Miracle-dz/UniFAR-FTUs huggingface dataset:
Technological Innovation Adoption in Manufacturing Sector: The Moderator Role of Government Support

Investment in technological innovation in Malaysia has been actively developed either through government support or through the firm’s initiative to adopt technological innovation. This study examines government support as a moderator between the influencing factors and technological innovation performance in the manufacturing sector. The present study is conducted in the context of Malaysian manufacturing sector using a sample of 445 innovative companies. The results indicate that five indicators were an essential factor in boosting technological innovation adoption. Such support includes technical consultancy, innovation activities, technical support services, proper training of personnel, implementing of productivity and registration of patents, and financial support including excluded duty on imported machinery or equipment, tax-exemption incentives and innovation grants to the firm.
Issues with the material point method for geotechnical modelling, and how to address them

: The Material Point Method (MPM) for solid mechanics was ﬁrst proposed by Sulsky and co-workers in the 1990s. Since then it has been developing a growing band of followers not least because of its ability to handle large deformation problems with ease. This feature has more recently come to the notice of geotechnical researchers who have plenty of problems to solve involving large deformations. It is clear from recent publications, however, that many geotechnical researchers have found difﬁculties with the use of the MPM in a number of areas. In this paper we visit three of these problem areas and highlight solutions we have developed. It is to be hoped that this can remove some of the roadblocks to the use and further development of the MPM for geotechnical problems in future.
Coagulation and sedimentation of particulate pollutants in marine waters after wastewater outfall discharge

Outfall discharge to seawater is generally used as a practical means of wastewater disposal in many coastal cities. However, deposition of solid pollutants from the wastewater on the seabed would affect the benthic communities. In the present study, both laboratory experiments and numerical simulations were carried out on the role of particle coagulation in the transport and sedimentation of sewage solid materials in marine waters after outfall discharge. Actual sewage samples were collected from two treatment plants in Hong Kong for the experimental study. A flocculator-imaging system was developed for characterization of the particle size distribution (PSD) dynamics during flocculation and dilution of sewage particles in seawater. The system consisted of an external flow-through cell, a microscopic CCD video recorder and an image analyzer. The laboratory results demonstrated that coagulation played an important role in regulating the transport of solid pollutants in marine waters. Flocculation of 30-45 min could shift the PSD considerably, transforming small particles to larger, fast-settling particle aggregates. With a growth in particle size by flocculation, the average settling velocity of the sewage particulates increased by a factor of 2 or more. In addition to the laboratory study, a mathematical model was developed to simulate the transport dynamic of wastewater particles in seawater after outfall discharge. The results of numerical simulations compared well with the experimental observations. Both laboratory and simulation results suggest the important role of coagulation in the transport and sedimentation dynamics of sewage particles in marine waters, especially in the early phase of outfall discharge. As a result, suspended solids from the wastewater discharge would deposit more closely in the vicinity of the outfall sites.
Fast species ranking for iterative species-oriented skeletal reduction of chemistry sets

A fast algorithm is developed for ranking the species in a chemistry set according to their importance to the modeled densities of user-specified species of interest. The species ranking can be constructed for any set of user-specified plasma conditions, but here we focus predominantly on low-temperature plasmas, with gas temperatures between 300 and 1500 K covering the typical range of ICP and CCP plasma sources. This ranking scheme can be used to acquire insight into complex chemistry sets for modeling plasma phenomena or for a species-oriented reduction of the given chemistry set. The species-ranking method presented is based on a graph-theoretical representation of the detailed chemistry set and establishing indirect asymmetric coupling coefficients between pairs of species by the means of widely used graph search algorithms. Several alternative species-ranking schemes are proposed, all building on the theory behind different flavors of the directed relation graph method. The best-performing ranking method is identified statistically, by performing and evaluating a species-oriented iterative skeletal reduction on six, previously available, test chemistry sets (including O2–He and N2–H2) with varying plasma conditions. The species-ranking method presented leads to reductions of between 10 and 75% in the number of species compared to the original detailed chemistry set, depending on the specific test chemistry set and plasma conditions.
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals

Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchoredSTFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.
Reinforcement Learning for Optimal Primary Frequency Control from Inverter-Based Resources: A Lyapunov Approach

