Reviewerly/exHarmony huggingface dataset:
Hybrid of Deep Learning and Word Embedding in Generating Captions: Image-Captioning Solution for Geological Rock Images

Captioning is the process of assembling a description for an image. Previous research on captioning has usually focused on foreground objects. In captioning concepts, there are two main objects for discussion: background object and foreground object. In contrast to the previous image-captioning research, generating captions from the geological images of rocks is more focused on the background of the images. This study proposed image captioning using a convolutional neural network, long short-term memory, and word2vec to generate words from the image. The proposed model was constructed by a convolutional neural network (CNN), long short-term memory (LSTM), and word2vec and gave a dense output of 256 units. To make it properly grammatical, a sequence of predicted words was reconstructed into a sentence by the beam search algorithm with K = 3. An evaluation of the pre-trained baseline model VGG16 and our proposed CNN-A, CNN-B, CNN-C, and CNN-D models used BLEU score methods for the N-gram. The BLEU scores achieved for BLEU-1 using these models were 0.5515, 0.6463, 0.7012, 0.7620, and 0.5620, respectively. BLEU-2 showed scores of 0.6048, 0.6507, 0.7083, 0.8756, and 0.6578, respectively. BLEU-3 performed with scores of 0.6414, 0.6892, 0.7312, 0.8861, and 0.7307, respectively. Finally, BLEU-4 had scores of 0.6526, 0.6504, 0.7345, 0.8250, and 0.7537, respectively. Our CNN-C model outperformed the other models, especially the baseline model. Furthermore, there are several future challenges in studying captions, such as geological sentence structure, geological sentence phrase, and constructing words by a geological tagger.
Towards East Asian Facial Expression Recognition in the Real World: A New Database and Deep Recognition Baseline

In recent years, the focus of facial expression recognition (FER) has gradually shifted from laboratory settings to challenging natural scenes. This requires a great deal of real-world facial expression data. However, most existing real-world databases are based on European-American cultures, and only one is for Asian cultures. This is mainly because the data on European-American expressions are more readily accessed and publicly available online. Owing to the diversity of huge data, FER in European-American cultures has recently developed rapidly. In contrast, the development of FER in Asian cultures is limited by the data. To narrow this gap, we construct a challenging real-world East Asian facial expression (EAFE) database, which contains 10,000 images collected from 113 Chinese, Japanese, and Korean movies and five search engines. We apply three neural network baselines including VGG-16, ResNet-50, and Inception-V3 to classify the images in EAFE. Then, we conduct two sets of experiments to find the optimal learning rate schedule and loss function. Finally, by training with the cosine learning rate schedule and island loss, ResNet-50 can achieve the best accuracy of 80.53% on the testing set, proving that the database is challenging. In addition, we used the Microsoft Cognitive Face API to extract facial attributes in EAFE, so that the database can also be used for facial recognition and attribute analysis. The release of the EAFE can encourage more research on Asian FER in natural scenes and can also promote the development of FER in cross-cultural domains.
Comparative Analysis of Photosynthetically Active Radiation Models Based on Radiometric Attributes in Mainland Spain

The aims of this work are to present an analysis of quality solar radiation data and develop several hourly models of photosynthetically active radiation (PAR) using combinations of radiometric variables such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) from their dimensionless indices atmospheric clearness index (kt), horizontal diffuse fraction (kd), and normal direct fraction (kb) together with solar elevation angle (α). GHI, DHI, and DNI data with 1-minute frequencies in the period from 2016 to 2021 from CEDER-CIEMAT, in a northern plateau, and PSA-CIEMAT in the southeast of the Iberian Peninsula, were used to compare two locations with very different climates according to the Köppen—Geiger classification. A total of 15 multilinear models were fitted and validated (with independent training and validation data) using first the whole dataset and then by kt intervals. In most cases, models including the clearness index showed better performance, and among them, models that also use the solar elevation angle as a variable obtained remarkable results. Additionally, according to the statistical validation, these models presented good results when they were compared with models in the bibliography. Finally, the model validation statistics indicate a better performance of the interval models than the complete models.
Deep-Learning-Based Complex Scene Text Detection Algorithm for Architectural Images

