Reviewerly/exHarmony huggingface dataset:
Unlocking the power of blockchain in education: An overview of innovations and outcomes

Blockchain is a revolutionary technology that has the potential to revolutionize various industries, including finance, supply chain management, healthcare, and education. Its decentralized, secure, and transparent nature makes it ideal for use in industries where trust, security, and efficiency are of paramount importance. The integration of blockchain technology into the education system has the potential to greatly improve the efficiency, security, and credibility of the educational process. By creating secure and transparent platforms for tracking and verifying students' academic achievements, blockchain technology can help to create a more accessible and trustworthy education system, making it easier for students to showcase their skills and knowledge to potential employers. While the potential benefits of blockchain in education are significant, there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational sector. Some of the major challenges include adoption, technical knowledge, interoperability, regulation, cost, data privacy and security, scalability, and accessibility. The necessary of equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative technology. Organizations should carefully consider these equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain network. Blockchain technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and organizations. Organizations should consider incorporating blockchain technology into their sustainability education programs, in order to enhance the transparency, verifiability, and efficiency of their sustainability-related activities. While the use of blockchain technology in education is still in its early stages, the available data suggest that it has significant potential to transform the education sector and improve the efficiency and transparency of educational systems.
Deep One-Class Fine-Tuning for Imbalanced Short Text Classification in Transfer Learning

The abundance of user-generated online content has presented significant challenges in handling big data. One challenge involves analyzing short posts on social media, ranging from sentiment identification to abusive content detection. Despite recent advancements in pre-trained language models and transfer learning for textual data analysis, the classification performance is hindered by imbalanced data, where anomalous data represents only a small portion of the dataset. To address this, we propose Deep One-Class Fine-Tuning (DOCFT), a versatile method for fine-tuning transfer learning-based textual classifiers. DOCFT uses a one-class SVM-style hyperplane to encapsulate anomalous data. This approach involves a two-step fine-tuning process and utilizes an alternating optimization method based on a custom OC-SVM loss function and quantile regression. Through evaluations on four different hate-speech datasets, we observe that significant performance improvements can be achieved by our method.
Vision-based environmental perception for autonomous driving

Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of Deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RGB-D sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.
Author and organization profiles in Dimensions and Lens information systems: The study of functionality

The paper demonstrates the features of author and organizational publication profiles in Dimensions and Lens information systems. In recent years, these systems have been successfully competing with many subscription-based systems. The authors discuss the advantages of these databases in terms of coverage of research content in publication profiles as compared to that of commercial bibliographic platforms, as well as the wide range of functional capabilities. As illustrated by publication profiles of two research organizations and their authors, we describe original approaches to searching bibliographic information, as well as editing bibliographic errors in databases. These features enable (a) to monitor effectively the scholarly output of research organizations and authors, and (b) to enhance visibility of research findings in the international information space. Solving information-retrieval problems and editing publication profiles of Russian organizations and authors in Dimensions and Lens prove to be similar to that of commercial systems, i. e. Web of Science and Scopus. The vast experience of working with proprietary databases and subscription-based systems, would enable Russian academic libraries to adapt to new interfaces in a short time frame.
Building Understanding of Experience Design in Digital Health: Preliminary Results Based on Semi-Structured Interviews

Design is expanding its influence on shaping future healthcare. Ideally, designers apply human-centered design and human factors that introduce theory, principles, and methods to design to optimize people's healthcare experiences in both digital and non-digital environments. To discuss and implement experience design in healthcare, consensus about experience design in healthcare is needed. Objectives: Therefore, the purpose of this study is to investigate designers' views on experience design in health, and to uncover their understanding about three experience design concepts, i.e., user experience (UX), patient experience (PEx), and digital patient experience (dPEx). We conducted online semi-structured interviews study with convenience samples who met the eligibility. We used ATLAS.ti for an in-depth data coding following thematic analysis. 24 international designers of digital health solutions, either in industry or in academia took part in the interviews. We found the similarities and differences mentioned between healthcare design and non-healthcare design relate to (1) design principles, (2) user attributes, and (3) design contexts. Furthermore, the differences between UX, PEx, and dPEx can be mapped on five dimensions: people, contexts, purposes, means, and usage scenarios. These insights can help designers and human factors specialists build a common design language for experience design in healthcare. Our study can also assist designers and human factors specialists with experience design in digital health by pointing out the areas where design thinking generally is appropriate and the places where particular expertise in healthcare design is needed.
Practices to Prototype Planar Link Mechanisms with 1 DOF for Elementary School, Junior High School and High School Students

