Reviewerly/exHarmony huggingface dataset:
"Pesawat Kertas", a Game that Motivates Students in Learning Bahasa Indonesia

Online learning implemented in Indonesia caused many negative impacts, such as decreased student motivation and the creation of loss of learning. One way to overcome this is through the implementation of learning while playing. This research aims to describe the pesawat kertas (paper plane) game in learning Bahasa Indonesia in VII-A class, SMP Hamparan 2. The research method used is descriptive qualitative. The data in this research were obtained from documentation and questionnaires. The data analysis technique used is Miles and Huberman model. The results of this research indicate that the preparation of the pesawat kertas game in learning Bahasa Indonesia for VII-A class, SMP Hamparan 2 has been done very well. The stages in the preparation of learning are: analyze the syllabus, choosing learning media, making evaluation tools, making learning plans, and compiling the concept of the game that is carried out. Second, the stages and plots of the pesawat kertas game are pretty good and coherent. Readers can also see videos of students' excitement on Instagram of SMP Hamparan 2. The response of subjects to the pesawat kertas game that has been done is excellent. This can be seen from as many as 90% subjects agreeing that the game is fun and can make the spirit of learning.
Hazy images dataset with localized light sources for experimental evaluation of dehazing methods

Image haze removal methods have taken increasing attention of researchers.At the same time, an objective comparison of haze removal methods struggles because of the lack of real data.Capturing pairs of images of the same scene with presence/absence of haze in real environment is a very complicated task.Therefore, the most of modern image haze removal datasets contain artificial images, generated by some model of atmospheric scattering and known scene depth.Among the few real datasets, there are almost no datasets consisting of images obtained in low light conditions with artificial light sources, which allows evaluating the effectiveness of nighttime haze removal methods.In this paper, we present such dataset, consisting of images of 2 scenes at 4 lighting levels and 4 levels of haze density.The scenes has varying "complexity"the first scene consists of objects with a simpler texture and shape (smooth, rectangular and round objects); the second scene is more complex -it consists of objects with small details, protruding parts and localized light sources.All images were taken indoors in a controlled environment.An experimental evaluation of state-of-the-art haze removal methods was carried out on the collected dataset.
Reflective Equilibrium on the Fringe

Reflective equilibrium, as a methodology for the "formation of logics," fails on the *fringe*, where intricate details can make or break a logical theory. On the fringe, the process of theorification cannot be methodologically governed by anything like reflective equilibrium. When logical theorising gets tricky, there is nothing on the pre-theoretical side on which our theoretical claims can reflect of---at least not in any meaningful way. Indeed, the fringe is exclusively the domain of theoretical negotiations and the methodological power of reflective equilibrium is merely nominal.
Separate Syntax and Semantics: Part-of-Speech-Guided Transformer for Image Captioning

Transformer-based image captioning models have recently achieved remarkable performance by using new fully attentive paradigms. However, existing models generally follow the conventional language model of predicting the next word conditioned on the visual features and partially generated words. They treat the predictions of visual and nonvisual words equally and usually tend to produce generic captions. To address these issues, we propose a novel part-of-speech-guided transformer (PoS-Transformer) framework for image captioning. Specifically, a self-attention part-of-speech prediction network is first presented to model the part-of-speech tag sequences for the corresponding image captions. Then, different attention mechanisms are constructed for the decoder to guide the caption generation by using the part-of-speech information. Benefiting from the part-of-speech guiding mechanisms, the proposed framework not only adaptively adjusts the weights between visual features and language signals for the word prediction, but also facilitates the generation of more fine-grained and grounded captions. Finally, a multitask learning is introduced to train the whole PoS-Transformer network in an end-to-end manner. Our model was trained and tested on the MSCOCO and Flickr30k datasets with the experimental evaluation standard CIDEr scores of 1.299 and 0.612, respectively. The qualitative experimental results indicated that the captions generated by our method conformed to the grammatical rules better.
Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data

Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using neural networks, which is a meta-learner in our proposed model. The meta-learner updates the regression coefficients using the confidence interval formula. We extensively compared our proposed model to other benchmark approaches on public datasets for regression task. The results showed that our proposed neural network-based interpretable model showed outperformed results compared to the benchmark models. We also applied our proposed model to the synthetic data to measure model interpretability, and we showed that our proposed model can explain the correlation between input and output variables by approximating the local linear function for each point. In addition, we trained our model on the economic data to discover the correlation between the central bank policy rate and inflation over time. As a result, it is drawn that the effect of central bank policy rates on inflation tends to strengthen during a recession and weaken during an expansion. We also performed the analysis on CO2 emission data, and our model discovered some interesting explanations between input and target variables, such as a parabolic relationship between CO2 emissions and gross national product (GNP). Finally, these experiments showed that our proposed neural network-based interpretable model could be applicable for many real-world applications where data type is tabular and explainable models are required.
Attention-Based Scene Text Detection on Dual Feature Fusion

