Reviewerly/exHarmony huggingface dataset:
Data Encryption Using Morse Code

Cryptography in general means hiding, while in technical terms cryptography means protecting data from unauthorized access of someone, or cryptography can be considered as a method that ciphers the data so that the data can neither be read nor understood by humans. With the increasing level of internet consumption, the security of our data is the main concern. Due to increasing cyber-attacks, and over-relying on the internet, a lot of users’ data has been compromised. This has brought our attention to data security and cryptographic algorithms. Data security ensures the data of users is not being compromised and is present in such a format that an intruder won’t get access to our data, it ensures that the data is accessible to the intended user only, and anybody else can neither read nor change the data. To achieve such security, some algorithms or processes are needed. One such algorithm can be derived using Morse code since data in Morse code uses dots and dashes to represent alphabets and numbers. Even if such encryption is used directly, it may cause some threat to the user’s data as anyone with knowledge of Morse code can easily decrypt it, so to get more safety, the encrypted data is needed to be encrypted again with some other algorithm. Now, other than data safety, the safety of the algorithm is also required, as if someone gets to know about the algorithm, they can decrypt the data. The Python programming language provides a module named Cryptography in which Fernet can be used to encrypt the algorithm file as well as the data file again. Cryptography in python uses a symmetric encryption technique, i.e., it uses the same key to encrypt and decrypt the data. This technique is faster compared to asymmetric techniques. This paper demonstrates how Morse code, time &amp; Python’s Cryptography module can be used together to provide maximum data safety.
Technology-Enhanced Paraphrasing Tool to Improve EFL Students’ Writing Achievement and Enjoyment

&lt;p&gt;&lt;em&gt;The issue of plagiarism and EFL students’ difficulties in writing course appeared to be still the challenging task for teachers. Various efforts in order to increase students’ competence in producing a decent writing production are still fascinating until the present time. Therefore, the aim of this research is to examine whether the use of Technology-Enhance Paraphrasing Tool (TEPT) can improve EFL students’ writing performance. Additionally, the way the students react emotionally towards the use of TEPT in writing class, especially in paraphrasing activities was also measured to by the use of Foreign Language Enjoyment (FLE) questionnaire. Paired sample t-test was employed to 48 students from English Department at one of Private Universities in Samarinda, East Kalimantan, Indonesia. The findings showed that there was a significant effect towards the use of TEPT in writing class to improve EFL students writing achievement. In addition, all of the students performed positive emotions during the implementations of TEPT in writing class where the results from FLE questionnaire indicated that the students enjoy and being motivated in joining the writing class. Pedagogical implication of the research also discussed further in this article.&lt;/em&gt;&lt;/p&gt;
Communicative Language Teaching and Its Implementation in Online Learning: The Teachers’ Voice

&lt;p&gt;&lt;em&gt;Communicative Language Teaching has been popular as a pedagogical approach in teaching L2 that may help students gain communicative competence. This research aimed to investigate teachers' perceptions of CLT and its implementation in online learning. This research adopted a survey research design, and 30 teachers were involved as the participants. The participants were from junior high school and senior high school teachers in Tanjungpinang-Bintan and Batam. Two questionnaires were used as the instruments. The findings of this study revealed that teachers had a good understanding of the principle of CLT. But most of the teachers showed a misconception about not teaching grammar in CLT. In addition, the teachers also practiced CLT in online learning, and they believed CLT was appropriate and effective. They were also supported with sufficient platform media and the internet even though the instructional materials were insufficient. It shows that the teachers had positive perceptions towards the practice and implementation of CLT in online learning. The result can be used as a reflection for the government to provide adequate instructional media and teachers' training in CLT that may be beneficial for teaching in online learning.&lt;/em&gt;&lt;/p&gt;
A MVMD–MMFE algorithm and its application in the flow patterns identification of horizontal oil–water two-phase flow

Abstract Aiming to extract the main information features of fluid multivariate conductance signals and identify the flow patterns under different flow velocities, we present a multichannel time series analysis algorithm based on the multivariate variational mode decomposition (MVMD) and multivariate multiscale fuzzy entropy (MMFE). Firstly, by simulating a multichannel complex signal and performing a series of sensitivity experiments within various noise intensities, we prove the feasibility of the MVMD in chaotic time series. Then, we employ the MVMD to decompose multivariate conductance signals into the intrinsic mode function (IMF) and calculate the MMFE of the IMFs for different flow patterns. Meanwhile, the multivariate empirical mode decomposition (MEMD) is also applied on the comparison of signal decomposition. Finally, we discuss the classification consequence under different mode values k to realize the optimal decomposition. The experimental results show that the MVMD–MMFE algorithm can extract the main information of fluid multichannel signals and distinguish three horizontal oil–water flow patterns effectively, which provides an idea for studying the nonlinear characteristics of the chaotic system.
Inventari di beni mobili della Venezia medievale: spogli lessicali

