Reviewerly/exHarmony huggingface dataset:
An Approach for Similarity Vietnamese Documents Detection from English Documents

Currently, many studies are measuring the similarity between documents in a specific language, such as Vietnamese - Vietnamese and English - English. However, situations have recently appeared in the problem of copying articles. For example, English sources have been translated into Vietnamese and edited into their manuscripts. As a result, it is considered cross-language plagiarism. Therefore, this study has applied a new approach: translate from English to Vietnamese documents, then calculate and compare the translated document with documents modified or copied from a translated document. In the study, the main focus is on stages such as Translating English documents into Vietnamese, preprocessing documents, and determining the similarity between documents. The determination of similarity between documents mentioned in this topic is Cosine similarity based on Term Frequency (TF), Inverse Document Frequency (IDF), and word order similarity in the text. Combine these two metrics to give a similar result that is more accurate and convincing. The data is collected in 7 topics with related topics with the number of 15 documents with lengths from 2000 to more than 8000 words, successfully built a document translation integration system based on Google Translate Application Programming Interface (API) and similarity checking, Precision and Recall measures show very positive results over 80%.
Double Security to Colour Images with DCT based Watermarking and Chaos based Encryption

Copyright protection, content authentication, and other applications have benefited from color image watermarking. Color image encryption can be used to secure color images and prevent unauthorized access. To protect a color image’s copyright, the paper proposes a powerful watermarking in CIE La*b* space using a block-based DCT approach. Initially, the color image has been transformed to CIE La*b* space and the watermark has been implanted into the b* channel because any changes made to the b* channel are almost undetectable by the human eye. Before the watermark embedding the b* channel is sub-divided into non-overlapping blocks of size [8$\times$ 8]. By making sure that the difference among two middle band frequencies is +ve within the situation where the encoded value is 1, the algorithm embeds a single bit of the watermark which is binary into the DCT coefficients of each sub-block of the original image. After watermarking, Encryption is done on the watermarked image before sending it to the receiver. This paper proposed a novel cryptographic system by constructing a new chaotic map for producing the key. The proposed method has been compared with different current works, and the experimental outcome shows the superiority of the work presented in this paper.
Classification of Ceramics Based on Improved AlexNet Convolutional Neural Network

As a carrier of Chinese culture, ceramics are of various kinds. In the era of artificial intelligence, the need for class recognition of ceramic types is even more pressing. This study aims to evaluate the performance of deep learning in ceramic type classification using the AlexNet model. By collecting the ceramic image data set and building the AlexNet convolutional network, the image classifier is trained by the training model to achieve accurate ceramic type classification, and the built ceramic 3D model can be retrieved by identifying the ceramic type. This study used 26,595 ceramic-type images. This paper proposes an improved AlexNet network structure, adjusts various hyperparameters, and compares the effects of various activation functions on the classification of ceramics in the AlexNet network. Finally, it is found that the ELU activation function has the best performance in ALexNet, and is accurate The degree reaches 0.9262, and adding multi-layer Dropout has a good effect on preventing over-fitting of the entire network. Finally, we further realize from ceramic classification to ceramic model retrieval.
Sarcasm Detection in News Headlines using Voted Classification

Sarcasm is a sentiment one uses to advocate the opposite of what they mean. Sarcasm is purely context-based, a common phenomena in social media and is inherently difficult to detect, which makes it sometimes difficult for humans to interpret. Over the past couple years, studies on sarcasm detection have been mainly focused on social media content or review analysis which are usually noisy in terms of labels and language. To overcome those issues hence, this paper deals particularly with sarcasm detection in News Headlines. The approach implemented is bag of words analysis using term frequency and n-grams frequency followed by voted classification. We consider comparing different features-based approach and the experimental results are generated by a voted classifier consisting of 7 different classifiers. Result metrics are included in the paper such as accuracy, precision and recall to give a better picture of how efficient the model is.
Towards High-Fidelity Single-View Holistic Reconstruction of Indoor Scenes

