Reviewerly/exHarmony huggingface dataset:
Colored-QRNet: Fast QR Code Color Image Embedding

Quick Response (QR) codes are widely used to connect offline and online content, and thus many efforts have aimed at improving the visual quality of QR codes to be easily included in publicity designs in billboards and magazines. The most successful approaches, however, are slow since optimization algorithms are required for the generation of each beautified QR code, hindering its online customization. The aim of this paper is the fast generation of visually pleasant and robust QR codes. The proposed framework leverages state-of-the-art deep-learning algorithms to embed a color image into a baseline QR code in seconds while keeping a maximum probability of error during the decoding procedure. Halftoning techniques that exploit the human visual system (HVS) are used to smooth the embedding of the QR code structure in the final QR code image while reinforcing the decoding robustness. Compared to optimization-based methods, our framework provides similar qualitative results but is significantly faster.
“I Feel Like I Am Teaching Myself” - An Exploratory Study of the Factors and Implications of Online Learning

Higher education institutions had to adapt to a new normal quickly brought about by the Covid-19 pandemic. This research set out to explore the factors impacting the adoption of online learning by students and the implications of these factors on future learning. An online survey was conducted and analysed using quantitative and qualitative methods, guided by an extended technology acceptance model. The research was necessary as online learning is anticipated to continue to be used for education by higher education institutions. The findings showed the benefits of learning flexibility of online learning. However, social isolation resulted in low motivation and perception of lowering the quality of education. The research concludes that students need reassurance that they are getting an adequate education through structured learning materials and processes with timely lecturer support. Students need access to peers and must be encouraged to engage. Furthermore, students need to find their optimal learning spaces because, as life-long learners, they need to teach themselves effectively.
Nonuniform illumination correction for underwater images through a pseudo-siamese network

The underwater environment is often poorly illuminated, making the images captured during underwater exploration missions poorly visible. Therefore, artificial light sources are often used to assist in underwater imaging. However, artificial light sources usually change the light conditions to a large extent, resulting in nonuniform illumination. To solve this problem, we propose a pseudo-siamese network that enables the separation of the nonuniform illumination layer and the ideal illumination image. And cascading iterative operation is utilized to achieve enhancement of image details and improve the image quality. To better balance the estimation quality of both, we propose residual reconstruction loss and structure loss as further guidance for network training. In addition, we simulate a nonuniform illumination model to create a dataset containing nonuniform illumination layers and ideally illuminated uniform images to alleviate the problem of insufficient data. The comprehensive experiments show that the algorithm can effectively correct the underwater nonuniform illumination image and recover the details of images better than the existing correction algorithms.
AI-Assisted Search for Missing Children

The fact that half of the 174 children who go missing each day in India are still unaccounted for is incredibly troubling. According to a National Crime Records Bureau (NCRB) data, more than one lakh children (1, 11,569 in all) were reported missing up until 2016, and 55,625 of them were still unaccounted for at the end of the year that was cited by the Ministry of Home Affairs (MHA) in Parliament (LS Q no. 3928, 20-03-2018).In order to tackle and help out Police, AI-Assisted Search for Missing Children a GUI application was created in Python that the police may use to open a new case and find missing children. The missing person’s image that has been supplied is processed on the backend, and important information are recorded. This is kept in a database together with more details like name, parent’s name, age, place, etc. An application using Android is also developed which can be made accessible to the general public. Using this application, a person’s photo can be uploaded if found suspicious by the user. There is an option of submitting the photo obliquely or anonymously. Along with the location, this image is saved in the database. The GUI application applies a machine learning algorithm to compare user-submitted photos with those uploaded by the police. Any matches along with the place where the missing individual was last seen if they are discovered can displayed.
Optimizing the Certainty Factor on K-Nearest Neighbor to Determine the Learning Model during the Pandemic

In the pandemic era, the lecture must use an online class, not an offline class. The online course has a significant problem in learning because students must adapt to model learning their lesson, students have their education, and teachers have their style. So it will happen throughout the pandemic era. It is a big problem in learning. At the end of the semester, the student usually gives feedback for their lecture on what kind of model they want. The data of feedback is put in a database online learning. The data can be used to know what students need and what course must choose a model for online learning. This research is applied to determine the kind of model learning suitable for students in the pandemic era. The determination process uses a k-nn algorithm that classifies all parameters and data and makes decisions based on all parameters and data. The data in this research is from database learning Universitas Negeri Malang and data feedback from students. All data is processed and calculated by k-nn. This application has a maximum accuracy of 88.37% in testing the variation of the k value, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=3,\ \mathrm{k}=5$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=8$</tex> .
EditAR: A Digital Twin Authoring Environment for Creation of AR/VR and Video Instructions from a Single Demonstration

