Reviewerly/exHarmony huggingface dataset:
FedMC: Federated Reinforcement Learning on the Edge with Meta-Critic Networks

Federated learning (FL) has been proposed as a novel paradigm to enable distributed learning on the edge with privacy protection. However, existing federated learning approaches mainly focus on training deep classification and clustering models, and no enough attention has been paid to solve the federated reinforcement learning task on the edge, a challenging task where multiple learning agents observe local state and take local actions to train a global learning model without revealing their local dataset. In this paper, we propose a generalised federated reinforcement learning framework called FedMC that integrates reinforcement learning models trained by multiple edge devices into a general model based on a meta-learning approach. In the proposed framework, each participant adopts a meta-value network (MVN) and task-actor encoder network (TAEN) locally to perform meta-learning training based on local task samples, and periodically uploads the weights of local MVN and TAEN to the server, which aggregate them to a global model with rapid adaptability and cross-task applicability. Extensive experiments based on a number of reinforcement learning tasks show that FedMC outperforms various federated learning baseline algorithms, and it is competitive with the methods that centrally train reinforcement learning task with global dataset.
Plaintext recovery and tag guessing attacks on authenticated encryption algorithm COLM

There are three main approaches related to cryptanalysis of Authenticated Encryption with Associated Data (AEAD) algorithms: Simulating the encryption oracle (universal forgery attack), simulating the decryption oracle (plaintext recovery attack) and producing the valid tag of a given ciphertext (tag guessing attack). In this work, we analyze the security of COLM in these approaches. COLM is one of the AEAD algorithms chosen in the final portfolio for defense-in-depth use case of the CAESAR competition. The ciphers in this portfolio are supposed to provide robust security with their multiple layered defense mechanisms. The main motivation of this work is to examine if COLM indeed satisfies defense-in-depth security. We make cryptanalysis of COLM, particularly in the chosen ciphertext attack (CCA) scenario, once its secret whitening parameter L = E K ( 0 ) is recovered. To the best of our knowledge, we give the first example of querying an EME/EMD (Encrypt-linearMix-Encrypt/Decrypt) AEAD scheme in its decryption direction for arbitrary ciphertexts, not produced previously by the oracle, namely either a forgery or tag guessing attack. We construct SEBC/SDBC (Simulation models of the Encryption/Decryption oracles of the underlying Block Cipher) of COLM, thereby forming the first examples of these models of an authenticated EME scheme simultaneously. The combination of our SEBC/SDBC is a powerful tool to mount a universal forgery attack, a tag guessing attack and a plaintext recovery attack. All of these attacks have polynomial time complexities once L is recovered in the offline phase, indicating that the security of COLM against plaintext recovery and tag guessing attacks is limited by the birthday bound. Apart from exploiting SEBC/SDBC, we mount a pair of plaintext recovery attacks and another universal forgery attack. Finally, we make some suggestions to prevent our attacks. • For the first time, we introduce plaintext recovery and tag guessing attacks against a CAESAR winner. • We mount key recovery attack against the underlying block cipher (BC) of COLM. • For the first time, we construct an SDBC (Simulation model of the Decryption oracles of BC) of an Encryption-Mix-Encryption AEAD Scheme. • We introduce new security criteria to prevent these attacks which will contribute to design secure AEAD algorithms.
Topic Discovery About Economy During COVID-19 Pandemic from Spanish Tweets

Automatic topic discovery from natural language texts has been a challenging and widely studied problem. The ability to discover the topics present in a collection of text documents is essential for information systems. Topic discovery has been used to obtain a compact representation of documents for grouping, classification, and retrieval. Some tasks that can benefit from topic discovery: recommendation systems, tracking misinformation, writing summaries, and text clustering. However, topic discovery from Spanish texts has been somewhat neglected. For this reason, this work proposes analyzing the behavior of topic discovery tasks in texts in Spanish, specifically in tweets about the Mexican economy during the COVID-19 pandemic, under three different approaches. A comparison was conducted, achieving promising results because the topic coherence metric indicates coherent topics. The highest score of 1.22 was obtained using PLSA with 50 topics, concluding that the topics encompassed the study domain.
Software as a Service Challenges: A Systematic Literature Review

Software as a Service (SaaS) as a type of Cloud computing is a type of computing that become a contentious topic among enterprise information technology (IT) professionals. In recent decades, rapid advances and breakthroughs in SaaS have enabled many businesses in numerous industries to consider it as a promising technology towards using. Nevertheless, such a new approach is up against a number of obstacles, a systematic literature review of SaaS cloud computing’s possible antecedents has been conducted to describe the significant challenges SaaS cloud computing adoptions are facing. Articles describing the difficulties of SaaS Adoption were compiled. For a concentrated discourse on solutions, we organized the key challenges in the ontology. As a result, out of more than 68 factors which has been addressed, 16 factors has been identified and deeply discussed as a significant factors that may affects CC SaaS adoption. A comprehensive framework of SaaS Adoption obstacles will be required to expedite the adoption of this Innovation.
SimLDA: A Tool for Topic Model Evaluation

