Reviewerly/exHarmony huggingface dataset:
Context-Based Structure Mining Methodology for Static Object Re-Identification in Broadcast Content

Technological advancement, in addition to the pandemic, has given rise to an explosive increase in the consumption and creation of multimedia content worldwide. This has motivated people to enrich and publish their content in a way that enhances the experience of the user. In this paper, we propose a context-based structure mining pipeline that not only attempts to enrich the content, but also simultaneously splits it into shots and logical story units (LSU). Subsequently, this paper extends the structure mining pipeline to re-ID objects in broadcast videos such as SOAPs. We hypothesise the object re-ID problem of SOAP-type content to be equivalent to the identification of reoccurring contexts, since these contexts normally have a unique spatio-temporal similarity within the content structure. By implementing pre-trained models for object and place detection, the pipeline was evaluated using metrics for shot and scene detection on benchmark datasets, such as RAI. The object re-ID methodology was also evaluated on 20 randomly selected episodes from broadcast SOAP shows New Girl and Friends. We demonstrate, quantitatively, that the pipeline outperforms existing state-of-the-art methods for shot boundary detection, scene detection, and re-identification tasks.
Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network

In the paper, we propose a novel prediction technique to predict Zernike coefficients from interference fringes based on Generative Adversarial Network (GAN). In general, the task of GAN is image-to-image translation, but we design GAN for image-to-number translation. In the GAN model, the Generator’s input is the interference fringe image, and its output is a mosaic image. Moreover, each piece of the mosaic image links to the number of Zernike coefficients. Root Mean Square Error (RMSE) is our criterion for quantifying the ground truth and prediction coefficients. After training the GAN model, we use two different methods: the formula (ideal images) and optics simulation (simulated images) to estimate the GAN model. As a result, the RMSE is about 0.0182 ± 0.0035λ with the ideal image case and the RMSE is about 0.101 ± 0.0263λ with the simulated image case. Since the outcome in the simulated image case is poor, we use the transfer learning method to improve the RMSE to about 0.0586 ± 0.0035λ. The prediction technique applies not only to the ideal case but also to the actual interferometer. In addition, the novel prediction technique makes predicting Zernike coefficients more accurate than our previous research.
Bidirectional IoT Device Identification Based on Radio Frequency Fingerprint Reciprocity

Existing research on Radio Frequency Fingerprint (RFF) mainly focus on unilateral device identification in one communication direction. In practice, it is difficult for IoT devices to identify the base station due to their hardware insufficiencies. In this paper, a bidirectional device identification method is proposed for IoT scenarios. The inherent reciprocity of the communication pair’s RFFs is exploited to offload the learning process, which is supposed to be proceeded by the IoT device, to the base station. An autoencoder-based RFF reciprocal conversion network is proposed to predict the downlink RFF based on the data samples acquired in the uplink, so that the training process of the downlink identification network can be accomplished by the base station and the computational complexity of IoT devices is reduced. Evaluations with real-world data show that, the IoT devices can achieve a high accuracy to identify the base station using the identification network trained by the base station.
Model of Business Intelligence Applied the Principle of Cooperative Society in the Business Forums

Business forums are activities between individuals and organizations that carry out the transactions on online media or within applications, which spread across countries. Along with the development of information technology towards business intelligence (BI), the business processes carried out in the business forum are modeled specifically in order to create an effort and attempt to follow the indicator and criteria from the industrial revolution 4.0. In this paper, a model is designed to combine three type of principles, namely the business forum, BI and the cooperative principle. Actually, cooperatives have been long abandoned since the existence of conventional and Islamic banking concept but it has kinship principle to divide the profits based on the size of the contribution given. Meanwhile, BI model is designed to obtain a formula from the cooperative principle, namely the residual income from operations where the transaction process is successfully implemented through the application to allocate a portion of the profits to the members based on the specified percent.
An Ensemble-Based Method for Predicting Facebook Check-ins

The world of technology continues to grow, and the use of digital information is giving rise to massive datasets. To find the most popular locations around the globe, it is very challenging to find the places in chunks. Social media provides a huge dataset, and researchers can use this dataset to predict the places where individuals can establish their businesses based on their popularity. In this research work, an ensemble classification strategy has been applied to several classification methods, such as Naïve Bayes, support vector machine, and multilayer perceptron algorithm. With these two test methods, k-cross-fold and training test methods are used to predict the results. The prediction results show that the 10-cross-fold method gives highest accuracy, as compared to the training test method. The ensemble method improves the overall efficiency in location prediction.
Decoherence of GHZ state under three noisy channels in non-inertial frames