Augmented/Virtual reality and video-based media play a vital role in the digital learning revolution to train novices in spatial tasks. However, creating content for these different media requires expertise in several fields. We present EditAR, a unified authoring, and editing environment to create content for AR, VR, and video based on a single demonstration. EditAR captures the user’s interaction within an environment and creates a digital twin, enabling users without programming backgrounds to develop content. We conducted formative interviews with both subject and media experts to design the system. The prototype was developed and reviewed by experts. We also performed a user study comparing traditional video creation with 2D video creation from 3D recordings, via a 3D editor, which uses freehand interaction for in-headset editing. Users took 5 times less time to record instructions and preferred EditAR, along with giving significantly higher usability scores.
Tracking Student Attendance in Virtual Classes Based on MTCNN and FaceNet

All classes are held online in order to ensure safety during the COVID pandemic. Unlike onsite classes, it is difficult for us to determine the full participation of students in the class, as well as to detect strangers entering the classroom. Therefore, We propose a student monitoring system based on facial recognition approaches. Classical models in face recognition are reviewed and tested to select the appropriate model. Specifically, we design the system with models such as MTCNN, FaceNet, and propose measures to identify people in the database. The results show that the system takes an average of 30 s for learning and 2 s for identifying a new face, respectively. Experiments also indicate that the ability to recognize faces achieves high results in normal lighting conditions. Unrecognized cases mostly fall into too dark light conditions. The important point is that the system was less likely to misrecognize objects in most of our tests.
Noise Reduction Method Based on Wavelet Analysis for RF side-channel Signals

The emergence of the RF side-channel leakage problem in 2018 reveals a significant hidden danger of wireless communication system, meanwhile, because the signal is in the wireless channel, making the collected RF side-channel leakage curve inevitably has the interference of noise, which affects the effectiveness of RF side-channel analysis. In order to reduce the influence of noise, this paper proposes a noise reduction algorithm based on wavelet filtering for RF side-channel signals, and achieves pre-processing of the leakage curve by collecting the leakage curve in a noisy environment, filtering, aligning and noise reduction operations on the curve, and successfully obtains the key information of the target device through key analysis. According to the experimental results, the method can improve the success rate of key analysis.
Quantum Time Complexity and Algorithms for Pattern Matching on Labeled Graphs

The problem of matching (exactly or approximately) a pattern P to a walk in an edge labeled graph $$G = (V,E)$$ , denoted PMLG, has received increased attention in recent years. Here we consider conditional lower bounds on the time complexity of quantum algorithms for PMLG as well as a new quantum algorithm. We first provide a conditional lower bound based on a reduction from the Longest Common Subsequence problem (LCS) and the recently proposed NC-QSETH. For PMLG under substitutions to the pattern, our results demonstrate (i) that a quantum algorithm running in time $$O(|E|m^{1-\varepsilon } + |E|^{1-\varepsilon }m)$$ for any constant $$\varepsilon > 0$$ would provide an algorithm for LCS on two strings X and Y running in time $$\tilde{O}(|X||Y|^{1-\varepsilon } + |X|^{1-\varepsilon }|Y|)$$ , which is better than any known quantum algorithm for LCS, and (ii) that a quantum algorithm running in time $$O(|E|m^{\frac{1}{2}-\varepsilon } + |E|^{\frac{1}{2}-\varepsilon }m)$$ would violate NC-QSETH. Results (i) and (ii) hold even when restricted to binary alphabets for P and the edge labels in G. We then provide a quantum algorithm for all versions of PMLG (exact, only substitutions, and substitutions/insertions/deletions) that runs in time $$\tilde{O}(\sqrt{|V||E|}\cdot m)$$ . This is an improvement over the classical O(|E|m) time algorithm when the graph is non-sparse.
Efficient Noise Generation Protocols for Differentially Private Multiparty Computation