Topic model evaluation is a well studied field. Two classes of metrics are typically used to evaluate the quality of extracted topics, namely held-out perplexity and coherence measures. Although these metrics have been improved and refined, they still have drawbacks. In this paper we propose using simulated data generated from our flexible corpus generation tool, SimLDA, combined with an exact measure of dissimilarity, the average Kulback-Leibler divergence (KLD), to achieve a more fine-grained method for detecting differences in topic quality. In this work, we use our proposed approach to evaluate and compare topics extracted from synthetic data using two inference algorithms for latent Dirichlet allocation (LDA), namely, variational Bayes (VB) and collapsed Gibbs sampling. We then evaluate the extracted topics using a coherence measure (the $$C_{\text {v}}$$ score). Using the same two inference algorithms we then extract topics from the popular 20 Newsgroups data set and evaluate the extracted topics based on the $$C_{\text {v}}$$ score. Through these three steps, we show that although collapsed Gibbs sampling consistently outperforms VB, the use of simulated data (evaluated using both coherence measures and KLD) provides more insight into the quality of the extracted topics and allows us to examine performance differences of the inference algorithms.
A Comparative Analysis of Local Explainability of Models for Sentiment Detection

Sentiment analysis is one of the crucial tasks in Natural Language Processing (NLP) which refers to classifying natural language sentences by their positive or negative sentiments. In many existing deep learning-based models, providing an explanation of a sentiment might be as necessary as the prediction itself. In this study, we use four different classification models applied to the sentiment analysis of the Internet Movie Database (IMDB) reviews, and investigate the explainability of results using Local Interpretable Model-agnostic Explanation (LIME). Our results reveal how the attention-based models, such as Bidirectional LSTM (BiLSTM) and fine-tuned Bidirectional Encoder Representations from Transformers (BERT) would focus on the most relevant keywords.
Vocabulary Expansion for the Sub-word WFST-Based Automatic Speech Recognition System

This paper reports the improvement of the Lithuanian Automatic Speech Recognition (ASR) system focusing on “vocabulary expansion”, i.e. enabling ASR system to recognize words never seen during training. These unseen words are called out-of-vocabulary (OOV) words and involve: 1) regular Lithuanian words appearing due to different topics or domains not covered in training; 2) complicated cases, i.e., foreign names, brand names, and loanwords pronounced not according to regular Lithuanian pronunciation rules. In weighted finite-state transducer (WFST) ASR OOV problem is typically solved by applying one of the following solutions: (1) making ASR vocabulary unlimited by performing recognition on sub-word level, (2) adding words directly to the WFST decoding graph or (3) by reconstruction of OOV words from ASR result. Our baseline Lithuanian ASR system already follows the first approach, however many OOV words are still not being recognized, because of low probability of corresponding sub-word sequences. Therefore, our offered approach can be seen as a combination of the first two solutions: we boost probabilities of sequences of sub-words (corresponding to words being “added” to vocabulary) in a sub-word weighted finite-state transducer (WFST) ASR system. In such way the vocabulary of the ASR is being “expanded”. The proposed approach allowed to achieve significant improvement over the baseline: the percentage of misrecognized out-of-vocabulary words dropped by $$\sim $$ 7%, while F1 reached 85.6%.
Learning a spatial-temporal texture transformer network for video inpainting

We study video inpainting, which aims to recover realistic textures from damaged frames. Recent progress has been made by taking other frames as references so that relevant textures can be transferred to damaged frames. However, existing video inpainting approaches neglect the ability of the model to extract information and reconstruct the content, resulting in the inability to reconstruct the textures that should be transferred accurately. In this paper, we propose a novel and effective spatial-temporal texture transformer network (STTTN) for video inpainting. STTTN consists of six closely related modules optimized for video inpainting tasks: feature similarity measure for more accurate frame pre-repair, an encoder with strong information extraction ability, embedding module for finding a correlation, coarse low-frequency feature transfer, refinement high-frequency feature transfer, and decoder with accurate content reconstruction ability. Such a design encourages joint feature learning across the input and reference frames. To demonstrate the advancedness and effectiveness of the proposed model, we conduct comprehensive ablation learning and qualitative and quantitative experiments on multiple datasets by using standard stationary masks and more realistic moving object masks. The excellent experimental results demonstrate the authenticity and reliability of the STTTN.
One thousand exercises in probability (3rd edition) by Geoffrey R. Grimmett and David R. Stirzaker, pp. 580, £29.99 (paper), ISBN 978-0198847618, Oxford University Press (2020)

