Reviewerly/exHarmony huggingface dataset:
On Taking Advantage of Opportunistic Meta-Knowledge to Reduce Configuration Spaces for Automated Machine Learning

The optimisation of a machine learning (ML) solution is a core research problem in the field of automated machine learning (AutoML). This process can require searching through complex configuration spaces of not only ML components and their hyperparameters but also ways of composing them together, i.e. forming ML pipelines. Optimisation efficiency and the model accuracy attainable for a fixed time budget suffer if this pipeline configuration space is excessively large. A key research question is whether it is both possible and practical to preemptively avoid costly evaluations of poorly performing ML pipelines by leveraging their historical performance for various ML tasks, i.e. meta-knowledge. This paper approaches the research question by first formulating the problem of configuration space reduction in the context of AutoML. Given a pool of available ML components, it then investigates whether previous experience can recommend the most promising subset to use as a configuration space when initiating a pipeline composition/optimisation process for a new ML problem, i.e. running AutoML on a new dataset. Specifically, we conduct experiments to explore (1) what size the reduced search space should be and (2) which strategy to use when recommending the most promising subset. The previous experience comes in the form of classifier/regressor accuracy rankings derived from either (1) a substantial but non-exhaustive number of pipeline evaluations made during historical AutoML runs, i.e. `opportunistic' meta-knowledge, or (2) comprehensive cross-validated evaluations of classifiers/regressors with default hyperparameters, i.e. `systematic' meta-knowledge. Overall, numerous experiments with the AutoWeka4MCPS package, including ones leveraging similarities between datasets via the relative landmarking method, suggest that (1) opportunistic/systematic meta-knowledge can improve ML outcomes, typically in line with how relevant that meta-knowledge is, and (2) configuration-space culling is optimal when it is neither too conservative nor too radical. However, the utility and impact of meta-knowledge depend critically on numerous facets of its generation and exploitation, warranting extensive analysis; these are often overlooked/underappreciated within AutoML and meta-learning literature. In particular, we observe strong sensitivity to the `challenge' of a dataset, i.e.~whether specificity in choosing a predictor leads to significantly better performance. Ultimately, identifying `difficult' datasets, thus defined, is crucial to both generating informative meta-knowledge bases and understanding optimal search-space reduction strategies.
DepCon: Achieving Network SLO for High Performance Clouds

As containers run in a distributed manner in clouds, it is important to satisfy network service level objectives (SLOs) for containers. In addition, it has been known that containers utilize more CPU resources for network processing than native processes because of the long networking stack of containers. Thus, for achieving network SLOs, containers require sufficient CPU resources as well as network resources, which we call inter-resource dependency. However, existing cloud schedulers have limitations in that they do not take CPU into account for the network SLO. This paper proposes DepCon that controls CPU resources for containers to satisfy network SLOs. DepCon consists of DepCon scheduler that works in the cloud-level and DepCon agent at each node. We implement DepCon in the most popular container orchestration platform, Kubernetes. Our evaluation results show that DepCon reduces the network performance variance by 20 times while improving the network throughput by 40%. In addition, DepCon reduces the scheduling overhead by 20 times compared to the representative multi-resource scheduling technique like DRF.
Chinese Mineral Question and Answering System Based on Knowledge Graph

Mineral knowledge acquisition is indispensable in geological research. Currently, mineral knowledge can be acquired though a search engine like Google, or though some mineral databases. But usually, the answers from the search engines are not professional enough, and the mineral databases cannot answer the questions input in natural language. In this paper, a Chinese mineral question and answering system based on knowledge graph is proposed with the aim of answering the mineral questions in Chinese natural language professionally, accurately and efficiently. Two deep learning models based on BERT are built to obtain mineral entities and relationships to construct the mineral knowledge graph. After the mineral knowledge graph is constructed, accordingly question templates are defined and another deep learning model based on BERT is built to recognize the intent and the entity/attribute of the input question. The query statement is generated according to the question template pre-defined and is input to the mineral knowledge graph constructed. Then the answer to the question is returned by the knowledge graph. Of the knowledge graph we constructed has 22,568 entities and 91,699 relationships, which is richer than most of the geological knowledge graph. The question and answering system achieve a 91.2% accuracy on the 2,000 test questions.
Multi-Domain Adaptation for Regression Under Conditional Distribution Shift ⋆

