Reviewerly/exHarmony huggingface dataset:
Using cross-examples in viola-jones algorithm for thermal face detection

Detection of the face region is a key step in a face recognition system. Thermal images are widely used in many applications where normal visibility is reduced, impaired or ineffective, such as night surveillance and fugitive searches. However, low spatial resolution brings challenges to face detection in thermal images. Viola-Jones is an object detection method widely used for face detection. The algorithm suffers from missed faces and wrongly detected non-face objects due to low resolution of thermal images. To improve the face detection performance for thermal images, we propose to incorporate cross-examples into our framework. In addition to using negative samples of non-face thermal images, we utilize non-face visible images as part of the negative samples (cross-examples). Cross-examples effectively increase the discriminability between the positive samples and negative samples. Experimental results show that the proposed scheme can effectively reduce the non-face objects and thus improve accuracy of face detection.
Improved Image Super-resolution Using Enhanced Generative Adversarial Network a Comparative Study

Super-resolution using generative adversarial networks is an approach for improving the quality of imaging system. With the advances in deep learning, convolutional neural networks-based models are becoming a favorite choice of researchers in image processing and analysis as it generates more accurate results compared to conventional methods. Recent works on image super-resolution have mainly focused on minimizing the mean squared reconstruction error and able to get high signal-to-noise ratios. But, they often lack high-frequency details and are not as accurate at producing high-resolution images as expected. With the aim of generating perceptually better images, this paper implements the enhanced generative adversarial model and compares with super-resolution generative adversarial model. The qualitative measures such as peak signal-to-noise ratio and structural similarity indices were used to assess the quality of the super-resolved images. The results obtained prove that, enhanced GAN model is able to recover more texture details when compared to super-resolution GAN models.
How to Make Text-to-Speech System Pronounce "Voldemort": an Experimental Approach of Foreign Word Phonemization in Vietnamese

Generating foreign words is one of the hardest tasks for any speech synthesis systems. This work deal with this problem in the case of Vietnamese, a low-resourced language, following an experimental approach. Base on a deep analysis of the usage of foreign words in Vietnamese, various types of pronunciation dictionaries for foreign words was proposed including rule-based phonemization, word-to-syllables mapping, and cross-lingual phone-to-phone mapping. These dictionaries were then used to train different types of grapheme-to-phoneme (G2P) converters. The perceptual evaluation of the Vietnamese synthesized speech confirms that the output of the proposed method can compare favorably with the pronunciation by the human on the unseen foreign words.
Adversarial Defense via Scatter and Alignment

Recently, adversarial examples have imposed a serious threat to the robustness of deep models and raise potential risks in AI-Security areas. To defend adversarial attacks, we develop a novel defense paradigm via embedding scatter and feature alignment to enhance model's robustness. We first propose a margin constraint loss to encourage better discriminative representations of samples, i.e., increasing inter-class distance and reducing intra-class distance simultaneously. Then a novel method is introduced to seek the representation anchors channel-wisely, which quantizes the learned representations into different scatters. During the inference stage, we first align the extracted representation from the input data onto the most similar anchors and use these anchors replace the representations. Finally, we rebuild and feed these aligned representations into the classifier to get the result. This novel paradigm for utilizing representation anchors channel-wisely provides a new viewpoint to understand the CNN's fragility. Extensive experiments on several datasets well demonstrate that the proposed method improves models' robustness without compromising performance.
Guidelines for braindraw conduction: looking at braindraw under the lens of abduction and semiotics

Braindraw is a participatory technique in which participants collaboratively design the interface for a proposed solution. Conducting braindraw in different design situations is still a factor of difficulty for its use by human-computer interaction professionals in the software industry, as there is no set of basic guidelines to be followed. In this sense, this article presents a narrative literature review, a set of exploratory case studies to identify initial elements for braindraw conduction, and a case study to evaluate and improve the initial set of elements for braindraw conduction. We analyse the collected data using the thematic analysis method. This resulted in a set of guidelines for braindraw conduction.
Multi-stage Attention based Visual Question Answering

