Reviewerly/exHarmony huggingface dataset:
ASKCC-DCNN-CTC: A Multi-Core Two Dimensional Causal Convolution Fusion Network with Attention Mechanism for End-to-End Speech Recognition

Aiming at the problems of difficulty in extracting key features and low prediction accuracy of traditional convolutional neural networks in Chinese speech recognition, we analyze the impacts of information leakage and unstandardized phoneme features on its performance, based on the deep convolutional neural network (DCNN)-connectionist temporal classification (CTC) model. In addition, a multi-core two dimensional causal convolution fusion network layer structure of SKNet is constructed, and we propose a DCNN-CTC model for fusion of attention mechanism and SKNet multi-core 2D causal convolution network (ASKCC-DCNN-CTC), which effectively improves the accuracy and training speed of Chinese speech recognition. The simulation results show that the error rate of our model on the ST-CMDS dataset is 12.201% lower than that of the DCNN-CTC model, the performance on the THCHS30 dataset is also improved, which reveals a good generalization ability.
MOODLE-BASED DIGITAL INTERACTIVE BOOK: STUDENT REFLECTIONS ON THE TECHNOLOGY INTEGRATED BLENDED CLASS

Online learning has been going on for almost 2 years now since pandemic and caused a dramatic change of education, with the rise of e-learning, where teaching takes place remotely and using a variety of digital platforms. One of the challenges is access internet and technology used in digital learning that forced the lecturers to innovate in providing a learning approach or using innovative teaching media as well as teaching material to facilitate students in the learning process independently wherever they are. The shift away from the education system caused by the COVID-19 pandemic raises the question of whether the adoption of online learning will continue post-pandemic, and how such a shift will impact education systems around the world. This study proposed some of the objective due to the recent condition of the education field. One of the objectives is to describe the students-teacher reflection toward the implementation of digital interactive book in their class. The study employed a descriptive analysis research design and gained the data from semi structured interview and questionnaire. The participant of this study was a class studying Grammar subject in a blended class of a second year at a higher educational institution (FKIP) in Universitas Pakuan. The result of the study showed that somehow, the students have positive reflections toward the use of digital interactive book designed by the researchers.
THE NEED ANALYSIS OF WRITING MODULE WITH EDMODO

Paper writing skills for Akamigas Polytechnic Students Palembang had not reached the expectation. They still had difficulties organizing the ideas into written forms and effective sentences. They did not understand the paper writing systematically, how to cite, and how to write the bibliography. They were caused by the use of limited teaching materials, by relying on PowerPoint as the main media. This research aimed to find out what the students needed of Edmodo-writing module. This research is a part of the identification of teaching material needs to explore the needs of the teaching materials. Therefore, this research applied the qualitative approach with descriptive method. The data were collected with a closed-questionnaire and interview. The need analysis showed a percentage of 97.1% of students preferred the Internet as the reference source to any other. Therefore, it is important to technology-based learning media that provided several applications. They could facilitate both lecturer and students to share and to learn the materials. The data showed 87.1% of students needed technology-based teaching materials, such as Edmodo. A percentage of 91.4% of students stated that they wanted technology-based teaching materials that were developed into modules assisted with Edmodo. Besides that, a percentage of 95.7% of students wanted guidelines to facilitate them applying the module and using the Edmodo.
Dynamic Sharding: A Trade-OFF Between Security and Scalability

To overcome the limitations on the scalability of current blockchain systems, sharding is widely considered as a promising solution that divides the network into multiple disjoint groups processing transactions in parallel to improve throughput while decreasing the overhead of communication, computation, and storage. However, most existing blockchain sharding systems adopt a static sharding policy that cannot efficiently deal with the dynamic environment in the blockchain system, i.e., joining and leaving of nodes, and malicious attack. This chapter presents SkyChain, a novel dynamic sharding-based blockchain framework to achieve a good balance between performance and security without compromising scalability under the dynamic environment. We first propose an adaptive ledger protocol to guarantee that the ledgers can merge or split efficiently based on the dynamic sharding policy. Then, to optimize the sharding policy under dynamic environment with high dimensional system states, a deep reinforcement learning-based sharding approach has been proposed, the goals of which include: (1) building a framework to evaluate the blockchain sharding systems from the aspects of performance and security; (2) adjusting the re-sharding interval, shard number and block size to maintain a long-term balance of the system’s performance and security. Experimental results show that SkyChain can effectively improve the performance and security of the sharding system without compromising scalability under the dynamic environment in the blockchain system.
An unsupervised aerial tracking method of camouflaged targets in complex environments