With the advent of smart cities, the text information in an image can be accurately located and recognized, and then applied to the fields of instant translation, image retrieval, card surface information recognition, and license plate recognition. Thus, people’s lives and work will become more convenient and comfortable. Owing to the varied orientations, angles, and shapes of text, identifying textual features from images is challenging. Therefore, we propose an improved EAST detector algorithm for detecting and recognizing slanted text in images. The proposed algorithm uses reinforcement learning to train a recurrent neural network controller. The optimal fully convolutional neural network structure is selected, and multi-scale features of text are extracted. After importing this information into the output module, the Generalized Intersection over Union algorithm is used to enhance the regression effect of the text bounding box. Next, the loss function is adjusted to ensure a balance between positive and negative sample classes before outputting the improved text detection results. Experimental results indicate that the proposed algorithm can address the problem of category homogenization and improve the low recall rate in target detection. When compared with other image detection algorithms, the proposed algorithm can better identify slanted text in natural scene images. Finally, its ability to recognize text in complex environments is also excellent.
A new non-entangled quantum secret sharing protocol among different nodes in further quantum networks

As an important branch of quantum secure multi-party computation, quantum secret sharing (QSS) can distribute secret information among dishonest network nodes without revealing the secrets. In this study, a new four-party QSS protocol based on locally indistinguishable orthogonal product (LIOP) states is first proposed for quantum network communication. Then, the general multiparty QSS model based on LIOP states will be expanded. Combined with the property of LIOP states and obfuscating operation, the source node can send the secrets to different destination nodes in the quantum network. Accordingly, it is proven that the destination nodes have to work together to recover the shared secrets against some existing attacks. Furthermore, no entangled resources and complicated operations are required in the presented protocol. We hope the results could make positive effects to the development of quantum secure communication in the future.
Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud

Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative method in GNNs, in the context of computer vision, utilize conventional Convolutional Neural Networks (CNNs) to process data supported by graphs. This paper proposes a one-stage GCN approach for 3D object detection and poses estimation by structuring non-linearly distributed points of a graph. Our network provides the required details to analyze, generate and estimate bounding boxes by spatially structuring the input data into graphs. Our method proposes a keypoint attention mechanism that aggregates the relative features between each point to estimate the category and pose of the object to which the vertices of the graph belong, and also designs nine degrees of freedom of multi-object pose estimation. In addition, to avoid gimbal lock in 3D space, we use quaternion rotation, instead of Euler angle. Experimental results showed that memory usage and efficiency could be improved by aggregating point features from the point cloud and their neighbors in a graph structure. Overall, the system achieved comparable performance against state-of-the-art systems.
Symboleo2SC

Smart contracts (SCs) are software systems that monitor and control the execution of legal contracts to ensure compliance with the contracts' terms and conditions. They often exploit Internet-of-Things technologies to support their monitoring functions, and blockchain technology to ensure the integrity of their data. Ethereum and business blockchain platforms, such as Hyperledger Fabric, are popular choices for SC development. However, there is a gap in the knowledge of SCs between developers and legal experts. Symboleo is a formal specification language for legal contracts that was introduced to address this issue. Symboleo specifications directly encode legal concepts such as parties, obligations, and powers. In this paper, we propose a tool-supported method for translating Symboleo specifications into smart contracts. We have extended the current Symboleo IDE, implemented the ontology and semantics of Symboleo into a reusable library, and developed the Symboleo2SC tool to generate Hyperledger Fabric code exploiting this library. Symboleo2SC was evaluated with three sample contracts. The results shows that legal contract specifications in Symboleo can be fully converted to SCs for monitoring purposes. Moreover, Symboleo2SC helps simplify the SC development process, saves development effort, and helps reduce risks of coding errors.
Knowledge organization in LAMs

IntroductionTo be useful and retrievable, collections of publications, documents, artifacts, and specimens in libraries, archives, and museums (LAMs) need to be feasibly organized and presented.This is done in a variety of ways.Books in libraries are sorted on shelves in repositories and described in, and accessible through, catalogues and indexes; documents in archives are organized and retrievable through a variety of search protocols and tools; objects in museums are organized in storerooms and described and displayed in catalogues and various exhibitions.These doings go by the name knowledge organization (KO).Due to different commissions, traditions, and material conditions, LAM institutions have developed diverse routines, techniques, and tools to work with KO.LAM institutions' tradition of organizing collections is long, but in recent decades, tools for KO have changed enormously due to technological developments; the resources to be included in the collections -including the tools for organizing them -are increasingly digital; and their retrieval is affected by these digital technologies.Today, an unprecedented number of resources are available through a quick search online and a majority of people in Scandinavia have technological devices that offer instant access to these Internet resources.Many of the constraints of the physical world are solved when ordering digital resources (Weinberger 2007).Even so, it is important to bear in mind that KO practices have developed out of the ordering of physical collections, hence principles from physical tools remain influential.Understanding KO tools today includes considering their historical roots.In this text, we will trace some historical roots and some challenges and opportunities that new technologies and societal situations bring to the KO work of LAM institutions.We will follow two trajectories of development that we find
Secret handshakes: Full dynamicity, deniability and lattice-based design