The segmentation-based scene text detection algorithm has advantages in scene text detection scenarios with arbitrary shape and extreme aspect ratio, depending on its pixel-level description and fine post-processing. However, the insufficient use of semantic and spatial information in the network limits the classification and positioning capabilities of the network. Existing scene text detection methods have the problem of losing important feature information in the process of extracting features from each network layer. To solve this problem, the Attention-based Dual Feature Fusion Model (ADFM) is proposed. The Bi-directional Feature Fusion Pyramid Module (BFM) first adds stronger semantic information to the higher-resolution feature maps through a top-down process and then reduces the aliasing effects generated by the previous process through a bottom-up process to enhance the representation of multi-scale text semantic information. Meanwhile, a position-sensitive Spatial Attention Module (SAM) is introduced in the intermediate process of two-stage feature fusion. It focuses on the one feature map with the highest resolution and strongest semantic features generated in the top-down process and weighs the spatial position weight by the relevance of text features, thus improving the sensitivity of the text detection network to text regions. The effectiveness of each module of ADFM was verified by ablation experiments and the model was compared with recent scene text detection methods on several publicly available datasets.
SimBlock Simulator Enhancement with Difficulty Level Algorithm Based on Proof-of-Work Consensus for Lightweight Blockchain

Proof-of-Work (PoW) was the first blockchain consensus protocol discovered, followed by Proof-of-Stake and others. The disadvantage of the PoW is that it requires high energy consumption compared to other consensus protocols. Based on this weakness, some researchers proposed a lightweight blockchain technology, a modified blockchain that has a simplified algorithm but does not reduce the security factor. This lightweight blockchain is suitable for applications requiring data reliability but with limited computing resources, such as Internet of Things devices. This paper discussed and modified the SimBlock simulator as one of the existing blockchain simulators. It has a visualization tool to look further into the propagation transition of the block. Unfortunately, the existing PoW consensus on the SimBlock simulator is unable to pinpoint the actual hash computation method. Therefore, the hashing process in the SimBlock simulator was modified by including the difficulty level for finding the hash target. The purpose of including the difficulty level was to determine how long it takes to create a block. By knowing the time needed, a recommendation could be obtained for the most suitable difficulty level for a lightweight blockchain and its implementation with IoT devices. There are two options of approaches to the difficulty level referred to in this paper; finding the number of zeros that appear sequentially and are in front of a hash value (leading zero) and finding the number of zeros that appear arbitrarily (count zero). For example, the first difficulty level on a leading-zero quest has the same meaning as searching for a leading zero, the second level of difficulty is the search for the two leading zeros, etc. The block generation time on a blockchain network using the PoW consensus highly depends on the difficulty level. Block generation time and resource utility have been analyzed and compared with other blockchain simulators and existing networks, such as Ethereum and Bitcoin. The modified SimBlock simulator was tested in this experiment using 100–600 nodes, with the expected result of creating 100–1000 blocks. Based on the experiments, creating a block using leading zeros as the hash target for the first to fourth difficulty levels took less than 1 s, whereas when using count zeros (zero-count) as the target hash, it took less than 1 s for the first to fifteenth difficulty levels. Using leading zeros took approximately 237.4 s at difficulty level 7, while count-zero took approximately 633.8 s at difficulty level 19. The experiment was not continued at the next difficulty level because it required a longer compilation time. With the modifications made, the creation of a block on a blockchain network using the PoW consensus can be clearly seen. The difficulty level added to the simulator can also provide information for determining the difficulty level to be implemented on the lightweight blockchain.
Frequency aggregation network for blind super-resolution based on degradation representation

Single image super-resolution (SISR) research has made significant progress with advances in convolutional neural networks (CNNs). However, most existing super-resolution (SR) methods ignore the differences in frequency distribution between uniformly degraded images (e.g., bicubic downsampling) and complicated degraded images, leading to unsatisfactory restoration performance in real-world images. Recent blind SR studies use degradation estimation to solve the problem, but they rarely capture the true degradation discrepancy from the perspective of the frequency domain. This gap motivates us to solve the blind SR problem by fusing the information of different frequency components to compensate for information loss comprehensively. Thus, we propose a frequency aggregation SR (FASR) network for blind SR that fully utilizes frequency information to guide high-quality image reconstruction based on degradation representation with flexible adaption to various degradations. Specifically, we design an effective degradation-aware module (EDAM), which incorporates a local cross-channel interaction (LCCI) method designed by us. LCCI performs feature adaption more effectively by degradation information. Furthermore, we design the detail-enhanced convolution block (DECB) within the EDAM to implicitly extract different spatial derivatives for retaining edge information and benefiting reconstruction quality. To comprehensively compensate for frequency information, we propose a frequency compensation module (FCM), which uses different frequency components to obtain different frequency information. This module further mitigates the problem of frequency information loss caused by complicated realistic degradation. Extensive experiments on synthetic and real images show that our network outperforms other advanced blind SR methods in both quantitative and qualitative terms.
Content Aspects of Professional Training of a Specialist in the Development of Individual Educational Trajectories