We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D shapes of indoor objects with limited geometry quality because of the heavy occlusion of indoor scenes. To solve this, we propose an instance-aligned implicit function (InstPIFu) for detailed object reconstruction. Combining with instance-aligned attention module, our method is empowered to decouple mixed local features toward the occluded instances. Additionally, unlike previous methods that simply represents the room background as a 3D bounding box, depth map or a set of planes, we recover the fine geometry of the background via implicit representation. Extensive experiments on the SUN RGB-D, Pix3D, 3D-FUTURE, and 3D-FRONT datasets demonstrate that our method outperforms existing approaches in both background and foreground object reconstruction. Our code and model will be made publicly available.
A Fractal Dimension Estimator For Multispectral Images

We propose a fractal dimension estimator for multispectral images. The estimator is based on the probabilistic box-counting classical approach extended to the multivariate domain of multiband images. In order to validate the estimator, we propose a fractal model for multispectral images based on the fractional Brownian motion. The generated synthetic multispectral fractal images have 7 statistically-independent spectral bands at specific wavelengths in the visible domain. We apply the proposed approach for the complexity characterization of a widely-known remotely-sensed data set and conclude the article.
Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements.
W-Net: Structure and Texture Interaction for Image Inpainting

Recent literature has developed two advanced tools for image inpainting: appearance propagation and attention matching. However, given the ineffective feature reorganization and vulnerable attention maps, existing works yield suboptimal results with distorted structures and inconsistent contents. Furthermore, we observe that deep sampling layers (DSL) and shallow skip connections (SSC) in U-Net separately promote image structure inference and texture synthesis. To address the above two issues, we devise a W-shaped network (W-Net), which consists of two key components: a texture spatial attention (TSA) module in SSC and a structure channel excitation (SCE) module in DSL. W-Net is a two-stage network, with coarse and refined structures derived at each stage. Meanwhile, the TSA module fills incomplete textures with reliable attention scores under the guidance of coarse structures, which effectively diminishes inconsistency from appearance to semantics. The SCE module rectifies structures according to the difference between coarse structures and refined structures enhanced by texture features. Then the module motivates them to produce more reasonable shapes. Complete textures and refined structures constitute desired inpainted images, as the output of W-Net. Experiments on multiple datasets demonstrate the superior performance of W-Net.
Pramanik - Cloud Based Certificate Repository

A digital certificate is issued by a (usually TRUSTED) certifying authority. The certificates are the basis for providing privacy, authenticity, integration and non-repudiation (referred to as PAIN properties). However, there may be cases when CAs are either negligent, compromised or corrupted enough to issue fake certificates, which can further compromise the entire PKI ecosystem and victimize millions through fake artefacts (fake websites, forged signatures etc.). Several initiatives like Certificate Transparency has been started to ensure proper transparency in the issuance, revocation and audit of the certificate. However, still some caveats need to be addressed. Through this paper, we propose an online system PRAMANIK for certificate validation before issuance, certificate store on a blockchain, certificate validator and a querier (augmented by database operation and API support) to build a robust PKI ecosystem for certificate management.
Decolonizing Design with Technology in Cultural Heritage Contexts - Systematic Literature Review

In this paper we present the results of a systematic literature review investigating the cross-sections of decolonizing design, cultural heritage, and technology. In total, 26 relevant publications were found and examined systematically. Decolonizing design is a growing trend and the most publications were published since 2017. By examining the results, we will introduce the current debates and discourses on using technology in the context of cultural heritage field by using decolonizing design approach. We report that whereas technology itself appears as neutral, it is applied through a lens of values which can be intertwined with politics and power. In order to improve this, the design processes should be integrated with the local and cultural context. This is accomplished through participatory and co-creation approaches. Altogether, whereas the theme of decolonizing design is gaining growing interest, there is still a relatively small number of studies which report applying the approach into the design practice.
Using of an Arithmetic Sequence to Estimate Undetected Existing Circle Choice Locations

Handling an optical mark recognition (OMR) answer sheet faces a problem of shape detection due to defections of color, resolution and shape. A typical method of shape detection thus creates false detection. The false detecting problem is that the general detecting approach fails to detect the locations of the filled shape or detects only the previous detected locations. For this work focuses on a circle choice shape. To solve such problem, an arithmetic sequence is proposed to estimate the existing circle-shaped locations undetected by the common detecting scheme. The proposed technique is verified by experimenting the OMR answer sheet having thirteen options per item. Two conditions are experimented. The first condition arbitrarily fills a circle in each item and the other one leaves the circle in each item unfilled. The experimental results have revealed that the average success rate of the proposed approach in locating the undetected existing circle shapes is 100% for both conditions.
FBIPT: A New Robust Reversible Database Watermarking Technique Based on Position Tuples

