Reviewerly/exHarmony huggingface dataset:
EmotionAlBERTo: Emotion Recognition of Italian Social Media Texts Through BERT

Social networks are perceived by users as a natural environment for publicly sharing their thoughts and emotions about different subjects. In these platforms, despite the availability of various forms of communication, text is the most widespread way of communication. Therefore, the development of automatic techniques for emotion recognition in social media texts, such as tweets or Facebook posts, gives the opportunity of extracting information that could be valuable for many application fields, ranging from the analysis of customer satisfaction to the optimization of political campaigns. Nowadays, several emotion classifiers and datasets have been built for English texts while few resources are available for other languages. For this reason, in this work we present a deep learning algorithm for emotion recognition in Italian social media texts. The algorithm was named EmotionAlBERTo as it is based on AlBERTo, a BERT-based language understanding model for the Italian language. We trained and evaluated EmotionALBERTo on two different Twitter datasets: the MultiEmotions-It dataset and TwIT, a novel Italian dataset for emotion recognition that we built by collecting and manually labelling a corpus of about 3100 Italian tweets. Experiments show that the models achieve remarkable performance on both 4- class and 6-class emotion classification, by respectively obtaining F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 0.91 and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 0.83 on MultiEmotions-It, F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 0.92 and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 0.86 on TwIT.
Summary of Experimental Curriculum Reform in the Information Age

With the continuous penetration of information technology into the field of education, educational informatization and educational modernization coexist. With its advanced technical means, information technology supports new educational methods such as interactive learning and virtual laboratory, realizes the multi-level development of “building resources, building platforms, expanding space, building systems and creating models”, and increasingly plays a key role in the process of educational reform and development. The continuous development of intelligent society and the trend of modernization have brought many challenges to education in the new era. The current education model urgently needs transformation and upgrading. Educational informatization promotes the rapid improvement of educational cognition and educational level, changes the time and space constraints of traditional educational model, and makes the optimization and upgrading of educational reform possible. Based on this trend, the cross research of education and information technology has gradually become a hot topic in the field of educational science. Based on the trend of educational informatization in the new era, this study explores the technical basis and platform guarantee of experimental curriculum reform, deeply studies the development process of experimental curriculum reform, summarizes the existing mechanism, looks forward to the future development direction, and puts forward the feasible path in construction and supervision.
Review of Photometric Compensation in Projection System

With the continuous development and advancement of computer vision related technologies and the improvement in projector performance in recent years, people are using projectors more and more widely in various scenarios of life and replacing traditional screens for various natural surfaces. However, when the projection surface is no longer the ideal white projection curtain, the projection effect is often greatly compromised, especially when there is color texture on the projection surface. In order to achieve a good perception of the projected image of some complex scenes, a series of compensation processes are needed for the projected image. This paper reviews the projection compensation techniques based on projector-camera systems that have emerged in recent years such as color mixing matrix, thin plate spline, and deep learning. An experimental comparison of the various compensation techniques is also presented.
Tackling Non-stationarity in Decentralized Multi-Agent Reinforcement Learning with Prudent Q-Learning

Multi-Agent Reinforcement Learning (MARL) is challenging due to the non-stationary issue of an agent’s learning environment caused by multiple co-evolving agents, i.e., the uncertainty rises with multiple agents learning and evolving simultaneously. Though centralized learning can solve the problem to some degree, it is hard to expand into numerous agents or real-world applications. We focus on the native property of the non-stationary environment and use a independent method to alleviate its harmful influence. Here are the two main contributions of this article: 1) We formalize the non-stationary environment by supposing it follows a pessimistic transition, and we explain the existence of a lower bound of accumulated reward expectation that the agent can learn. 2) We devise a scheme called Prudent Q-Learning (PQL) based on independent learning for controlling multi-agent systems in non-stationary environments. We present both tabular and Deep Q-Learning based implementations of the proposed Prudent Q-Learning scheme.
A Performance Calculation Method of Hypersonic Astronomical Attitude Determination with Corrected Star Map

Astronomical observation of hypersonic vehicles is affected by aero-optical effects. A method for calculating the astronomical attitude determination performance of hypersonic vehicles with a corrected star map is proposed in the paper. Firstly, the flow field structures on the surface of the observation window of the hypersonic vehicle is analyzed. The impact of the main structure of the shock layer and turbulence layer is refined. The shock wave deflection correction and optical image restoration results are used to achieve the quantitative calculation method of the astronomical attitude accuracy of the hypersonic vehicles based on the corrected star map. The simulation experiments show that the field of view of the star sensor has almost no change on different flight speeds; the standard deviation of the attitude angle after turbulent image restoration is about 13 arcseconds, concentrated in the yaw angle; after correcting shock wave deflection, the residual error has little influence on the attitude determination.
Recursions for modified Walsh transforms of some families of Boolean functions

