Reviewerly/exHarmony huggingface dataset:
A large-scale real-world comparative study using pre-COVID lockdown and post-COVID lockdown data on predicting shipment times of therapeutics in e-pharmacy supply chains

Purpose The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects. Design/methodology/approach The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets. Findings The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains. Research limitations/implications The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects. Practical implications E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times. Originality/value The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.
Handwritten numeral recognition through superimposition onto printed form

Handwriting is, eventually, a variation of the printed forms where the characters are little larger, smaller, angled and deformed than the printed forms. The small changes in handwriting define the parameters of the character to be recognized. Handwritten numeral recognition (HNR) poses significant challenges due to the deformations and other variations. This study proposes a new notion of HNR on the hypothesis that the handwritten numerals are distinct deformations of the printed forms, which leads to easier recognition task with higher accuracy when superimposing handwritten numeral images onto the corresponding printed numeral images. In the proposed HNR, auto-encoder and convolutional auto-encoder have been adapted for the superimposing task that transform HNIs into PNIs, while neural network and convolutional neural network are employed for classification of PNIs. The superimposition method reduces the computational overhead. Moreover, this method employs simple pre-processing without feature extraction whereas the traditional methods employ pre-processing, feature extraction, and recognition with machine learning tools, which add to the computational overhead. The performance of HNRSP has been evaluated for recognizing handwritten numerals of Bengali, Devanagari, and English on benchmark datasets and the proposed system achieves 99.68%, 99.73%, and 99.62% recognition accuracy for Bengali, Devanagari, and English handwritten numerals, respectively.
A novel PID controller for BLDCM speed control using dual fuzzy logic systems with HSA optimization

Abstract In order to enhance the speed control performance of the brushless DC motor (BLDCM), a novel proportion integration differentiation (PID) is proposed in this paper by using dual fuzzy logic systems (FLSs) with harmony search algorithm (HSA) optimization, which is called DFPID-HSA. Firstly, the FLS1 in DFPID-HSA locks the three coefficients of the PID controller in an extensive range on the basis of the system error and error change rate. Then, the FLS2 is optimized by HSA (HSA-F2) to obtain the precise correction of the three coefficients. To get the optimal global harmony better, the improved dynamic adjustment mode is used for the pitch adjustment rate (PAR) and distance bandwidth (BW) in HSA, and the triple selection method is adopted in the composition harmony section to realize the global search. Finally, DFPID-HSA provides the optimal supply control signal to BLDCM so that it can control the speed effectively. Moreover, the stability of the system is analyzed by the pole, Lyapunov, and Nyquist determination methods. And the sensitivity analysis of DFPID-HSA is carried out under the condition of different motor’s mechanical parameters to check its robustness. In addition, the superiority of DFPID-HSA is verified by MATLAB simulation and experiment platform.
Exploring the viability of augmented reality game- enhanced education in WhatsApp flipped and blended classes versus the face-to-face classes

The application of augmented reality games (ARG) as an emerging innovative technology has become a significant component of instructional learning contexts in recent years. ARG-based education as a form of student-centered learning situates students in a learning environment that integrates virtual elements with physical environments through three-dimensional pictures and videos on mobile devices for educational purposes. To connect the use of digital tools into the language classrooms and allow learners to view the real world, this study examined the viability of ARG-enhanced education on English foreign language (EFL) learners' learning of giving and asking for directions in flipped and blended contexts. The study involved 60 EFL elementary students of homogenous English proficiency, organized into two comparative and one control group, with 20 participants in each group. For the pre-test, an 18-item multiple-choice test with one written and oral question was administered to assess the learners' knowledge of asking for and giving directions. After taking the pre-test, the two comparative groups received 16 sessions of ARG-enhanced education (one with a blended and the other with a flipped classroom approach), while the control group received placebo instructions. The flipped group received the instructional materials preceding the online group, while the blended group received instruction in both online and face-to-face classes. The control group received instruction in a face-to-face context. After 8-weeks of treatment sessions, all participants of the study took the post-test. According to the results, both flipped and blended groups receiving ARG-enhanced education performed better than the control group in learning how to give and ask for directions. The results of this study may pave the way for EFL teachers and students to use ARG-based technology in online and traditional classes.
Chicken Swarm-Based Feature Subset Selection with Optimal Machine Learning Enabled Data Mining Approach

Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability of DM includes several areas such as education, healthcare, business, and finance. Educational Data Mining (EDM) is an interdisciplinary domain which focuses on the applicability of DM, machine learning (ML), and statistical approaches for pattern recognition in massive quantities of educational data. This type of data suffers from the curse of dimensionality problems. Thus, feature selection (FS) approaches become essential. This study designs a Feature Subset Selection with an optimal machine learning model for Educational Data Mining (FSSML-EDM). The proposed method involves three major processes. At the initial stage, the presented FSSML-EDM model uses the Chicken Swarm Optimization-based Feature Selection (CSO-FS) technique for electing feature subsets. Next, an extreme learning machine (ELM) classifier is employed for the classification of educational data. Finally, the Artificial Hummingbird (AHB) algorithm is utilized for adjusting the parameters involved in the ELM model. The performance study revealed that FSSML-EDM model achieves better results compared with other models under several dimensions.
Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics

Due to the increasing use of the different composite materials in lightweight applications, such as in aerospace, it becomes crucial to understand the different damages occurring within them during life cycle and their possible inspection with different inspection techniques in different life cycle stages. A comprehensive classification of these damage patterns, measuring signals, and analysis methods using a taxonomical approach can help in this direction. In conjunction with the taxonomy, this work addresses damage diagnostics in hybrid and composite materials, such as fibre metal laminates (FMLs). A novel unified taxonomy atlas of damage patterns, measuring signals, and analysis methods is introduced. Analysis methods based on advanced supervised and unsupervised machine learning algorithms, such as autoencoders, self-organising maps, and convolutional neural networks, and a novel z-profiling method, are implemented. Besides formal aspects, an extended use case demonstrating damage identification in FML plates using X-ray computer tomography (X-ray CT) data is used to elaborate different data analysis techniques to amplify or detect damages and to show challenges.
Exploiting deep and hand-crafted features for texture image retrieval using class membership

In the modern digital era, with the availability of low-cost hardware like sensors and cameras, a huge amount of image databases are being created for diverse applications. These databases give rise to the need of developing efficient content-based image retrieval (CBIR) systems. Major efforts have been put over the past two decades to develop different global and low-level texture features to build efficient CBIR systems. However, designing texture features that are suitable for distance-based retrieval is always a challenging task. Recently, Convolution Neural Networks have shown promising results for object detection and classification. CNNs are also applied to build classifier-based retrieval systems. However, the classifier-based retrieval methods can retrieve images only from the predicted class. Therefore, the performance of such system greatly depends on classification performance of the classifier. This paper proposes a method that exploits the strength of the Convolutional Neural Networks for predicting the class membership of the query image for all output classes and retrieve images using a modified distance function in the wavelet feature space. The performance of the proposed method is evaluated using three popular texture datasets of varying complexity and found to be superior to all competing methods considered.
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner and support online adaptation space reduction only for specific goals. To tackle these limitations, we present “Deep Learning for Adaptation Space Reduction Plus”—DLASeR+ for short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach and supports three common types of adaptation goals beyond the state-of-the-art approaches.
Authenticated Semi-Quantum Key Distribution Protocol Based on W States

In 2019, Wen et al. proposed authenticated semi-quantum key distribution (ASQKD) for identity and message using the teleportation of W states and GHZ-like states without pre-shared keys. However, the ASQKD protocol presents a vital issue in the teleportation of W states owing to its inappropriate design. Bob recovers the teleported W states without obtaining the position of the corresponding photons and then returns the recovered photons back to Alice. Hence, the teleportation of W states in Wen et al.'s ASQKD protocol was malfunctioning. Moreover, Wen et al.'s ASQKD protocol requires quantum memory, which strongly disobeys the definition of semi-quantum proposed by Boyer et al. Therefore, in this study, we discover the flaws of Wen et al.'s ASQKD protocol and propose an authenticated semi-quantum key distribution protocol. When compared to Wen et al.'s ASQKD protocol, the proposed ASQKD protocol has the following advantages: legal semi-quantum environment (i.e., does not require quantum memory), reduced quantum hardware requirement (i.e., based only on W states), does not involve classical cryptography (i.e., the hash function), and provided 1.6 times higher qubit efficiency.
A hybrid granular-evolutionary computing method for cooperative scheduling optimization on integrated energy system in steel industry

