Reviewerly/exHarmony huggingface dataset:
A Deep Learning-Based Segregation of Housing Image Data for Real Estate Application

Pictures have become an important part of our life nowadays. Humans tend to analyze all the images they come across to find which category they belong to and annotate them. This analysis will become difficult for large volumes of data and we introduce a machine learning methodology to work more efficiently to do such classification. The input dataset contains images belonging to different categories of parts of the house for the real estate work thus reducing hectic work of an agent and will increase his working efficiency. The approach to this type of classification algorithm contains a convolution layer network with a fully connected layer. The algorithm for the same approach also comes with an image enhancement technique as the quality of images of the dataset might not be sufficient for the architecture. So, the preprocessing technique ‘contrast limited adaptive histogram equalization (CLAHE)’ has been implemented for the enhancement of images. The image classification results of the CNN-based architecture are superior to other traditional methods used for classification.
Storyboard as One of the Representations of the Scenario Prototype of Computer Games

The work is devoted to the study and improvement of the design, development, and testing of video game storytelling. The existing practices of writing and keeping up-to-date scripts for interactive works have been studied. The definition of a scenario prototype and requirements for its form are formulated. An idea was put forward about the efficiency of automating the creation of a scenario prototype in the form of a generator tool. A vision of such a tool has been drawn up. The impact of such a tool on development order is presented. Implemented a tool component and conducted an experiment that proves its effectiveness with an example such as generating storyboards from the text. Plans for future development have been formulated.
Multimodal encoders and decoders with gate attention for visual question answering

Visual Question Answering (VQA) is a multimodal research related to Computer Vision (CV) and Natural Language Processing (NLP). How to better obtain useful information from images and questions and give an accurate answer to the question is the core of the VQA task. This paper presents a VQA model based on multimodal encoders and decoders with gate attention (MEDGA). Each encoder and decoder block in the MEDGA applies not only self-attention and crossmodal attention but also gate attention, so that the new model can better focus on inter-modal and intra-modal interactions simultaneously within visual and language modality. Besides, MEDGA further filters out noise information irrelevant to the results via gate attention and finally outputs attention results that are closely related to visual features and language features, which makes the answer prediction result more accurate. Experimental evaluations on the VQA 2.0 dataset and the ablation experiments under different conditions prove the effectiveness of MEDGA. In addition, the MEDGA accuracy on the test-std dataset has reached 70.11%, which exceeds many existing methods.
LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models cannot make a proper trade-off between the graph context and the model complexity, which makes them still far from satisfactory. In this paper, we propose a lightweight framework named LightCAKE for context-aware KGE. LightCAKE explicitly models the graph context without introducing redundant trainable parameters, and uses an iterative aggregation strategy to integrate the context information into the entity/relation embeddings. As a generic framework, it can be used with many simple KGE models to achieve excellent results. Finally, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.
Public key authenticated encryption with multiple keywords search using Mamdani system

The public cloud environment has attracted massive attackers to exploit insecure ports and access to data, services and other resources. Techniques, such as Public Key Encryption with Keyword Search (PEKS), could be deployed in cloud security to avoid accidents. PEKS allows users to search encrypted documents by a specific keyword without compromising the original data security. The first PEKS scheme was proposed in 2004, since then, PEKS has been experienced a great progress. Recently, Kazemian and Ma firstly incorporated with Fuzzy Logic technique to PEKS scheme, namely “Public Key Encryption with Multi-keywords Search using Mamdani System (m-PEMKS)”, in order to support Fuzzy Keyword (i.e. “latest”, “biggest”) Search. However, the m-PEMKS scheme has the ability to prevent Off-line Keyword Guessing Attack (OKGA) but it may suffer Inside Keyword Guessing Attack (IKGA). This paper will revisit the m-PEMKS scheme and propose a robust m-PEMKS mechanism. The proposed scheme has the properties of Ciphertext Indistinguishability, Trapdoor Indistinguishability and User Authentication which can prevent OKGA and IKGA. Besides, the proposed scheme supports both Fuzzy Keyword Search and Multiple Keywords Search and therefore, it is more practical and could be applied to the general public networks.
Formation of soft skills among students of higher educational institutions

The objective of the article was to analyze the experience of training and development of soft skills among students of higher education institutions. The methodology used in the study presents a comparative analysis of hard and soft skills, presents the results of student surveys on the advantages of hackathons in the training of soft skills. The conclusions determine that institutions of higher education for the formation of soft skills of students introduce innovative methods and means in the learning process. Hackathons and meetups act as real tools for soft skills training and competitive specialist training in general.
LatentMap: Effective auto-encoding of density maps for spatiotemporal data visualizations

