Reviewerly/exHarmony huggingface dataset:
Using Classkick Application to Promote Constructive Feedback in Writing Lessons

Objective: As there is a drastic change in the nature of written literacy, both tertiary level students and educators are expected to maximize the opportunities offered by digital tools like Classkick application when giving and receiving constructive feedback about their language lessons. The purpose of this study was to get feedback from the students, especially about their perceptions of Student Interaction, Student Engagement, Instructor Presence, Student Satisfaction &amp; Perceived Learning in using ‘Classkick’ App for writing lessons as well as to get the learners’ suggestions onto how do we use ‘Classkick’ App to create more interesting lessons.&#x0D; Methodology: The methods used were an adapted questionnaire named Student Learning and Satisfaction in Online Learning Environments Instrument (SLS-OLE) questionnaire and two semi-formal interview questions. 96 students from the Law Faculty at Universiti Teknologi MARA (UiTM) Kampus Dengkil located in Malaysia were chosen as an informant group.&#x0D; Findings: The results indicate that the most common condition concerning students’ readiness in getting constructive feedback is that students get to interact with others using this application in order for them to be engaged with the lecturer’s presence so that it will lead to students’ satisfaction in enabling the construction of their own perceived learning in online classrooms.&#x0D; Implications: The main conclusion was that more constructive feedback received by the students will lead to more motivating factors to learn writing digitally.
A New Thumbnail Preserving Encryption Scheme

Cloud services can store a large number of images, but cannot protect the security of user privacy. The traditional image encryption scheme improves the privacy security, but reduces the visibility of the image, which makes the image invisible in the cloud service. To maintain both cloud storage security and visual usability, Tajik et al. proposed a thumbnail preserving encryption (TPE) scheme so that the encrypted image is presented as a low-resolution version of the plaintext image. However, this scheme only scrambles and replaces pixels, and has low security. Aiming at the problem of low security of this method, this paper proposes a new thumbnail preserving encryption scheme (New-TPE). The plaintext image information is used as part of the key to construct the correlation between the encryption process and the plaintext image; The optimal random value is selected from multiple random values and is added to the pixel value replacement process, which increases the degree of variation of pixel values before and after the replacement and reduces the correlation between adjacent pixels; The simulated annealing idea is introduced to select the optimal scrambling sequence within the range; A displacement function scrambling method is proposed to reduce the correlation between adjacent pixels. The experimental results show that the proposed scheme has higher security and a better balance between image privacy and security.
Knowledge Graph Tracing and Query System Based on Blockchain

In order to solve the problem that the personal rights and interests of knowledge map producers cannot be protected, this paper proposes a query system for the management and traceability of knowledge maps based on blockchain technology. This system contains the following modules: a user information module, a knowledge map production and production information record module, a knowledge map comparison and calculation module, a revenue distribution module and an information query module. The system provides a transparent and reliable unified information platform; it can trace the entire production, updating and circulation process of knowledge graphs and protect the privacy information of producers.
Cognitive-efficient component in professional-role communication of the modern Primary School teacher

The author raises the question concerning the importance of professional- role communication of primary school teachers; reveals the efficiency of communication in achieving partnerships and productive interaction in educational process. Empirical evidence has shown that there is a correlation between knowledge, cognitive skills, reflexive role assessment and communication skills.
Theoretical review of interpersonal problems of teacher and students’ interaction

The article is devoted to the theoretical problems of interpersonal interaction between teacher and students in higher education institutions.Researchers consider pedagogical communication and relationships in the student academic group as an important prerequisite for pedagogicalmanagement of the educational process in higher education institutions. It is asserted that the term “interrelation” is often used for relationshipsbetween people. It is determined that pedagogical communication is the interaction of the teacher with students and the content is the exchangeof information. It is noted that the interrelations “teacher-student” mean purposeful interaction of subjects of the pedagogical process and aredetermined by general socio-psychological mechanisms, social and role functions of partners and social norms of the institution of higher education. Relationships are distinguished that differ into primary and secondary ones, where it is established that, in contrast to primary relationships, secondary ones (“teacher-student” relationship) are rarely characterized by high emotional involvement and the participants in the relationship can be easily replaced. The three-component model of the mindset is characterized: cognitive (knowledge, understanding by the teacher of the child's inner world), emotional (the teacher's attitude to the student) and behavioral (the choice of means and methods of influence). It is noted that sympathy and antipathy determine the circle of persons with whom contact is maintained in the collective and the assimilation of group norms of behavior is carried out. It has been proven that the enthusiasm for the common cause of students and teachers, friendly disposition, combined with an interest in science, in a future profession, give rise to a joint creative search. The emphasis is made on the fact that every teacher of a higher education institution must constantly analyze his activities, behavior, relationships with colleagues and students.
From carbon copy paper to AI: 36 years as a reporter for the BBC