Domain adaptation (DA) methods facilitate cross-domain learning by minimizing the marginal or conditional distribution shift between domains. However, the conditional distribution shift is not well addressed by existing DA techniques for the cross-domain regression learning task. In this paper, we propose \textit{Multi-Domain Adaptation for Regression under Conditional shift} (DARC) method. DARC constructs a shared feature space such that linear regression on top of that space generalizes to all domains.In other words, DARC aligns different domains and makes explicit the task-related information encoded in the values of the dependent variable.It is achieved using a novel Pairwise Similarity Preserver (PSP) loss function. PSP incentivizes the differences between the outcomes of any two samples, regardless of their domain(s), to match the distance between these samples in the constructed space. We perform experiments in both two-domain and multi-domain settings. The two-domain setting is helpful, especially when one domain contains few available labeled samples and can benefit from adaptation to a domain with many labeled samples. The multi-domain setting allows several domains, each with limited data, to be adapted collectively; thus, multiple domains compensate for each other's lack of data. The results from all the experiments conducted both on synthetic and real-world datasets confirm the effectiveness of DARC.
Phishing Detection System Using Extreme Learning Machines with Different Activation Function based on Majority Voting

Phishing is a type of software-based cyber-attack carried out to steal private information such as login credentials, user passwords, and credit card information. When the security reports published in recent years are examined, it is seen that there are millions of phishing spoofing web pages. Therefore, in this study, it is aimed to develop an effective phishing detection model. In the study, an extreme learning machine based model using different activation functions such as sine, hyperbolic tangent function, rectified linear unit, leaky rectified linear unit and exponential linear unit was proposed and comparative analyses were made. In addition, the performances of the models when combined with the majority vote were also evaluated and it was seen that the highest accuracy value of 97.123% was obtained when the three most successful activation functions were combined with the majority vote. Experimental results show the effectiveness and applicability of the model proposed in the study.
Algorithm of Multi-Target Dynamic Path Search Decision Maker Based on Otcm Pattern Recognition Robot

In wind farm robot equipment fault detection, the application of multi-objective dynamic path decision algorithm can simplify the operation process to improve detective efficiency and enhance accuracy. However, the algorithm has not been effectively popularized. Based on this, this study innovatively proposes the global scanning center method for optimal tracking to strengthen the tracking and monitoring speed of the robot and provide a convenient way for the application of multi-objective dynamic path decision algorithm. This method integrates the basic idea and operation mode of artificial intelligence immune algorithm to design and optimize the whole process monitoring path of wind farm fault monitoring. This paper uses principal component analysis method to transform the problem of multiple wind farms in the wind farm into a comprehensive index multivariate statistical analysis problem, so as to achieve the purpose of reduction. In addition, based on the optimal tracking center method (OTCM), the failure tracking mode of the controllable robot in the detection process is designed to minimize the feedback interval of the detection results. Finally, in order to test the rationality and effectiveness of the proposed method in practical application, a calculation example of random matrix positioning coordinates is introduced to verify it.
Fluid Space: Digitisation of Cultural Heritage and its Media Dissemination

From the mechanical reproduction outlined by Benjamin to the virtual reality depicted by Baudrillard. With the advent of the information society and the digital age, the dissemination of cultural heritage is gradually moving away from the traditional face-to-face embodied presence toward digital communication that transcends the limits of space and time. Our visual experience has also shifted from the traditional creation and reception of visual images to the invention and immersion in new worlds of spectacle. Virtual reality is a representative new technology in the digital media era, which not only realises the digital simulation of cultural heritage from physical space to cyberspace but also incorporates the characteristics of virtual visual culture into it, freeing heritage sites from the inherent material bulkiness and becoming a lighter, more fluid space, releasing the unlimited possibilities of spectacle simulation. Virtual reality technology has not only reshaped the technology of image viewing and production, but also the technology of image dissemination. Taking cultural heritage sites as an example, this paper will explain the role and impact of virtual technology on the digitisation of cultural heritage and its media dissemination from three aspects: interactivity, spatial perception and cultural value presentation.
Understanding Leadership in Agile Software Development Teams: Who and How?

Abstract The principles in the Agile Manifesto, the Scrum Guide and most other approaches to agile software development emphasize self-organizing teams, but rarely address issues of leadership. In this paper we report on a study of the nature of different aspects of leadership in agile teams. We used an established model of leadership, distinguishing transactional and transformational styles, and asked IT professionals a set of questions about the leadership they experience, both from direct supervisors (hierarchical leadership) and from the team itself (shared leadership). We determined correlation measures of these four types of leadership with the extent of agility in the whole organization. Our results show that agility is indeed related to the transformational style, but that the transactional style also plays a part, especially as shared leadership. Furthermore, even in highly agile software development, leadership by direct supervisors still plays an important role. We propose that, as software development becomes more agile, the transactional aspects of leadership may shift away from the leadership dyad between supervisor and employee into the agile team, while transformational leadership is important for both the team and supervisors. We discuss our results in light of applications for both research and practice.
Testing the Homogeneity of Topic Distribution Between Documents of a Corpus