—The increase in penetration of inverter-based resources provides us with more ﬂexibility in frequency regulation compared to conventional linear droop controllers. Using their power electronic interfaces, inverter-based resources can realize almost arbitrary responses to frequency changes. Reinforcement learning (RL) has emerged as a popular method to design these nonlinear controllers to optimize a host of objective functions. The key challenge with RL based approaches is how to enforce the constraint that the learned controller should stabilize the power system. In addition, training these controllers is nontrivial because of the time-coupled dynamics of power systems. In this paper, we propose to explicitly engineer the structure of neural network-based controllers such that they guarantee system stability by design. This is done by constraining the network structures using a well-known Lyapunov function. A recurrent RL architecture is used to efﬁciently train the controllers. The resulting controllers only use local information and outperform linear droop as well as controllers trained using existing state-of-the-art learning approaches.
Cyber LOPA: An Integrated Approach for the Design of Dependable and Secure Cyber-Physical Systems

Safety risk assessment is an essential process to ensure a dependable cyber-physical system (CPS) design. Traditional risk assessment considers only physical failures. For modern CPSs, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety–security lifecycle integration and the expansion of modeling formalisms for risk assessment to incorporate security failures. The interaction between safety and security lifecycles and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures, are some of the overlooked research questions. This article addresses these research questions by presenting a new safety design method named cyber layer of protection analysis (CLOPA) that extends the existing layer of protection analysis (LOPA) framework to include failures caused by cyber attacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the tradeoff between designing a highly reliable and a highly secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice.
Functional Pearl: Zero-Cost, Meta-Programmed, Dependently-Typed Stateful Functors in F ★

Writing code is hard; proving it correct is even harder. As the scale of veriﬁed software projects reaches new heights, the problem of eﬃciently verifying large amounts of software becomes more and more salient. Nowhere is this issue more evident than in the context of veriﬁed cryptographic libraries. To achieve feature-parity and be competitive with unveriﬁed cryptographic libraries, a very large number of algorithms and APIs need to be veriﬁed. However, the task is oftentimes repetitive, and factoring out commonality between algorithms is fraught with diﬃculties, requiring until now a signiﬁcant amount of manual eﬀort. This paper shows how a judicious combination of known functional programming techniques leads to an order-of-magnitude improvement in the amount of veriﬁed code produced by the popular HACL ★ cryptographic library, without compromising performance. We review three techniques that build upon each other, in order of increasing sophistication. First, we use dependent types to crisply capture the speciﬁcation and state machine of a block algorithm , a cryptographic notion that was until now only informally and impre-cisely speciﬁed. Next, we rely on partial evaluation to author a higher-order, stateful functor that transforms any unsafe block API into a safe counterpart. Finally, we rely on elaborator reﬂection to automate the very process of authoring a functor, using a code-rewriting tactic. This culminates in a style akin to templatized C++ code, but relying on a userland tactic and partial evaluation, rather than built-in compiler support. We demonstrate our techniques in the F ★ programming language, and show that they require no change in the F ★ compiler whatsoever. Our techniques require no user intervention either; the proofs are carried once and for all, and users can instantiate our higher-order functors without incurring any proof obligations. A
Piecewise Growth Modeling Using SAS PROC MIXED

ABSTRACT Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant’s age, or conversely, participants may experience an intervention that causes trajectories to change. Specifically, this article outlines how to build and estimate piecewise growth models (PGM) using SAS PROC MIXED in order to estimate discontinuous growth models over time in a hierarchical linear model (HLM) framework. Details of data coding schemes, model parameterization, analysis using SAS PROC MIXED, and interpretation of parameter estimates will be discussed using a simulated education intervention data set.
Measurement in Physical Education and Exercise Science (MPEES): A Summary of MPEES-Related Activities in 2020

