Reviewerly/exHarmony huggingface dataset:
Summarization of Educational Videos with Transformers Networks

This paper presents an approach to summarize educational videos using Deep Learning Transformers models. The approach focuses on educational content by summarizing captions and using the text results to summarize the videos. Tests were conducted using the EDUVSUM dataset, which improved upon the original paper’s results, achieving an accuracy of 26.53% in a multi-class problem, with a mean absolute error of 1.49 per video frame and 1.45 per video segment. Transformer techniques for automatic text summarization have proven effective in creating multimedia learning objects. The results suggest that these techniques can generate more efficient and high-quality digital educational resources, reducing the time and effort required for their creation.
Efficiency Scoring for Subway Tunnel Construction Based on Shield-Focused Big Data and Gaussian Broad Learning System

During subway tunnel construction, the consumed time for each ring (unit of construction progress) is highly dependent on objective factors such as geological conditions and shield performances, as well as subjective reasons including workers’ proficiency and contractors’ management skills. It is nontrivial to score each ring’s efficiency from an objective angle, so that workers and contractors can receive fair evaluations. In this paper, a scoring method driven by construction big data is proposed. First, to resolve the high-dimensionality problem, a Gaussian mixture model (GMM) was employed to cluster rings of similar conditions in a probabilistic style so that detailed information can be retained. Second, the standard time to finish one ring was analyzed, so that each ring can be labeled as fail or pass, and our task can be considered as a classification problem. Third, for each cluster, a broad learning system (BLS) was developed as a classifier due to its advantages of fast computation and incremental learning. Finally, the BLS was trained with real tunneling data of 23,822 rings, and then scorecards were developed, where results of validation and statistical tests suggested that our method outperforms conventional ones. Feedback from the subway company and two compared contractors suggested that the proposed method is fair and practical, and it could reveal management problems that were easily overlooked.Practical ApplicationsFor subway shield tunnels, this study proposes a construction progress scoring method driven by big data for subway companies to evaluate the performance of each construction unit, e.g., each ring of the shield tunnel is treated as a sample analysis. First, in view of the difficulty of many geological types and risk variables, the Gaussian mixture method is used to classify the samples and obtain probability that each ring belongs to a certain geological type. Statistical analysis is used to calculate the shield standard time of each geological type, so that the time interval of pass can be determined. Second, a broad learning model with low calculation and updating cost is constructed to fit the mapping relationship between geological factors and construction progress. With this model, each ring can be scored. Finally, through the model training and verification of 23,822 ring data in seven subway intervals, results suggest that the method is practical. Our method can objectively reflect the work efficiency and management ability of different contractors and workers. In addition, suggestions can be elicited to improve the management level of construction units.
From Collatz Conjecture to chaos and hash function

The non-linear property of Chaos is a promising approach to information security, and many accomplishments have been made by combining Chaos with several sub-security domains, including chaos-based stream ciphers, block ciphers, image encryption, and hash functions. Most Chaos-based hash functions are insecure or inefficient due to their dependence on complex, attacked multi-dimensional maps or uncertain, weak one-dimensional maps like logistic and tent. The Collatz Conjecture is a mystery that has stumped mathematicians for decades and still has not been solved. This paper aims to introduce a novel approach to addressing current security challenges by utilizing our generalized Collatz process to create a chaos-based hash function. By leveraging the unpredictable behaviour of the Collatz sequence, the proposed hash function aims to enhance ergodicity and entropy properties, thereby making it well-suited for cryptographic applications. In the proposed method, the chaotic variables are governed by cryptographic keys, crucial in generating data sequences. These sequences are then utilized within the diffusion and confusion structures of the hashing function. The design of the chaos-hash model is carefully optimized to exhibit desirable characteristics such as randomness, collision resistance, uniformity, sensitivity to initial conditions, speed, and resistance against cryptanalysis. The primary goal of this research is to develop a robust and efficient chaos-based hash function that addresses the requirements of cryptographic systems. By incorporating the Collatz process and carefully considering key-controlled variables, the proposed model aims to offer enhanced security properties while meeting the necessary criteria for a reliable and effective hashing mechanism. The effectiveness and dependability of the proposed hash function are evaluated by comparing it with two well-known hash algorithms, namely SHA-3 and SHA-2, as well as several other Chaos-based hash algorithms. Through comprehensive evaluations conducted under various circumstances and with different datasets, the suggested hash function consistently outperforms the state-of-the-art alternatives. Experiments involving diverse input scenarios consistently demonstrate that the proposed hash function surpasses the performance of the current leading alternatives. Its superior performance is observed across various evaluation metrics, confirming its effectiveness in generating reliable and secure hash values. The results obtained from the comparative analysis highlight the superiority of the proposed hash function over existing alternatives, validating its potential as a robust solution for various cryptographic applications.
More-than-human practices of making and unmaking the smart home: A socioemotional investigation

