Reviewerly/exHarmony huggingface dataset:
Training room management based on speech recognition and artificial intelligence

This paper starts from the basic principles of speech recognition, starting from acoustic models and features, and applies deep convolutional neural networks to speech recognition processing. It also introduces the overall process of speech recognition and mainstream algorithms to classify and summarize the speech recognition system. Due to the technological innovation of speech recognition and offline speech recognition systems, we found that traditional systems have low interactivity, poor flexibility, poor recognition command library, and other problems. We provide solutions for this paper according to the specific application environment. This paper introduces the design and implementation of an artificial intelligence teaching management system based on the Internet of Things (IoT) technology. Based on the design of the entire system, the system databases such as the video surveillance module, artificial intelligence security module, and remote control module are designed and realized the functions of these modules. Finally, RFID technology and database technology will be used to realize the informatization of training room personnel and equipment management, and the positioning and storage inspection of training room equipment. Secondly, from the perspective of environmental monitoring of the training room, temperature/ humidity sensors and optical sensors are used to complete the collection of real-time environmental data in the training room, thereby transmitting the data to the host system. It realizes the pairing through the Zigbee wireless communication module. Real-time monitoring of training room and management system. This paper introduces speech recognition and artificial intelligence into the management system of the training room so that the training can be better recorded and managed.
ISO/TC 67 Framework for Developing Semantic, Data/Term Centric Information Model

Abstract ISO/TC 67 Advisory Group 1 – "Digital Implementation" has studied current status on the ISO/IEC Information Model along with current Internet (W3C) standards and systems engineering principles in order to conclude on a possible future ISO/IEC Information Model facilitating production, publication, and utilization of content in standards and other governing type documents on a digital format that is interoperable and consumable by all types of end-user applications and digital markets. The resulting Information Model aims at becoming standardized data/term-centric, directly consumable by semantic applications. This Information Model framework can be applied by: —ISO/IEC to revise its Information Model to facilitate production and publication of content on a semantic, data/term-centric machine-readable and -interpretable format—Government bodies creating and publishing laws and regulations—Companies and their project organizations creating and publishing company- and project-specific governing documents covering projects life cycles—Developers of end-user applications supporting the actual corporate work processes and their projects through their life cycles—Developers of digital markets A globally standardized Information Model facilitating semantic, data/term-centric interoperability is possible to develop, and a framework is provided by this presentation. This Information Model differs significantly from the current ISO/IEC version that is very low-granular and demands massive manual interaction. This Information Model framework presents a granularity level that can facilitate true machine-to-machine interoperability with minimized human interference – in contrast to current manual management of content in standards (and laws, regulations, company governing docs, etc) that is a very resource-consuming process prone to errors.
Design and Implementation of Dancing Information Management System Based on Visual Processing

With the advent of the information age and the continuous updating of modern educational methods, the learning form of schools has developed into a diversified structure, and the status of network teaching in the modern educational mechanism has also been continuously improved, achieving irreplaceable results in modern teaching. The advantages of network teaching in its breadth and ubiquity have been recognized and now accepted by students. At present, most of the schools relax students’ study areas and increase students’ interest in self-directed learning. A good online teaching method is an educational method formed by comprehensive use of modern educational technology. To sum up, it is imperative to develop a system that is suitable for schools to manage many dance resource websites. The dance resource information management platform organically integrates related digital resources around a certain theme to form a resource combination covering text, pictures, audio, video, and other types of materials, specifically related to content associations and subject indexing. This dance digital resource information can be revealed from multiple dimensions through the information system. It is convenient for users to retrieve and browse various information. This method facilitates the interaction between teachers and students, thereby greatly improving the utilization rate of digital information resources. The three-dimensional teaching of dance classroom is realized. Through the platform, a wealth of characteristic dance resource information can be collected from all over the world. Through the system’s reorganization of dance information, value-added services of characteristic dance resources are realized, making the dance material novel and professional. The system has strong operability and practicability to achieve the integration of teaching design and diversification of learning applications.
The Case of SPARQL UNION, FILTER and DISTINCT