To stop the trend of elementary, junior high, and high school students away from the mathematics and sciences, and to encourage their interest not only in sciences but also in engineering, the author has planned and conducted visiting classes to design and prototype a mechanism. Paper craft models of a leg mechanism for biped walking machine and a dancing mechanism are designed as planar closed-loop link mechanisms with only 1 DOF based on the systematic kinematic analysis of planar closed-loop link mechanisms. The fabrication processes which children can build safely and quickly with inexpensive materials such as a tack seal sheet and construction papers and simple tools such as scissors and an eyelet punch are established. These visiting classes were conducted at several elementary and high schools, and almost of children and students were pleased to have completed a working paper craft model and were interested in mechanisms and machines.
An EMG dataset for Arabic sign language alphabet letters and numbers

Nowadays, surface electromyography (sEMG) is evolving as a technology for hand gesture recognition. Detailed studies have revealed the capacity of EMG signals to access detailed information, particularly in the classification of hand gestures. Indeed, this advancement emerges as an interesting element in refining the recognition and interpretation of sign languages and exploring deeper into the phonology of signed languages. Aligned with this advancement and the need for a reliable and mobile sign language recognition system, we introduce a specialized sEMG dataset, acquired using the Myo armband. This device is adept at capturing recordings at frequencies of up to 200 Hz. The dataset focuses on the 28 letters of the Arabic alphabet and 10 digits using hand gestures, with each gesture captured into 400 frames. This considerable collection of 18,716 samples was achieved with the cooperation of three contributors, providing a varied and comprehensive range of gestural data.
Improving speech recognition systems for the morphologically complex Malayalam language using subword tokens for language modeling

Abstract This article presents the research work on improving speech recognition systems for the morphologically complex Malayalam language using subword tokens for language modeling. The speech recognition system is built using a deep neural network–hidden Markov model (DNN-HMM)-based automatic speech recognition (ASR). We propose a novel method, syllable-byte pair encoding (S-BPE), that combines linguistically informed syllable tokenization with the data-driven tokenization method of byte pair encoding (BPE). The proposed method ensures words are always segmented at valid pronunciation boundaries. On a text corpus that has been divided into tokens using the proposed method, we construct statistical n-gram language models and assess the modeling effectiveness in terms of both information-theoretic and corpus linguistic metrics. A comparative study of the proposed method with other data-driven (BPE, Morfessor, and Unigram), linguistic (Syllable), and baseline (Word) tokenization algorithms is also presented. Pronunciation lexicons of subword tokenized units are built with pronunciation described as graphemes. We develop ASR systems employing the subword tokenized language models and pronunciation lexicons. The resulting ASR models are comprehensively evaluated to answer the research questions regarding the impact of subword tokenization algorithms on language modeling complexity and on ASR performance. Our study highlights the strong performance of the hybrid S-BPE tokens, achieving a notable 10.6% word error rate (WER), which represents a substantial 16.8% improvement over the baseline word-level ASR system. The ablation study has revealed that the performance of S-BPE segmentation, which initially underperformed compared to syllable tokens with lower amounts of textual data for language modeling, exhibited steady improvement with the increase in LM training data. The extensive ablation study indicates that there is a limited advantage in raising the n-gram order of the language model beyond $$n=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> . Such an increase results in considerable model size growth without significant improvements in WER. The implementation of the algorithm and all associated experiments are available under an open license, allowing for reproduction, adaptation, and reuse.
LS-MVSNet: Lightweight self-supervised multi-view stereo