This article examines the complex and dynamic practices in the smart home and how these practices are socially and emotionally engaged in daily encounters with different human and non-human actors, drawing on a 14-month autoethnography. Based on the investigation of my own experiences of setting up, using and getting rid of smart home technologies, this article develops a more-than-human understanding of the time-space of home. It concentrates on the significant role of the dynamic human-technology assemblage in the process of making and unmaking smart home and the social and emotional negotiations between the operation of smart home technologies, the doings of household relationship, and the management of the physical, virtual, and imaginative home. These findings may open new dialogues on the dynamic spatiotemporalities of the digital mundane. Ultimately, this research suggests future studies to develop a more embodied and emotional way and take the social and material links and transactions between home and the broader digital world to unravel how digital technologies are normalised and integrated into our day-to-day experiences.
Revenue-optimized photovoltaic orientation in a northern competitive electricity market with carbon offsets

Solar photovoltaic (PV) farms are typically designed to maximize energy generation; however, their impacts on market prices can create conditions where revenues may be maximized by orienting the system to produce more energy at more valuable times of the day. PV generation offers its energy at a zero-dollar price in competitive electric markets, which tends to result in a drop in hourly price ("merit-order effect") and reduction of revenues ("cannibalization effect"). We present an analysis of optimizing the orientation of PV systems for revenues, accounting for cannibalization in three pricing dynamics, with flat, medium, and steep merit-order curves. We employ a dataset of historical price-quantity offers from 2011 to 2019 from Alberta, Canada, a market that also has significant opportunities to generate carbon credits (CC). The market has an average maximum hourly load of ∼9 GW; we introduced 15, 400, 1000, and 3000 MW PV-capacity inputs, using tilt angles from 0° to 90° and azimuths from 90° to 270°. Our results show that for small PV capacities compared to the load, the market revenue-optimized orientations are close to the energy-optimized orientations, as would be expected. At higher levels of PV capacity, market revenue-optimized orientations deviate from the energy-optimized orientation as price cannibalization increases. The extent of this deviation depends on the slope of the historical merit order. When CC are introduced into the simulation, the total revenue-optimized orientation moves back towards the energy-optimized orientation as the CC overcome cannibalized market losses but exasperate overall market price depression.
A Distributed Alignment-free Pipeline for Human SNPs Genotyping

Identification of known genetic traits and disease-related variants within an individual requires a fundamental task: genotyping a set of variants from a database. However, the efficiency of this process is challenged by the growing volume of sequencing data and variant databases. At such scale, even the fastest genotyping tool available can deliver a result in a time that is unacceptable.
Makine Öğrenimi ve Hibrit Altuzay Sınıflandırıcılar için Yalıtılmış Kelime Tanıma Performanslarının Karşılaştırılması

One of the essential factors affecting recognition rates in speech recognition studies is environmental background noise. This study used a speech database containing different noise types to perform speaker-independent isolated word recognition. Thus, it will be possible to understand the effects of speech signals having noise on the recognition performance of classifiers. In the study, K-Nearest Neighbors (KNN), Fisher Linear Discriminant Analysis-KNN (FLDA-KNN), Discriminative Common Vector Approach (DCVA), Support Vector Machines (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) were used as classifiers. MFCC and PLP coefficients were used as feature vectors. The DCVA classifier has been deeply tested for isolated word recognition for the first time in the literature. The recognition process was carried out using various distance measures for the KNN, FLDA-KNN, and DCVA classifiers. In addition, new (DCVA)PCA and (FLDA-KNN)PCA classifiers were designed as hybrid algorithms using Principle Component Analysis (PCA), and better recognition results were obtained from those of DCVA and FLDA-KNN classifiers. The highest recognition rate of RNN-LSTM was 93.22% in experimental studies. For the other classifiers, the highest recognition rates of the CNN, KNN, DCVA, (DCVA)PCA, SVM, FLDA-KNN, and (FLDA-KNN)PCA were 87.56%, 86.51%, 74.23%, 79%, 77.78%, 71.37% and 84.90%, respectively.
Design Principles of an Interactive Tangible Mobile Application for Students With Specific Learning Disabilities