SPARQL’s Basic Graph Pattern (BGP) queries are well-researched for query optimisation and RDF indexing techniques. They resemble SQL inner joins and benefit from reorderability of triple patterns in their performance optimisation. But other components of SPARQL such as OPTIONAL, UNION, FILTER, DISTINCT pose more challenges, as they are part of the SPARQL recursive grammar and impose restrictions on the reorderability of triple patterns. These components are important because they help in querying the semi-structured data as opposed to the strictly structured relational data with a stringent schema. In this paper, we use previously published optimisation techniques for BGP-OPTIONAL queries as primitives, and show how they can be used for SPARQL queries with any intermix of UNION, FILTER, and DISTINCT clauses. We mainly focus on the structural aspects of these queries, identify UNION, FILTER, and DISTINCT queries that can use these BGP-OPTIONAL optimisation techniques, and extend some of the previously published theoretical results.
Application of an innovative narrow band vegetation index to corn leaves to explore the characteristics of corn spectra under Cu2+ stress

In this study, an innovative narrow band vegetation index (INBVI) was proposed to explore the sensitive regions of corn leaves spectra under copper stress. In 2016, the spectra and Cu2+ contents of corn leaves under different copper stress concentrations were measured by designing corn stress experiments with different copper gradients. Firstly, we divided the spectra of corn leaves into 11 subintervals, and chose the spectral reflectance which corresponded to the middle wavelength of each subinterval to build INBVI. Then, the Pearson correlation coefficient (r) and Root Mean Square Error (RMSE) were calculated, and we analysed correlation of the INBVI and Cu2+ contents in corn leaves. What’s more, we compared it with the three conventional vegetation indexes of Water Band Index (WBI), Modified Chlorophyll Absorption Ratio Index (MCARI) and Normalized Difference Water Index (NDWI). Finally, the spectra of corn leaves obtained under the same experimental conditions in other year were used to verify the stability and effectiveness of INBVI. The results showed that the four subintervals of Green peak, Red edge, Near valley and Near peak A were sensitive intervals, because the absolute value of r exceeded .9, and the RMSE values were lower than other subintervals and the three conventional vegetation indexes. We verified the above results and found the sensitive intervals were Green peak, Red edge, Near valley and Near peak A, which were consistent with the sensitive subintervals obtained from the experiment data in 2016, and confirmed the effectiveness and stability of INBVI. The INBVI proposed in this paper can be used as a method to monitor copper pollution in corn leaves, and provide some theoretical support for the research of heavy metal pollution in other crops.
One Parameter Discrete Ailamujia Distribution with Statistical Properties and Biodiversity and Abudance Data Applications

The discretization of continuous distributions among researchers become important issue, so several discretization methods exists in the literature for obtaining discrete version of continuous distribution which can be pragmatic to discrete data. The present study proposes the discretization of continuous Ailamujia distribution. Subsequently various statistical properties has been studied including moment generating function, characteristic function, mode, reliability, probability generating function etc. Nature of density function and hazard function has been studied graphically. The technique of maximum likelihood estimation is used to estimate the unknown parameter of the said model. Biodiversity and abundance data applications are provided to flexibility and applicability of the new distribution in ecological studies.
A Contrastive Learning Pre-Training Method for Motif Occupancy Identification

Motif occupancy identification is a binary classification task predicting the binding of DNA motif instances to transcription factors, for which several sequence-based methods have been proposed. However, through direct training, these end-to-end methods are lack of biological interpretability within their sequence representations. In this work, we propose a contrastive learning method to pre-train interpretable and robust DNA encoding for motif occupancy identification. We construct two alternative models to pre-train DNA sequential encoder, respectively: a self-supervised model and a supervised model. We augment the original sequences for contrastive learning with edit operations defined in edit distance. Specifically, we propose a sequence similarity criterion based on the Needleman-Wunsch algorithm to discriminate positive and negative sample pairs in self-supervised learning. Finally, a DNN classifier is fine-tuned along with the pre-trained encoder to predict the results of motif occupancy identification. Both proposed contrastive learning models outperform the baseline end-to-end CNN model and SimCLR method, reaching AUC of 0.811 and 0.823, respectively. Compared with the baseline method, our models show better robustness for small samples. Specifically, the self-supervised model is proved to be practicable in transfer learning.
A Participatory Design Approach to Develop Visualization of Wearable Actigraphy Data for Health Care Professionals: Case Study in Qatar