In recent years, significant progress has been made in multi-view stereo through the adoption of learning-based methods. However, current state-of-the-art approaches exhibit high computational and memory costs, owing to their reliance on semantic segmentation networks, neural rendering, and other advanced technologies for enhancing the accuracy of depth map prediction. To improve the efficiency of resource utilization and maintain accuracy in depth prediction, we present a self-supervised multi-view stereo framework that is lightweight and combines depth feature extraction modules, edge feature extraction modules, and data augmentation techniques. Specifically, the depth feature extraction module is employed to adaptively extract significant information from the input images for depth estimation. The edge feature extraction module is utilized to extract object contour features from the input images to augment the accuracy of depth inference at the edges. Experimental results on the DTU dataset show that our proposed lightweight self-supervised multi-view stereo framework, referred to as LS-MVSNet, outperforms the most advanced self-supervised MVS methods in terms of accuracy. Furthermore, extensive experiments on the Tanks and Temples dataset demonstrate the effective generalization ability of our model. The code will be released.
Feedforward neural network-based augmented salp swarm optimizer for accurate software development cost forecasting

This research proposes the use of feed-forward backpropagation neural networks (FFNN) to develop an accurate cost forecasting model in light of the challenges associated with forecasting software development costs (FSDC). The salp swarm algorithm (SSA) is first augmented and then employed to optimize the parameters of the developed FFNN predictor. A search enhancement mechanism and an elitism technique have been developed and incorporated into the SSA optimization process as two fresh and effective techniques for this goal. The search enhancement mechanism is employed to keep up a high rate of global exploration while also driving convergence towards the optimal area. Whereas elitism is used throughout the research phase to prevent stagnation in the local optima. Nineteen benchmark test functions and twelve benchmark software development cost forecasting data sets are utilized to assess the performance of the recommended enhancement techniques and developed algorithms. The results obtained from experiments show the superiority of the proposed techniques. In addition, the developed technique has been compared with many state-of-the-art methods, which demonstrates its durability. This is in addition to the statistical validation carried out on the results obtained, which also supports the robustness of the proposed technique.
The true futures

Abstract In this paper various branching time semantics are compared with the aim of clarifying the role of true futures of counterfactual moments, that is, true futures of moments outside the true chronicle. First we give an account of Arthur Prior’s Ockhamistic semantics where truth of a formula is relative to a moment and a chronicle. We prove that this is equivalent to a version of a semantics put forward by Thomason and Gupta where truth is relative to a moment and what is called a chronicle function which assigns a chronicle to each moment. Later we discuss how a semantic theory considered by Belnap and Green may be formalised. It comes about by assuming a chronicle function to be given once and for all. However, this semantics invalidates an intuitively valid formula, so we present an alternative semantics where the formula in question is valid. Furthermore, we shall exhibit an intuitively invalid formula which is invalid in our alternative semantics, but which is valid in Prior’s Ockhamistic semantics. So we can conclude that Prior’s Ockhamistic validity does not imply validity in the alternative semantics. On the other hand, the converse implication does hold, as we shall prove. Summary of mathematical results: We have proved that Prior’s Ockhamistic semantics has the same valid formulas as Thomason and Gupta’s semantics, and we have proved that Prior’s Ockhamistic semantics has strictly more valid formulas than the alternative semantics.
SL-TeaE: An Efficient Method for Improving the Precision of Teaching Evaluation

