Reviewerly/exHarmony huggingface dataset:
Mathematical Model of Curriculum Development Taking into Account Individual Preferences of Students

The research is aimed at improving the effectiveness of network interaction management in the implementation of educational programs of higher education institutions through the use of intelligent methods and algorithms for decision support. The obtained results allow to improve the management of network interaction between universities in the modern system of higher education through the use of intelligent decision-making support tools when building and updating of individual educational plans of students enrolled in the online learning program and determining the most optimal combination of individual training plans from the standpoint of the needs of the students and performance of restrictions on resources of universities.
Enhanced Artistic Image Style Transfer Using Convolutional Neural Networks

Have you ever wondered how applications like Prisma and other artistic applications work, we input the image from the camera roll to application software and then we select the design to extract the image with the selected artistic design which is a completely different from the initial style? In the context of Artificial Intelligence this is also called style transfer. We use convolutional neural networks in the artistic style transfer, style transfer basically transfers the images by mixing it style of the another images. CNN is sub-branch of neural networks which is very useful in classification of the images it also recognizes the images, they spot the objects in the images with the human faces which empower the automated robots. We use 64,128,512 filters to change the artistic feature of the image. VGG is visual geometry group which can give most success arte of clustering of 93% with only 7% of error which also can be rectified by taking certain measurements. We recreate the images whose features mix the selected convolution layer of the input content image. By mixing the image with selected convolution layer we can construct the beautiful artistic image.
Signature-Based Video Watermarking using SVD DTCWT

With the development in the electronics and communication industry, i.e., super high-speed internet connectivity and super-fast hardware and software, it is not so hard to get access to most of the content on the internet. Most of the multimedia content on the internet, either it is copyrighted or not, is on the fingertips; because of some websites and torrent sites. Cinema industry is on its knees because of this much illegal distribution of millions of copies of unreleased movies, pirated copies of movies. Therefore, to limit the unlawful distribution of multimedia content, watermarking techniques have been used for years. In this paper, a blind video watermarking technique based on Dual-Tree Complex Wavelet Transform (DTCWT), Singular Value Decomposition (SVD), and chrominance channel, is presented, which uses a signature-based approach for authentication.
MRLS-TPS-Based Nonrigid Image/Surface Deformation under Manifold Regularization Constraint

This paper solves the problem of nonrigid deformation based on labeled and unlabeled feature points between two images or surfaces. The principle is to obtain the labeled deformation control point pairs on the correspondence images and the unlabeled feature control points in the source image, and estimate the deformation function between the deformation control point pairs for each pixel or voxel. To achieve a deformation function with more structure information preserved, we consider the nonrigid deformation function estimating as a semi-supervised learning problem, which exploits the manifold regularization to preserve the intrinsic geometry information of the points contained in the object. Moreover, we choose to define the nonrigid deformation function in a reproducing kernel Hilbert space and a closed-form solution can be derived. To reduce the computation complexity, we also adopt a sparse approximation to realize a fast implementation. The method can be applied to 2D image deformation and 3D surface deformation, where extensive experiments on the data show that our methods are superior to state-of-the-art methods.
“Early earth” structural data from the Memve'ele area in the northwestern Congo Craton (Ntem complex-southwestern Cameroon)

Structural data, meso‑ and micro-photographics were collected from Archean basement of the Memve'ele area (Ntem Complex, southwestern Cameroon). The analyses were acquired using field and laboratory investigations. Meso-photographics were obtained by a camera Canon SX160 IS, 16X digital zoom, HD 16.0 Mega pixels. Micro-photographics were carried out by electronic microscope Olympus BX60 type with a camera and entire thin section picture scan. Structural data were acquired by a topochaix compass type and stereographic data were obtained by a stereonet program. The data presented in this paper are further interpreted and discussed in the Ntomba et al., 2020 [1].
A Data Integrity Verification Scheme Based on Blockchain and Blind Homomorphic Tags

With the advent of big data, users’ data is usually outsourced to the cloud. However, users will lose the absolute control over the data and its integrity is hard to be guaranteed. Currently, the most effective way to detect data corruption in the cloud is through data integrity verification that usually relies on third party. However, the third party is not always credible. This paper proposes a data integrity verification scheme based on blockchain and blind homomorphic tags to tackle the over-reliance on the third party auditor. Firstly, our approach is explored to weaken the centralization of the third party through the blockchain technology. Secondly, the smart contract is used to control the access of different auditors, and this allows users to change auditors freely. Lastly, blind homomorphic tags are proposed to avoid recomputing of tags when users change auditors. Based on the experiment results, our proposed scheme is more credible and has a higher recognition rate under the same computational overhead when compared with other mechanisms.
Local-hidden-state models for T-states using finite shared randomness