Several tools have been developed for health care professionals to monitor the physical activity of their patients, but most of these tools have been considering only the needs of users in North American and European countries and applicable for only specific analytic tasks. To our knowledge, no research study has utilized the participatory design (PD) approach in the Middle East region to develop such tools, involving all the stakeholders in the product development phases, and no clear use cases have been derived from such studies that could serve future development in the field.This study aims to develop an interactive visualization tool (ActiVis) to support local health care professionals in monitoring the physical activity of their patients measured through wearable sensors, with the overall objective of improving the health of the Qatari population.We used PD and user-centered design methodologies to develop ActiVis, including persona development, brainwriting, and heuristic walkthrough as part of user evaluation workshops; and use cases, heuristic walkthrough, interface walkthrough, and survey as part of expert evaluation sessions.We derived and validated 6 data analysis use cases targeted at specific health care professionals from a collaborative design workshop and an expert user study. These use cases led to improving the design of the ActiVis tool to support the monitoring of patients' physical activity by nurses and family doctors. The ActiVis research prototype (RP) compared favorably with the Fitbit Dashboard, showing the importance of design tools specific to end users' needs rather than relying on repurposing existing tools designed for other types of users. The use cases we derived happen to be culturally agnostic, despite our assumption that the local Muslim and Arabic culture could impact the design of such visualization tools. At last, taking a step back, we reflect on running collaborative design sessions in a multicultural environment and oil-based economy.Beyond the development of the ActiVis tool, this study can serve other visualization and human-computer interaction designers in the region to prepare their design projects and encourage health care professionals to engage with designers and engineers to improve the tools they use for supporting their daily routine. The development of the ActiVis tool for nurses, and other visualization tools specific to family doctors and clinician researchers, is still ongoing and we plan to integrate them into an operational platform for health care professionals in Qatar in the near future.
Dynamics of Multimodal Families of m-Modal Maps

In this work, we introduce families of multimodal maps based on logistic map, i.e., families of m-modal maps are defined on an interval <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>I</mi> <mo>⊂</mo> <mi>ℝ</mi> </math> , which is partitioned into non-uniform subdomains, with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>m</mi> <mo>∈</mo> <mi>ℕ</mi> </math> . Because the subdomains of the partition are not uniform, each subdomain contains a unimodal map, given by the logistic map, that can have different heights. Therefore, we give the necessary and sufficient conditions for these modal maps present a multimodal family of m-modal maps, i.e., a bifurcation parameter can set a unimodal map, a bimodal map, up to a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>m</mi> </math> -modal map. Some numerical examples are given according to the developed theory. Some numerical examples are given in accordance with the developed theory.
Chapter 8: Conclusion – strategies and policy decisions in the new digital-era governance

This concluding chapter discusses how to create viable technology adoption strategies and policies in the new digital-era governance. Specifically, the chapter elucidates the policy processes and decisions that need to be taken to implement AI, blockchain, and big data solutions in public governance processes. Building on the research insight about the use of digital technologies in the fight against fraud, the chapter expounds on the drivers for the implementation of the AI, big data, and blockchain technologies in administrative decision-making processes. The chapter extrapolates new challenges and issues practitioners need to face in the new digital-era governance, and concludes with a new research agenda for the role of data-processing and new digital technologies in digital public governance.
Enhancing Boundary Attack in Adversarial Image Using Square Random Constraint

An adversarial image is a sample with intentional small perturbations that causes deep learning models to classify the image incorrectly. In the image recognition field, adversarial images have become an attractive research topic because they can efficiently attack many state-of-the-art and even commercial models. The challenge now for any deep learning models is how to find out potentially sophisticated adversarial images and prepare proactive prevention against adversarial attacks. Among various existing adversarial attacks, Boundary Attack, proposed in 2018, is one of the state-of-the-art attack methods due to its efficiency, extreme flexibility, simplicity, and high utilization in real-world applications. However, we found a severe drawback existing in the Boundary Attack. First, when randomizing the direction for the next perturbation, it uses a Gaussian distribution over the entire image space to choose the next movement. This causes losing various useful statistic information from the models, such as the high usage of the convolutional layers. Therefore, in this paper, we aim to investigate an enhancement for the Boundary Attack. In the perturbation direction randomization step, we restrict the perturbation direction in a square shape in the geometrical presentation of the image. Compared to the existing randomization strategy, as described in more detail in Section 1.2, our approach can exploit the nature of most image recognition models originating from the convolutional layers that capture the image features in square patterns. We experimented with our proposed method with the well-known CIFAR-10 image dataset on the ResNet-v2 model. Our experimental result showed that the proposed method could successfully reduce the similarity between the adversarial image and the original image by 41.06% with the same number of queries.
Creating Signature-Based Views for Description Logic Ontologies with Transitivity and Qualified Number Restrictions