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An improved algorithm for three-dimensional reconstruction of nonwoven microstructures by using multi-focus images

The filtration performance of nonwovens, such as virus protection and air purification, depends largely on their microstructure, that is, their internal structure. With the development of manufacturing techniques, nonwovens can be produced with complex microstructures to deliver low air resistance and high filtration efficiency. Therefore, it seems important to reconstruct an accurate three-dimensional (3D) model to describe the various microstructures for performance evaluation and product development. Multi-focus image fusion is one of the most effective ways used for the 3D nonwoven reconstruction. However, the overlapping and crossing of fibers in multi-focus images taken on an optional microscope make 3D fiber reconstruction incomplete and discontinuous. In this study, we developed a novel fiber segmentation and 3D nonwoven reconstruction algorithm to restore fiber continuity. The fiber web image was firstly thinned into the fiber mid-axis or skeleton, and fiber fragments were detected by searching the intersections. The features, such as the angle and depth, of each fiber fragment were obtained using Harris corner detection. A cluster technique was used to group the head-points of the fiber fragments that have similar features into one cluster. All fiber fragments in the same cluster are fitted with a polynomial curve to form a complete fiber in the 3D space. The experimental results show that this new method is suitable for the reconstruction of nonwovens with complex structures, such as fiber bonding or crossing, and can automatically detect and connect fiber fragments in 3D space. It provides an effective way for the subsequent simulation/evaluation of filtration characteristics.
Theoretical development of discrete-modulated continuous-variable quantum key distribution

Continuous-variable quantum key distribution offers simple, stable and easy-to-implement key distribution systems. The discrete modulation scheme further reduces the technical difficulty. The main regret is that the security of discrete modulation schemes has not been sufficiently demonstrated. Schemes with different signal state distributions use various physical conditions to obtain the key rate formula, resulting in different security levels, computation complexities and implementation difficulties. Therefore, a relatively systematic and logically consistent security proof against most general attacks is worth exploring. On the other hand, extending the discrete modulation scheme and its variants to different applications, such as satellite-to-earth communication, can further activate and advance this field. Here, we briefly review the achievements that have been made in discrete-modulated continuous-variable quantum key distribution, and openly discuss some issues worthy of further research.
Selective video enhancement in the Laguerre–Gauss domain

Traditional image and video enhancement techniques do not consider the subjective preferences of the user, who might be interested in modifying or highlighting specific elements of a scene. In fact, most image or video post-processing techniques are applied to the entire image or frame. In this work, a framework for selective video enhancement is presented. It is based on an adaptive multi-resolution edge enhancement technique performed in the Laguerre–Gauss complex wavelet domain. In the proposed scheme, edges are selectively enhanced or attenuated according to the inputs of the user, taking into account the characteristics of the content, the response of the human visual system, and the masking effects induced by textured background. Two scenarios have been implemented and tested: (i) an interactive video editing system, where the user selects the objects to enhance through a graphical interface and the system automatically propagates the selection through the following video frames belonging to the same shot, (ii) a perceptually-driven technique to improve the quality of RGB plus Depth videos, which uses depth maps as additional features to guide the enhancement.
The Experience of the People's Republic of China in the Development of the Digital Educational Environment

Digitalization of higher education is a major strategic issue affecting and even determining the qualitative development of the entire educational system of the country. China's higher education has a huge historical contribution to economic and social development, especially in the context of digitalization. The use of various educational software and hardware and cooperation with data processing technologies are designed to improve the quality of education and help education become more effective.
IoT-Deep Learning Based Face Mask Detection System for Entrance and Exit Door

During the pandemic, it has been seen that the global population follows the guidelines issued by the health organization regarding wearing face masks, but some people do not take care of this and do not use masks. The objective of the proposed system, Wollega University Face Mask Detection System (WUFMDS), is to restrict people who are not wearing a mask on the door side by identifying the face mask from the face or open the door if the incoming person is wearing the mask. This system is based on the Internet of Things (IoT) and a Deep Learning algorithm called Convolutional Neural Network (CNN). For this purpose, images with and without masks were collected as samples from the university. The CNN algorithm is used to detect the mask and classify it as with or without masks. The IoT module controls the door operation based on the classification response sent to the IoT module by the CNN algorithm. The system was tested lively with the dummy door system in order to ensure the functionality of the face mask detection system and developed software applications for the system model are working as defined objectives. Our model had 99.36% accuracy with the training dataset and 99.29% accuracy with the validation set. Hence, the proposed system could be used for the automatic identification and classification of masks on the face and to operate the door to allow the person who is wearing the mask to pass through while keeping it closed when no mask is found on the face.
Systematic literature review on approaches of extracting image merits