I joined the British Broadcasting Corporation (BBC) as a 22-year-old radio reporter in the city of Cambridge, in the east of England. At the time, I had the intention of becoming another John Cole, the late, great political editor of the BBC. Politics and social issues were what fascinated me. But a chance meeting one spring afternoon with a scientist at the city&amp;#8217;s famous Laboratory of Molecular Biology changed the direction of my career. I was stunned by what this man had achieved (he would later win a Chemistry Nobel) and committed to becoming a journalist specialising in the reporting of science. This was problematic as I&amp;#8217;d had no real science education at school. But seven years with the Open University as a mature student put that right, and in 1998 I found myself in the position of leading the science coverage on the fledgling BBC News website. I&amp;#8217;ve been a full-time science hack ever since. When I started in journalism my tools were a reel-to-reel recorder, a typewriter and several sheets of carbon copy paper to produce my radio scripts in duplicate. Today, as I approach the end of my career, I operate in a fully digital newsroom with mp3 recorders, cloud computing and AI. My medal lecture will detail the journey from the old to the new. I will pass on some of the lessons learned (which should be of interest to those wanting to interact with journalists) and consider some of the challenges ahead for my profession.
Comparative Study on Generating and Predicting Swarm Satellite Data by Deep Neural Networks

This study will present the latest development of anomaly detection methods underpinned with Deep Neural Networks (DNN), which focuses on predicting and generating electromagnetic data from the Swarm historic data. We report our investigation into the architectures of Recurrent Neural Networks (RNN) and generative adversarial network (GAN), particularly illustrating the development of a Long-Short Term Memory (LSTM) based methods and a flow-based generative model. We present the implementation of the algorithms developed as well as comparisons between the predicted and generated results of applying these algorithms to the Swarm historic data. We also describe error metrics used for measuring the accuracy of Swarm data reconstruction and the methods of detecting anomalies in the synthesized Swarm data.
Lessons learned from the demonstrator 5G RuralDorset project: a pilot landslide monitoring system using Internet of Things, Machine Learning and 5G/NB-IoT mobile networks

The 5G RuralDorset project (https://5gruraldorset.org/) was a large (&amp;#163;9M; 2020-2022), multi-disciplinary project funded by the UK Department for Culture, Media and Sport that aimed to understand how 5G mobile network technologies could address some specific challenges in rural communities in Dorset, UK: public safety, economic growth, food production and environmental. &amp;#160;Work Package X aimed to develop and trial a novel landslide monitoring system for coastal cliffs using 5G/NB-IoT (Narrow Band - Internet of Things) technologies. &amp;#160;The system comprised a set of small, fully autonomous, highly integrated and power efficient sensing devices that were able to collect sensory data to identify landslide activity and landslide movement. These data were transmitted wirelessly using 5G/NB-IoT to a cloud-based Data Management Platform, where they were presented to the end user over a web interface for processing by Machine Learning algorithms. It is important to note that the term &amp;#8216;Internet of Things&amp;#8217; has been used widely in recent years in application to landslide monitoring to in fact describe real-time telemetry of data. However, the true added value of IoT-enabled systems lies in their ability to extract knowledge from collected data, make decisions and take actions based on ambient conditions and evolution of physical processes. &amp;#160;This talk will describe the lessons learned from this work and highlight some of the obstacles to overcome when attempting to develop and commission such a system.
Detecting, monitoring and modeling volcanic clouds with EarthObservation (EO) satellites data: a systematic review