In the study of spatiotemporal data visualization, compression and morphing of density maps are challenging tasks. Many existing methods require adjustment of multiple parameters and rich experience, but still cannot get accuracy or smooth results. In this paper, we propose a GAN-based method (LatentMap) to explore the latent space of density maps, which is an end-to-end method. First, we find that small latent codes can be used as the results of compression, which can greatly save transmission time in a front-end system. We collect density maps to make a dataset. Our model learns from the dataset and samples from the Gaussian distribution to encode and decode density maps. Second, based on the latent codes, we explore the smooth dynamic visualization of density maps, and our method can generate dynamic and smooth results. We show the results of our method in a variety of situations and evaluations from multiple aspects. The results demonstrate the effectiveness and practicality of our approach. Our method has practical applications, such as speed up front-end loading, completing or predicting stream data information and visual query.
Entity Embedding as Game Representation.

Procedural content generation via machine learning (PCGML) has shown success at producing new video game content with machine learning. However, the majority of the work has focused on the production of static game content, including game levels and visual elements. There has been much less work on dynamic game content, such as game mechanics. One reason for this is the lack of a consistent representation for dynamic game content, which is key for a number of statistical machine learning approaches. We present an autoencoder for deriving what we call entity embeddings, a consistent way to represent different dynamic entities across multiple games in the same representation. In this paper we introduce the learned representation, along with some evidence towards its quality and future utility.
On the properties of the classifier in case of significant deviations of blood parameters from the norm

A mathematical model is described and implemented, intended for the numerical study of the ability of the statistical classification method to interpolate and extrapolate. The classifier developed by the authors is based on the polynomial-regression approach and has probabilistic estimates. It is used to assess the state of human health based on the parameters of laboratory analysis of peripheral blood. The blood base is considered with a significant deviation from the norm.
EDUCATION, SOCIAL JUSTICE AND POST-PANDEMIC IN SPAIN

The health crisis caused by the COVID pandemic-19 has had a profound impact on all areas of human life, and especially in education, with consequences that in many cases can be irreversible, specific in terms of social justice and equity, since the current situation seems to have exacerbated educational inequalities. These inequalities are reflected in the lack of equal opportunities for access, teaching and results, in many cases, as a result of the digital divide that became evident when moving from face-to-face education to a virtual modality. That is why this theoretical and descriptive work conducts a review of literature about social justice in education in Spain with projection towards an immediate post-pandemic future. The analysis of the revised work shows that there are still many outstanding challenges that the Spanish education system faces, but perhaps the most important is to correct or alleviate at least the inequality of educational opportunities, bearing in mind that the digital divide is only another side - the most visible - of inequality. Resumen La crisis sanitaria ocasionada por la pandemia de COVID-19 ha impactado profundamente en todos los ámbitos de la vida humana, y muy especialmente en el educativo, con consecuencias que en muchos casos pueden ser irreversibles, especialmente en términos de justicia social y equidad, ya que la situación actual parece haber exacerbado las desigualdades educativas. Esas desigualdades quedan reflejadas en la falta de igualdad de oportunidades de acceso, enseñanza y resultados, en muchos casos, como producto de la brecha digital que se hizo evidente al pasar de una educación presencial a una modalidad virtual. Es por ello que en este trabajo de tipo teórico y descriptivo se realiza una revisión de la literatura sobre el tema de la justicia social en la educación en España con proyección hacia un futuro inmediato de post pandemia. El análisis de los trabajos revisados pone evidencia que quedan aun muchos retos y desafíos pendientes que el sistema educativo español debe afrontar, pero tal vez el más importante es corregir o paliar al menos, la desigualdad de oportunidades educativas, teniendo en cuenta que la brecha digital no es más que otra cara -la más visible- de la desigualdad.
Uncertainty-Guided Robust Training For Medical Image Segmentation

For medical image segmentation tasks, some of foreground objects have more ambiguities than other areas because of confusing appearances. It is critical to seek a proper method to measure such ambiguity of each pixel and use it for robust model training. To this end, we design a Bayesian uncertainty estimate layer, and propose an uncertainty-guided training for standard convolutional segmentation models. In particular, the proposed Bayesian uncertainty estimate layer provides the confidence on each pixel's prediction independently, and works with prediction correctness to obtain the rescaling weights of training loss for each pixel. Through this mechanism, the learning importance of the regions with different ambiguities can be distinguished. We validate our proposal by comparing it with other loss rescaling approaches on medical image datasets. The results consistently show that the uncertainty-guided training brings significant improvement on lesion segmentation accuracy.
Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks

As cryptographic tokens and altcoins are increasingly being built to serve as utility tokens, the notion of useful work consensus protocols is becoming ever more important. With useful work consensus protocols, users get rewards after they have carried out some specific tasks useful for the network. While in some cases the proof of some utility or service can be provided, the majority of tasks are impossible to verify reliably. To deal with such cases, we design “Proof-of-Prestige” (PoP)—a reward system that can run directly on Proof-of-Stake (PoS) blockchains or as a smart contract on top of Proof-of-Work (PoW) blockchains. PoP introduces “prestige,” which is a volatile resource that, in contrast to coins, regenerates over time. Prestige can be gained by performing useful work, spent when benefiting from services, and directly translates to users minting power. Our scheme allows us to reliably reward decentralized workers while keeping the system free for the end-users. PoP is resistant against Sybil and collusion attacks and can be used with a vast range of unverifiable tasks. We build a simulator to assess the cryptoeconomic behavior of the system and deploy a full prototype of a content dissemination platform rewarding its participants. We implement the blockchain component on both Ethereum (PoW) and Cosmos (PoS), provide a mobile application, and connect it with our scheme with a negligible memory footprint. Finally, we adapt a fair exchange protocol allowing us to atomically exchange files for rewards also in scenarios where not all the parties have Internet connectivity. Our evaluation shows that even for large Ethereum traces, PoP introduces sub-millisecond computational overhead for miners in Cosmos and less than 0.013$ smart contract invocation cost for users in Ethereum.
A CMA‐ES‐Based Adversarial Attack Against Black‐Box Object Detectors

Object detection is one of the essential tasks of computer vision. Object detectors based on the deep neural network have been used more and more widely in safe-sensitive applications, like face recognition, video surveillance, autonomous driving, and other tasks. It has been proved that object detectors are vulnerable to adversarial attacks. We propose a novel black-box attack method, which can successfully attack regression-based and region-based object detectors. We introduce methods to reduce search dimensions, reduce the dimension of optimization problems and reduce the number of queries by using the Covariance matrix adaptation Evolution strategy (CMA-ES) as the primary method to generate adversarial examples. Our method only adds adversarial perturbations in the object box to achieve a precise attack. Our proposed attack can hide the specified object with an attack success rate of 86% and an average number of queries of 5, 124, and hide all objects with a success rate of 74% and an average number of queries of 6, 154. Our work illustrates the effectiveness of the CMA-ES method to generate adversarial examples and proves the vulnerability of the object detectors against the adversarial attacks.
Clustering for Private Interest-based Advertising

We study the problem of designing privacy-enhanced solutions for interest-based advertisement (IBA). IBA is a key component of the online ads ecosystem and provides a better ad experience to users. Indeed, IBA enables advertisers to show users impressions that are relevant to them. Nevertheless, the current way ad tech companies achieve this is by building detailed interest profiles for individual users. In this work we ask whether such fine grained personalization is required, and present mechanisms that achieve competitive performance while giving privacy guarantees to the end users. More precisely we present the first detailed exploration of how to implement Chrome's Federated Learning of Cohorts (FLoC) API. We define the privacy properties required for the API and evaluate multiple hashing and clustering algorithms discussing the trade-offs between utility, privacy, and ease of implementation.
Melanoma in the blink of an eye: Pathologists’ rapid detection, classification, and localization of skin abnormalities

Expert radiologists can quickly extract a basic “gist” understanding of a medical image following less than a second exposure, leading to above-chance diagnostic classification of images. It is currently unclear whether this pattern of results is applicable to pathology, another medical imaging domain demanding visual diagnostic interpretation. To further characterize the rapid interpretation medical images, this study examined eye movements and diagnostic decision-making when pathologists were briefly exposed to digital whole slide images of melanocytic skin biopsies. Twelve resident (N = 5), fellow (N = 5), and attending pathologists (N = 2) with experience interpreting dermatopathology briefly viewed 48 cases presented for 500 ms each, and we tracked their eye movements towards abnormalities, their ability to classify images as containing or not containing invasive melanoma, and their ability to localize critical image regions. Results demonstrated rapid shifts of the eyes towards critical abnormalities, high diagnostic sensitivity and specificity, and surprisingly high accuracy localizing critical diagnostic regions. Furthermore, when pathologists fixated critical regions with their eyes, they were subsequently more likely to successfully localize that region on an outline of the image. Results are discussed relative to models of medical image interpretation and innovative methods for monitoring and assessing expertise development during medical education and training.
New Binary Quantum Codes Derived from Quasi-Twisted Codes with Hermitian Inner Product

