Reviewerly/exHarmony huggingface dataset:
Generating Compositional Color Representations from Text

We consider the cross-modal task of producing color representations for text phrases. Motivated by the fact that a significant fraction of user queries on an image search engine follow an (attribute, object) structure, we propose a generative adversarial network that generates color profiles for such bigrams. We design our pipeline to learn composition - the ability to combine seen attributes and objects to unseen pairs. We propose a novel dataset curation pipeline from existing public sources. We describe how a set of phrases of interest can be compiled using a graph propagation technique, and then mapped to images. While this dataset is specialized for our investigations on color, the method can be extended to other visual dimensions where composition is of interest. We provide detailed ablation studies that test the behavior of our GAN architecture with loss functions from the contrastive learning literature. We show that the generative model achieves lower Frechet Inception Distance than discriminative ones, and therefore predicts color profiles that better match those from real images. Finally, we demonstrate improved performance in image retrieval and classification, indicating the crucial role that color plays in these downstream tasks.
Пунктуационные знаки и смайлики – объединяющие и разделяющие функции в цифровой коммуникации

This article aims to give an overview of similarities and differences in the function and use of smileys and emojis on the one hand and punctuation marks on the other hand in digital communication. It becomes clear that the traditional punctuation marks in their current use and the relatively new graphic symbols used in chats, messengers and forums are not so different at all, but show certain similarities, in that they can supplement, modify or even completely replace the written message itself.
Towards a Data Driven Natural Language Interface for Industrial IoT Use Cases

The ubiquitous availability of sensors and smart devices makes IoT networks more and more complex to manage and control. A natural language interface (NLI) would allow users to interact with the devices via human language by translating the user command into a machine-interpretable meaning representation, often called logical forms.Despite the rapid development of conversational interfaces in smart home and personal intelligent assistant use cases, there are limited research and applications in industrial sensor and actuator networks, usually referred to as Industrial Internet of Things (IIoT). In this paper, we show an early phase design principle of a semantic representation to express IIoT device interactions and propose a data-focused workflow of IIoT automation system architecture.
Recognition of Indian Sign Language using Machine Learning Algorithms

Auditory perception enables an individual to sense sound vibrations caused due to the variations in pressure present. In accordance with WHO, 6.3% of the Indian population suffers from hearing disability. People that suffer from hearing impairment communicate using hand gestures. Unfortunately, the vast majority of the people in India are not aware of the semantics of these gestures. To bridge the gap between the people suffering from hearing disabilities and those who are not, we have proposed an Indian sign language Recognition system using machine learning algorithm techniques. Our method utilizes several images of people demonstrating the alphabets in Indian Sign Language. These images are pre-processed, and further, we utilize these obtained images for training and testing our Machine Learning Algorithms. Out of all the six machine learning algorithms that we used, Random Forest Machine Learning Algorithm gave the highest accuracy of 98.44%.
Adaptive Style Transfer Using SISR

Style transfer is the process that aims to recreate a given image (target image) with the style of another image (style image). In this work, a new style transfer scheme is proposed that uses a single-image super resolution (SISR) network to increase the resolution of the given target image as well as the style image and perform the transformation process using the pre-trained VGG19 model. The Combination of perceptual loss and total variation loss is used which results in more photo-realistic output. With the change in content weight, the output image contains different semantic information and precise structure of the target image resulting in visually distinguishable results. The generated outputs can be altered accordingly by the user from artistic style to photo-realistic style by changing the weights. Detailed experimentation is done with different target image and style image pairs. The subjective quality of the stylised images is measured. Experimental results show that the quality of the generated image is better than the state of the art existing schemes. This proposed scheme preserves more information from the target image and creates less distortion for all combinations of different types of images. For more effective comparison, the contour of the stylizing images are extracted and also similarity is measured. This experiment shows that the result images have contour closer to the target images, also measured similarity is found maximum which indicates more preservation of semantic information than other existing schemes.
Ant-Based Generation Constructive Hyper-heuristics for the Movie Scene Scheduling Problem