Secret handshake (SH), as an essential privacy-preserving primitive, entitles members of the same group to anonymously authenticate each other. Among all the existing designs, only the ones separately proposed by Zhang et al. over coding theory and An et al. over lattice are post-quantum secure. However, both schemes fall short of being practical due to their huge overhead (≫ 100 MB), and the code-based one is even insecure under impersonator attacks. Besides, all the known SH constructions are powerless to handle the problem of dynamically managing users. To fill the gap, in this work we first formalize the model of fully dynamic secret handshakes (FDSH), where members have the freedom of joining or leaving a group. Then, to achieve anonymous mutual authentication with ease, we introduce a newly-defined cryptographic primitive called anonymous message exchange (AME), where legal participants can exchange their messages anonymously. Further, two generic approaches of AME from non-interactive zero-knowledge arguments of knowledge (NIZKAoK) are provided, along with heuristic applications. Finally, by combining a well-designed AME and other suitable techniques, we present the first lattice-based FDSH in the random oracle model offering another attractive property: full deniability, which enables users to plausibly deny their participating in interactions. Our scheme sharply reduces the communication cost (< 10 MB) and surpasses the existing post-quantum systems in terms of security, efficiency, and functionality.
National-Scale Geochemical Baseline of 69 Elements in Laos Stream Sediments

Geochemical baselines are crucial to explore mineral resources and monitor environmental changes. This study presents the first Laos geochemical baseline values of 69 elements. The National-scale Geochemical Mapping Project of Lao People’s Democratic Republic conducted comprehensive stream sediment sampling across Laos, yielding 2079 samples collected at 1 sample/100 km2, and 69 elements were analyzed. Based on the results of LGB value, R-mode factor analysis, and scatter plot analysis, this paper analyzes the relationship between the 69 elements and the geological background, mineralization, hypergene processes and human activities in the study area. The median values of element contents related to the average crustal values were: As, B, Br, Cs, Hf, Li, N, Pb, Sb, Zr, and SiO2, &gt;1.3 times; Ba, Be, Cl, Co, Cr, Cu, F, Ga, Mn, Mo, Ni, S, Sc, Sr, Ti, Tl, V, Zn, Eu, Al2O3, Tot.Fe2O3, MgO, CaO, and Na2O, &lt;0.7 times; and Ag, Au, Bi, Cd, Ge, Hg, I, In, Nb, P, Rb, Se, Sn, Ta, Th, U, W, Y, La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and K2O, 0.7–1.3 times. R-mode factor analysis based on principal component analysis and varimax rotation showed that they fall into 12 factors related to bedrock, (rare earth, ferrum-group, and major Al2O3 and K2O elements; mineralization–Au, Sb, and As) and farming activities–N, Br, S, and C). This study provides basic geochemical data for many fields, including basic geology, mineral exploration, environmental protection and agricultural production in Laos.
Automated assessment of visual aesthetics of Android user interfaces with deep learning

Visual aesthetics are seen as an essential success factor for mobile apps, affecting user experience and perception, which makes their evaluation crucial as part of the interface designing process. Machine learning approaches have shown to be very promising in this context, but so far, there are only solutions for assessing the aesthetics of web-based graphical user interfaces (GUIs). This article presents a deep learning approach to automatically quantify the visual aesthetics of GUIs of Android apps developed with App Inventor, based on a convolutional neural network (CNN) and adopting a regression-based supervised learning approach. The performance results demonstrate that the CNN can learn to evaluate the visual aesthetics of GUIs with a mean squared error of 0.023 for the validation set and 0.017 for the test set. The model ratings are also highly correlated with human ratings (Spearman rank correlation coefficient ρ = 0.86 for the validation set and ρ = 0.95 for the test set). And the results of a Bland & Altman analysis show that more than 95% of them agree with the human ratings. These promising results indicate that the model can be an effective and efficient means to automate the visual aesthetics assessment during the GUI design process for mobile apps.
Recommendation Algorithm Using SVD and Weight Point Rank (SVD-WPR)