ABSTRACT The purpose of this manuscript is to provide a summary of Measurement in Physical Education and Exercise Science (MPEES)-related activities in 2020. In 2020 original submissions (i.e., not counting revised manuscripts) increased by ~84% as compared to 2019. Fifty-three countries were represented across the 386 original manuscripts submitted to MPEES in 2020. MPEES continues to welcome high-quality submissions from around the world in 2021. The seven sections of MPEES are as follows: (a) exercise science, (b) physical activity, (c) physical education pedagogy, (d) psychology, (e) research methodology and statistics, (f) sport management and administration, and (g) tutorial and teacher’s toolbox. More than three-fourths of the manuscripts that were submitted to MPEES in 2020 were submitted to one of the three sections: exercise science or physical activity or research methodology and statistics. MPEES continues to welcome high-quality submissions within each of the seven sections that comprise the journal in 2021. All of the original manuscripts that were submitted to MPEES in 2020 had received an initial decision by May 3, 2021 (i.e., 386 of the 386 manuscripts received). When an immediate decision (e.g., desk-rejection) was rendered by the editor-in-chief it always (i.e., for 288 of 288 manuscripts) occurred within 10 days of receiving the manuscript. When a manuscript was assigned to a section editor by the editor-in-chief it typically (i.e., for 90 of 98 manuscripts) received an initial decision within 90 days of receiving the manuscript. Twenty-three countries were represented across reviewers for the original manuscripts that were sent out for external review by MPEES in 2020. We gratefully acknowledge, and list in the Appendix, the many scholars (N = 176) from around the world who served as a reviewer for MPEES in 2020. Volume 24 (i.e., the volume published in 2020) of MPEES published a total of 32 manuscripts. More than three-fourths of the published manuscripts were submitted to one of the three sections: exercise science or physical activity or research methodology and statistics. MPEES continues to strive for publishing high-quality manuscripts within each of the seven sections that comprise the journal in 2021. Eleven countries were represented across the manuscripts published in Volume 24 of MPEES. MPEES continues to strive for publishing high-quality manuscripts written by one or more authors from around the world in 2021. The 2019 impact factor for MPEES (released in ~June 2020) was 1.75. The value of this impact factor placed MPEES in the second quartile (i.e., ranked #115 out of 263 journals) within the Education & Educational Research category of Clarivate Analytics.
Mining Process Data to Detect Aberrant Test Takers

ABSTRACT Item harvesters who memorize, record and share test items can jeopardize the validity and fairness of credentialing tests. Item harvesting behaviors are difficult to detect by the existing statistical modeling approaches due to the absence of operational definitions and the idiosyncratic nature of human behaviors. Motivated to detect the hard-to-define aberrant test-taking behaviors like item harvesting, we proposed a data-mining approach that utilized the process data and identified the examinees whose test-taking processes deviate from the majority of examinees. Specifically, two steps were implemented in the proposed approach: First, archetypes of test-taking processes are learned with the k-means clustering algorithm; second, examinees whose behavioral patterns deviate from the archetypes are flagged for further investigation. Given that the process data makes it possible to capture more subtle differences between the aberrant test-takers and normal examinees, the proposed approach is expected to be complementary of the statistical modeling methods, capture additional types of aberrant test-takers and increase the probability of discovering the elusive item harvesters.
Applied artificial intelligence : A bibliometric study of an International Journal

Purpose: The study aims to conduct a bibliometric analysis of an international journal “Applied Artificial Intelligence (AAI)” to analyze publication trends, authorship patterns, collaborative networks, citation behaviors, and research hotspots of authors, organizations, and countries. Research Design/Methodology: “Applied Artificial Intelligence” is a peer-reviewed international journal, published by Taylor & Francis. The journal has published more than 1100 articles in 34 volumes so far. The idea was conceived to conduct the bibliometric study of the AAI journal. The data were collected from the Web of Science (WOS) database, owned by the Clarivate Analytics. A total of 1109 articles were retrieved and their metadata was collected for further analysis and interpretation. Additionally, the VOS viewer software was used for mapping and visualization of the bibliographic information. Findings: The journal has experienced positive growth in research productivity and negative growth in citations. Authors of 74 prominent countries have contributed to the journal and the USA has occupied the first position in publication count followed by Italy, India and England respectively. All the countries work in close collaboration and created a collaborative network and sub-networks. The USA is the pivot of the collaborative network, mostly collaborating with England, Japan, Italy, China and Germany. The keywords like the classification, optimization, algorithms and neural networks are the most common and hence the hot topics of research in the journal. Originality/Value: The main advantage of this study is that it provides profound knowledge of the content structure and developmental process of the journal to date. It is also valuable for researchers in the field of artificial intelligence to identify the research hotspots in this field.
Wafer-scale heterostructured piezoelectric bio-organic thin films