Integrated Energy System (IES) in steel industry, typically involving gas, heat and electricity, exhibits distributed nature and complex coupling relationship, which demands effective scheduling optimization to cooperatively utilize the multiple energy media. For this purpose, a Granular-Evolutionary Hybrid Computing (GEHC) framework is proposed in this study to solve the scheduling of IES as a Constrained Multi-Objective Problem (CMOP). More specifically, a Collaborative Fuzzy C-Means (CFCM) clustering model is firstly established, where the horizontal and vertical structures are well constructed to obtain information granules regarding the cooperative relationship among the units in different energy subsystems. Then, a Probability-based Granular Computing (PGrC) approach is proposed for the initial optimization of the IES scheduling, which is guided by the expert knowledge compacted in the information granules. To determine the collaborative parameters in CFCM that essentially controls the cooperation among the multiple energy media, as well as satisfying the complex practical constraints, a Multi-Objective Differential Evolution (MODE)-based method is proposed for further scheduling optimization. To evaluate the performance on cooperativity, convergency and diversity, the proposed GEHC and the compared methods that either has partial collaborations or use other optimization frameworks are applied to a real-world optimization problem. The experimental results demonstrate that the proposed GEHC performs superior to the other methods considering both the objective functions and performance indicators, which can be potentially helpful to achieve well-performed unmanned IES scheduling in steel industry.
An ASP Approach for Reasoning on Neural Networks under a Finitely Many-Valued Semantics for Weighted Conditional Knowledge Bases

Abstract Weighted knowledge bases for description logics with typicality have been recently considered under a “concept-wise” multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of multilayer perceptrons (MLPs). In this paper we consider weighted conditional $\mathcal{ALC}$ knowledge bases with typicality in the finitely many-valued case, through three different semantic constructions. For the boolean fragment $\mathcal{LC}$ of $\mathcal{ALC}$ we exploit answer set programming and asprin for reasoning with the concept-wise multipreference entailment under a $\varphi$ -coherent semantics , suitable to characterize the stationary states of MLPs. As a proof of concept, we experiment the proposed approach for checking properties of trained MLPs.
Graphs can be succinctly indexed for pattern matching in $O(\vert E\vert ^{2}+\vert V\vert ^{5/2})$ time

For the first time we provide a succinct pattern matching index for arbitrary graphs that can be built in polynomial time, while improving both space and query time bounds from [SODA 2021]. We show that, given an edge-labeled graph <tex>$G=(V, E)$</tex>, there exists a data structure of <tex>$\vert E/_{\leq G}\vert (\lceil\log\vert \Sigma\vert \rceil+\lceil\log q\rceil+2)\cdot(1+o(1))+\vert V/_{\leq G}\vert \cdot(1+o(1))$</tex> bits which supports pattern matching on <tex>$G$</tex> in <tex>$O(\vert P\vert \cdot q^{2}\cdot\log(q\cdot\vert \Sigma\vert))$</tex> time, where <tex>$G/_{\leq G}=(V/_{\leq G}, E/_{\leq G})$</tex> is a quotient graph obtained by collapsing some nodes in <tex>$G$</tex> and <tex>$q$</tex> is the width of the maximum co-lex relation on <tex>$G$</tex>. Our results have relevant applications in automata theory: we can use our data structure to decide whether a string belongs to the language recognized by a given automaton, and we can capture the degree of nondeterminism of an NFA.
A two-stage stacked-based heterogeneous ensemble learning for cancer survival prediction