We show that, under certain conditions, restricted and biased exponential sums and Walsh transforms of symmetric and rotation symmetric Boolean functions are, as in the case of nonbiased domain, C-finite sequences. We also prove that under other conditions, these sequences are P-finite, which is a somewhat different behavior than their nonbiased counterparts. We further show that exponential sums and Walsh transforms of a family of rotation symmetric monomials over the restricted domain En,j={x∈𝔽2n:wt (x)=j} (wt (x) is the weight of the vector x) are given by polynomials of degree at most j, and so, they are also C-finite sequences. Finally, we also present a study of the behavior of symmetric Boolean functions under these biased transforms.
Inputting Writing Systems with Medium Complexity: A Generalized Input Method Editor AKKHARA and Case Study on Myanmar Script

In this study, an input method editor called AKKHARA is developed to accommodate writing systems comprising several tens to hundreds of symbols. As an engineering realization, AKKHARA accepts and applies a set of rewrite rules with priorities such that the alternation, substitution, and normalization of character strings are applied alongside the keystrokes. Compared with general key-character editors, AKKHARA provides a greater flexibility for Romanization-based rule editions. Compared with the input methods developed for Chinese and Japanese, AKKHARA is lightweight and easy to maintain. As an application case of AKKHARA, this study illustrates the realization of a Romanization-based Myanmar input method using the Unicode standard. A version of AKKHARA for Microsoft Windows was released that supports Unicode characters with customizable functions for rewriting rule editions.
Geometry-Aware Network for Table Structure Recognition in Wild

Table is a common representation format used to record and summarize important data in our daily life. Table detection, cell detection and table structure recognition have been widely discussed in recent years. The recognition of table structure in clean and noiseless images or documents has achieved good results, but in the real world with distorted images containing noise disturbance, the existing methods cannot get good results. The reason for this problem is that the image in the real world contains various distortions, such as curve, which causes the model to fail to parse the table correctly. To solve this problem, we propose a network with geometry awareness, which can enhance the ability of the model when facing the real-world images containing distortion.
A Scalable and Auditable Secure Data Sharing Scheme With Traceability for Fog-Based Smart Logistics

Smart logistics (s-Logistics) has become more and more popular driven by the intelligent Internet of Things (IoT) which deploys pervasive smart devices in s-Logistics systems. The explosive growth of s-Logistics data collected by these resource-limited IoT devices enables Fog-based s-Logistics that provides data outsourcing and sharing services via multiple clouds within small latency. Nevertheless, it also gives rise to prominent security risks of user privacy leakage considering malicious users and data integrity violation with untrusted cloud servers, which are severe to s-Logistics systems and cannot be addressed by simple encryption. To solve these issues, in this article, we propose an efficient large universe and traceable privacy-preserving data sharing (LUTPDS) for Fog-based s-Logistics. It simultaneously achieves data access control, data integrity protection, key escrow and abuse resistance, user privacy preserving, and scalability. We devise a large universe and multiauthority ciphertext-policy attribute-based encryption (CP-ABE) scheme in which access policy hiding mechanism is used for user privacy preserving, while white-box tracing and certificateless public data integrity auditing techniques are employed to resist key abuse and escrow problems. In addition, online/offline encryption and verifiable outsourced decryption are leveraged for high efficiency and cloud encryption is utilized to extend to multiple clouds. In the end, we formally prove the security of our scheme for indistinguishability of chosen plaintext attack (IND-CPA) security and traceability. Detailed performance evaluation with extensive experiments shows that our scheme is practicable for s-Logistics compared with the existing schemes.
Four-Round Black-Box Non-malleable Schemes from One-Way Permutations

We construct the first four-round non-malleable commitment scheme based solely on the black-box use of one-to-one one-way functions. Prior to our work, all non-malleable commitment schemes based on black-box use of polynomial-time cryptographic primitives require more than 16 rounds of interaction. A key tool for our construction is a proof system that satisfies a new definition of security that we call non-malleable zero-knowledge with respect to commitments. In a nutshell, such a proof system can be safely run in parallel with any (potentially interactive) commitment scheme. We provide an instantiation of this tool using the MPC-in-the-Head approach in combination with BMR.
What can a line of sight tell us? Working with a gantry crane at 50 m above ground level