Nowadays, data is essential in several fields, such as science, finance, medicine, and transportation, which means its value continues to rise. Relational databases are vulnerable to copyright threats when transmitted and shared as a carrier of data. The watermarking technique is seen as a partial solution to the problem of securing copyright ownership. However, most of them are currently restricted to numerical attributes in relational databases, limiting their versatility. Furthermore, they modify the source data to a large extent, failing to keep the characteristics of the original database, and they are susceptible to solid malicious attacks. This paper proposes a new robust reversible watermarking technique, Fields Based Inserting Position Tuples algorithm (FBIPT), for relational databases. FBIPT does not modify the original database directly; instead, it inserts some position tuples based on three Fields―Group Field, Feature Field, and Control Field. Field information can be calculated by numeric attributes and any attribute that can be transformed into binary bits. FBIPT technique retains all the characteristics of the source database, and experimental results prove the effectiveness of FBIPT and show its highly robust performance compared to state-of-the-art watermarking schemes.
A Web-Based Zakat Recipient Determination System using the Naïve Bayes Algorithm

As the third pillar of Islam, zakat must be fulfilled by Muslims who have met the requirements of Islamic law. In Indonesia, zakat is managed by by the National Amil Zakat Agency (BAZNAS). BAZNAS is obliged to distribute zakat to eligible people who call in Islamic is Mustahiq to receive zakat according to the applicable category rules. Mustahiq (beneficiaries) are groups entitled to receive zakat according to the priority scale arranged in the Zakat program. As a case study, BAZNAS in Pekanbaru city determines the recipient of zakat manually by interviewing each candidate. Factors influencing decisions, such as feelings, emotions, sentiments, and moods, can lead to wrong decisions. Human weakness contrasts with the advantages of artificial intelligence. Therefore, this study aimed to design and build a web-based application system to determine the category of the recipient of zakat automatically and quickly using the Naïve Bayes algorithm. A total of 3222 data in 2015–2017 were obtained in BAZNAS Pekanbaru for training data. Based on the experimental testing results, the highest accuracy was 93.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and F-measure was 96.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in 80% of training data. Based on the test results, it can be concluded that web-based applications using the Naïve Bayes method has the potential to be used in determining the recipient of zakat in BAZNAS Pekanbaru.
Research on Lightweight Network of Human Posture Estimation for Physical Training

Human posture estimate based on openpose is lightweight and allows for real-time analysis on mobile devices. Contrapose the problem that the real-time of physical training and detection process based on computer vision needs to be improved. Firstly, the MobileNets network was used to achieve model lightweight. MobileNets has the advantage of reducing more parameters and faster speed. Then the image was input, and the coordinate information of human skeleton key points was obtained in real time through OpenPose network, and each key point was connected through PAFs. Finally, the joint Angle value was calculated by the selected coordinates of six bone key points, and the obtained joint Angle value was compared with the standard value to judge whether the posture detected in human physical training is normal. Through experimental verification, the lightweight network can reduce the computational complexity by 8-9 times, improve the accuracy by 3%, and realize the detection function of human body physical training posture.
SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection From Multi-view Camera Images With Global Cross-Sensor Attention

Based on the key idea of DETR this paper introduces an object-centric 3D object detection framework that operates on a limited number of 3D object queries instead of dense bounding box proposals followed by non-maximum suppression. After image feature extraction a decoder-only transformer architecture is trained on a set-based loss. SpatialDETR infers the classification and bounding box estimates based on attention both spatially within each image and across the different views. To fuse the multi-view information in the attention block we introduce a novel geometric positional encoding that incorporates the view ray geometry to explicitly consider the extrinsic and intrinsic camera setup. This way, the spatially-aware cross-view attention exploits arbitrary receptive fields to integrate cross-sensor data and therefore global context. Extensive experiments on the nuScenes benchmark demonstrate the potential of global attention and result in state-of-the-art performance. Code available at https://github.com/cgtuebingen/SpatialDETR .
An Adversarial Training Framework for Sentinel-2 Image Super-Resolution