In this paper, we investigate the decoherence of GHZ state under three noisy channels in non-inertial frames. The phase flip, the bit flip and the phase damping channels are considered as noisy channels, respectively. By using three-tangle [Formula: see text] as the measurement of entanglement, we numerically calculate the genuine tripartite entanglement of GHZ state under noisy environments in non-inertial frames. Unlike the case of phase damping channel, in the cases of the phase flip and the bit flip ones, we find that the effect of environment cannot only decay the genuine tripartite entanglement, but also revive it.
Simplified facial capture with head mounted cameras

We present a unified pipeline for high-resolution facial capture that replaces the initial traditional seated capture with a head-mounted camera setup. At its core, our approach relies on improving roughly personalized blendshapes by fitting handle vertices, in a Laplacian framework, to depth and image data. Thus, refining the geometry. This pipeline has been used in production to generate high quality animation to train our proprietary marker-based solution, leading to large time and cost savings.
Negative Learning in Ant Colony Optimization: Application to the Multi Dimensional Knapsack Problem

In this paper we continue our recent work on the development of a negative learning component for ant colony optimization, which is a metaheuristic algorithm that is mostly based on positive learning, that is, on learning from positive examples. In particular, we apply our approach to the well-known multi dimensional knapsack problem as a test case. The obtained results show that our negative learning approach significantly outperforms the standard ant colony optimization approach.
PapSmear Image Recording System for Artificial Intelligence Data Collection

Abstract Cervical cancer is one of the most leading causes of death for women worldwide. To reduce the mortality caused by cervical cancer, early screening techniques such as pap smear need to be carried out more extensively. Forthat, the availability of an automatic screening system is essential. In this paper, we proposed a system that can collect the dataset needed to train an Artificial Intelligence (AI) system for the automatic pap smear screening system. The proposed system can be integrated seamlessly to the current pap smear result recording procedure; hence avoiding any possible complication.
Intelligent Control of a Semi-autonomous Assistive Vehicle

A control system for a powered wheelchair is described. The wheelchair is equipped with sensors to help a disabled user to steer their wheelchair. An innovative intelligent control schemes is presented. A model reference controller for veer regulation that can deal with variable operating conditions is presented. It is based on compensating the non-linear terms using an automatic adaptive scheme. The method specifically focuses on the design of a reliable veer controller capable of mitigating for uncertainties such as slopes, bumps, hills, differences in wheels and tires and changes to surfaces (for example one side more uneven than the other). The controller has been designed with a quasi-linear closed-loop behavior so that outer control loops can be added later such as path-following. A single powered wheelchair assistive agent was created to allow for future cooperation between wheelchair systems by sharing information. The work foresees the potential employment of semi-autonomous assistive agents within cooperative wheelchair systems.
XLNet For Knowledge Graph Completion

As the one of databases in artificial intelligence systems, knowledge graph has been widely used nowadays. Although the number of entities in current knowledge graphs has reached tens of millions or even billions level, directed cyclic graphs of their relations and entities composition were still relatively sparse. Knowledge graph completion can make the structure and content of the knowledge graphs completer and richer. In this paper, the knowledge graph completion was converted into classification and scoring tasks, and several knowledge graph completion models based on XLNet were proposed. Besides, a new negative sampling method was also proposed to improve the quality of the negative samples. Several comparative experiments were done for different output layers. Experiments on several benchmark knowledge graphs shown that our methods achieved state-of-the-art performance in common evaluation methods for knowledge graph completion.
Development of a Web-based Jahai – Malay Language Repository

The Digital Jahai - Malay Language Repository is a platform that was created to store and translate Jahai terms into two different languages which are English and Bahasa Melayu. The system was developed to assist the needs to translate a Jahai term using a text input. Previously, the only way to translate Jahai terms to English is by using a corpus in a paper published by Burenhalt. Until recently, no system was developed to translate the Jahai language to Bahasa Melayu. Since ethnic Jahai resides in Malaysia, it is more beneficial if we can develop our own language repository system that will translate the Jahai terms to Bahasa Melayu, and preserve the language using a sustainable format which is digitization. The proposed system is capable of identifying the Jahai terms using International Phonetic Alphabet (IPA) symbols by entering the special characters using a virtual keyboard. Several additional functions such as insert new Jahai terms and listening to Jahai term pronunciation are also provided. In the future, a mobile translation application will be developed to increase the usability of the existing system.
CSWAP Operator as 2×1 Multiplexer to Design CNOT and CCNOT Gates based Carry Select Adder