A methodology is introduced to test the equality of topic distribution between documents of a corpus. This is achieved by using Latent Dirichlet Allocation (LDA) to estimate the topic distributions and the Kullback-Leibler divergence to measure the dissimilarity between the distributions. The testing approach combines Bayesian and frequentist statistics. Since the sampling distribution of the proposed statistics is unknown, a bootstrap test is suggested. The methodology is illustrated using scientific abstracts from the CMStatistics conference.
A survey on attribute-based encryption for Internet of Things

The Internet of Things (IoT) is a new technology that has drawn interest from both industry and academia in the modern digital era. The IoT provides a platform for any device to connect and share data over the Internet. Because of the diverse IoT environment and resource-constrained IoT devices, this nascent technology has numerous security challenges. Due to the lack of fine-grained access control and flexibility, traditional cryptographic algorithms such as private key encryption and public-key encryption are not suitable for providing access control mechanisms. ABE (Attribute-Based Encryption) is a new cryptographic approach that not only provides security but also fine-grained access control. This paper provides a comprehensive survey of ABE and its various forms, such as key policy ABE and ciphertext policy ABE, based on access structure, multi-authority, and hierarchy features. Further access structures used in ABE and applications of the ABE technique in IoT are also discussed. A comparison of different ABE schemes is presented regarding features, efficiency, and security. In addition, numerous schemes are analyzed to uncover obstacles, issues, and gaps that need to be investigated further.
A Transferable Turbidity Estimation Method for Estimating Clear-Sky Solar Irradiance

A transferable turbidity estimation method is proposed for estimating the turbidity and clear-sky solar irradiance. Instead of using on-site irradiance measurements (i.e., the local model), a transferable model is developed involving stations with sufficient information, and then applied at locations with limited data availability. Compared with the local method, the transferable model yields results with slightly higher discrepancies regrading normalized root mean squared error (nRMSE, 2.80% vs 2.45%). When compared with the Ineichen-Perez (PVLIB) model, the nRMSE of clear-sky global horizontal irradiance (GHIcs) estimation is reduced from 5.46% to 2.56%, and the normalized mean bias error (nMBE) is improved from -4.12% to -0.99%. The GHIcs estimation is comparable with physical models (i.e., McClear and REST2), where the McClear produces a nRMSE of 2.60% and the nMBE is 1.16%, while the REST2 generates results with a nRMSE of 2.43% and a nMBE of 0.81%. We further compare aforementioned models for day-ahead GHIcs forecasts using a day persistent way. GHIcs forecast from the transferable method has lower nRMSE while the nMBE is slightly larger than the physical models. Considering the complexity of physical models, the transferable turbidity estimation method with comparable performance demonstrates valuable potential for solar resourcing and forecasting applications.
Tukey Depth for Fuzzy Sets

Tukey depth is the first and one of the most notorious statistical depth functions. It has very good properties and measures in a proper way the centrality of points with respect to a distribution. In a forthcoming paper by the authors, the Tukey depth function is extended to the fuzzy setting. Here, we comment on this extension and some interesting properties satisfied by this function. Additionally, we include a novel illustration of this extension on a real dataset on the luminous intensities of light emitting diodes (LED) sources.
Cluster Validity Measures for Fuzzy Two-Mode Clustering

Two-mode clustering consists in simultaneously partitioning rows (mode 1, e.g. objects) and columns (mode 2, e.g., variables) of a data matrix. Recently, several soft two-mode clustering techniques have been developed according to the fuzzy approach, but how to determine the optimal numbers of clusters for objects and variables is an open problem not yet investigated. In this paper some new cluster validity measures for fuzzy two-mode clustering are introduced. Such measures, defined in terms of the compactness within each cluster and separation between clusters, can be seen as generalizations of well-known indices widely used in the standard fuzzy clustering framework. The adequacy of these proposals is assessed by means of a simulation study.
Multiscale Feature Fusion Network for Monocular Visual Gesture Pose Estimation

Hand pose estimation based on a single RGB image has low accuracy due to the complexity of the pose, local self-similarity of finger features, and occlusion. A multiscale feature fusion network (MS-FF) for monocular vision gesture pose estimation is proposed to address this problem. The network can take full advantage of different channel information to enhance important gesture information, and it can simultaneously extract features from feature maps of different resolutions to obtain as much detailed feature information and deep semantic information as possible. The feature maps are merged to obtain the hand pose results. The InterHand2.6M dataset and Rendered Handpose Dataset (RHD) are used to train the MS-FF. Compared with the InterNet method (which can estimate interacting hand poses from a single RGB image), our method reduces the average error by 7.6% for interacting hand joints, 10.8% for single joints, and 6.5% for root joints on InterHand2.6M. The MS-FF obtains the smallest average error of hand joints on RHD, verifying its effectiveness.
Lightweight Blockchain-Based Architecture for 5g Enabled Iot