The task of generation constructive hyper-heuristics concerns itself with generating new heuristics for problem domains via some kind of mechanism that combines low-level heuristic components into new heuristics. The movie scene scheduling problem is a recently developed combinatorial problem for which there are relatively few low-level heuristics. This paper focused on the application of a novel ant-based generation constructive hyper-heuristic to develop new constructive heuristics for the problem. The ant-based generation constructive hyper-heuristic was applied to create components that were themselves produced from existing heuristics and domain knowledge regarding the movie scene scheduling problem. The results of the research demonstrated that the ant-based hyper-heuristic was successful in the domain. It outperformed the existing set of human-derived constructive heuristics across a wide variety of problem classes and over several instances within the movie scene scheduling problem. The success of this research suggests that other hyper-heuristic methods, such as a generation perturbative one, could be applied to the movie scene scheduling problem in the future.
Information Visualization and Design Activism: An Emerging Relationship

Information Visualization is a practice of visual communication that aims to make visible and understandable phenomena whose invisibility, abstraction or complexity defy human perception and knowledge. It involves a visual codification of categories and quantities, relationships and processes, embodied in artefacts such as charts, thematic maps, infographics or interactive data visualizations. This kind of devices are designed in order to enable insights and new interpretations about different subject matters, taking advantage of human perceptual and cognitive faculties. In recent years, Information Visualization has become widespread in several fields of activity, from science to the arts, through journalism and has gained significant media coverage. This growth in the use of visualizations makes it necessary to have critical tools which may allow a better understanding of these visual devices – What do they communicate? Why? For what purpose? This paper presents a set of arguments which relates Information Visualization with Design Activism, a multidisciplinary approach to design that is identified by the ethical motivation of the designer, by the sense of public intervention that underlies it and its counter-narrative or disruptive discourse. Based on the literature review, we arrive at the assumption that it may be possible to characterize an emerging sub-domain that results from the intersection between Information Visualization and Activist Design. The validation of this assumption implies the development of this research in order to identify and characterize visualization projects in which the Motivation, Purpose and Discourse factors can be compatible with those that identify Design Activism.
A Shared Two-way Cybersecurity Model for Enhancing Cloud Service Sharing for Distributed User Applications

Cloud services provide decentralized and pervasive access for resources to reduce the complex infrastructure requirements of the user. In decentralized service access, the implication of security is tedious to match the user requirements. Therefore, cloud services incorporate cybersecurity measures for administering standard resource access to users. In this paper, a shared two-way security model (STSM) is proposed to provide adaptable service security for the end-users. In this security model, a cooperative closed access session for information sharing between the cloud and end-user is designed with the help of cybersecurity features. This closed access provides less complex authentication for users and data that is capable of matching the verifications of the cloud services. A deep belief learning algorithm is used to differentiate the cooperative and non-cooperative secure sessions between the users and the cloud to ensure closed access throughout the data sharing time. The output of the belief network decides the actual session time between the user and the cloud, improving the span of the sharing session. Besides, the proposed model reduces false alarm, communication failures, under controlled complexity.
The future of security in a remote-work environment

Cyber security threats were a concern before the Covid-19 pandemic. But a year after businesses and employees vacated office complexes, the risks of falling victim to a cyber attack have only grown. In an article published by the Journal of Medical Internet Research, researchers found that the number of cyber attacks increased by 500% throughout the pandemic, and by 2021 are on track to cost world businesses $6tr annually. 1
E-Learning in the Developing World

Abstract Medicine is a changing field and so is the art of teaching medicine. To cope up with technology and science is a major challenge. We all have been trained by conventional classroom teaching, which includes seminars, demonstrations, and bedside clinics but should now be aware of e-learning, blended learning, use of smartphones and tablets, webinars, telemedicine, and tele-education. There is lots of medical content in social media (Facebook, Twitter, and LinkedIn), which is very easy to access. This chapter gives an overview of how electronics have changed the way we practice, keep our records, learn, and teach.
Multi-Task Self-Supervised Learning for Script Event Prediction

Most existing approaches to script event prediction rely on manually labeled data heavily, which is often expensive to obtain. To cope with the training data bottleneck, we investigate methods of combining multiple self-supervised tasks, i.e. tasks where models are explicitly trained with automatically generated labels. We propose two self-supervised pre-training tasks:one is End Identification and the other is Contrastive Scoring. Multi-task learning framework is then leveraged to combine these two tasks to jointly train the model. The pre-trained model is then fine-tuned using human-annotated script event prediction training data. Experimental results on the commonly used dataset show that our approach can achieve competitive performance compared to the previous models which are trained with the whole dataset by using just 10% of the training data, and our model trained on the whole dataset outperforms previous models significantly.
A Multi-agent Approach for Graph Classification