Quantum computing is emerging as an alternative to the classical computer, based on the fundamental properties of quantum physics. This leads to develop quantum systems to solve complex problems which a classical system is unable to do. In this article, we propose the design of adders using low quantum cost full adder. Initially, full adder is proposed using CNOT and CCNOT gates, and later parallel adder and carry select adder are designed using it. Apart from full adder, additional gates are not required to design parallel adder while CSWAP gates are utilized to propose carry select adder. CSWAP gate is modeled as 2×1 multiplexer and used to select the output from any one among the two parallel adders that constitutes carry select adder. An open source IBM Qiskit tool is used to design and simulate the adder circuits and observed the metrics.
Evolutionary-based Federated Ensemble Learning on Face Recognition

Federated learning, as a distributed training framework of machine learning compared to the traditional training on a single, centralized dataset has been increasingly popular with advantages of addressing privacy, data ownership, data isolation and so on. One of the major challenges of federated learning framework is how to design model aggregation as data owners in the network may be different in data characteristics and communication delay which deceases training efficiency or even make the training invalid. To overcome these issues, we proposed a novel learning strategy based on evolutionary theory (EFEL) where we maintain a group of diversified models rather than a single, complex model and allow them to evolve independently. The proposed federated ensemble learning framework is evaluated on both benchmark and real-world databases and achieves better results than FedAvg and FedFS to train the state the art models in face recognition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Evolving Fuzzy System with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy - eFCE

This paper suggests a new evolving fuzzy approach called eFCE (evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy). The approach uses a single pass learning procedure based on a recursive clustering algorithm with participatory learning and multivariable Gaussian membership functions. The eFCE employs first-order Takagi-Sugeno functional rules that may be added, excluded, merged and/or updated depending on the input data. The rules' antecedent is extracted from the clusters, and the consequent parameters are updated by a recursive algorithm of maximum correntropy. The method to create rules uses a compatibility measure and an arousal index. The compatibility measure employs both Euclidean and Mahalanobis distance. The rules elimination procedure combines age and population to exclude inactive rules. Redundant rules are merged if there is a noticeable overlap between two clusters. The performance of the approach is evaluated using instances of times series forecasting. Computational results and comparisons against alternative state-of-the-art evolving models show that the eFCE has better or comparable performance.
Face Recognition Algorithm based on Traditional and Artificial Intelligence: A Systematic Review

Face recognition is one of the hot topics for researchers in computer vision and image processing. Face recognition is used in the security field such as authentication, for using ATM card in China, social media, and military Feature-based and traditional algorithms are now superseded by artificial neuron networks. Previous algorithms of face recognition have to be integrated with artificial intelligence and neural network for improving accuracy. Neural networks take a huge part in improving facial extraction and detection of facial features and improving accuracy. This study discussed and analyzed some traditional, artificial intelligence, and integrated these two face recognition algorithms. Along with, their advantage, strength, drawbacks, and new challenges in different fields and condition, along with suggestions for future work of authors are mentioned.
A New Technique for Image Enhancement by Adaptive Power-Law Intensity Transformation

This study is devoted to the problem of improving digital image and video in real-time applications. This paper looks at the task of enhancing images in automatic mode without distortions and loss of information. This work addresses the challenge of improving the effectiveness of image enhancement based on the use of the technique of power-law transformation (gamma correction). To that end, we offer a new technique of image enhancement by adaptive power-law transformation based on the APLIT approach using proposed basis functions and thresholds. The proposed method gives results close to the traditional gamma correction but is less costly computationally.
Intelligent Path Planning and Obstacle Avoidance Algorithms for Autonomous Vehicles Based on Enhanced RRT Algorithm

This manuscript studies the intelligent path planning and obstacle avoidance algorithms for autonomous vehicles based on enhanced RRT algorithm. The selection of the optimal path in this research gives priority to security, so the function of the weight value of each cost function is designed according to the threshold of the security cost function. When the value of the security cost function is above the threshold, the security is low. With this replacement, the traditional RRT is implemented with the genetic algorithms and the neural computation model is combined for the accuracy concern. The designed model is tested on the random data sets. Compared with the traditional RRT, the neural network based model and the fuzzy planning based model, the proposed is efficient.
Detecting two photons with one molecule