Abstract With the widespread use of UAVs and the development of various technologies related to computer vision, much attention has been devoted to UAV-based target tracking. For the task of tracking camouflaged people in complex environments, there is a shortage of annotated datasets and poor backbone feature extraction network capabilities, which will cause tracking drift and tracking lag. To achieve unsupervised UAV target tracking under unsupervised conditions, a new target tracking model has been designed. First, we roughly detect moving targets using a camouflaged segmentation network, and then apply dynamic programming to generate smooth candidate boxes. Secondly, a deeper convolutional neural network is designed for feature extraction to replace the original backbone network, and the residual structure is modified to ensure tracking effectiveness. Extensive comparative experiments have shown that the proposed unsupervised method performs the task of UAV target tracking well.
The basic ability of Chinese teachers online teaching in the post-epidemic era

At the beginning of 2020, the COVID-19 epidemic swept the world, and countries all over the world adopted online teaching, and international Chinese education has faced unprecedented challenges.In the post-epidemic era, online Chinese teaching is bound to become a new normal of teaching Chinese as a foreign language, and teaching has changed from offline education to online education.The change of teaching platform and teaching mode puts forward higher requirements for the ability of Chinese teachers.This paper starts with the analysis of the International Standard for Chinese Teachers, and combines with the actual situation of Chinese teaching in the epidemic environment, trying to define the specific basic abilities of online teaching in Chinese teachers.
Digitalization of Accounting Applications: From Clay Tablets to Smart Accounting Applications

In the historical process, accounting has changed and developed in order to adapt to technological developments. Accounting has shown a serious technological development from clay tablets to accounting with pen and paper and then with electronic tablet. Accountants using technology in accounting practices are the natural carriers of this process. Technological innovations such as blockchain, artificial intelligence and cloud technology, big data systems are changing, in other words, transforming the functions of accounting, the competencies and competencies of accountants. In this context, the aim of the study is to create a theoretical framework for the potential impact of technological developments in the form of blockchain, artificial intelligence, big data, cloud computing system, which are defined as smart accounting applications in the development process of accounting and the digital transformation of accounting.
The Role of Cybersecurity on the Performance of Malaysian Higher Education Institutions

Cybersecurity, as a security management requirement, is essential to understanding data security in higher institutions.This study aims to explore the role of cybersecurity in the performance of Malaysian higher education institutions through semi-structured qualitative interviews with 10 cybersecurity risk management officers from 10 public universities.The data were analysed using thematic analysis to identify the themes and sub-themes that revealed the strengths and deficiencies of the current cybersecurity frameworks.Results showed that cybersecurity implementation is considered a successful innovation in Malaysian universities and has contributed to protecting the data of students and staff, which in turn allowed the universities to improve their reputation.This study contributed significantly to the understanding of the performance and applicability of cybersecurity in universities.It showed the efficient use of resources, identification and detection of risk exposures, and improved cybersecurity communication between the technical team and top management are essential for a good decisionmaking process.
Privacy-Preserving Local Energy Markets: A Systematic Literature Review

In recent years, different models of local energy markets have emerged due to the rapid growth of renewable energy resources. These models introduce privacy and security concerns that may deter many from participating and benefiting from these markets. Consequently, researchers have shown increased interest in enhancing the privacy and security of local energy markets through several approaches by applying diverse privacy-enhancing techniques. This paper presents a systematic literature review of 61 peer-reviewed papers to examine the proposed privacy-preserving approaches in the current literature and systematise them based on three market phases: bids generation, market clearance and payment and settlement. The paper also provides a detailed analysis and evaluation of different aspects of these approaches in terms of integration with blockchain technology, reliance on a trusted third party, type of participants protected, adversarial behaviour adopted as well as fulfilment of security, privacy, traceability and scalability requirements. Finally, it identifies five significant gaps and limitations that require further research if such privacy solutions are to be deployed in the real world. These gaps are lack of: privacy solutions that integrate all market phases; consideration of physical constraints while maintaining privacy; satisfying both identity and data protection; provision of clearly defined adversarial models; and, consideration of how these privacy solutions will scale.
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification

Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotation of DAs, which are then used to train DA classifiers. However, these studies have paid little attention to sample informativeness, which can reflect the information quantity of the selected samples and inform the extent to which a classifier can learn patterns. Notably, the informativeness level may vary among the samples and the classifier might only need a small amount of low informative samples to learn the patterns. Random sampling may overlook sample informativeness, which consumes human labelling costs and contributes less to training the classifiers. As an alternative, researchers suggest employing statistical sampling methods of Active Learning (AL) to identify the informative samples for training the classifiers. However, the use of AL methods in educational DA classification tasks is under-explored. In this paper, we examine the informativeness of annotated sentence samples. Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process. The results reveal that most annotated sentences present low informativeness in the training dataset and the patterns of these sentences can be easily captured by the DA classifier. We also demonstrate how AL methods can reduce the cost of manual annotation in the AL sampling process.
Performance Analysis of Different Applications of Image Inpainting Based on Exemplar Technique

In this age of rapidly developing image processing, inpainting has been a popular and practical art. Researchers have paid considerable attention to image inpainting throughout the years due to its enormous significance and effectiveness in a wide range of image processing applications, including the removal of scratches, the elimination of objects, and the modification of faces. It is one of the most challenging issues in image processing, demanding a comprehensive understanding of the image's texture and structure. The quality of inpainted image is a crucial factor which determines how close the inpainted image is to the original image. Many improvements have been implemented in the exemplar-based approach to increase the quality of inpainted regions containing structure and texture information. There are numerous ways to assess the quality of an inpainted image. In this study, the applications of exemplar based inpainting are evaluated using standard analytical measures including Sum of Absolute Difference (SAD), Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient, and Structural Similarity Index Measure (SSIM).
Automated verification of countermeasure against detector-control attack in quantum key distribution

Abstract Attacks that control single-photon detectors in quantum key distribution using tailored bright illumination are capable of eavesdropping the secret key. Here we report an automated testbench that checks the detector’s vulnerabilities against these attacks. We illustrate its performance by testing a free-running detector that includes a rudimentary countermeasure measuring an average photocurrent. While our testbench automatically finds the detector to be controllable in a continuous-blinding regime, the countermeasure registers photocurrent significantly exceeding that in a quantum regime, thus revealing the attack. We then perform manually a pulsed blinding attack, which controls the detector intermittently. This attack is missed by the countermeasure in a wide range of blinding pulse durations and powers, still allowing to eavesdrop the key. We make recommendations for improvement of both the testbench and countermeasure.
Estratégias desenvolvidas por conselheiros tutelares e assistentes sociais frente às dificuldades impostas pela Covid-19

Objective:To analyze the factors that hinder the actions of Guardianship Counselors and Social Workers during the Covid-19 pandemic.Methods: Exploratory and descriptive study, developed in inland cities of Ceará, participating 7 guardianship counselors and 3 social workers, acting in the fight against intrafamily violence against children and adolescents in the pandemic period, conducted between August 2020 and May 2021.Data were collected online through videos authored by the participants with subsequent transcription of excerpts of the content.Results and Discussion: An increase in cases of violence against children and adolescents was observed, with fear of the victim being the most recurrent reason.Regarding the facilities, one can mention the greater access to the legal sector and the increase in partnerships with the Secretariat of Labor and Social Development.Conclusion: The relevance of the professionals mentioned in combating violence against children and adolescents was verified, even with the difficulties imposed by the pandemic.
Sign Language Recognizer: A Deep Learning Approach

This research article deals with an approach towards sign language recognition with deep learning algorithms. Different algorithms like LSTM and CNN which are deep learning techniques are used for better results, LSTM which is the advanced version of RNN and Convolutional Neural Network which is good at extracting features from uneven background, we use CNN and LSTM and classify the features extracted from the video frames. The model is trained with the dataset which is generated on its own, and the dataset includes the video frames of the gestures, and the model uses the dataset to extract the key features and classify them. The motive of this research is to recommend the best algorithm which classifies the hand gestures with accurate results. A model is developed which recognizes and classify the hand gestures and display the meaning of those gestures in the form of text.
Surveillance using Ml