The study of local models using finite shared randomness originates from the consideration about the cost of classically simulating entanglement in composite quantum systems. We construct explicitly two families of local-hidden-state (LHS) models for T-states, by mapping the problem to the Werner state. The continuous decreasing of shared randomness along with entanglement, as the anisotropy increases, can be observed in the one from the most economical model for the Werner state. The construction of the one for separable states shows that the separable boundary of T-states can be generated from the one of the Werner state, and the cost is 2 classical bits.
Deep Convolutional Neural Network with Wavelet Decomposition for Automatic Modulation Classification

In cognitive radio, signal recognition is an important technology and modulation recognition plays a key role in it. With the development of artificial intelligence, deep learning algorithms applied in automatic modulation recognition have developed quickly, whereas they usually depend on a large number of labeled samples for training. Few samples directly affect the network convergence, which will lead to network overfitting and cannot achieve good results. The loss of prior information makes feature extraction more difficult. In this paper, we propose a wavelet-decomposition-based algorithm for modulation recognition to solve the small sample size problem. To obtain rich information relatively, we adopt the wavelet function to analyze signals from multiple scales, extract the time domain features of the signals without any prior information by Residual Blocks, and fuse these features by attention blocks. In order to reduce the risk of overfitting, we use the Batch Normalization layer, Global Average Pooling, and Additional Random Noise to improve robustness. The proposed algorithm has a classification accuracy of 95.6% with only 20 samples per category when the SNR is 20dB, which outstrips other classical methods under the same condition.
Dataflow and microarchitecture co‐optimisation for sparse CNN on distributed processing element accelerator

Accelerators that utilise the sparsity of both activation data and network structure of convolutional neural networks (CNNs) have demonstrated efficient processing of CNNs with superior performance. Previous research studies have shown three critical design concerns when designing accelerators for sparse CNNs, including data reuse, parallel computing performance, and effective sparse computation. These factors were each used in the previous accelerator designs, but none of the designs have considered all the factors at the same time. This study provides analytical approaches and experimental results to reveal the insight of accelerator design for sparse CNNs. The authors have shown that the architectural aspects need to be all considered to avoid performance pitfalls, including their mutual effects. Based on the proposed analytical approach, they proposed enhancement techniques and co-designed among the factors discussed in this study. The improved architecture shows up to 1.5× data reuse and/or 1.55× performance improvement in comparison with state-of-the-art sparse CNN accelerators while still maintaining equal area and energy cost.
Financial Shared Service Model Based on Block Chain and Evaluation

In the innovative environment of “big wisdom moving cloud”, the implementation of financial sharing services across regions, boundaries or group companies can better enhance the competitiveness of enterprises. However, in the implementation, there are some problems such as unreasonable total score structure, poor sharing effect of finance, layer upon layer approval and return, and poor sharing effect of local dependent business. Based on this, this paper proposes the block chain +financial sharing mode, which divides the entire financial sharing system into three modules, changes the hierarchical operation into flat operation, liberates labor and saves costs. After the implementation, the balanced scorecard was used for evaluation.
Detection-free framework for cabinet switch state recognition

In this paper, we propose a novel detection-free framework for cabinet switch state recognition. Different from prior works which adopt object detection or detection followed by per-switch recognition, our approach processes the image as a whole. Specifically, a semantic segmentation model is used to generate a coarse semantic map as a temporary result, which will be further refined by an object counting module. Moreover, for higher efficiency, we augment the Fully Convolutional Network (FCN) by introducing a hierarchical feature aggregation structure, forming a lightweight yet effective model called HFA-FCN. The experimental results show that the proposed pipeline outperforms those based on object detection, especially in hard cases: images are low-quality, targets are densely distributed or distorted. To the best of our knowledge, we are the first to formulate the task of switch state recognition as a task of semantic segmentation and object counting.
FTR-NAS: Fault-Tolerant Recurrent Neural Architecture Search