In this paper, a special class of two-generator quasi-twisted (QT) codes with index 2 will be presented. We explore the algebraic structure of the class of QT codes and the form of their Hermitian dual codes. A sufficient condition for self-orthogonality with Hermitian inner product is derived. Using the class of Hermitian self-orthogonal QT codes, we construct two new binary quantum codes [[70, 42, 7]]2, [[78, 30, 10]]2. According to Theorem 6 of Ref.[2], we further can get 9 new binary quantum codes. So a total of 11 new binary quantum codes are obtained and there are 10 quantum codes that can break the quantum Gilbert-Varshamov (GV) bound.
Provably Strict Generalisation Benefit for Equivariant Models

It is widely believed that engineering a model to be invariant/equivariant improves generalisation. Despite the growing popularity of this approach, a precise characterisation of the generalisation benefit is lacking. By considering the simplest case of linear models, this paper provides the first provably non-zero improvement in generalisation for invariant/equivariant models when the target distribution is invariant/equivariant with respect to a compact group. Moreover, our work reveals an interesting relationship between generalisation, the number of training examples and properties of the group action. Our results rest on an observation of the structure of function spaces under averaging operators which, along with its consequences for feature averaging, may be of independent interest.
Robust Cross-Scene Foreground Segmentation in Surveillance Video

<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Training only one deep model for large-scale cross-scene video foreground segmentation is challenging due to the off-the-shelf deep learning based segmentor relies on scene-specific structural information. This results in deep models that are scene-biased and evaluations that are scene-influenced. In this paper, we integrate dual modalities (foregrounds’ motion and appearance), and then eliminating features without representativeness of foreground through attention-module-guided selective-connection structures. It is in an end-to-end training manner and to achieve scene adaptation in the plug and play style. Experiments indicate the proposed method significantly outperforms the state-of-the-art deep models and background subtraction methods in un-trained scenes – LIMU and LASIESTA. Source Code is available at: https://github.com/WeiZongqi/HOFAM
eDify: Enhancing Teaching and Learning Process by Using Video Streaming Server

The study investigates the nature and degree of influence of the range of application usability variables on the learning experience of the students at a specific institution of higher education institution in Oman. The study was carried out via eDify implementation encompassing the usability variables and learning experience of the students. Literature does little to suggest the aforementioned relationship in the context of the Omani higher education. The current study would reveal the variables that are critical to effective technology-based learning of the students. The implications generated through the study would allow the institution involved in the study to effectively implement the variables required for enhanced teaching and learning. The methodology used in the study is divided into an exploratory and the main research. Principal component analysis and a range of regression analyses are conducted to test the relationships between the independent and the dependent variable, “learning experience”. Results suggest that the usability variables have both positive and significant effects on the dependent variable of the learning experience. One usability variable that stands out more than others is the usage of mobile media.
Research on the development trend of foreign education based on machine learning and artificial intelligence simulation analysis

The development trend of foreign education is affected by many factors, so its future development trend is difficult to judge. Therefore, it is necessary to simulate and analyze the development trend of foreign education through artificial intelligence. According to actual needs, based on artificial intelligence algorithms, this paper builds artificial intelligence simulation analysis model to realize the simulation analysis of foreign education development. Moreover, starting from the overall design architecture of the online education platform, this paper builds functional modules, uses the machine learning constructed in this paper for data training and data prediction, and outputs prediction results. In order to study the performance reliability of the model, we predict and judge the development trend of foreign education and determine the model reliability through empirical judgment. The research results show that the model constructed in this paper has a certain effect.
Cooking in the dark: a mixed reality empathy experience for the embodiment of blindness

In the context of promoting a sense of empathy for the difference in people without disabilities, we propose a gaming experience that allows users to embody having a visual impairment. By occluding the user’s vision and providing spatialized audio and passive haptic feedback, allied with a speech recognition digital assistant, our goal is to offer a multi-sensory experience to enhance the user’s sense of embodiment inside a mixed reality blindness simulation. Inside the game environment, while expecting a guest to arrive, the player is required to cook a meal completely in the dark. Being aided solely by their remaining senses and a digital assistant, players must go through several tasks as to prepare dinner in time, risking to lose a love interest.