Reviewerly/exHarmony huggingface dataset:
New error function designs for finite-time ZNN models with application to dynamic matrix inversion

The zeroing neural network (ZNN), as a special kind of recurrent neural network (RNN), is often utilized to solve dynamic matrix inversion problems in the many fields recently. In this work, two finite-time ZNN (termed as ZNN-A and ZNN-B) models with the sign-bi-power (SBP) activation function are proposed by designing two novel error functions. The theoretical analysis shows the superior stability and finite-time convergence properties of the ZNN-A and ZNN-B models. Furthermore, three simulative examples show the effectiveness of the proposed ZNN-A and ZNN-B models for finding dynamic matrix inversion and the correctness of the corresponding theorems. To reveal the superior performance of the proposed ZNN-A and ZNN-B models with SBP activation function, the standard ZNN model with the linear activation function is comparatively applied in experiments.
Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning

Natural language processing (NLP) covers a large number of topics and tasks related to data and information management, leading to a complex and challenging teaching process. Meanwhile, problem-based learning is a teaching technique specifically designed to motivate students to learn efficiently, work collaboratively, and communicate effectively. With this aim, we developed a problem-based learning course for both undergraduate and graduate students to teach NLP. We provided student teams with big data sets, basic guidelines, cloud computing resources, and other aids to help different teams in summarizing two types of big collections: Web pages related to events, and electronic theses and dissertations (ETDs). Student teams then deployed different libraries, tools, methods, and algorithms to solve the task of big data text summarization. Summarization is an ideal problem to address learning NLP since it involves all levels of linguistics, as well as many of the tools and techniques used by NLP practitioners. The evaluation results showed that all teams generated coherent and readable summaries. Many summaries were of high quality and accurately described their corresponding events or ETD chapters, and the teams produced them along with NLP pipelines in a single semester. Further, both undergraduate and graduate students gave statistically significant positive feedback, relative to other courses in the Department of Computer Science. Accordingly, we encourage educators in the data and information management field to use our approach or similar methods in their teaching and hope that other researchers will also use our data sets and synergistic solutions to approach the new and challenging tasks we addressed.
Artificial intelligent environments: Risk management and quality assurance implementation

AbstractAbstractThis research not only elaborates literature on AI techniques for risks management but also proposes a new Neuro-Fuzzy workflow process for mitigating identified software risks. This Neuro-Fuzzy process considers high accuracy levels by training the system with Bayesian regulation approach and Fuzzy logic to construct the fuzzy inference system. The proposed workflow may be implemented by MATLAB or Java Frameworks. The authors have implemented the benchmarking of AI softwares for quantitative performance analysis of the competing products as per technology. The intelligent vendor collects all the metrices of the application by scanning its code and donates them anonymously to its customers across the globe. The organizations use these metrices to benchmark their application against particular technology, application properties and the business drivers. The implementation results of the benchmarking process assists in software robustness and defect probability estimation to assure the software quality. The paper contributes in providing a strong foundation for developing new models, techniques and tools for software quality assurance in AI environments by establishing correlation among risk management and quality assurance in AI environments .Subject Classification: 97R50Keywords: (Artificially Intelligent)Risk MitigationFuzzy LogicBenchmarkingQuality Assurance (QA)Artificial Neural Networks (ANN)Bayesian Regulation Technique
Multi-view region-adaptive multi-temporal DMM and RGB action recognition

Abstract Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel multi-view region-adaptive multi-resolution-in-time depth motion map (MV-RAMDMM) formulation combined with appearance information. Multi-stream 3D convolutional neural networks (CNNs) are trained on the different views and time resolutions of the region-adaptive depth motion maps. Multiple views are synthesised to enhance the view invariance. The region-adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multi-class support vector machines. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human–object interaction. Three public-domain data-sets, namely MSR 3D Action, Northwestern UCLA multi-view actions and MSR 3D daily activity, are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.
A Principled Approach Using Fuzzy Set Theory for Passage-Based Document Retrieval