This study introduces an effective method for analyzing emotional states in evaluation data with enhanced precision by constructing a sentiment lexicon for teaching evaluation (SL-TeaE). We expand a general basic sentiment lexicon based on teaching evaluation data from our university’s academic system by creating a list of adverbs of degree and negative words. We use the TextRank algorithm to select sentiment seed words from user data and the SO-PMI algorithm to generate a user-based domain sentiment vocabulary in the teaching domain. Finally, we merge the user-based domain sentiment vocabulary and the expanded general foundation sentiment lexicon to construct the SL-TeaE. The experimental results indicate that the proposed method has excellent performance in both sentiment classification and quantitative evaluation score. Specifically, the F1 values for positive and negative teaching comments have been improved by 27.3% and 16.7%, respectively, compared to the general basic sentiment lexicon. Furthermore, the sentiment classification performance of our method surpasses that of commonly used sentiment classification algorithms. In addition, compared to the general basic sentiment lexicon and the expanded general basic sentiment lexicon, the proposed method in this paper achieves the lowest mean absolute error and root mean square error for course comprehensive evaluation scores (MAE = 2.57, RMSE = 2.62).
Determining the Viscosity Coefficient of Fluids Using a Simple Viscosity Practical Tool Aided by Arduino Uno and a Magnetic Sensor

This study aims to design and create fluid viscosity test equipment in the form of used oil, sunlight, soklin, liquid rinso, and harpick. The working principle of the viscometer uses a free-falling ball system according to Stokes' law principles. The stages in making a viscometer are designing a tool model, designing hardware and software, designing a mechanical system and testing. Designing hardware includes hall effect magnet sensor, arduino uno, lCD, and power supply. The software uses the Arduino IDE-assisted programming language. The design of the tool includes supports, boxes, magnets, and glass tubes. the test was carried out by measuring the viscosity coefficient of used oil, sunlight, so klin, rinso, and harpick. The viscosity coefficients of used oil, sunlight, floor soklin, liquid Rinso, and harpick from the test results have been able to be measured with a viscometer that has been made.
A Systematic Review on Deep-Learning-Based Phishing Email Detection

Phishing attacks are a growing concern for individuals and organizations alike, with the potential to cause significant financial and reputational damage. Traditional methods for detecting phishing attacks, such as blacklists and signature-based techniques, have limitations that have led to developing more advanced techniques. In recent years, machine learning and deep learning techniques have gained attention for their potential to improve the accuracy of phishing detection. Deep learning algorithms, such as CNNs and LSTMs, are designed to learn from patterns and identify anomalies in data, making them more effective in detecting sophisticated phishing attempts. To develop a comprehensive understanding of the current state of research on the use of deep learning techniques for phishing detection, a systematic literature review is necessary. This review aims to identify the various deep learning techniques used for phishing detection, their effectiveness, and areas for future research. By synthesizing the findings of relevant studies, this review identifies the strengths and limitations of different approaches and provides insights into the challenges that need to be addressed to improve the accuracy and effectiveness of phishing detection. This review aims to contribute to developing a coherent and evidence-based understanding of the use of deep learning techniques for phishing detection. The review identifies gaps in the literature and informs the development of future research questions and areas of focus. With the increasing sophistication of phishing attacks, applying deep learning in this area is a critical and rapidly evolving field. This systematic literature review aims to provide insights into the current state of research and identify areas for future research to advance the field of phishing detection using deep learning.
Depth Map Super-Resolution Based on Semi-Couple Deformable Convolution Networks

Depth images obtained from lightweight, real-time depth estimation models and consumer-oriented sensors typically have low-resolution issues. Traditional interpolation methods for depth image up-sampling result in a significant information loss, especially in edges with discontinuous depth variations (depth discontinuities). To address this issue, this paper proposes a semi-coupled deformable convolution network (SCD-Net) based on the idea of guided depth map super-resolution (GDSR). The method employs a semi-coupled feature extraction scheme to learn unique and similar features between RGB images and depth images. We utilize a Coordinate Attention (CA) to suppress redundant information in RGB features. Finally, a deformable convolutional module is employed to restore the original resolution of the depth image. The model is tested on NYUv2, Middlebury, Lu, and a Real-Sense real-world dataset created using an Intel Real-sense D455 structured-light camera. The super-resolution accuracy of SCD-Net at multiple scales is much higher than that of traditional methods and superior to recent state-of-the-art (SOTA) models, which demonstrates the effectiveness and flexibility of our model on GDSR tasks. In particular, our method further solves the problem of an RGB texture being over-transferred in GDSR tasks.