To bound information leakage in outputs of protocols, it is important to construct secure multiparty computation protocols which output differentially private values perturbed by the addition of noise. However, previous noise generation protocols have round and communication complexity growing with differential privacy budgets, or require parties to locally generate non-uniform noise, which makes it difficult to guarantee differential privacy against active adversaries. We propose three kinds of protocols for generating noise drawn from certain distributions providing differential privacy. The two of them generate noise from finite-range variants of the discrete Laplace distribution. For <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta )$</tex-math></inline-formula> -differential privacy, they only need constant numbers of rounds independent of <inline-formula><tex-math notation="LaTeX">$\epsilon,\delta$</tex-math></inline-formula> while the previous protocol needs the number of rounds depending on <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> . The two protocols are incomparable as they make a trade-off between round and communication complexity. Our third protocol non-interactively generate shares of noise from the binomial distribution by predistributing keys for a pseudorandom function. It achieves communication complexity independent of <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> for the computational analogue of <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta )$</tex-math></inline-formula> -differential privacy while the previous protocols require communication complexity depending on <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> . We also prove that our protocols can be extended so that they provide differential privacy in the active setting.
DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep Learning and YOLOv5 Model

Blood cell identification and counting is critical for doctors and physicians nowadays in order to diagnose and treat a variety of disorders. Platelet identification and counting are frequently performed in the context of many types of sickness such as COVID-19 and others. However, it is frequently costly and time intensive. Additionally, it is not widely available. From this vantage point, it is necessary to develop an efficient technical model capable of detecting and counting three fundamental types of blood cells: platelets, red blood cells, and white blood cells. Thus, this study proposes a deep learning-based model based on the YOLOv5 model with a precision of 0.799. The model consists of thre different layers such as backbone, neck and output layer The model is extremely capable of detecting and counting individual blood cells. Doctors, physicians, and other professionals will be able to detect and count blood cells using real-time images. It will significantly minimise the cost and time associated with detecting and counting blood cells by utilizing real-time blood images.
Learning-to-Learn Agent Adaptation Policy for Abstractive Summarization

Neural abstractive text summarization models have gained a lot of popularity. Recent researches on this tasks resort to a single generative model to generate high-quality summaries. However, people adopt adaptive strategies in the process of obtaining text abstracts, such as searching for combinations from textbooks or abstracts from domain knowledge. In this paper, we use dynamic reasoning to address abstractive summarization tasks. To achieve dynamic reasoning for high flexibility when faced with diverse summarizations, we propose a Learning-to-Learn Agent Adaption policy (LLAA-Policy) network to seamlessly adapt the selection over generative agent and retrieval agent, guided by the final summarization reward. Extensive experiments on the TT News Corpus dataset demonstrate the superiority of LLAA-Policy model over previous methods and the automatically learned policy reveals the underlying summarization aspects.
Recent Trends in Image Information Recovery Using Leaked Electromagnetic Wave from Electronic Equipment

Information input/output technology has been employed in various fields in our modern lives, and it is almost impossible for society to function without the devices based on this technology. Therefore, security issues associated with such electronic devices have attracted extensive attention. This study thoroughly reviews various TEMPEST-related research outcomes and trends. It contains both conventional and deep learning-based image processing techniques and research involving realistic scenarios, including concrete walls, information recovery under multiple devices, and reconstruction of color information.
Analysing the Interactions Between Training Dataset Size, Label Noise and Model Performance in Remote Sensing Data

In this work we analyse how training datasize affects the ability of a deep neural network to deal with noisy training labels in a semantic segmentation task with labels from OpenStreetMap. To this end, several versions of the training set were created by introducing varying amounts of label noise, and a model was then trained on subsets of varying size of these versions. The results indicate that the relationship between noise level and model performance is largely independent of the datasize except for very small datasizes where adding label noise has an even more deteriorating effect than usual.
Regional Distributed Photovoltaic Short Term Power Prediction Method Based on Cluster Analysis and Stacking Ensemble Learning

Accurate and reliable photovoltaic short term power prediction is of great significance to the improvement of photovoltaic consumption capacity, the day ahead scheduling and the safe and stable operation of the power grid. To ensure an accurate prediction, in this paper, a regional distributed photovoltaic short term power prediction method based on stacking ensemble learning and cluster analysis is proposed. In the proposed method, several weather patterns are firstly identified by using the K-means++ algorithm based on the historical data. Then, for each weather pattern, all the photovoltaic panels are clustered into several groups based on the k-Shape algorithm, where a prediction model for each photovoltaic cluster is established by employing the Stacking ensemble learning algorithm. Finally, based on the numerical weather forecast (NWP) of the forecast day, we select the best suited weather pattern and obtain the final prediction result by employing the trained prediction model under this weather pattern. The effectiveness of the proposed strategy is verified by the actual dataset.
Development of the Recommended Coffee Shops Application based Twitter Sentiment Analysis