In this article, we present a novel principled approach to passage-based (document) retrieval using fuzzy set theory. The approach formulates passage score combination according to general relevance decision principles. By operationalizing these principles using aggregation operators of fuzzy set theory, our approach justifies the common heuristics of taking the maximum constituent passage score as the overall document score. Experiments show that this heuristics is only the near best, with some fuzzy set aggregation operators stipulated in our approach being better methods. The significance of our principled approach is the applicability of many passage score combination methods, potentially bringing further performance enhancement. Experiments on several text retrieval conference collections demonstrate that our approach performs significantly better than document-based retrieval. While recent works in the literature mostly employ document-based rather than passage-based retrieval due to the common conception that document length normalization solves the problem of varying document lengths, our results show that document length normalization alone is not sufficient, especially in pseudo-relevance feedback retrieval.
Knowledge discovery in medical and biological datasets by integration of Relief-F and correlation feature selection techniques

Feature selection is a pre-processing method that identifies the significant features from high-dimensional data and able to diminish the computational cost of the learning algorithm because of removing the irrelevant and redundant features. It has traditionally been applied in a wide range of prob lems that include biological data processing, pattern recognition, and computer vision. The aim of this paper is to identify the best feature subsets from the benchmark datasets which improve the performance of the classifiers. Existing filter-based feature selection approaches fail to choose the relevant features from the original feature sets. To obtain the tiny subset of relevant features, we have introduced a novel filter-based feature selection method, called ReCFS. The proposed method is a combination of both feature-feature correlation and nearest neighbor weighted features to find an optimal subset of features to minimize correlation among features. The effectiveness of the selected feature subset by proposed method is evaluated by using two classifiers such as Naïve Bayes and K-Nearest Neighbour on real-life datasets. For the diverse performance measurements, the experiments are conducted on eight real-life datasets of varied dimensionality and number of instances. The result demonstrates that the proposed method has found promising feature subsets which improved the classification accuracy over competing feature selection methods
Surface patterning for combined digital image correlation and electron backscatter diffraction in-situ deformation experiments

A considerable amount of information can be gained from combined collection of both digital image correlation (DIC) measurements, and of electron backscattered diffraction (EBSD) data from the same area during in-situ deformation experiments. Such data collection remains a challenge, however, due the influence of the surface markers required for DIC on the EBSD data quality. In this work we demonstrate that by careful control of the sample preparation process, a two-step electropolishing method can be used to generate excellent surface markers for DIC where the markers are integral features on the surface. The method does not require any special equipment and can be used to collect high quality EBSD data and DIC data with sub-micron resolution on the same area during repeated loading as part of in-situ deformation experiments. Combined EBSD and DIC measurements are illustrated in this work for samples of Al deformed in tension to a strain of 10%. Due to the integral nature of the surface markers we demonstrate also that it is possible to perform DIC measurements up to large plastic strains, including the necked region within a tensile sample. A comparison of EBSD and DIC data collected from the same area of a tensile-deformed sample shows that although the EBSD data provide excellent information on the lattice rotations developed during deformation, these data cannot be directly used as a measure of local plastic strain on the scale of the deformation microstructure. The ability to collect both EBSD and DIC data during an in-situ experiment provides, therefore, an opportunity to explore further the complex relationship between local plastic strain heterogeneity and EBSD-based measures of crystal lattice rotation.
AWARE-CNN: Automated Workflow for Application-Aware Real-Time Edge Acceleration of CNNs

This article presents the application-aware real-time edge acceleration of CNNs (AWARE-CNNs) accelerators, which is a novel architecture design methodology for real-time execution of deep learning algorithms on IoT devices. AWARE leverages the reconfigurability of field-programmable gate arrays (FPGAs) to create application-specific architectures customized to match the inherent dataflow of targeted deep neural networks and user-specified real-time requirements. The customized datapath is combined with a customized memory path to guarantee deterministic latency-aware execution over streaming data. For results and evaluation, we have developed a Chisel-based implementation of AWARE-CNN with a full integrative framework for application-specific architecture generation and synthesis (AWARE-CNN architecture compiler). Our results demonstrate the ability to execute Tiny DarkNet and shallow MobileNet inference at 120 frames/s (FPS) and 75 FPS, using only 2.8 and 3.4 W, respectively, on a Xilinx XCZU9EG FPGA. In addition, AWARE-CNN framework's flexibility with respect to the targeted convolutional neural networks and user constraints is validated by targeting additional design points for AlexNet (as a baseline network) and Tiny YOLOv2.
Device-based manipulation technique with separated control structures for 3D object translation and rotation in handheld mobile AR