With the popularity of the applications equipped with neural networks on edge devices, robustness has become the focus of researchers. However, when deploying the applications onto the hardware, environmental noise is unavoidable, in which errors may cause applications crash, especially for the safety-critic applications. In this paper, we propose FTR-NAS to optimize recurrent neural architectures to enhance the fault tolerance. First, according to real deployment scenarios, we formalize computational faults and weight faults, which are simulated with Multiply-Accumulate (MAC)-independent and identically distributed (i.i.d) Bit-Bias (MiBB) model and Stuck-at-Fault (SAF) model, respectively. Next, we establish a multi-objective NAS framework powered by the fault models to discover high-performance and fault-tolerant recurrent architectures. Moreover, we incorporate fault-tolerant training (FTT) in the search process to further enhance the fault tolerance of the recurrent architectures. Experimentally, C-FTT-RNN and W-FTT-RNN we discovered on PTB dataset have promising fault tolerance for computational and weight faults. Besides, we further demonstrate the usefulness of the learned architectures by transferring it to WT2 dataset well.
Shaping Deep Feature Space Towards Gaussian Mixture for Visual Classification

The softmax cross-entropy loss function has been widely used to train deep models for various tasks. In this work, we propose a Gaussian mixture (GM) loss function for deep neural networks for visual classification. Unlike the softmax cross-entropy loss, our method explicitly shapes the deep feature space towards a Gaussian Mixture distribution. With a classification margin and a likelihood regularization, the GM loss facilitates both high classification performance and accurate modeling of the feature distribution. The GM loss can be readily used to distinguish the adversarial examples based on the discrepancy between feature distributions of clean and adversarial examples. Furthermore, theoretical analysis shows that a symmetric feature space can be achieved by using the GM loss, which enables the models to perform robustly against adversarial attacks. The proposed model can be implemented easily and efficiently without introducing more trainable parameters. Extensive evaluations demonstrate that the method with the GM loss performs favorably on image classification, face recognition, and detection as well as recognition of adversarial examples generated by various attacks.
Study of GAN-based image reconstruction for diffractive optical systems

In previous works, the generative adversarial network (GAN) was successfully used to remove the image-wise color distortion of images captured by diffractive optics. It showed good image quality for a test sample. However, some reconstruction artifacts were produced by GAN for real scene images. In the paper, we study the nature of these artifacts. We show how overexposure and cross-like markers affect the occurrence of artifacts.
Blockchain and the Personal Data Protection Act 2010 (PDPA) in Malaysia

Blockchain is a time stamped ledger that is used to keep immutable records. This technology has gained immense popularity due to the use of its decentralized architecture in cryptocurrency platforms. Blockchain has been increasingly adopted in other sectors due to its ability to ensure data integrity. The increasing use of blockchain by the public has made it become a subject to data privacy laws. Based on the study conducted by other researchers, there are features of blockchain that conflict with certain elements in data privacy laws. The European Union's General Data Protection Regulation (GDPR), which becomes a model for data privacy act of many other countries, has been identified to be incompatible with blockchain. One of the research works in the area of blockchain is to figure out how blockchain can be made to comply with such privacy laws. In Malaysia, blockchain is still relatively new and a study needs to be done to evaluate its compatibility with Personal Data Protection Act 2010 (PDPA). Hence, the aim of this paper is to identify the gaps between blockchain and PDP A in terms of their compatibility and to propose solutions to bridge the gaps. Based on the gaps identified, the paper proposed the use permissioned or private blockchain, off-chain storage and stealth address to enable a blockchain application to be compliant with PDPA.
Research on Person Re-Identification Method Based on Fine-tune ResNet50 Network

Aiming at the problem that the unreasonable design of the traditional network structure may easily affect the performance of person re-identification, this paper provides a method for person re-identification based on fine-tuning ResNet50 network. This method builds a model based on the ResNet50 network structure. In the model training stage, the data set is preprocessed, the data enhancement method is adopted to avoid over-fitting, select the appropriate loss function abate the influence of the gradient disappearance, and use the cosine distance measurement function to complete the spatial distance calculation and similarity ranking between sample. The experimental results show that the designed new method can improve the performance of person re-identification to a certain extent.
Using Basic Natural Language Processing for Effective Project Closeout Process