Nowadays, the coffee industry trend is rapidly growing due to the demand of the consumers. It directly affects the increase in the number of coffee shops and coffee cafes. Then, the coffee shops have become the appointment place or the informal meeting area. Moreover, many people visit coffee shops and upload their opinion and feedback on social networks such as Twitter and Facebook. Feedback on social networks can be considered to be one of the decision tools to visit coffee shops. However, the limitation of the data from social networks is unstructured and uncategorized information. Therefore, our paper proposed the application to recommend a coffee shop based on Twitter data. Chiang Mai province is the focused area. This application can visualize the Twitter data on the map with all coffee shops mentioned on Twitter. In addition, the application used sentiment analysis with the help of the Naïve Bayes method. After implementing the sentiment analysis, the application can classify whether it should be recommended or not. The proposed method can perform with high performances in terms of the average accuracy, precision, recall, and F1-score.
Multimodal Violent Video Recognition Based on Mutual Distillation

Violent video recognition is a challenging task in the field of computer vision and multimodal methods have always been an important part of it. Due to containing sensitive content, it is not easy to collect violent videos and resulting in a lack of big public datasets. Existing methods of learning violent video representations are limited by small datasets and lack efficient multimodal fusion models. According to the situation, firstly, we propose to effectively transfer information from large datasets to small violent datasets based on mutual distillation with the self-supervised pretrained model for the vital RGB feature. Secondly, the multimodal attention fusion network (MAF-Net) is proposed to fuse the obtained RGB feature with flow and audio feature to recognize violent videos with multi-modal information. Thirdly, we build a new violent dataset, named Violent Clip Dataset (VCD), which is on a large scale and contains complete audio information. We performed experiments on the public VSD dataset and the self-built VCD dataset. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods on both datasets.
Leveraging Multi-aspect Semantics for Conversation Structure Modeling

With the advent of online chatting systems, conversation structure modeling has attracted increasing attention and can be beneficial to many downstream tasks such as topic summarization and dialog generation. Existing studies mainly focus on modeling the pairwise or thread-level text semantics in multiple interleaved conversations, and don’t fully utilize the rich semantics such as the dialogue actions and speaker relationships. For this purpose, we propose to leverage broader semantics to better disentangle the conversations. Concretely, we devise a novel deep neural networks to capture the text contents, discourse structures and speaker attention in one framework, to better identify the utterance pairs with the “reply-to” relationships. We evaluate our framework on two datasets, the publicly available Ubuntu IRC dataset and our proposed WeChat& QQ dataset. The results show that our model significantly outperforms the state-of-the-art baselines.
Domain Adaptation and Super-Resolution Based Bi-Directional Semantic Segmentation Method for Remote Sensing Images

Image semantic segmentation methods based on convolutional neural network rely on supervised learning with ground truth, thus cannot be well extended to datasets that all of the data are unlabeled. Domain adaptation can solve the problem of inconsistent feature distribution between target and source domains. However, when the spatial resolution of remote sensing images in the source and target domains are not the same, those domain adaptation methods are not effective. In this paper, we propose a bi-directional semantic segmentation method based on super-resolution and domain adaption (BSSM-SRDA). With the help of generative adversarial learning, the method accomplishes semantic segmentation task from a low-resolution labelled data source domain to a high-resolution unlabelled data target domain by reducing differences in resolution and feature distribution. In addition, we propose a self-supervised learning algorithm that helps the domain discriminator to focus on those target data that has not been aligned with the source domain. The experiments demonstrate the superiority of the proposed method over other state-of-the-art methods on two remote sensing image datasets.
Combinational Features with centrality measurements on GCN+LR classification of Adversarial Attacks in homogenous Graphs.

In the present emergence of artificial intelligence, deep learning is at the core of the process. For applications ranging from self-driving vehicles to surveillance and security, computer vision has emerged as the workhorse in the area of artificial intelligence. While deep neural networks have demonstrated phenomenal success (often exceeding the capabilities of humans) in solving complex problems, recent studies have revealed that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that cause a model to predict incorrect outputs, according to the researchers. Such disturbances are typically too subtle to be seen in photographs, but they fully trick the deep learning models, which are trained to detect them. Adversarial assaults are a severe danger to the success of deep learning in practice, and should be taken seriously. This fact has lately resulted in a significant increase in the amount of money being donated in this manner. The primary goal of this work is to improvise a design feature of attack and its defence mechanism with centralities on the each neighbours and its performance measure. The overall reduction of accuracy for the before attack and after attack with a marginal error of 5% percent is observed for each type of the Neighbours set with matrix size or even list of elements repeatedly.