Piezoelectric bioorganic thin films Piezoelectric materials enable a reversible conversion between mechanical pressure and electric charge and are useful for sensors, actuators, and high-precision motors. Yang et al. developed a method for making high-quality crystalline thin films of piezoelectric γ-glycine crystals that are grown and refined between layers of polyvinyl alcohol (PVA) (see the Perspective by Berger). The PVA layers are essential to promoting the crystallization of the preferred crystal phase with the polar axis oriented perpendicular to the film plan because of hydrogen bonding at the PVA-glycine interface. The thin films show a macroscopic piezoelectric response and high stability in aqueous environments. The films are water soluble and, when suitably packaged, could be implanted into a biodegradable energy-harvesting device. Science, abf2155, this issue p. 337; see also abj0424, p. 278 Implantable piezoelectric thin films are made from crystalline γ-glycine sandwiched between two polyvinyl alcohol thin films. Piezoelectric biomaterials are intrinsically suitable for coupling mechanical and electrical energy in biological systems to achieve in vivo real-time sensing, actuation, and electricity generation. However, the inability to synthesize and align the piezoelectric phase at a large scale remains a roadblock toward practical applications. We present a wafer-scale approach to creating piezoelectric biomaterial thin films based on γ-glycine crystals. The thin film has a sandwich structure, where a crystalline glycine layer self-assembles and automatically aligns between two polyvinyl alcohol (PVA) thin films. The heterostructured glycine-PVA films exhibit piezoelectric coefficients of 5.3 picocoulombs per newton or 157.5 × 10−3 volt meters per newton and nearly an order of magnitude enhancement of the mechanical flexibility compared with pure glycine crystals. With its natural compatibility and degradability in physiological environments, glycine-PVA films may enable the development of transient implantable electromechanical devices.
DataPlanner: data-budget driven approach to resource-efficient ABR streaming

Over-the-top video (OTT) streaming accounts for the majority of traffic on cellular networks, and also places a heavy demand on users' limited monthly cellular data budgets. In contrast to much of traditional research that focuses on improving the quality, we explore a different direction---using data budget information to better manage the data usage of mobile video streaming, while minimizing the impact on users' quality of experience (QoE). Specifically, we propose a novel framework for quality-aware Adaptive Bitrate (ABR) streaming involving a per-session data budget constraint. Under the framework, we develop two planning based strategies, one for the case where fine-grained perceptual quality information is known to the planning scheme, and another for the case where such information is not available. Evaluations for a wide range of network conditions, using different videos covering a variety of content types and encodings, demonstrate that both these strategies use much less data compared to state-of-the-art ABR schemes, while still providing comparable QoE. Our proposed approach is designed to work in conjunction with existing ABR streaming workflows, enabling ease of adoption.
A Software/Hardware Co-Design of Crystals-Dilithium Signature Scheme

As quantum computers become more affordable and commonplace, existing security systems that are based on classical cryptographic primitives, such as RSA and Elliptic Curve Cryptography (ECC), will no longer be secure. Hence, there has been interest in designing post-quantum cryptographic (PQC) schemes, such as those based on lattice-based cryptography (LBC). The potential of LBC schemes is evidenced by the number of such schemes passing the selection of NIST PQC Standardization Process Round-3. One such scheme is the Crystals-Dilithium signature scheme, which is based on the hard module-lattice problem. However, there is no efficient implementation of the Crystals-Dilithium signature scheme. Hence, in this article, we present a compact hardware architecture containing elaborate modular multiplication units using the Karatsuba algorithm along with smart generators of address sequence and twiddle factors for NTT, which can complete polynomial addition/multiplication with the parameter setting of Dilithium in a short clock period. Also, we propose a fast software/hardware co-design implementation on Field Programmable Gate Array (FPGA) for the Dilithium scheme with a tradeoff between speed and resource utilization. Our co-design implementation outperforms a pure C implementation on a Nios-II processor of the platform Altera DE2-115, in the sense that our implementation is 11.2 and 7.4 times faster for signature and verification, respectively. In addition, we also achieve approximately 51% and 31% speed improvement for signature and verification, in comparison to the pure C implementation on processor ARM Cortex-A9 of ZYNQ-7020 platform.