One of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve a more accurate recommended ranking. However, this algorithm suffers in the execution time with an increased number of items. Therefore, this study proposes a new recommendation algorithm that combines the matrix decomposition method and ranking aggregation to reduce the time complexity. The matrix decomposition method utilizes singular decomposition value (SVD) to predict the unrated items. The ranking aggregation method applies weight point rank (WPR) to obtain the recommended items. The experimental results with the MovieLens 100K dataset result in a faster running time of 13.502 s. In addition, the normalized discounted cumulative gain (NDCG) score increased by 27.11% compared to the WP-Rank algorithm.
A Federated Transfer Learning Framework Based on Heterogeneous Domain Adaptation for Students’ Grades Classification

In the field of educational data mining, the classification of students’ grades is a subject that receives widespread attention. However, solving this problem based on machine learning algorithms and deep learning algorithms is usually limited by large datasets. The privacy problem of educational data platforms also limits the possibility of building an extensive dataset of students’ information and behavior by gathering small datasets and then carrying out the federated training model. Therefore, the balance of educational data and the inconsistency of feature distribution are the critical problems that need to be solved urgently in educational data mining. Federated learning technology enables multiple participants to continue machine learning and deep learning in protecting data privacy and meeting legal compliance requirements to solve the data island problem. However, these methods are only applicable to the data environment with common characteristics or common samples under the alliance. This results in domain transfer between nodes. Therefore, in this paper, we propose a framework based on federated transfer learning for student classification with privacy protection. This framework introduces the domain adaptation method and extends the domain adaptation to the constraint of federated learning. Through the feature extractor, this method matches the feature distribution of each party in the feature space. Then, labels and domains are classified on each side, the model is trained, and the target model is updated by gradient aggregation. The federated learning framework based on this method can effectively solve the federated transfer learning on heterogeneous datasets. We evaluated the performance of the proposed framework for student classification on the datasets of two courses. We simulated four scenarios according to different situations in reality. Then, the results of only source domain training, only target domain training, and federated migration training are compared. The experimental results show that the heterogeneous federated transfer framework based on domain adaptation can solve federated learning and knowledge transfer problems when there are little data at the data source and can be used for students’ grades classification in small datasets.
Experimental evaluation of virtual reality applications running on next-gen network scenarios with edge cloud assistance

Volumetric video is an emerging key technology for immersive representation of 3D spaces and objects. Rendering volumetric video at client's end requires significant computational power which is challenging especially for mobile devices. One of the ways to mitigate this is to offload the rendering at the edge cloud and stream the video and audio to the thin client. Remote edge-cloud rendering may increase the end-to-end delay of the system due to the added network and processing latency which is greater than local rendering system. We investigate network latency in edge-based remote rendering over NextG networks and identify the bottleneck deteriorating the application performance. Further, we delve into the current state of the art and challenges of performing rendering remotely at the edge cloud and study the associated problems that need to be addressed in order to realize remote augmented reality(AR)/virtual reality (VR). Our prototype implementation shows effectiveness of maintaining the application QoE by prioritizing data at the level of a sub-flow and reducing the motion-to-photon latency.
The Enredo Game-Installation: A Proposal to Counter Hate Speech Online

In this article we encourage conflict sensitive attitudes and multicultural awareness based on the potential for creative responses. Through digital media art and serious games strategies we seek to create safe areas for dialogue, debate, and awareness of hate speech. These solutions will be even more effective if they are based on the understanding of how different forms of expression emerge, interact, and potentially dissipate in the virtual environment. Grounded on the possibilities of digital media art, through a practice-based research methodology, we explore the process of creating a gamified counter-narrative, designed with the objective of responding to hate speech, and, at the same time, capable of providing an experience of aesthetic enjoyment. Foreseeing the establishment of a collaborative network with the educational community and the non-specialized public (parents, youth, associations, educators), this project is also based on the key concepts of media and information literacy, which are important not only to understand and analyze the phenomenon of online hate speech, but also to develop strategies and tools that allow the containment of this type of speech.
Entutela. A Digital Tool to Facilitate the Drafting and Creation of Tutelas in Colombia

The restrictions imposed by the Covid-19 pandemic, meant that many services had to accelerate their digital transformation processes if they were to continue to exist. In the case of justice services, specifically the tutela action, a digital filing tool called Tutela en Línea was adapted. This tool allowed citizens to continue using the tutela remedy to protect their fundamental rights. However, Tutela en línea is only the beginning of what is expected for the digital transformation of legal services in Colombia. Accordingly, Entutela is intended to overhaul people’s current experience of creating and visualizing a tutela in digital media, presenting a tool built based on the study of the different views involved in the system, dispelling myths and traditional practices, to simplify access to the tutela for people with no legal knowledge.