Abstract From the point of view of the historical sciences, inventories of movable property constitute a particularly interesting type of text since they include more detailed information of the material realities of past epochs. The lexical study of 14th-century Venetian inventories carried out in the present paper provides relevant new evidence for Italian historical lexicography.
Development Status and Trend Analysis of Internet of Medical Things Industry in China

This article analyzes the development status, development trend and prospects of China’s Internet of Medical Things (IoMT) industry from a macro perspective. Our survey mainly includes: analyzing the necessity and urgency of China’s medical system reform from the various dilemmas faced by China’s medical system, and analyzing the development of the IoMT industry based on the current basic conditions of development of the Internet of Things (IoT), information technology and background of COVID-19 epidemic. Opportunities and the evolution of China’s IoMT policy were also analyzed. Moreover, from the five aspects of medical industry informatization, Internet hospitals, smart wearable devices, medical AI industry and medical industry digitization, the development status and trends of China’s IoMT industry are analyzed. Finally, it looks forward to the development prospects and directions of IoMT industry for health care in China.
Reconstructing non-Markovian open quantum evolution from multiple time measurements

For a quantum system undergoing non-Markovian open quantum dynamics, we demonstrate a tomography algorithm based on multitime measurements of the system, which reconstructs a minimal environment coupled to the system, such that the system plus environment undergoes unitary evolution and that the reduced dynamics of the system is identical to the observed dynamics of it. The reconstructed open quantum evolution model can be used to predict any future dynamics of the system when it is further assumed to be time-independent. We define the memory size and memory complexity for the non-Markovian open quantum dynamics which characterize the complexity of the reconstruction algorithm.
Analysis of Hybrid Wavelet Transformed Images to Improve Image Pixel Quality

Visual information exchange in the form of a digital picture has become ubiquitous in the world of communication. During the transmission of images, the information is distorted due to noise. This noise component distorts the quality of images. De-noising methods are used to expand the grade of picture pixels or to restore the original form of incoming data. In this article, we will be introducing a de-noising method for upgrading the excellence of the image. A multiscale LMMSE estimation method using un-decimated wavelet transforms (UWT) has been proposed. To generate the wavelet transformed images, Daubechies & Biorthogonal filters are used. In this proposed method, a Hybrid filter is generated using these two filters. Image produced using this method seems to be agreeable as compared to individual filters. The result is observed by the PSNR & MSE value for the quantitative measure.
Evaluation of Covid-19 Ontologies Through OntoMetrics and OOPS! Tools

Ontology provides a way to encode human intelligence so that machines can understand and make decisions by referring to this intelligence. For this reason, ontologies are used in every domain, specifically in the domains that relate to the emergency situation. Covid-19 became a serious concern and emerged as the most significant emergency for the world. Many Covid-19 ontologies are available on the Web to analyse the Covid-19 data semantically. However, a few questions arise from work done so far: How many Covid-19 ontologies are available? Which is a good Covid-19 ontology in terms of richness? What is the pitfall rate of the available Covid-19 ontologies? This paper focuses on these questions by providing a comprehensive survey on available Covid-19 ontologies. By this paper, analysts and researchers find a road map, an overview of research work that exists in terms of Covid-19 ontologies.
Improvisation of Music Therapy with Repertoire of Marineras Trujillanas in Online Higher Education Learning

University students face and faced cases of anxiety, stress, shyness in front of the public which caused effects on learning and if it was online learning were stronger those negative effects, so the improvisation music therapy with workshops had to intervene as a fundamental tool in online learning, the objective of this research was to analyze the results of the development of an improvisation music therapy program, with Peruvian marinera repertoire in general studies students of the music workshop of the National University of Trujillo with a total of 133 out of 156 participants using the methodology of the online survey resulted in significant effects and group participation of the students of the music workshop. It was concluded that music therapy significantly helps students to improve their self-esteem, motivates them to continue studying, motivates them to participate and/or relate to their environment and decreases stress and anxiety.
Lattice-Based Cryptography: A Quantum Approach to Secure the IoT Technology