In this paper, we propose and discuss a prototypical framework for graph classification.The proposed algorithm (Graph E-ABC) exploits a multi-agent design, where swarm of agents (orchestrated via evolutionary optimization) are in charge of finding meaningful substructures from the training data.The resulting set of substructures compose the pivotal entities for a graph embedding procedure that allows to move the pattern recognition problem from the graph domain towards the Euclidean space.In order to improve the learning capabilities, the pivotal substructures undergo an independent optimization procedure.The performances of Graph E-ABC are addressed via a sensitivity analysis over its critical parameters and compared against current approaches for graph classification.Results on five open access datasets of fully labelled graphs show interesting performances in terms of accuracy, counterbalanced by a relatively high number of pivotal substructures.
An alternative algorithm for the <i>n</i>–Queens puzzle

Abstract In this paper a new method for solving the problem of placing n queens on a n×n chessboard such that no two queens directly threaten one another and considering that several immovable queens are already occupying established positions on the board is presented. At first, it is applied to the 8–Queens puzzle on a classical chessboard and finally to the n Queens completion puzzle. Furthermore, this method allows finding repetitive patterns of solutions for any n .
Visual perception driven collage synthesis

Abstract A collage is a composite artwork made from the spatial layout of multiple pictures on a canvas, collected from the Internet or user photographs. Collages, usually made by skilled artists, involve a complex manual process, especially when searching for component pictures and adjusting their spatial layout to meet artistic requirements. In this paper, we present a visual perception driven method for automatically synthesizing visually pleasing collages. Unlike previous works, we focus on how to design a collage layout which not only provides easy access to the theme of the overall image, but also conforms to human visual perception. To achieve this goal, we formulate the generation of collages as a mapping problem: given a canvas image, first, compute a saliency map for it and a vector field for each sub-region of it. Second, using a divide-and-conquer strategy, generate a series of patch sets from the canvas image, where the salient map and the vector field are used to determine each patch’s size and direction respectively. Third, construct a Gestalt-based energy function to choose the most visually pleasing and orderly patch set as the final layout. Finally, using a semantic-color metric, map the picture set to the patch set to generate the final collage. Extensive experimental and user study results show that this method can generate visual pleasing collages.
Design and Implementation of University Network Education Platform Based on Cloud Computing

Abstract With the continuous progress of science and technology, the information society has also come. The emergence of cloud computing has made China’s information platform more open, and at the same time, it has given unlimited opportunities for the development of all walks of life. The traditional single teaching mode in universities is no longer suitable for the development of modern students. In the internet plus era, universities built a network teaching platform to enrich students’ learning methods. The use of the network teaching platform improved students’ learning interest and stimulated students’ learning potential. Cloud computing itself has obvious characteristics, which guarantees services under various measures. It is outstanding in scalability, virtualization and high versatility, and can be applied in different fields. People can get more educational resources through the network education platform of universities based on cloud computing, so as to make the education of universities exert greater value. Based on this, this paper mainly discusses the construction strategy of university network education platform based on cloud computing.
MUSE: Textual Attributes Guided Portrait Painting Generation

We propose a novel approach, MUSE, to automatically generate portrait paintings guided by textual attributes. MUSE takes a set of attributes written in text, in addition to facial features extracted from a photo of the subject as input. We propose 11 attribute types to represent inspirations from a subject's profile, emotion, story, and environment. Then we design a novel stacked neural network architecture by extending an image-to-image generative model to accept textual attributes. Experiments show that our approach significantly outperforms several state-of-the-art methods without using textual attributes, with Inception Score score increased by 6% and Frechet Inception Distance (FID) score decreased by 11%, respectively. We also propose a new attribute reconstruction metric to evaluate whether the generated portraits preserve the subject's attributes. Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
MIMO-OFDM channel detection algorithm in multi-station and multi-satellite uplink system based on deep learning