Abstract Cancer survival prediction is one of the three major tasks of cancer prognosis. To improve the accuracy of cancer survival prediction, in this paper, we propose a priori knowledge- and stability-based feature selection (PKSFS) method and develop a novel two-stage heterogeneous stacked ensemble learning model (BQAXR) to predict the survival status of cancer patients. Specifically, PKSFS first obtains the optimal feature subsets from the high-dimensional cancer datasets to guide the subsequent model construction. Then, BQAXR seeks to generate five high-quality heterogeneous learners, among which the shortcomings of the learners are overcome by using improved methods, and integrate them in two stages through the stacked generalization strategy based on optimal feature subsets. To verify the merits of PKSFS and BQAXR, this paper collected the real survival datasets of gastric cancer and skin cancer from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute, and conducted extensive numerical experiments from different perspectives based on these two datasets. The accuracy and AUC of the proposed method are 0.8209 and 0.8203 in the gastric cancer dataset, and 0.8336 and 0.8214 in the skin cancer dataset. The results show that PKSFS has marked advantages over popular feature selection methods in processing high-dimensional datasets. By taking full advantage of heterogeneous high-quality learners, BQAXR is not only superior to mainstream machine learning methods, but also outperforms improved machine learning methods, which indicates can effectively improve the accuracy of cancer survival prediction and provide a reference for doctors to make medical decisions.
The Design of Cyber-Physical Exercises (CPXS)

This paper explores the objectives, tactics, and strategies for identifying, planning, conducting, and evaluating an international cyber-physical exercise (CPX). The goal of a CPX is to improve defense capabilities for defending national critical infrastructure via global coordination. Lessons about CPX have been derived from a series of annual cyber-physical defense exercises conducted since 2015, referred to as Critical Infrastructure Security Showdowns (CISS). The cyber range of a CISS consists of a realistic and operational enterprise network coupled to water treatment and distribution plants in the form of physical testbeds and digital twins. These systems simulate and integrate information technology (IT) and operational technology (OT) scenarios that are ubiquitous in modern-day critical infrastructure controlled by industrial control systems (ICS). Participants from the red, blue, green, and white teams are assigned specific roles to attack, defend, visualize, and manage the plant, respectively. Each of these roles is evaluated via a specific set of metrics by a panel of judges and automated systems. The scoring criteria incentivize the red teams to design and launch novel attacks to contribute to and improve the cybersecurity community’s knowledge base regarding offense and defense. The lessons distilled from these positive-sum games are analyzed and shared in the form of post-event reports.From 2015 to 2021, CISS has constantly evolved to mimic contemporary cyber-physical security scenarios in the real world. These evolutions have forced the CISS organizing team to adapt and design novel infrastructure to support the changing needs of the event and its stakeholders, from tooling, logistics, and network infrastructure to scoring criteria and cross-disciplinary collaboration.The cited reports on techniques, tactics, and procedures will be valuable to stakeholders from the military, industry, government, and academia.
An Improved Mixture Density Network for 3D Human Pose Estimation with Ordinal Ranking

Estimating accurate 3D human poses from 2D images remains a challenge due to the lack of explicit depth information in 2D data. This paper proposes an improved mixture density network for 3D human pose estimation called the Locally Connected Mixture Density Network (LCMDN). Instead of conducting direct coordinate regression or providing unimodal estimates per joint, our approach predicts multiple possible hypotheses by the Mixture Density Network (MDN). Our network can be divided into two steps: the 2D joint points are estimated from the input images first; then, the information of human joints correlation is extracted by a feature extractor. After the human pose feature is extracted, multiple pose hypotheses are generated via the hypotheses generator. In addition, to make better use of the relationship between human joints, we introduce the Locally Connected Network (LCN) as a generic formulation to replace the traditional Fully Connected Network (FCN), which is applied to a feature extraction module. Finally, to select the most appropriate 3D pose result, a 3D pose selector based on the ordinal ranking of joints is adopted to score the predicted pose. The LCMDN improves the representation capability and robustness of the original MDN method notably. Experiments are conducted on the Human3.6M and MPII dataset. The average Mean Per Joint Position Error (MPJPE) of our proposed LCMDN reaches 50 mm on the Human3.6M dataset, which is on par or better than the state-of-the-art works. The qualitative results on the MPII dataset show that our network has a strong generalization ability.