Texture analysis is gaining popularity among the scientific community. A wide variety of applications use texture analysis method. Texture analysis methods can be used for image segmentation, pattern analysis and pattern classification tasks. The application areas range from remote sensing, biomedical imaging, image synthesis, image inpainting and image processing. However, the preliminary step in all these applications refers to the extraction of intricate features from the given image. As a result, a wide verity of feature extraction methods exists in the literature. All the feature extraction methods have their own advantages and shortfalls. For example, some of the methods are computationally expensive, some are rotation and scale invariant whereas the others are easy to implement. This article provides an insight regarding different texture feature extraction techniques. The article bifurcate these techniques into different techniques according to their working principles. Besides provision of the basic working principle of every technique, the article provides an insight regarding their advantages and shortfalls. Moreover, this article considers deep learning and entropy based methods interesting for texture evaluation. Besides, the article also proposes a thorough study of these methods in texture analysis.
Design and simulation of an Unmanned Aerial Vehicle based on a motor glider concept

Due to the miniaturization of optoelectronic devices, the increase in computing power, and the improvement of control systems and navigation systems, Unmanned Aerial Vehicles (UAVs) have become an indispensable tool in areas such as agriculture, industry (critical infrastructure inspection), and surveying. These systems are available in fixed-wing and rotary-wing (multiple rotors) configurations. The multiple rotor system is characterized by advantages such as vertical take-off and landing and simplicity of the structure design and control algorithm. Still, there are also disadvantages: the short flight time and very high energy consumption. Based on the requirement for a platform with high reliability and high flight range, the paper proposes a solution for fixed-wing UAVs with an extended flight time, self-launching, short landing, and optimized energy consumption. The presented solution is inspired by the model of the Romanian glider IS-28B2 produced by ICA Ghimbav, which has been redesigned and optimized to become a 1:10 scale UAV type glider. The paper presents the 3D model design, simulation, and optimal autopilot integration for this robotic aerial system.
Study of the characteristics of ear animations used to convey information and emotion in remote communication without web camera

The use of remote communication has grown globally due to the COVID-19 outbreak. In some remote communication, meeting participants use audio only with their web cameras turned off, resulting in a lack of exchange of nonverbal information. In this study, we defined an "ear animation" as an avatar composed of a simple face-like body with no facial features and ear-like parts coming out from this body which can be animated. The purpose of this study was to design the ear animation and evaluate user impressions of it as nonverbal information. While setting conveying information and conveying emotion as dependent variables, the independent variables we set in this study were three different conditions: when ear animations were presented with no sound, when ear animations were presented simultaneously with simple voice, when only voice was played, and three different kinds of content: "agreement", "skepticism", and "disagreement" conveyed from ear animations. Using Two-way ANOVA (repeated) with these variables, we conducted comparative analysis. The results showed that ear animations presented simultaneously with voice had the potential to be a new way of conveying nonverbal information by combining relevant ear animation movement forms.
Critical Evaluation of Reinforced Concrete Beams Using Digital Image Correlation Technique

Abstract Digital Image Correlation technique (DIC) is a non-contact optical method for rapid structural health monitoring of critical infrastructure. DIC is used to extract Moment (M) – Curvature (κ) relationships using random speckle patterns. The random speckle pattern is evaluated for 2D DIC measurements and shows an improved correlation with conventional measurements. Characteristics of random speckle patterns are quantified and their applicability to DIC is explored. The ultimate moment-curvature values computed from the random speckled pattern are found to be in good agreement with conventional measurements.
Agent coalitions for load balancing in cloud data centers

The workload of Cloud data centers is constantly fluctuating causing imbalances across physical hosts that may lead to violations of service-level agreements. To mitigate workload imbalances, this work proposes a concurrent agent-based problem-solving technique supported by cooperative game theory capable of balancing workloads by means of live migration of virtual machines (VMs). Nearby agents managing physical hosts are partitioned into coalitions in which agents play coalitional games to progressively balance separate sections of a data center while considering the coalition's benefit of migrating a VM as well as its associated network overhead. Simulation results show that, in general, the proposed coalition-based load balancing mechanism outperformed a load balancing mechanism based on a hill-climbing algorithm used by a top data center vendor when considering altogether (i) the standard deviation of resource usage, (ii) the number of migrations, and (iii) the number of switch hops per migration.