3D object manipulation is one of the most critical tasks for handheld mobile Augmented Reality (AR), which can contribute towards its practical potential, especially for real-world assembly support. In this context, the study of techniques which are used to manipulate 3D objects is an important research area. This study has developed an improved device-based technique within handheld mobile AR interfaces, to solve the large-range 3D object rotation problem, as well as issues related to position and orientation deviations in manipulating 3D objects. We firstly enhanced the existing device-based 3D object rotation technique (named as HoldAR) with an innovative control structure (named as TiltAR) that utilizes the handheld mobile device tilting and skewing amplitudes to determine the rotation axes and directions of the 3D object. Whenever the device is tilted or skewed in a way that exceeds the threshold values of the amplitudes, the 3D object rotation will start continuously with a pre-defined angular speed per second, to prevent over-rotation of the handheld mobile device. This over-rotation is a common occurrence when using the existing technique to perform large-range 3D object rotations. The problem of over-rotation of the handheld mobile devices needs to be solved since it causes a 3D object registration error and a 3D object display issue, where the 3D object does not appear consistent within the user's range of view. Secondly, restructuring the existing device-based 3D object manipulation technique (named as DI) was done by separating the degrees of freedom (DOF) of the 3D object translation and rotation, to prevent deviations of the 3D object position and orientation, caused by the DOF integration that utilizes the same control structure, which is HoldAR, for both tasks. Next, an improved device-based manipulation technique (named as DS), with better performance on task completion time for 3D object manipulation within handheld mobile AR interfaces, was developed. A pilot test was carried out before other main tests to determine several pre-defined values designed in the control structure of TiltAR. A series of 3D manipulation tasks were designed and developed to benchmark DS (the proposed manipulation technique) with DI (the existing technique) on task completion time (s). Sixteen participants aged 19–24 years old were selected. Each participant had to complete twelve trials, which came to a total 192 trials per experiment for all the participants. Repeated measure analysis was used to analyze the data. The results obtained have statistically proven that DS markedly outpaced DI with significant shorter task completion times in all tasks consisting of different difficulty levels and rotation amounts. Based on the findings, an improved device-based 3D object manipulation technique has been successfully developed to address the insufficient functionalities of the current technique.
A Hybrid Modified Lightweight Algorithm Combined of Two Cryptography Algorithms PRESENT and Salsa20 Using Chaotic System

Cryptography algorithms play a critical role in information technology against various attacks witnessed in the digital era. Many studies and algorithms are done to achieve security issues for information systems. The high complexity of computational operations characterises the traditional cryptography algorithms. On the other hand, lightweight algorithms are the way to solve most of the security issues that encounter applying traditional cryptography in constrained devices. However, a symmetric cipher is widely applied for ensuring the security of data communication in constraint devices. In this study, we proposed a hybrid algorithm based on two cryptography algorithms PRESENT and Salsa20. Also, a 2D logistic map of a chaotic system is applied to generate pseudo-random keys that produce more complexity for the proposed cipher algorithm. The goal of the proposed algorithm is to present a hybrid algorithm by enhancing the complexity of the current PRESENT algorithm while keeping the performance of computational operations as minimal. The proposed algorithm proved working efficiently with fast executed time, and the analysed result of the generated sequence keys passed the randomness of the NIST suite.
Collective disambiguation in entity linking based on topic coherence in semantic graphs

Entity Linking (EL) consists of determinating the entities that best represent the mentions in a document. Mentions can be very ambiguous and can refer to different entities in different contexts. In this paper, we present ABACO, a semantic annotation system for Entity Linking (EL) which addresses name ambiguity assuming that the entity that annotates a mention should be coherent with the main topics of the document. ABACO extracts a sub-graph from a knowledge base which interconnects all the candidate entities to annotate each mention in the document. Candidate entities are scored according to their degree of centrality in the knowledge graph and their textual similarity with the topics of the document, and worst candidates are pruned from the sub-graph. The approach has been validated with 13 datasets and compared with other 11 annotation systems using the GERBIL platform. Results show that ABACO outperforms the other systems for medium/large documents.
Computation-efficient image watermarking architecture with improved performance

This work presents a computation efficient image watermarking very large-scale integration (VLSI) architecture with improved robustness and throughput. The computational complexity is reduced by the incorporation of integer DCT, whereas the throughput is improved by the implementation of a four-stage pipeline architecture. Further, in the present work, the secret watermark bits are inserted in the DCT low-frequency coefficients using a mean adaptive threshold value, which not only results in a minimum degradation to the host image quality but also increases the robustness of the proposed algorithm. The VLSI implementation of the proposed algorithm using CadenceⓇ circuit design tools in 180 nm technology demonstrates an overall power consumption of 4.47 mW and a throughput of 2.5 Gbps that are better than the results reported in the literature.
DREAM Principles and FAIR Metrics from the PORTAL-DOORS Project for the Semantic Web