Volcanic clouds are a major hazard to air traffic, public health, infrastructure, and economic sectors. Therefore, monitoring and tracking volcanic clouds and determining eruptive source parameters (e.g., erupted volume, plume height, mass eruption rate) is crucial to characterizing eruption dynamics and assessing associated natural hazards.A literature review is proposed in this study to understand better how Earth Observation (EO) satellite sensors are used to monitor, track, and model ash and SO2 during volcanic eruptions, ranging from optical (multispectral, hyperspectral, and LiDAR) to radar and thermal data. This review seeks to characterize the different sensors algorithms and models, their accuracy, advantages, and limitations. A systematic literature review was carried out to accomplish this goal utilizing the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) standard. To the best of the author'sknowledge, it is the first systematic literature review fully dedicated to satellite Remote Sensing-based approaches (RS) to monitor, track and model volcanic cloud monitoring, prediction, and forecasting methods.The review was performed on academic papers on the Web of Science to find relevant scientific publications on volcanic cloud monitoring, published from January 1 st , 2010, to September 30 th , 2022. The search parameters used were keywords chosen based on the review topic. They were combined as follows: "Volcanic cloud" OR "Volcanic plume" OR "Volcanic Column" AND "Ash plume" OR "Ash cloud" OR "plume" AND "Remote Sensing" OR "Satellite" AND "Monitoring" AND "Eruptive Source Parameters" OR "SO2 mass Flux" OR "SO2 Flux". From this search, 84 papers were chosen, the selection was based on the use of satellites to detect and monitor volcanic clouds, model and forecast, and combining both approaches in order to estimate the eruptive source parameters. This work assesses the state of the art in satellite remote sensing across the globe to identify and comprehend the major gaps, constraints, and prospective advancements in the sensors, algorithms, and models.
Synthesis and characterization of TiO2-Fe for photocatalytic applications of domestic waste

Wastewater originating from household, industrial, and public places that contains materials can endanger human life and living things and disrupt environmental sustainability. Various methods have recycled domestic wastewater. One is through the photodegradation process utilizing ultraviolet light in the photocatalytic process. Inorganic materials are semiconductors with photocatalyst activity that can absorb photons and simultaneously carry out material interface transformation reactions. The research aimed to determine the effect of adding Fe metal dopants on TiO2 photocatalyst activity in simulated methylene blue and domestic waste treatment. The precursor used was TiO2 P25 Degussa Anatase. Fe dopant was added using the hydrothermal method with variations of 2%, 4%, and 6%. The XRD characterization results show that TiO2 has a high degree of crystallinity, so the overall process of electron diffusion and electron transfer in SSPT is higher to increase its efficiency. SEM-EDX characterization results show that TiO2-Fe particles are scattered in a spherical shape with a size of less than (&lt;)100 nm and tend to agglomerate. Test results of simulated methylene blue (MB) waste with a UV-Vis spectrophotometer at variations in mass, concentration, pH, and time showed that the addition of doping could increase photocatalytic activity and optimum conditions at 6% (F6) were used in the analysis of parameters pH, COD and TSS of domestic wastewater. The pH value increased from 5 to 6, % COD degradation was 59.17%, and TSS was 23.07%. This research showed that TiO2-Fe was effective enough for domestic wastewater treatment.
SUSTAINED SILENT READING AND READING RATE GAINS OF FEMALE PAKISTANI EFL LEARNERS IN A RURAL HIGHER SECONDARY SCHOOL

Reading speed has long been recognized in scholarly literature as an essential component of reading fluency and successful reading. A variety of instructional techniques and activities are used to improve reading speed of learners. Sustained Silent Reading (SSR) is one such way that encourages the development of reading fluency and reading speed. This study investigated the impact of SSR on reading speed of female Pakistani EFL learners. A nine-week long SSR program was designed with 10 minutes timed-reading activity to improve students’ reading rate. Using random sampling procedures, fifty female students were selected to participate in the study. After the pre-test, twenty five students were kept in treatment group and twenty five in control group. Results of the study show that the experimental group’s average reading speed considerably increased from 115.60 wpm to 347.60 wpm after the intervention which indicates positive impact of SSR on reading speed of the participants. Statistically significant differences were found in reading rate gains between the two groups. Interestingly, a slight increase was also noted in the reading speed of students in the control group. The findings of the study indicate that SSR is a very effective instructional technique in developing EFL learners’ reading rate. Keywords: Reading rate, Fluency, Sustained Silent Reading, Reading fluency, EFL learners, Pakistan
Comparison of Attention Mechanism in Convolutional Neural Networks for Binary Classification of Breast Cancer Histopathological Images