Developing ontologies for the Semantic Web is a time-consuming and error-prone task that typically requires the investment of considerable manpower and resources, as well as collaborative efforts. A potentially better idea is to reuse the “off-the-shelf” ontologies, whenever possible, somehow as per certain demands and requirements. A promising way to achieve ontology reuse is through creating views of ontologies, analogous to creating views of databases, with the resulting views focusing on specific topics and content of the original ontologies. This paper explores the problem of creating views of ontologies using a uniform interpolation approach. In particular, we develop a novel and practical uniform interpolation method for creating signature-based views for ontologies specified in the description logic , a very expressive description logic for which uniform interpolation has not been fully addressed. The method is terminating and sound, and computes uniform interpolants of -ontologies by eliminating from the input ontologies the names not used in the view using a forgetting procedure. This makes it the first and so far the only approach to eliminate both concept and (non-transitive) role names from -ontologies. Despite the inherent difficulty of uniform interpolation for this level of expressivity, an empirical evaluation with a prototypical implementation show very good success rates on a corpus of real-world ontologies, and demonstrates clear algorithmic advantage over the state-of-the-art system LETHE. This is extremely useful from the semantic web perspective, as it provides knowledge engineers with a powerful tool to create views of ontologies for ontology reuse.
Application of Image Encryption Algorithm for Wireless Sensor Network in the Security Analysis of Public Big Data

To improve the security of the Wireless Sensor Network (WSN), the image encryption algorithm is studied. Firstly, the shortcomings of current image encryption algorithms are compared. Secondly, based on the chaotic system, the color image encryption algorithm of double chaotic cross-diffusion is built, and the encryption platform is constructed in combination with the current development of intelligent equipment. Finally, the performance of the constructed algorithm and platform is evaluated, and the correlation of adjacent pixel pairs in different color channels and different directions of image encryption is verified. The results indicate that in the designed image encryption platform, the channels of different color are encrypted in different directions, and the encryption effect is good. Compared with the evaluation platform, the practicability of the encryption algorithm is good. After the image is encrypted, the correlation between adjacent pixels is significantly reduced. The image is highly correlated with adjacent pixels in three directions in the first three channels of encryption. After the studied algorithm is encrypted, the correlation coefficient value of adjacent pixel pairs is greatly reduced, and the maximum reduction is 0.97. The information entropy of the red, green, and blue channels before encryption is lower than the information entropy of the three-color channels after encryption. The information entropy of the algorithm is closer to the optimal value of the information entropy. For the index values of Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR), the average UACI of the algorithm is 99.62%, while the average of the UACI of the literature algorithm is 98.33%. The NPCR value of the algorithm is 33.52%, while the NPCR value of the literature algorithm is 31.23%. The encryption and decryption time of the algorithm is shorter than that of the literature algorithm. The constructed algorithm has high efficiency of encryption and decryption. It provides a reference for the enhancement of the security of image data in the WSN.
ParClick: A Scalable Algorithm for EM-based Click Models

Research on click models usually focuses on developing effective approaches to reduce biases in user clicks. However, one of the major drawbacks of existing click models is the lack of scalability. In this work, we tackle the scalability of Expectation-Maximization (EM)-based click models by introducing ParClick, a new parallel algorithm designed by following the Partitioning-Communication-Aggregation-Mapping (PCAM) method. To this end, we first provide a generic formulation of EM-based click models. Then, we design an efficient parallel version of this generic click model following the PCAM approach: we partition user click logs and model parameters into separate tasks, analyze communication among them, and aggregate these tasks to reduce communication overhead. Finally, we provide a scalable, parallel implementation of the proposed design, which maps well on a multi-core machine. Our experiments on the Yandex relevance prediction dataset show that ParClick scales well when increasing the amount of training data and computational resources. In particular, ParClick is 24.7 times faster to train with 40 million search sessions and 40 threads compared to the standard sequential version of the Click Chain Model (CCM) without any degradation in effectiveness.