In the Earth-star uplink system composed of multiple measurement and control stations and multiple satellites, the multipath effect caused by cloud reflection or refraction and the Doppler effect generated by the relative motion between the measurement and control station and the satellite will cause the injection signal distortion and attenuation. This paper proposes a channel estimation algorithm based on deep learning theory, which is different from the traditional orthogonal frequency-division multiplexing (OFDM) system in which the channel state information is estimated first. And then the estimated channel state information (CSI) is used to design the equalization module. In the process of restoring the transmission signal, this paper designs and uses a five-layer fully connected neural network to restore the useful information transmitted by the measurement and control station to the satellite in an end-to-end manner. The simulation results show that the channel estimation algorithm based on deep learning can effectively reduce the use of pilots, submit spectrum utilization, and reduce the system error rate. When pilots with the same length as the signal are used in OFDM symbols, the bit error rate performance of channel estimation algorithms based on deep learning is better than the traditional Least Squares (LS) algorithm. The performance of the LMMSE (Linear Minimum Mean Square Error) algorithm is equivalent to the proposed algorithm also in terms of bit error rate. When the pilot sequence length is reduced to 1/8 of the OFDM symbol, the algorithm proposed in this paper is far superior to the traditional Channel estimation algorithm. This result can be seen that the algorithm can greatly improve the spectral efficiency by reducing the use of pilots.
Dual-Layer Barcodes

With the emergence of mobile networks and smartphones, Quick Response Code (QR Code) has been widely used in many scenes in life, e.g. mobile payments, advertisement, and product traceability. However, when the encoded information of QR Codes is leaked, the QR Codes can be easily copied, which increases the risk of mobile payment and the difficulty of product traceability. To solve these problems, this paper proposes a novel approach to expand the information channels of QR Code based on invisible data hiding. The proposed architecture consists of an information encoder to hide messages into QR Codes while maintaining the original appearances of the QR codes and an information decoder to extract the hidden messages. To make the hidden messages detectable by smartphones, we use a series of noise layers to process the encoder output between the end-to-end training of the encoder and the decoder. The noise layers simulate the general distortions caused by camera imaging, e.g. noise, blur, JPEG compression, and light reflection. Experimental results show that the proposed method can achieve a high decoding accuracy of the hidden messages without affecting the decoding rate of the QR Codes used as the containers.
Emoticons

In the new age of digital communication, when most of our human-to-human interaction takes place on the internet, new strategies to make up for the lack of nonverbal cues, such as body language or voice tone, needed to be invented. This, in turn, led to the popularization of digital pictograms and ideograms, which have evolved greatly over the past two decades, allowing people to express themselves with a new type of nonverbal cue. Digital pictograms and ideograms developed into the virtual “body language” of the digital age, which, at first glance, might appear understandable without knowing the interlocutor’s language or the background to it. As the phenomenon spread around the world and across different cultures, it became clear that some digital pictograms and ideograms differ. In particular, differences between Western- and Eastern-style pictograms became a focus for researchers. However, even different ‘Eastern-style emoticons’ may create confusion among the users. Two countries known for their extensive use of digital pictograms and ideograms in everyday communication are Japan and Korea. Their cultures are often compared, and although they share numerous characteristics, there are some important linguistic differences between them that should not be overlooked. Of particular interest here are the differences resulting from diversity in digital communication between Japan and Korea, and these have not been paid the attention they deserve. This research aims to address the problem of cultural bias (by users from seemingly similar backgrounds) in interpreting and understanding ‘Eastern-style emoticons’. Using a specially designed survey (a mix of forced choice and open questions), we investigate main differences in the reception of keurim mal in Korea and kaomoji in Japan. Moreover, we attempt to anticipate problems that may emerge from possible misunderstandings (or misinterpretations) of such.
Event-based hand shadow recognition with varied light intensity and background subtraction

In this paper, we propose the use of event-based hand shadow images for the hand gesture recognition problem and we aim at a portable deep learning shadow-based detection application. Such an interaction-based application requires fast sensing, and limited data transmission (since classification is performed at the edge or back-end server). In addition, it needs to easily adapt to different testing environments with varied lighting conditions and environment backgrounds. In order to overcome these limitations, we introduce an image pre-processing step based on special features of event-based cameras, which reduces the information contained in ’gray-scale’ hand shadow images that are needed for classifying gestures, and at the same time reduces the impact of light illuminations and background interference while maintaining a high classification accuracy.