Recent developments in the field of Visual Question Answering (VQA) have witnessed promising improvements in performance through contributions in attention based networks. Most such approaches have focused on unidirectional attention that leverage over attention from textual domain (question) on visual space. These approaches mostly focused on learning high-quality attention in the visual space. In contrast, this work proposes an alternating bi-directional attention framework. First, a question to image attention helps to learn the robust visual space embedding, and second, an image to question attention helps to improve the question embedding. This attention mechanism is realized in an alternating fashion i.e. question-to-image followed by image-to-question and is repeated for maximizing performance. We believe that this process of alternating attention generation helps both the modalities and leads to better representations for the VQA task. This proposal is benchmark on TDIUC dataset and against state-of-art approaches. Our ablation analysis shows that alternate attention is the key to achieve high performance in VOA.
The Computational Complexity of the Elliptic Curve Factorization Algorithm over Real Field

Abstract Several mathematical concepts from analytics number theory, especially the counting functions (CFs) are employed to solve some modern problems in mathematics, computer sciences, engineering and others. One of them is the factorization problem for integers into two primes. This work proposes an extended factorization problem using the CFs, as main part from analytics number theory, to factor a real number r into two primes p and q . The computations of the points doubling and addition on the elliptic curves E modulo a composite number n are also used in this extension to give new two versions of the factorization over real field R . On the first version, the primes are obtained from doubling and addition on elliptic curve points that are computed modulo composite number which is resulting as output of the CFs algorithm with the input r . Whereas, the primes, on the second version, are got directly from applying the CFs algorithm with r as input. The successful computation of the scalar multiplication kP on the elliptic curve E modulo primes are determined. The computational complexities of extended elliptic curve factorization versions are determined using the counting operations. These operations are elliptic curve and finite field operations. New numerical results on two versions are computed as study cases. The elliptic curve factorization algorithm over R (ECF-R) with two versions, is more efficient for factoring-based cryptography in comparing to the original ECM.
A background-induced generative network with multi-level discriminator for text-to-image generation

Most existing text-to-image generation methods focus on synthesizing images using only text descriptions, but this cannot meet the requirement of generating desired objects with given backgrounds. In this paper, we propose a Background-induced Generative Network (BGNet) that combines attention mechanisms, background synthesis, and multi-level discriminator to generate realistic images with given backgrounds according to text descriptions. BGNet takes a multi-stage generation as the basic framework to generate fine-grained images and introduces a hybrid attention mechanism to capture the local semantic correlation between texts and images. To adjust the impact of the given backgrounds on the synthesized images, synthesis blocks are added at each stage of image generation, which appropriately combines the foreground objects generated by the text descriptions with the given background images. Besides, a multi-level discriminator and its corresponding loss function are proposed to optimize the synthesized images. The experimental results on the CUB bird dataset demonstrate the superiority of our method and its ability to generate realistic images with given backgrounds.
1-Bit Radar Imaging Based on Adversarial Samples

Radar imaging with 1-bit data is attractive thanks to its low storage and transmission burden. Existing 1-bit radar imaging methods cannot satisfactorily suppress the artifacts in the imaging result induced by 1-bit quantization error and noise. In this article, we propose a new 1-bit compressive sensing (CS) based algorithm, i.e., the adversarial-sample-based binary iterative hard thresholding (AS-BIHT) algorithm, to improve the 1-bit radar imaging performance. First, we formulate a parametric model for 1-bit radar imaging with a new adjustable quantization level parameter. The parametric 1-bit radar imaging model updates the imaging scene and the quantization level parameter in an iterative fashion based on adversarial samples. Then, we design a mechanism to generate adversarial samples by attacking the 1-bit radar imaging model to resist the quantization consistency condition, such that forcing quantization consistent reconstruction on adversarial samples mitigates the quantization error and noise. The quantization level parameter is then tuned based on the adversarial samples. In this way, the ability of the model to adapt to echo data contaminated by noise and quantization error is enhanced, and the artifacts are well suppressed. Simulation and experimental results on real radar data demonstrate the effectiveness of the proposed AS-BIHT algorithm in 1-bit radar imaging.
Correlation alignment with attention mechanism for unsupervised domain adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.
Improvement of Contact Tracing with Citizen’s Distributed Risk Maps