Project closeout process is a crucial stage to complete every contractual obligation between the project stakeholders. However, the closeout process usually result in non-adding value activities to satisfy the contract's requirement. In many cases of the project closeout process, key stakeholders work in a negative environment to finish the project's requirements by generating and executing a list of punch list items. Many contractors consider the execution of punch list items including rework or missing work as non-value added activities. This perspective can create a strained relationship between the project's stakeholders including subcontractors and clients. Therefore, this study aims at improving the project closeout phase by analyzing textual data of punch list item recorded by one construction company. The text analysis aims at determining the most recurring punch list items across different projects. Based on the results, a proactive approach to manage punch list items during the project closeout process is proposed to effectively minimize the number of punch list items for a given project. One case study is conducted to test the validity of the proposed approach. It is expected that the proposed approach will help contractors to formally document the project closeout phase as well as predict and proactively address defects in a timely manner.
The Utilisation of Information Systems for VSAT Development in Rural Areas

One problem that is still common in Indonesia is the problem of the distribution of Internet network infrastructure. Meanwhile, as is well known, the internet has many roles in human life and especially in the technological advancements for the development of a country. To overcome this, several operators have developed internet networks using Very Small Aperture Terminal (VSAT) telecommunications technology for rural areas. VSAT be a choice because it has several advantages compared to other telecommunications media. In the VSAT development process, operators and customers do all the processes manually so that it requires a relatively long time. Therefore, this research will discuss the use of information systems to help the VSAT development process. With the change of the system manually into an information system, for example, based on web applications or with the internet. Therefore it is hoped that all activities in the system can make it easy for operators to run VSAT builder projects. Other than that, by using information systems, the companies can be connected with consumers and sellers, thus enabling operators of VSAT development service providers to market their services more easily and efficiently. In this research will provide the best flowchart for system information that can be used for VSAT development.
Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval

Traditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect the geometric structure of the original data. For this purpose, we introduce discrete hashing methods and propose a novel non-negative matrix factorization to generate binary codes from the original data. In this paper, we construct an affinity graph to encode the geometrical structure of the original data, and the learned binary code subspace achieved by matrix factorization respects the structure. The proposed problem can be formulated as a mixed integer optimization problem. Therefore, we transform it into several sub-problems including an integer optimization problem, two convex problems with the non-negative constraint and a quadratic programming problem. Optimizing each sub-problem alternately until we achieve a local optimal solution. Image clustering and retrieval on image datasets show the excellent performance of our method in comparison to other dimensional reduction methods.
Can micro interactions in user interfaces affect their perceived usability?

User interface elements such as buttons and controls should appear tangible and engender affordance even though these elements are behind a 'layer of glass'. Visual and motion cues can bridge this gap by acknowledging the user's input immediately and using animation in ways that look and feel like direct manipulation akin to real world physics. Hence, user interface buttons can mimic real world interaction with common physical objects. Simply put, you can add clarity to user interactions through visual responses to the user's input using 'micro interactions. To date, user interface animations have been used by smart phone operating systems to provide user feedback and to show fluidity between user interface transitions as opposed to instant flickering between screens which is somewhat unnatural. This paper aims to determine the effect that micro interactions has on the perceived usability of smart phone apps.
A Model-View-ViewModel (MVVM) Application Framework for Hearing Impairment Diagnosis - Type Dependency Architecture

Around 466 million people worldwide (over 5% of the world's population) have disabling hearing loss, and out of these 34 million are children. Estimates suggest that by 2050, over 900 million people worldwide will have disabling hearing loss. The annual global cost of unaddressed hearing loss amounts to US$ 750 billion. Early detection of hearing loss can reduce its impact on an individual's life in addition to saving a huge cost. This paper, the second in a series of three papers, presents the type dependency architecture of an open-source application framework for hearing impairment diagnosis. The framework is built using the Model-View-ViewModel (MVVM) pattern which separates the development of graphical user interface (GUI) from the development of business and back-end logic. Some of the benefits of the MVVM pattern include reusable components, independent development of GUI and business or back-end logic, flexibility to modify GUI without having to change business or back-end logic, ease of unit testing, and reduced maintenance overhead. The proposed framework along with the open-source code makes it possible to easily extend the application functionality thus enabling other researchers and practitioners to develop their own versions of hearing loss diagnosis applications. The proposed software was evaluated by an otolaryngologist and found to be very beneficial in assisting a clinician to reach a hearing impairment diagnosis conclusion more methodically, swiftly and accurately.