In this work is presented a new adversarial training framework for deep learning neural networks for super-resolution of Sentinel 2 images, exploiting the data fusion techniques on 10 and 20 meters bands. The proposed scheme is fully convolutional and tries to answer the need for generalization in scale, producing realistic and detailed accurate images. Furthermore, the presence of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{L}_{1}$</tex> loss limits the instability of GAN training, limiting possible problems of spectral dis-tortion. In our preliminary experiments, the GAN training scheme has shown comparable results in comparison with the baseline approach.
Convolutional Autoencoder Algorithm for On-Board Image Compression

The growing amount of data currently collected by earth observation satellites requires new processing procedures able to manage huge quantity of information. Among these, data reduction techniques represent a viable solution. In particular, data reduction on-board is significant because allows to save on-board storage space and bandwidth for data transmission to the ground. However, the algorithm used for compression must be able to preserve the key information contained in the acquired data, so that the applicability of the collected information is still guaranteed in the different fields of work. Artificial intelligence, and in particular deep learning, are well suited for this purpose because of their ability to extract valuable information from complex data. This work proposes a lossy image compression procedure based on a Convolutional Autoencoder (CAE) that can be performed on-board the satellite. The images acquired by the sensor can be compressed through the algorithm, stored, and sent to the ground where they are reconstructed, saving space and bandwidth for data transmission. The performance of the compression algorithm will be evaluated in terms of original-reconstructed image similarity and also with regard to the applicability of the reconstructed images to common applicative cases. The algorithm here proposed has been idealized and is currently in development in the context of the European Space Agency's (ESA) PhiSat-2 mission, that aims at demonstrating the advantages of the Artificial Intelligence (AI) on-board for Earth observation applications.
Research on Improved Double RSA Algorithm Based on RSA

Aiming at the exposure of the public key in the RSA algorithm, this paper proposes to use the double RSA algorithm to perform the second encryption of the public key modulus to generate a false modulus, thereby improving the security of the RSA algorithm. Secondly, the problem of generating large prime numbers is a time-consuming problem in the RSA algorithm. In addition to generating large prime numbers in encrypted plaintext, double RSA algorithm also generates large prime numbers that create fake modulus, which undoubtedly increases the time-consuming algorithm. Aiming at the above-mentioned time-consuming problem, this paper proposes an improved random incremental search algorithm, which uses low-order four prime numbers instead of traditional high-order two prime numbers. Experimental data shows that the improved algorithm improves the running speed and has certain feasibility.
Review Mining for Experiential Products Incorporating Word2vec and Review Sentiment Tendencies

In view of the difficulty in mining the characteristics of experience-based products, this paper proposes a method combining LDA and Word2vec to extract product features. This method uses the emotion dictionary to carry out fine-grained analysis on these features, which improves the defect that the effect of topic clustering is not obvious. The result shows that it can effectively mine the feature words with high relevance, and put forward more targeted suggestions for producers.
Cross-Resolution Person Re-Identification via Deep Group-Aware Representation Learning

Person re-identification (Re-ID) aims to identify the same person from samples taken by different cameras. However, in practical application scenarios, due to the quality of the camera equipment and the distance between the camera and the pedestrian, the captured pedestrian images usually have different resolutions, which will cause the mismatch problem of person Re-ID. To mitigate the resolution discrepancy issue, in this paper, we propose a method called deep group-aware representation learning (DGRL) for effective Re-ID. Firstly, We use the residual Transformer block in the feature extraction stage to thoroughly extract richer local and global information from variable resolution shallow images. Then our proposed multi-layered group-aware representation (MGAR) scheme can generate diverse representations different from the main branch, thereby improving the representation capability of the deeply embedded features. In addition, we calculate the kullback leibler divergence loss (KLDivLoss) values on the probability prediction outputs of any two branches, forcing the entire network to be well optimized. Plenty of evaluations on four benchmark datasets have demonstrated the effectiveness of our method.