The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society’s resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone. The results are compared with the pilot project’s impact testing of the Spanish contact tracing app (RadarCOVID). The paper also depicts the results combining both strategies: contact tracing to detect potential infections and risk maps to avoid movements into conflictive areas. Although some works affirm that contact tracing apps need 60% of users to control the propagation, our results indicate that a 40% could be enough. On the other hand, the elaboration of risk maps could work with only 20% of active installations, but the effect is to delay the propagation instead of reducing the contagion. With both active strategies, this methodology is able to significantly reduce infected people with fewer participants.
THE IMPACT OF THE COVID-19 PANDEMIC ON STUDENTS BEHAVIOUR DURING ONLINE LEARNING AT PELANGI KASIH ELEMENTARY SCHOOL IN JAKARTAv

This study aims to determine the impact of the Covid-19 pandemic on students’ behaviour during online learning. This research was conducted at Pelangi Kasih Elementary School, Jakarta. Respondents were grade 6 students, total of 102 respondents, consisting of 53 girls and 49 boys. The study used is quantitative and exploratory method with an descriptive analisys. The results showed that students behaviours are able to be formed successfully during online learning when teachers and parents communicate authentically more often and work together more cooperatively, and the high unfulfilled needs of interaction among students can affect their psychosocial issues.&#x0D; &#x0D;
Human Aware Robot Motion Planning using RRT Algorithm in Industry4.0 Environment

This work has been carried out to implement the Rapidly exploring Random Tree (RRT) Algorithm for designing a path in a 2D workspace cluttered with different human activities perceived as obstacles of different densities in a collaborative human-robot search operation-based Industry 4.0 environment. RRT based learning is an autonomous path planning algorithm suitable for perceiving environments with differential and non-holonomic constraints. Paths have been generated between an initial point, START to destination point, GOAL amidst uncertain temporal objects met when they are near and far. The algorithm starts to expand the search tree from the START node by randomly exploring the workspace until the GOAL is reached in the presence of these obstacles. The solution is a locus of points in the workspace between source and destination. This has been done for sparsely distributed human activity as obstacles encapsulating waypoint and lower and higher obstacle densities as observed in a real collaborative search environment. The generated paths were evaluated for closeness and impact of different kinds of dynamic obstacle configurations in a real experimental test bed.
A Duplex Spatiotemporal Filtering Network for Video-based Person Re-identification

Video-based person re-identification plays important roles in surveillance video analysis. This paper proposes a duplex spatiotemporal filtering network (DSFN) to re-identify persons in videos. A video sequence is represented as a duplex spatiotemporal matrix. DSFN model containing a group of filters performs filtering at feature levels in both temporal and spatial dimensions, by which the model focuses on feature-level semantic information rather than image-level information as in the traditional filters. We propose sparse-orthogonal constraints to enforce the model to extract more discriminative features. DSFN seeks to characterize not only the appearance features but also dynamic information such as gaits embedded in video sequences and obtains performance improvement as a result. Experimental results show that the proposed method outperforms other comparison approaches.
Map style transfer using pixel-to-pixel model

Abstract The image style transfer is a well-known task and it is applied commonly in the field of artificial intelligence, which converts an image to an image with a specific style without modifying the image content and can be applied to many fields such as changing styled automatically for photos in the cellphone and map wrapping. Recent works focus on a general image style transfer. In this paper, we aim to solve the problem in a navigation application, which is to transfer the satellite maps into map images automatically by deep learning techniques. The methodology we choose for this project is a special GAN named pix2pix model. We decide to use the satellite images as our inputs for the model and let the corresponding Google map images be the output sources. Similar to a traditional GAN, we train the discriminator and generator simultaneously and we attempt to run the training process for 50 epochs in total. PNSR and SSIM are two features we are going to use to test the performance of our results. Finally, the accuracy needs to be improved.
Injection Shilling Attack Tool for Recommender Systems