Traditional computer-assisted instruction applications fail to meet the changing needs of children with specific learning disabilities (SLD) and the new systems are expected to cultivate multi-sensory interaction as well as create a physical engagement. In this context, interactive tangible objects used with multi-touch tablets have the potential to enrich learning experiences of students with SLD. Tangible technologies serve as an instrument that can be used for students with SLD. The main purpose of this study is to determine design principles for an interactive tangible mobile application for students with SLD. Design-based research was employed in order to determine the design principles for interactive tangible mobile applications for students with SLD. Overall, the findings of the study indicate that 33 design principles for tangible mobile applications in four categories—educational content, visual design, tangible object use, and interaction—were determined for students with SLD.
Morphology Extraction and Analysis of Calligraphy Fonts Images: A Novel Perspective

With the development of the computers, people's demand for image processing technology is increasing day by day, and many new algorithms have been created, further promoting the development of the field of image processing. Among them, morphological processing of color images plays an important role. In this research study, it is proposed the novel perspectives on the morphology extraction and analysis of calligraphy fonts images. In the classical morphological image processing, the structured information reflected by the pixel values in the image is not considered, to deal with this, the proposed model has 3 focuses: (1) image invariant moments as initial processing is used to serve as the basic step of processing the information. The Krawtchouk moment and Tchebichef orthogonal scale polynomial model are combined to make the algorithm efficient; (2) color morphology extraction and analysis of images is conducted as well as keeping the structural information; (3) novel neural network model is proposed to extract the features. Through the experiment, it can be seen that the proposed model can effectively obtain the image features.
A 1.25-GHz multi-amplitude modulator driver in 0.18 <i>μ</i>m SiGe BiCOMOS technology for high speed quantum key distribution

Quantum key distribution (QKD) research has yielded highly fruitful results and is currently undergoing an industrialization transformation. In QKD systems, electro-optic modulators are typically employed to prepare the required quantum states. While various QKD systems operating at GHz repetition frequency have demonstrated exceptional performance, they predominantly rely on instruments or printed circuit boards to fulfill the driving circuit function of the electro-optic modulator. Consequently, these systems tend to be complex with low integration levels. To address this challenge, we have introduced a modulator driver integrated circuit in 0.18 µm SiGe BiCMOS technology. The circuit can generate multiple-level driving signals with a clock frequency of 1.25 GHz and a rising edge of ∼50 ps. Each voltage amplitude can be independently adjusted, ensuring the precise preparation of quantum states. The measured signal-to-noise ratio was more than 17 dB, resulting in a low quantum bit error rate of 0.24% in our polarization-encoding system. This work will contribute to the advancement of QKD system integration and promote the industrialization process in this field.
A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification

Given the threat posed by cancer to human health, there is a rapid growth in the volume of data in the cancer field and interdisciplinary and collaborative research is becoming increasingly important for fine-grained classification. The low-resolution classifier of reported studies at the journal level fails to satisfy advanced searching demands, and a single label does not adequately characterize the literature originated from interdisciplinary research results. There is thus a need to establish a multilabel classifier with higher resolution to support literature retrieval for cancer research and reduce the burden of screening papers for clinical relevance.The primary objective of this research was to address the low-resolution issue of cancer literature classification due to the ambiguity of the existing journal-level classifier in order to support gaining high-relevance evidence for clinical consideration and all-sided results for literature retrieval.We trained a multilabel classifier with scalability for classifying the literature on cancer research directly at the publication level to assign proper content-derived labels based on the "Bidirectional Encoder Representation from Transformers (BERT) + X" model and obtain the best option for X. First, a corpus of 70,599 cancer publications retrieved from the Dimensions database was divided into a training and a testing set in a ratio of 7:3. Second, using the classification terminology of International Cancer Research Partnership cancer types, we compared the performance of classifiers developed using BERT and 5 classical deep learning models, such as the text recurrent neural network (TextRNN) and FastText, followed by metrics analysis.After comparing various combined deep learning models, we obtained a classifier based on the optimal combination "BERT + TextRNN," with a precision of 93.09%, a recall of 87.75%, and an F1-score of 90.34%. Moreover, we quantified the distinctive characteristics in the text structure and multilabel distribution in order to generalize the model to other fields with similar characteristics.The "BERT + TextRNN" model was trained for high-resolution classification of cancer literature at the publication level to support accurate retrieval and academic statistics. The model automatically assigns 1 or more labels to each cancer paper, as required. Quantitative comparison verified that the "BERT + TextRNN" model is the best fit for multilabel classification of cancer literature compared to other models. More data from diverse fields will be collected to testify the scalability and extensibility of the proposed model in the future.
Image encryption algorithm based on reversible information hiding and physical chaos in images