There is a strong demand for smart vision based surveillance systems owing to the increase in crime at a frightening rate at various public places. As crime increases at a disturbing rate, public security violations and high cost of security personnel have motivated the strategic survey of existing vision and image processing based techniques. It discusses the system to analyze human’s normal and abnormal activities recognition along with various feature selectors and detectors.Deep learning approach is used to detect suspicious or normal activity in an academic environment, and which sends an alert message to the corresponding authority, in case of predicting a suspicious activity.
Defending from Physically-Realizable Adversarial Attacks through Internal Over-Activation Analysis

This work presents Z-Mask, an effective and deterministic strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. The presented defense relies on specific Z-score analysis performed on the internal network features to detect and mask the pixels corresponding to adversarial objects in the input image. To this end, spatially contiguous activations are examined in shallow and deep layers to suggest potential adversarial regions. Such proposals are then aggregated through a multi-thresholding mechanism. The effectiveness of Z-Mask is evaluated with an extensive set of experiments carried out on models for semantic segmentation and object detection. The evaluation is performed with both digital patches added to the input images and printed patches in the real world. The results confirm that Z-Mask outperforms the state-of-the-art methods in terms of detection accuracy and overall performance of the networks under attack. Furthermore, Z-Mask preserves its robustness against defense-aware attacks, making it suitable for safe and secure AI applications.
Neural Dynamic Focused Topic Model

Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by previous work, such models implicitly assume that the probability of a topic to be active and its proportion within each document are positively correlated. This correlation can be strongly detrimental in the case of documents created over time, simply because recent documents are likely better described by new and hence rare topics. In this work we leverage recent advances in neural variational inference and present an alternative neural approach to the dynamic Focused Topic Model. Indeed, we develop a neural model for topic evolution which exploits sequences of Bernoulli random variables in order to track the appearances of topics, thereby decoupling their activities from their proportions. We evaluate our model on three different datasets (the UN general debates, the collection of NeurIPS papers, and the ACL Anthology dataset) and show that it (i) outperforms state-of-the-art topic models in generalization tasks and (ii) performs comparably to them on prediction tasks, while employing roughly the same number of parameters, and converging about two times faster.
ConvNTM: Conversational Neural Topic Model

Topic models have been thoroughly investigated for multiple years due to their great potential in analyzing and understanding texts. Recently, researchers combine the study of topic models with deep learning techniques, known as Neural Topic Models (NTMs). However, existing NTMs are mainly tested based on general document modeling without considering different textual analysis scenarios. We assume that there are different characteristics to model topics in different textual analysis tasks. In this paper, we propose a Conversational Neural Topic Model (ConvNTM) designed in particular for the conversational scenario. Unlike the general document topic modeling, a conversation session lasts for multiple turns: each short-text utterance complies with a single topic distribution and these topic distributions are dependent across turns. Moreover, there are roles in conversations, a.k.a., speakers and addressees. Topic distributions are partially determined by such roles in conversations. We take these factors into account to model topics in conversations via the multi-turn and multi-role formulation. We also leverage the word co-occurrence relationship as a new training objective to further improve topic quality. Comprehensive experimental results based on the benchmark datasets demonstrate that our proposed ConvNTM achieves the best performance both in topic modeling and in typical downstream tasks within conversational research (i.e., dialogue act classification and dialogue response generation).
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-Cost Proxies

Designing neural architectures requires immense manual efforts. This has promoted the development of neural architecture search (NAS) to automate the design. While previous NAS methods achieve promising results but run slowly, zero-cost proxies run extremely fast but are less promising. Therefore, it’s of great potential to accelerate NAS via those zero-cost proxies. The existing method has two limitations, which are unforeseeable reliability and one-shot usage. To address the limitations, we present ProxyBO, an efficient Bayesian optimization (BO) framework that utilizes the zero-cost proxies to accelerate neural architecture search. We apply the generalization ability measurement to estimate the fitness of proxies on the task during each iteration and design a novel acquisition function to combine BO with zero-cost proxies based on their dynamic influence. Extensive empirical studies show that ProxyBO consistently outperforms competitive baselines on five tasks from three public benchmarks. Concretely, ProxyBO achieves up to 5.41× and 3.86× speedups over the state-of-the-art approaches REA and BRP-NAS.