This article lists the prerequisites for the emergence of new interdisciplinary professions at the intersection of pedagogy, information and communication technologies. A new promising profession of a developer of individual educational trajectories is given, described in the “Atlas of New Professions 3.0”, developed under the auspices of the Agency for Strategic Initiatives. As conceived by the authors, this is a professional who creates a route for training new specialists from courses offered by educational institutions, including those available online, as well as simulators, simulators, internships, etc., and develops an educational track based on them, considering the psychotype, abilities and goals individual person. Since a brief description of this profession in the Atlas does not give a complete picture of its content, for the purposes of understanding the meaningful aspect of the professional training of such specialists in a multidisciplinary university, a description of the methodological basis of the developer of individual educational trajectories is given. The authors of this article describe the principle of operation of the constructor, developed at SPbPU and allowing the selection of the content of professional training of the developer of individual educational trajectories in a multidisciplinary university.
DM<sup>2</sup>S<sup>2</sup>: Deep Multimodal Sequence Sets With Hierarchical Modality Attention

There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combines the feature representations from multiple encoders. However, as the number of modalities increases, several potential problems with the mid-fusion model structure arise, such as an increase in the dimensionality of the concatenated multimodal features and missing modalities. To address these problems, we propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets (DM2S2). Our set-aware concept consists of three components that capture the relationships among multiple modalities: (a) a BERT-based encoder to handle the inter- and intra-order of elements in the sequences, (b) intra-modality residual attention (IntraMRA) to capture the importance of the elements in a modality, and (c) inter-modality residual attention (InterMRA) to enhance the importance of elements with modality-level granularity further. Our concept exhibits performance that is comparable to or better than the previous set-aware models. Furthermore, we demonstrate that the visualization of the learned InterMRA and IntraMRA weights can provide an interpretation of the prediction results.
Retracted: Collaborative learning‐assisted recommendation of social trust over Web of Things

Abstract The above article from IET Communications, published online on 24 November 2022 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System

Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (&gt;40), and GDD values exceeded (0.92). Key space is determined by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>u</mi> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </mfenced> </mrow> </mrow> </msup> </mrow> </mfenced> </math> where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.
Periodical Press In The Provinces Of The Middle Volga Region (1914-1918)

The article attempts to carry out a comprehensive retrospective study of the provincial press activities of pro-government mass media in the provinces of the Middle Volga region (Simbirsk and Samara provinces) during the First World War.The relevance of the problem lies in the importance of a detailed study and rethinking of the periodical press role of pro-government mass media in the communication space of Russia and an objective assessment of the contribution it has made to the development of residents perceptions about military events, the course pursued by the imperial government, as well as socio-economic realities in the country.The article was formed on the principles of science, complexity, comprehensiveness, historicism, and objectivity.In the course of the work, specific methods such as: problem-chronological, historical-system, historical-typological, historical-genetic, historical-comparative and content analysis became fundamental.The paper considered transformations in the themes and activities of the pro-government periodical press in the realities of changing situation at the front, as well as the social, political, and economic situation in the country.Along with this, the article studies the peculiarities of the implementation of propaganda and information tasks by the mass media and their impact on the national consciousness of citizens.The authors came to the conclusion that the provincial press during the First World War was engaged in the formation of images of an external and internal enemy, and systematically condemned the "decadent" policy in the government and society.
Comparing NLP Solutions for the Disambiguation of French Heterophonic Homographs for End-to-End TTS Systems

This paper presents a study on different NLP solutions for French homographs disambiguation for text-to-speech systems. Solutions are compared using a home-made corpus of 8137 sentences extracted from the Web, comprising roughly one hundred instances of each of 34 pairs of prototypical words. A disambiguation system based on per-case Linear Discriminant Analysis (LDA) classifiers using contextual word embeddings as input features achieves state-of-the-art F-scores superior to 0.96.
Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems

A recent poll suggests that a lot of people don’t vote because they feel like they already have too much to do. This is because of many things, like the fact that everyone has to go to a polling place, wait in a long line, and be mentally and physically tired before they can vote. We have pushed for people to be able to vote online, but there are a growing number of problems and risks that could come with this. When websites are attacked by things like phishing, it might be hard to log in. With the help of Visual Cryptography (VC), We proposed Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems we made a voting system that lets people discuss and decide on important business ideas without giving away any information.
A multi-layer fusion image enhancement method for visual odometry under poor visibility scenarios

Visual odometry (VO) is important in robotic rescue and navigation operations; however, poor visibility scenarios, which are characterised by weak illumination, few textures, and self-similarity, seriously influence image quality and image matching capability, thus degrading VO performance. This study proposes a multi-layer fusion image enhancement method to ensure VO performance under poor visibility scenarios. Our method can achieve a trade-off between naturalness preservation, texture feature enhancement, contrast improvement, and noise suppression. To achieve this, we first decomposed input images into illumination, reflectance, and detail layers. Subsequently, we designed multiple weighting strategies in the illumination layer to preserve naturalness and improve contrast and applied a linear operation to enhance the colour contrast in the reflectance layer. A double-interval frequency optimisation model was also applied to enhance the textures in the detail layer. Finally, these layers were recombined to obtain an enhanced image. Our experimental results demonstrated our quantitative and qualitative superiority over six other methods under poor-visibility scenarios. The overall performance revealed that our method can effectively improve image matching and VO results.