This article proposes a reversible information hiding algorithm based on chaos and histogram translation in ciphertext images to address the shortcomings of traditional algorithms such as separation of encryption and information hiding processes, small embedding capacity, and inflexible operation. This article first uses grayscale images as a carrier for digital images, analyzes and studies existing reversible information hiding algorithms in the ciphertext domain of grayscale images, and improves them to solve the problems of existing reversible information hiding algorithms in the ciphertext domain. Secondly, the sender first encrypts the image and then embeds the information; The receiving end can either decrypt and extract information first, or extract and decrypt information first. Finally, we selected a Lena grayscale image with 513 pixels x 513 pixels for the experiment, dividing the image into 4 blocks (r=4), and scrambling the pixel positions using DLKL transform for each block, with parameters k=4, n=7, q=59. The simulation results show that the algorithm is easy to implement and can effectively improve the capacity of information embedding.
Unknown Building Exploration Simulator (UBES)

The Unknown Building Exploration Simulator (UBES) software is built to investigate effective strategies for multi-robot exploration of unknown, indoor environments. The software allows the assessment of different exploration techniques, offering the flexibility to modify initial and target conditions, and account for potential agent loss. A total of 12 distinct algorithms commonly used in modern multi-robot exploration are implemented for baseline analysis. The simulation software includes complex building environments that can be user-defined or randomly generated according to several adjustable parameters. Agents also include user-modifiable features such as agent count, sensor range, LiDar characteristics, and inter-agent communication capability.
MOTT: A new model for multi-object tracking based on green learning paradigm

Multi-object tracking (MOT) is one of the most essential and challenging tasks in computer vision (CV). Unlike object detectors, MOT systems nowadays are more complicated and consist of several neural network models. Thus, the balance between the system performance and the runtime is crucial for online scenarios. While some of the works contribute by adding more modules to achieve improvements, we propose a pruned model by leveraging the state-of-the-art Transformer backbone model. Our model saves up to 62% FLOPS compared with other Transformer-based models and almost as twice as fast as them. The results of the proposed model are still competitive among the state-of-the-art methods. Moreover, we will open-source our modified Transformer backbone model for general CV tasks as well as the MOT system.
Ontology-based Conversational Recommender System for Recommending Bicycle

With the increasing popularity of bicycles as ecofriendly transportation, the market offers a wide range of options with diverse specifications and functions. However, navigating through these options can be overwhelming for users lacking technical expertise, leading to time-consuming searches for a suitable bicycle. To address this challenge, we propose a conversational recommender system (CRS) that prioritizes functional needs, providing personalized recommendations through interactive user engagement. The CRS acts as a professional sales support, guiding users to express their functional requirements and aiding decision-making. Leveraging ontology as a knowledge representation, the system effectively maps functional requirement to specific product features, facilitating accurate recommendations. Our evaluation of the CRS considers user satisfaction and recommendation accuracy, which attains an accuracy rate of 87.91% with positive user satisfaction feedback. These results highlight the system’s efficacy in assisting users in selecting bicycles based on their unique functional requirements and streamlining the decision-making process.
AMAN: Token-based Authentication to Improved Single Sign-On Security Between Systems

The problems caused during the Covid-19 pandemic included disrupting the learning process, which was usually done face-to-face, and had switch to online. Many applications provide online learning features, requiring users to create multiple accounts for each application used. Therefore, the proposed solution is build a distributed system to combine various applications into one system. This research proposes AMAN (Authentication Model as Node) with the application of the concept of Single Sign On (SSO) and JSON Web Token (JWT) in Islamic Boarding School Academic Systems and Moodle E-Learning. Single Sign On is a log-in authentication system allowing users to access multiple resources with only one authentication, while Moodle is a popular platform for online learning media. By supplying a JSON Web Token (JWT), the server can provide the client permission to access protected data stored on the server via the OAuth2 protocol. The test method are SSO testing between academic systems and e-learning Moodle, token validation, JWT structure verification, and Network Sniffing Attack. The test results in this study show that the implementation of Single Sign On (SSO) using the OAuth2 protocol can form a secure system and interact with multiple platforms.