The quality of classification is crucial in medical applications. Especially when it comes to confirm that the patient does not have a malignant tumor. An example of such an application is a binary classification of breast tumor malignancy based on histopathological images. This paper explains the most popular attention mechanism in convolution neural networks as follows. Convolutional Block Attention Module, Attention Augmented Convolution, and Attention Guided Convolutional Neural Networks. Four neural networks are built and compared. Each is evaluated in the classification problem of histopathological images of breast cancer. On the basis of the results, it is clear that some attentional neural networks can outperform standard convolutional networks in the classification of breast cancer. In our investigation, the convolution networks reached an accuracy level of 90% and an AUC-ROC of 95.9%. It is worse compared to the Convolutional Block Attention Module Network (accuracy 90.7%, AUC-ROC 96.9%) and the Attention-Guided Convolutional Network (accuracy 91.2%, AUC-ROC 96.6%). Attention-augmented convolution remains behind the standard convolutional network, achieving 88.9% accuracy and 94.8% AUC-ROC. The Attention-Guided Convolution Network was the best network of all four. We also compared precision, NPV, sensitivity, specificity, and $$F_{1}$$ -score. We came to the conclusion that the Convolutional Block Attention Module network has the highest NPV (90.8%) and sensitivity (96.2%), while the Attention-Guided Convolutional Neural Network scored the highest in precision (92.4%) and specificity (82.9%).
Securing Supply-Chain Network with Blockchain

Blockchain is known to be a disruptive technology of the modern era. Businesses, especially, supply chain firms can get benefit by keeping production records in a shared ledger, providing total data visibility and a single source of truth. Supply-chain can validate the status and location of a product at any moment since transactions are constantly time-stamped. The key challenge in this study is to explore how blockchain technology can be utilized to improve asset traceability in today’s food supply chain cycle. In a business network, a blockchain is a distributed, shared ledger that employs encryption to validate and record transactions and monitor assets. The objective is to develop a blockchain model that should be feasible across all food supply chains and to demonstrate the benefits and drawbacks of doing so. One of the most difficult situations that Food Supply-Chain (FSC) firms confront today is food provenance. End-to-end food tracking is unavoidable in the food sector due to a worldwide supply-chain network with diverse operational processes and uneven food standards between nations. This study also includes a thorough evaluation of the present issues in food tracking, food safety standards, and food supply-chain design. An analysis of the foodborne disease outbreak dataset in the United States from 2007 to 2021 is also given, with an emphasis on two major findings. First, the blockchain model should be focused on food goods with the most food sickness outbreak occurrences, and second, IoT scanning points should be increased at particular phases of the supply chain that have recorded more than 50% of total contamination episodes. Finally, using Smart Contract, a blockchain model is developed, and its advantages over traditional information technology systems and global food monitoring techniques are discussed. A future study might look into leveraging blockchain-enabled models to replace ERP systems, minimize food waste, and enhance supply management between stages of the food supply chain.
Parallel FPTCM$$^+$$: Parallel FP-Tree Based Compression Method for Large Datasets

The volume of data is one of the important challenges of big data because a very large amount of data is daily generated and have to be stored for timely analysis. Storing data requires increasingly more storage space. Compression can be a solution to reduce the size of data and thus store a larger volume but this data must be decompressed to be analysed. The compression and decompression process can be time consuming. For this, we propose in this paper a new parallel compression method based on Spark, k-means clustering and frequent pattern tree (FP-Tree) to compress voluminous dataset. Experiments show that the size of datasets is considerably reduced in a reasonable time and without loss. The structure of compressed dataset allows data analysis without being decompressed.
Development of Assessment Tools for Depth of Understanding Quantitatively with Cognitive Diagnostic Models

Fueled by the demand to cultivate deep understanding in 21st-century education, schoolteachers have a burning desire to assess the elements of understanding that students struggle to achieve. However, there has been limited consideration of quantitatively identifying these strengths and weaknesses. Hence, this study proposes a way to assess these by using a class of statistical models called cognitive diagnostic models. This study consists of three parts: development and administration of the diagnostic mathematical test, analysis using the statistical models, and development of feedback sheets for students and teachers. We believe this assessment system can be implemented in digital tools in the future.
Speedup of Merkle-Root Hash Value Computation Using Groups

Every block in a blockchain network contains specific values in its header part. One of those values is the Merkle-root hash, which represents cryptographically the included in the block body transactions. In the bitcoin network, the computation of this value relies on the double SHA-256 hash function (the SHA-256 function takes, as input, the result of the same SHA-256 function, previously computed from a transaction or an intermediate value). The entire binary tree formed by the transactions included in the given block bears the name of its inventor - Ralph Merkel. Therefore, the root of that tree is called Merkle-root. This research proposes a way to speed up the time of Merkle-root hash value computation by dividing the Merkle tree into groups. The overall results are based on previously measured times for hash calculations within three cloud infrastructures (Amazon AWS, Microsoft Azure, and Google GCP). The idea is to use various hash algorithms, spread in different groups and applying multiple groups for the root computation. One of the essential attributes in each block is the Merkle-root hash, which must undoubtedly represent the included transactions. The proposed solution improves security by using a few hash functions (instead of one) and improves the overall time for Merkle-root hash value computation.