfer learning (ATTL) method to resolve the transferability between source and target with minimal data requirements. The ATTL method decides how much target data is essential for model training, along with selected source data, to obtain the skateholder's specified performance metrics. The ATTL framework optimizes the system to select minimal target data based on two approaches: combinatorial coverage and adaptive selection methodology, along with specific source data for fine-tuning given a pre-trained source model. We evaluated the ATTL method on the Kaggle's ‘planes in satellite imagery’ dataset and the results identified that acquiring a small number of intentionally well-chosen samples from the target environment can achieve model performance of 97% in comparison to the baseline transfer learning accuracy of 92%.
Hybrid attention network with appraiser-guided loss for counterfeit luxury handbag detection

Abstract Recently, convolutional neural networks have shown good performance in many counterfeit detection tasks. However, accurate counterfeit detection is still challenging due to the following three issues: (1) fine-grained classification, (2) class imbalance, and (3) high imitation samples. To address these issues, we propose a hybrid attention network (HANet) for counterfeit luxury handbag detection. In HANet, a hybrid attention module is first designed. Compared with existing methods that directly use classic CNNs for counterfeit detection, the HA module jointly uses a channel attention unit and a spatial attention unit to learn important information on both the channel and spatial dimensions. The HA modules can be easily integrated into the ResNet architecture to enhance the discriminative representation ability of CNNs, so as to help the network find subtle differences between the real and counterfeit products. In addition, an appraiser-guided loss is proposed to train HANet. Considering the factor of class imbalance and high imitation samples, the proposed loss gives the counterfeit class a higher weighting, and meanwhile gives the high imitation samples a much higher weighting. The proposed loss introduces the knowledge of appraisers, which allows HANet to not only treat real and counterfeit samples relatively fairly, but also pay more attention to the learning of difficult samples. To evaluate the performance of our method, we have constructed a well-benchmarked luxury handbag dataset. On this dataset, the performance of HANet, ResNet50, and the state-of-the-art attention methods is compared. The results demonstrate that HANet achieve superior performance against all its competitors.
An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion

Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model.
CurL-AutoML: Curriculum Learning-based AutoML

AutoML aims to find the best Machine Learning (ML) pipeline in a complex and high-dimensional search space by evaluating multiple algorithm configurations. However, training multiple ML algorithms is time-consuming, and as AutoML tools are frequently time-constrained, the exploration of the search space may find sub-optimal results. In this work, we explore the application of curriculum learning techniques to overcome this limitation. Curriculum and anti-curriculum learning have improved model performance and accelerated the training process on previous empirical investigations using optimization-based models by ordering examples during model training based on their difficulty. We apply and compare curriculum strategies on an AutoML system to accelerate the search space exploration and find good-performing machine learning pipelines efficiently. The results indicate that AutoML can benefit from a curriculum strategy. Furthermore, in most of the evaluated scenarios, the curriculum strategies led to better classification results.