The security of the Internet of Things (IoT) is a key worry since it deals with personal data that must be dependable and can be used dangerously to control and manipulate device operations. In addition, the data generating process is heterogeneous, and the data is enormous in bulk, requiring complicated management. Quantum IoT, or Quantum Computing and IoT, is a notion of higher security design that utilizes quantum-mechanical principles on IoT security management. On the one hand, IoT is beneficial to us, but it also poses several major security risks, such as data leaks, side-channel attacks, malware, and data authentication. Classical cryptographic techniques, such as the Rivest-Shamir-Adleman (RSA) algorithm, perform admirably on traditional machines. However, quantum computing, which has enormous processing capacity and is more than capable of readily breaking current cryptographic algorithms, is steadily gaining traction. As a result, even before quantum computers are commercially available, we must create post-quantum cryptography algorithms to protect our systems from security breaches. Lattice-based cryptography, as a possible option for the future post-quantum cryptography standard, has the features of strong security guarantees and great efficiency, making it ideal for IoT applications. We discuss the benefits of lattice-based cryptography and its implementations for IoT devices in this study.
Adaptive Synthetic Oversampling Algorithm for Handling Class Imbalance in Multi-Class Data Stream Classification

Concept drift and class imbalanced data are major challenging processes involved in modern streaming data classification.Particularly, when integrated with difficult factors like the existence of noise, overlapping class distribution, concept drift, and data imbalance can considerably affect the classifier results.In addition, various challenges affect the performance of the existing oversampling schemes such as SMOTE and its derivatives.Regardless of that, several existing models concentrate on the data imbalance in the binary classification problems, whereas the complex multi-class counterparts are yet to be explored.With this motivation, this study develops an Adaptive Synthetic Oversampling Algorithm (ASYNO) based Multiclass Streaming Data Classification (ASYNO-MCSDC) model on Class Imbalance Handling and Concept Drift.The presented ASYNO-MCSDC method initially performs different stages of preprocessing such as label encoding, data normalization, and data splitting.Besides, the Adaptive Synthetic oversampling technique (ASYNO) is applied for handling class imbalance data problems.Also, the online bagging ensemble classifier is employed for the data classification process in which the Hoeffding Tree (HT) was utilized as the base classification and the number of estimators used in online bagging is set to 10.For the process of experimentation, two types of learning are used, one is batch learning and other is incremental learning.The experimental validation of the ASYNO-MCSDC model is tested using two datasets namely stationary imbalance stream and dynamic imbalance stream.The experimental results pointed out that the ASYNO-MCSDC model has accomplished promising results over other models.
A Contrastive Pre-training Approach to Discriminative Autoencoder for Dense Retrieval

Dense retrieval (DR) has shown promising results in information retrieval. In essence, DR requires high-quality text representations to support effective search in the representation space. Recent studies have shown that pre-trained autoencoder-based language models with a weak decoder can provide high-quality text representations, boosting the effectiveness and few-shot ability of DR models. However, even a weak autoregressive decoder has the bypass effect on the encoder. More importantly, the discriminative ability of learned representations may be limited since each token is treated equally important in decoding the input texts. To address the above problems, in this paper, we propose a contrastive pre-training approach to learn a discriminative autoencoder with a lightweight multi-layer perception (MLP) decoder. The basic idea is to generate word distributions of input text in a non-autoregressive fashion and pull the word distributions of two masked versions of one text close while pushing away from others. We theoretically show that our contrastive strategy can suppress the common words and highlight the representative words in decoding, leading to discriminative representations. Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval.
Robust Node Classification on Graphs

Node classification using Graph Neural Networks (GNNs) has been widely applied in various real-world scenarios. However, in recent years, compelling evidence emerges that the performance of GNN-based node classification may deteriorate substantially by topological perturbation, such as random connections or adversarial attacks. Various solutions, such as topological denoising methods and mechanism design methods, have been proposed to develop robust GNN-based node classifiers but none of these works can fully address the problems related to topological perturbations. Recently, the Bayesian label transition model is proposed to tackle this issue but its slow convergence may lead to inferior performance. In this work, we propose a new label inference model, namely LInDT, which integrates both Bayesian label transition and topology-based label propagation for improving the robustness of GNNs against topological perturbations. LInDT is superior to existing label transition methods as it improves the label prediction of uncertain nodes by utilizing neighborhood-based label propagation leading to better convergence of label inference. Besides, LIndT adopts asymmetric Dirichlet distribution as a prior, which also helps it to improve label inference. Extensive experiments on five graph datasets demonstrate the superiority of LInDT for GNN-based node classification under three scenarios of topological perturbations.
Applications of Intelligent Artificial and Communication Technologies in Intelligent Buildings