With the support of gantry crane operators who load and unload containers between vessels and trailers, we visualized the gap in the line of sight between beginners and experienced operators. The aim was to explore training methods for skills transfer. Through an analysis of eye-movement data, we found that the working environment at 50 m above ground level was psychologically oppressive, so the beginners had an imbalanced gaze to the vital parts of the object. On the other hand, experienced operators made efforts to collect information by widening their gaze range. When it comes to training, in a stressful environment, too much input may instead lead to panic for beginners. Gradually expanding the scope of the inputs becomes important. In addition, beginners also need to carefully imitate the experienced operators in focusing on objects, so that they may truly grasp the skills.
Convolutional Neural Network model in Different dataset for Pose Face Recognition

Face recognition has progressed significantly thanks to recent developments in convolutional neural networks and the expanding amount of data. Security, healthcare, finance, criminal identification, payment, and advertising all employ face recognition. In face recognition difficulties, deep learning models approached and finally surpassed human performance. A deep structure feed forward neural network is a type of convolutional neural network. This is the one of the utmost well-known deep learning algorithms. The comparative research presented in this article looks at a variety of cuttingedge deep learning-based facial recognition classification algorithms to see which are the most successful in terms of accuracy and other metrics and also discussed are the numerous datasets for facial recognition that are accessible. It is Mainly focused on Comparison of various datasets for face recognition and also provide an accuracy and performance of the datasets such that LFW and YTF. Then analyse the performance of Multi-PIE dataset with different pose angle.
Comparative Ranking of Ontologies with ELECTRE Family of Multi-criteria Decision-Making Algorithms

Ontology selection is an active research topic to date. Essentially, ontology selection is a Multi-Criteria Decision-Making (MCDM) problem, as there are multiple ontologies to choose from whilst considering multiple criteria. The ELimination Et. Choix Traduisant la REalité (ELECTRE) is a family of popular MCDM methods. However, there has been little usage of the methods in ontology ranking to aid ontology selection and the comparisons between them on the same decision problem is missing in the literature. In this study, 4 ELECTRE methods were applied on a dataset of 200 ontologies along with 13 complexity metrics for each ontology. The experimental results show that all 4 ELECTRE methods have successfully ranked each ontology. The performances of the ELECTRE methods were further compared with 3 Statistical Rank Correlation Metrics. The correlation coefficients depicted a high level of agreement amongst the rankings of all ELECTRE versions despite some dissimilarities.
Domain specific Named Entity Recognition in Tamil

This paper presents a domain specific Tamil Named Entity Recognizer for history domain. The system uses a manually annotated corpus of 23k tokens and the dataset is tagged with 36 tags related to history domain. NER model is trained for Tamil based on Conditional Random Fields (CRF) with the use of features extracted based on the domain of interest and language. Hyper parameter tuning is applied with random search algorithm to find the best hyper parameters for the model. Tamil is a low resourced and morphologically rich language which makes the task challenging. Despite that, the system achieved a fair results with micro-averaged Precision, Recall and Fl-score of 87.9%, 67.1% and 76.1% respectively.
FuzzyPene: a Data-driven Genetic Fuzzy Tree Approach for Agent Safe Penetrations

An agent’s safe decision and control in an adversarial condition is difficult to make because of the decision environment’s highly dynamic and variable nature. In this work, we investigated the problem of an agent penetrating to reach its target, against the intercepting agents (i.e., defending agents) whose behaviors are unknown to the penetrating agent. We developed a method (named “FuzzyPene”) combining demonstration data of an ordinary human operator (not necessarily an expert), fuzzy rules and evolution. First, we obtain the ordinary operator’s successful penetrations trajectories and record critical information of each step of the trajectories. Then, the operator’s personal experience and preference (not necessarily perfect) are extracted and expressed by fuzzy rules whose parameters are tunable. Encountering that the parameter space of a monothetic fuzzy rule controller is exponentially high, we transform the monothetic controller into a fuzzy tree controller. After that, the parameters of the fuzzy rule controller are adjusted through demonstration data evaluation and genetic evolution. The controller parameters are initialized by the operator and may be suboptimal, but the ultimate success rate has improved relatively by 20% via self-evolution. Compared to the pure behavior cloning and pure DQN methods, FuzzyPene shows a higher performance (respecting final success rate and extra length ratio, times and samples needed), higher generalizability and decision interpretability.
Fundamentals and Challenges of Generative Adversarial Networks for Image-based Applications

Significant advances in image-based applications have been achieved in recent years, many of which are arguably due to recent developments in Generative Adversarial Networks (GANs). Although the continuous improvement in the architectures of GAN has significantly increased the quality of synthetic images, this is not without challenges such as training stability and convergence issues, to name a few. In this work, we present the fundamentals and notable architectures of GANs, especially for image-based applications. We also discuss relevant issues such as training problems, diversity generation, and quality assessment (metrics).
“When We See Strange Words”: Student-centered Experiences Using Dictionary Apps Within and Beyond the English Language Classroom in Palestine