Recommender systems help people in finding a particular item based on their preference from a wide range of products in online shopping rapidly. One of the most popular models of recommendation systems is the Collaborative Filtering Recommendation System (CFRS) that recommend the top-K items to active user based on peer grouping user ratings. The implementation of CFRS is easy and it can easily be attacked by fake users and affect the recommendation. Fake users create a fake profile to attack the RS and change the output of it. Different attack types with different features and attacking methods exist in which decrease the accuracy. It is important to detect fake users, remove their rating from rating matrix and recognize the items has been attacked. In the recent years, many algorithms have been proposed to detect the attackers but first, researchers have to inject the attack type into their dataset and then evaluate their proposed approach. The purpose of this article is to develop a tool to inject the different attack types to datasets. Proposed tool constructs a new dataset containing the fake users therefore researchers can use it for evaluating their proposed attack detection methods. Researchers could choose the attack type and the size of attack with a user interface of our proposed tool easily.
Image feature extraction and analysis algorithm based on multi-level neural network

Image feature extraction and analysis algorithm based on multi-level neural network is studied in this paper. As a research direction of machine learning, deep learning method has been widely concerned. This method obtains more abstract and effective high-level semantic information by combining low-level features to discover different feature representations of data. This research work aims to design the model based on the multilevel neural network with the implementation of the feature extraction pipeline. The core of deep learning is feature learning, which aims to obtain the hierarchical feature information through a hierarchical network. The framework is validated through the image processing. The proposed algorithm is simulated on on the public database, and the result is efficient.
Mobile Trolley for Medical Applications

This paper provides a mobile apparatus for medical application which comprises a medical trolley, automatic guidance, a wireless controller, human follower using ultrasonic sensors, an obstacle avoiding detection device, an IP camera, a smartphone touch screen controller, an integrated power supply, and a processor. The trolley with stainless steel shelves is ideal for use in clinical environments and operation theatres. The wireless remote drives the trolley to move forward and backward. An IP camera is attached to the trolley to provide visual guidance controlled using a smartphone. The system uses ultrasonic sensors for human follower. Arduino Development Board is used as the processor for the automated mobile trolley system. The implementation of the system can save a large amount of space. The workload of medical workers will be greatly relieved and the working efficiency can be improved.
A Novel Image Retrieval Method for Image Based Localization in Large-Scale Environment

Recently, indoor localization has become a hot research field. Although there are already different localization technologies, image-based localization is attracting more attention from researchers due to its unique advantages. The image retrieval algorithm is still a key part of the existing image-based localization system in a large scale environment. It is a challenging task for image retrieval to achieve a balance among image retrieval ratio, retrieval accuracy, and time complexity under such an environment. The state of the art image retrieval methods for image-based localization system still suffers from these problems. Therefore, we propose a novel image retrieval method aiming at reducing the time complexity without losing the retrieval accuracy. We compare the performance of the proposed method with the popular algorithms including the state of art in the indoor localization field, moreover, they are validated in a public dataset. It demonstrates that our method provides better results, which is verified by the well-known image recognition and visual localization algorithm.
eSTEMIA – Education in Science, Technology, Engineering, Mathematics, Informatics and Arts

The paper presents a new transdisciplinary educational concept based on creative thinking, recommended for teaching science, technology and engineering.
The history of forming libraries of the Russian Academy of Sciences in the Siberian-Far East region and trends of their development in the 2000s

In the context of the active development of information and communication technologies and a changing society in the XXI century, it becomes relevant to comprehend the experience accumulated by the scientific libraries of the Russian Academy of Sciences, which have experienced serious organizational and structural transformations that affect the formation of their ideological and resource base, the construction of adequate models for the organization, preservation, use and promotion of their resources. The article objective is to present the history of forming scientific institution libraries in Siberia and the Far East, emerging and developing the library system of a department of the USSR Academy of Sciences – the Siberian Branch of the USSR Academy of Sciences – Russian Academy of Sciences (SB USSR AS and SB RAS) in 1920–2020 to determine further trends in their activities. To carry out the work, the author used such techniques as statistic, system and factor analysis, modeling, forecasting. The main tasks of the library system of the Siberian Branch of the Russian Academy of Sciences (as well as other libraries of RAS) were to provide information for scientists and specialists of research institutions and preserve the historical, cultural and scientific heritage of the Russian Academy of Sciences. Disintegration of the network connections, the model destruction of centralizing the library community of the Russian Academy of Sciences, which has occurred recent years, required the development of new models of interaction between libraries and adjustments of their functions in the scientific and information space of the region. The authors represents reasoning about the trends in the further development of the libraries of RAS, SB RAS and SPSTL SB RAS as the central library of the system.