With the development of society and the progress of science and technology, more and more technologies are being applied to the intelligent building system. The study summarizes and analyzes the development status of intelligent buildings, then analyzes and generalizes the advantages, disadvantages, and applications of several artificial intelligence and communication technologies in the intelligent building system. Results show that this paper introduces the three applications of artificial intelligence systems in intelligent buildings, including expert control systems, artificial neural systems, and intelligent decision systems, as well as the application of communication technology in intelligent buildings, including virtual LAN technology, communication network technology and broadband network technology. Artificial intelligence technology can reflect the current situation of buildings to people in real-time, judge emergencies instead of experts, and make the best choice to prolong the service life and stability of buildings. Communication technology can help users realize remote offices and home offices. The application of these technologies in intelligent buildings may not be mature at this stage, but the development and application potential are still very considerable.
SRA

Deep Neural Network (DNN) accelerators are increasingly developed to pursue high efficiency in DNN computing. However, the IP protection of the DNNs deployed on such accelerators is an important topic that has been less addressed. Although there are previous works that targeted this problem for CMOS-based designs, there is still no solution for ReRAM-based accelerators which pose new security challenges due to their crossbar structure and non-volatility. ReRAM's non-volatility retains data even after the system is powered off, making the stored DNN model vulnerable to attacks by simply reading out the ReRAM content. Because the crossbar structure can only compute on plaintext data, encrypting the ReRAM content is no longer a feasible solution in this scenario.
3D Audio + Augmented Reality + AI Chatbots + IoT: An Immersive Conversational Cultural Guide

Typical Augmented Reality (AR) cultural heritage (CH) guides adopt a visuo-centric approach, wherein visual virtual elements are superimposed onto the physical world. Recent research has investigated the use of Audio AR to evoke multisensory immersive experiences to visitors of CH sites adopting screen-free interfaces to ensure that user attention is not distracted from the physical exhibits. A parallel trend in the audience engagement programs of cultural institutions involves the employment of AI chatbots which are engaged in dialogues with visitors to provide meaningful responses to user questions. Herein, we present Exhibot, an intelligent audio guide system aiming at enhancing the user experience of CH site visitors. Exhibot represents the first-ever approach to combine Audio AR and chatbot technologies to enable natural visitor-exhibit interaction, while also leveraging IoT devices to contextualize the delivered information. The usability and utility of Exhibot has been tested in a case study in outdoors environment with the preliminary results indicating a very positive user experience.
Design and Implementation of TLS Accelerator

Communication devices deploy messages to another one over either a controlled channel or a non-controlled channel. When the communication channel is non-controlled, the deployed message is available for anyone that is able to listen. But, the deployed message may comprise private information that needs to be shared securely. Therefore, to establish a secure communication over a controlled or a non-controlled channel, cryptography algorithms are used. The Transport Layer Security (TLS) is a protocol that includes several cryptography algorithms. With its algorithms, TLS provides authentication, confidentiality, and data integrity features. These features achieved with repeated calculations. Yet, these calculations increase the power consumption and degrade the performance of a sequential processor. To overcome these problems, an accelerator can be used for the calculations of TLS protocol. In this work, a TLS accelerator is designed and implemented on an FPGA. Our design aims to increase the performance and decrease the power consumption of sequential processor during the TLS calculations. The proposed accelerator is implemented with Xilinx Vivado and the implementation results show that the proposed accelerator consumes 0.86 Watt power. The accelerator is simulated with Vivado in order to measure the throughput of the proposed accelerator. The encryption throughput of the proposed accelerator at 100 MHz operating frequency is observed as 700 Mbps. Also, the proposed accelerator provides 52.4 handshake connections per second.
About Training Educators to Become Drivers for Change

Despite the increasing need to respond to changes in society and work, Higher Education (HE) remains slow to innovate and adapt (Serdyukov, 2017). The traditional model of education which was developed for a uniform, elite cohort, excludes many individuals. To realise the value of education in a modern society we need to consider Innovative and inclusive approaches to learning which can be enabled by digital technologies. This requires educators to re-examine their role and how they can influence change within current, rigid HE structures. Higher Education Institutes (HEIs) are in unusual circumstances in that academic staff have significant influence over change (Armstrong 2014). This can be advantageous as encouraging an innovative mind-set at individual levels of the organization can potentially lead to significant impact (Serdyukov, 2017; Jakovljevic, 2018). This chapter will explore barriers to innovation in HE, and the role of educators in enabling digital transformation. It will discuss a transformative Faculty Development model aimed at nurturing educators as agents for change in a digital age.