We apply input-output theory with quantum pulses [AH Kiilerich, K M\o lmer, Phys. Rev. Lett. {\bf 123}, 123604 (2019)] to a model of a new type of two-photon detector consisting of one molecule that can detect two photons arriving sequentially in time. The underlying process is distinct from the usual two-photon absorption process where two photons arriving simultaneously and with frequencies adding up to the resonance frequency are absorbed by a single molecule in one quantum jump. Our detector model includes a Hamiltonian description of the amplification process necessary to convert the microscopic change in the single molecule to a macroscopic signal.
Domain Concept Extraction and Ontology Visualization

Abstract A semantic navigation method based on ontology was proposed to solve the problem of digital library information trek. In this paper, the domain concept was extracted based on the knowledge base, linguistics, combined word length ratio, domain correlation and domain consistency.Finally, the domain ontology visualization scheme was put forward, and the related updating technology, wide-angle and aggregation technology were analyzed.The process of using Touch Graph to realize the navigation of ontology visual information was also analyzed.
Automatic Extraction of Document Information Based on OCR and Image Registration Technology

Recent have witnessed significant advances in scene text detection and recognition. In this paper, we propose a novel pipeline for real-life State Grid image OCR, named Rectification two-branch OCR, that guarantees the robustness and performance of the real scene recognition. Specially, we first consider the influence of image quality and adopt specific preprocessing techniques for different kinds of data, containing both traditional image processing methods and deep learning-based methods. Then, we propose a simple but effective end-to-end neural network based trainable model to further locate and recognize scene text. At last, we combine the image rectification module and OCR module as a new pipeline for the State Grid image OCR. Extensive experiments on several real scene datasets demonstrate the effectiveness of our proposed methods and also prove it can handle more irregular input image such as rotate and crop.
Towards an Artificial Intelligence (AI)-Driven Government in Sultanate of Oman

Many tasks that require human intelligence to perform changed to being executed by artificial intelligence such as voice recognition, image recognition, and various predictions. This study investigates how adopting AI-based technologies could redefine leadership roles and identify the gap of critical leadership competencies of AI-based technologies in Oman's public sector. The study used secondary data sources of four Omani ministries. The results confirm that the work of the leaders in Oman's public sector focuses more on administrative coordination, control, developing strategies, and problem solving. On the other hand, there is little attention given to innovation and focusing on developing people. AI-based technologies enhance leader performance and productivity in many areas such as mindful tech-savvy humanist, fostering systemic intelligence, building trust and innovation, developing creative capabilities, fostering leadership skills, enhancing strategic thinking skills, managing uncertainty, and having creative foresight.
JPEG Fake Media: a provenance-based sustainable approach to secure and trustworthy media annotation

Media assets can easily be manipulated with photo editing software or artificially created using deep learning techniques. This can be done with the intention to mislead, but also for creative or educational purposes. Clear annotation of media modifications is a crucial element to assess trustworthiness. However, these annotations should be attached securly to prevent them from being compromised. In addition, to achieve a wide adoption, interoperability is essential. This paper gives an overview of the media manipulation history, discusses the state-of-the-art and challenges related to AI-based detection methods. The paper then introduces JPEG Fake Media as a provenance-based sustainable approach to secure and trustworthy media annotation. JPEG Fake Media has the objective to produce a standard that can facilitate secure and reliable annotation of media asset creation and modifications. The standard shall support good faith usage scenarios as well as those with malicious intent.
Study and improved data storage in cloud computing using cryptography

The Due to security issues, information over the Internet is becoming important. We have suggested a method to protect critical file data. Cryptography algorithms are an efficient way to encrypt sensitive data. It is a retrieval and transmission system that is readable only by intended users. The low cost and data usability of cloud computing is advantageous. Cloud storage has many advantages at low cost and data connectivity through the Internet. Ensured cloud data security is an important aspect in the cloud computing community, as customers often store confidential information with cloud storage services, but such providers are not trustworthy. So, it remains a challenge to exchange data in a safe way while storing data from an unconfident cloud. Our solution guarantees consumer confidential data protection and privacy by using the AES, RC2 algorithm to store data in a single cloud. The cloud computing concept and transitional discussion under cloud computing will be provided previously proposed. The cryptographic algorithm used in cloud is then addressed and the latest trend for cloud security