Articles published in Scientific Data by Wilkinson et al. argued for the adoption of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles of data management without citing any of the prior work published by Taswell. However, these principles were first proposed and described by Taswell in 2006 as the foundation for work on the PORTAL-DOORS Project (PDP) and the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure, and have been published in numerous conference presentations, journal articles, and patents. This work on PDP and NPDS has been continuously available since 2007 from a publicly accessible web site at www.portaldoors.org, and discussed in person at conferences with several key authors of the Wilkinson et al. papers. Paraphrasing without citing the PDP and NPDS principles while renaming them as the FAIR principles raises questions about both the `FAIRness' and the fairness of the authors of the Wilkinson et al. papers. Promoting these principles with the use of the term `metrics', which are not metrics by definition of the term metric as used in most fields of science, also raises questions about their commitment to maintaining consistency of usage for basic terminology across different fields of science as should be expected for terms in ontology mapping with knowledge engineering for the semantic web. Therefore, in the present report, we clarify the origin of their FAIR principles by identifying our PDP and NPDS principles that constitute the historical precedent for their FAIR principles. Moreover, as the comprehensively summarizing phrase for all of our PDP and NPDS principles, we rename them the DREAM principles with the acronym DREAM for Discoverable Data with Reproducible Results for Equivalent Entities with Accessible Attributes and Manageable Metadata. Finally, we define numerically valid quantitative FAIR metrics to monitor and measure the DREAM principles from the perspective of the most important principle, ie, the Fair Acknowledgment of Information Records and Fair Attribution to Indexed Reports, for maintaining fair standards of citation in scholarly research and publishing.
A Method for Knowledge Representation to Design Intelligent Problems Solver in Mathematics Based on Rela-Ops Model

Knowledge-base is a fundamental platform in the architecture of an intelligent system. Relations and operators are popular knowledge in practice knowledge domains. In this paper, we propose a method to represent the model by combining these kinds of knowledge, called the Rela-Ops model. This model includes foundation components consisting of concepts, relations, operators, and inference rules. It is built based on ontology and object-oriented approaches. Besides the structure, each concept of the Rela-Ops model is a class of objects which also have behaviors to solve problems on their own. The processing of algorithms for solving problems on the Rela-Ops model combines the knowledge of relations and operators in the reasoning. Furthermore, we also propose a knowledge model for multiple knowledge domains, in which each sub-domain has the form as the Rela-Ops model. These representation methods have been applied to build knowledge bases of Intelligent Problems Solver (IPS) in mathematics. The knowledge base of 2D-Analytical Geometry in a high-school is built by using the Rela-Ops model, and the knowledge base of Linear Algebra in university is designed by using the model for multiple knowledge domains. The IPS system can automatically solve basic and advanced exercises in respective courses. The reasoning of their solutions is done in a step-by-step approach. It is similar to the solving method by humans. The solutions are also pedagogical and suitable for the learner's level and easy to be used by students studying 2D-Analytical Geometry in high-school and Linear Algebra in university.
The relationship between the body and the environment in the virtual world: The interpupillary distance affects the body size perception

Previous research suggests that the size of one's body is used as a metric to scale the external world. On the other hand, the influence of information from the external world on the perception of body size is unclear. It has been suggested that increased inter-pupillary distance (IPD) leads people to perceive the external world as smaller than it actually is. The present study investigated the effect of the IPD on body size perception, and the relationship between the perceived scale of the body and the external world when the IPD is manipulated. To this end, in a virtual environment, we manipulated the IPD as well as the size and presence of participants' hands, while participant's eye height was increased vertically. Results showed that, when participants' eye height was increased and their hands were enlarged, people with a fixed IPD perceived the size of their body to be large (like a giant) while the external world was perceived to be changed minimally. Alternatively, people with increased IPD perceived that the external world as having shrank, whereas their perception of their body size changed little. However, when a viewers' virtual hands were not shown, the IPD did not affect the individual's percept of body size, although the IPD did affect one's perception of the external world. These results suggest that, when the ratio of the size between one's body and the external world are explicit, the perceived size of one's body is affected by the IPD or perceived scale of the external world that is affected by the IPD.