Mobile technologies have been shown to be potential aids in improving language learning a long with computer technology, The purpose of this study was to examine students’ perceptions of their socio-affective learning practices when utilizing dictionary applications on their mobile phones. Twenty-five undergraduates were chosen at random from a group of 110 students to engage in five focus group discussions about their experiences using mobile dictionaries as learning tools in the West Bank and Palestine. The analysis of the data identified four main themes in the described experiences, each of which is related to specific socio affective aspects of learning and feelings of self-efficacy: feeling confident in their knowledge, independence in their learning, changing the pace of learning outside of the classroom, and overcoming obstacles like multiple meanings, Arabic accents, and technological distractions. The study’s proposals for adding more socio-technical pedagogies to English language instruction for Arabic learners are its conclusion.
Leveraging Non-Experts and Formal Methods to Automatically Correct Robot Failures

State-of-the-art robots are not yet fully equipped to automatically correct their policy when they encounter new situations during deployment. We argue that in common everyday robot tasks, failures may be resolved by knowledge that non-experts could provide. Our research aims to integrate elements of formal synthesis approaches into computational human-robot interaction to develop verifiable robots that can automatically correct their policy using non-expert feedback on the fly. Preliminary results from two online studies show that non-experts can indeed correct failures and that robots can use the feedback to automatically synthesize correction mechanisms to avoid failures.
Consortium Blockchain Based Anonymous and Trusted Authentication Mechanism for IoT

IoT devices always work in an open network environment, and their security has been widely concerned. Authentication is the most important part of IoT security. The traditional centralized authentication scheme has the defects of prolonging the authentication time and being difficult to be supervised. The blockchain technology with decentralized characteristics provides a new solution for the distributed IoT system. This paper proposes an anonymous two-way authentication mechanism for IoT terminals based on the Consortium blockchain. An anonymous communication authentication code is designed during the authentication process to ensure the security of the terminal’s real public key. The decentralization and non-tampering characteristics of the blockchain are used to ensure the security of anonymous keys. The randomness of the key is guaranteed through the negotiation of multiple sets of random numbers, and the confidentiality and integrity of the communication data are guaranteed through the 256-bit elliptic curve algorithm and digital signature algorithm. Compared with the existing mobile IoT authentication schemes through experiments, the result shows that this scheme has better security and communication performance.
A Flexible and Configurable Combined Public Key Scheme

In order to solve the problem of collusion attack and hard updating of classical COMBINED PUBLIC KEY (CPK), FLEXIBLE AND CONFIGURATION COMBINED PUBLIC KEY (FC-CPK) improves the classical CPK, and proposes a certificateless configurable combined public key with optional private key factor. This paper describes a certificateless encryption algorithm constructed based on FC-CPK encryption algorithm, and proves that it can be reduced to GDH complexity hypothesis under stochastic prediction model. At the same time, it also makes a security analysis of the against collusive attacks and resistance to forgery accompanying public key security. Therefore, the constructed algorithm has demonstrable security, and the cryptosystem of the algorithm has the characteristics of certificateless, unlimited, light-weight and low-delay, which is very suitable for the scenarios of the Internet of Things (IoT) era.
Blockchain Application in Incentivizing Students on Participation in Online Classes

The world we know has changed over a brief period, with the ascent and spread of Covid-19. This affected the education sector resulting in offline classes to online classes. Technologies made it easy with numerous websites, materials, video lectures, courses, and techniques for the students. In this situation, the main problem arises with students not participating in the class having many reasons such as illness, being introverted, and feeling that they may be wrong. If we are not interested in something to talk about or are shy, we must face it so that we will make the best out of it. People remember the things they listen to carefully, so we can probably study less if we listen. In this research paper, we proposed a Blockchain based application, so students who are going to attend online classes would be able to participate more in the class. They will be able to gain incentives that are based on crypto currency and by using those cryptocurrencies they can spend it on fees or any other resources in the university which would be beneficial for students. They will be able to gain incentives that are based on cryptocurrency and by using those crypto currencies they can spend it on fees or any other resources in the university. We are using the most popular technology which is Block chain technology to make sure that students who are attending online classes will be able to pay more attention. Also, we are using the most famous functionality of Blockchain which is incentivization. To give rewards to the students we will be using incentivization so they can pay more attention to the classes. This design is beneficial for teachers also to look at the status of each student and get in contact with them.