Fast and secure network connectivity is essential for emerging Internet of Things (IoT) applications and services. Distributed Blockchain technology offers compelling opportunities to solve IoT security, privacy, and reliability issues. However, using blockchain in IoT networks introduces a new set of issues, including high computational overhead, latency, and bandwidth overhead that can adversely affect the proper operation of the IoT. This paper proposes a new lightweight blockchain-based architecture for 5G-enabled IoT. This architecture combines small cells, network functions virtualization (NFV), software-defined networking (SDN), and distributed clouds with a lightweight Blockchain consensus algorithm to guarantee high availability, real-time data delivery, security, resiliency, and low latency. We compare the performance of the proposed architectures in six different scenarios with conventional 5G in terms of processing time and power consumption. The proposed structure added data overload, superior hashing, and encryption protocols to conventional 5G to improve the security level of IoT networks against data manipulation and fraud. However, it reduces the traffic passed through the Internet and local transaction processing time.
Tk-Merge: Computationally Efficient Robust Clustering Under General Assumptions

We address general-shaped clustering problems under very weak parametric assumptions with a two-step hybrid robust clustering algorithm based on trimmed k-means and hierarchical agglomeration. The algorithm has low computational complexity and effectively identifies the clusters also in the presence of data contamination. Its generalizations and an adaptive procedure to estimate the amount of contamination are also presented.
Stego Detection: Image Steganalysis Using a Novel Hidden Stego Visual Geometry Group–Based CNN Classification

Steganography is the concept of embedding or hiding secret information into a cover image by maintaining the visual quality. Various algorithms are designed to classify stego images but the race still continues between Steganographer and Steganalyser. Advances in deep learning provided a solution to detect stego images. In this article, we coin a new paradigm to detect stego image as a three-step process with the following repercussions: (1) employing preprocessing step to enhance the input image, (2 feature extraction using the Mustard honey bee optimization algorithm and, thus, the extracted features will be dimensionally reduced (3) by classification using HSVGG-based CNN. Experimentation carried out on ALASKA2 data set and the results were compared.
Incentive-Based Fault Tolerant Control of Evolutionary Matrix Games

This chapter considers the fault tolerant control problem of evolutionary matrix games modeled by replicator dynamics. Under the evolutionary matrix game framework, the faults can maliciously provide additional payoffs to particular strategies for all players. The local asymptotical stability of the equilibria as well as the domains of attraction are analyzed in both healthy and faulty cases, based on which an incentive-based fault tolerant control method is proposed. The results are further extended to single population with switching payoff matrices, single population with mutation and multiple populations on the network.
An INDSCAL-Type Approach for Three-Way Spectral Clustering

A spectral clustering method for three-way data using an INDSCAL-type approach is introduced. Our proposal takes into account the single geometrical structures for each occasion and returns a common low-dimensional space which is representative for all occasions. Finally, a real application to three-way data is presented to illustrate the potential of the method.
Video Games Classification Based on Their Spatial Quality Scores

This article discusses video games from the spatial quality and within the context of a GIS framework. In this direction, literature was searched, prominent concepts for each field were identified, and the concepts shared by the fields were stated. 14 questions based on common concepts were directed to 5 different experts. The levels of sample video games were determined using the framework of spatial thinking and GIS as a result of expert interviews. Another goal of the research is to look at video game classification from a different angle. The analysis reveals that the results are consistent with one another. Using the method developed in this article, video games to be used in spatial thinking studies can be chosen.
Evaluation of Internet Transaction Fraud in Thailand

This research examines the factors influencing internet transaction fraud in the digital era in Thailand. The population of this study consists of Thai internet users, and the samples were selected using convenience sampling. The statistical method used to test the hypothesis is analysis of covariance (ANCOVA).Internet transaction fraud in this study is evaluated as the total value of damage caused by internet scams, which is a dependent variable. The findings show that age, income and social media plat forms influence the total value of damage significantly. In this examination, the value of economic damage caused by internet transaction fraud equals 214,488.2 million baht or US$7,149.605 million. It is recommended that public sectors create policies to prevent internet fraud, such as spreading useful information about internet scams. Additionally, Internet users should be careful about revealing personal information via online platforms and double-check all the information before confirming a transaction. Finally, providers and online platform owners should improve cyber security systems by updating to the most recent security software.