Reviewerly/exHarmony huggingface dataset:
Longitudinal reading outcome and cognitive load in individual- and collaboration-based environments

In this paper, we report a 12-week longitudinal study aiming at exploring the students’ reading outcome and cognitive load with individual-based print, mobile app of Rain Classroom and collaboration-based social media of WeChat. Administered to 186 postgraduate students in a research university were the weekly reading materials and comprehension tests, together with the self-designed CLCR scale after the reading sessions. A combination of one-way and repeated measures ANOVA along with multi-regression analysis showed that social media produced the best reading outcome; The same pattern occurred about enhanced reading outcome in the initial sessions followed by a decline at the end; Reading outcome in print other than in mobile and social media significantly increased from the first session to the ending session; Reading in mobile generated the highest level of extraneous, intrinsic, and germane load; Multi-regression analysis showed a negative relationship between intrinsic load and the prediction of collaborative reading across all the reading contexts. Further efforts may contribute to more complex patterns of reading outcome and cognitive load status by arranging individual- and collaboration-based contexts in different sequences for students with high and low level of cognitive load.
Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

The volume of digital documents available online is growing exponentially due to the increasing use of the internet. Categorization of information obtained online is needed to make it easier for recipients of information to determine and filter which information is needed. Classification of web pages can be based on titles and descriptions, which are text data that can be done by utilizing deep learning technology for text classification. This study aimed to conduct data training and analysis experiments to determine the accuracy of the proposed deep learning architecture in classifying web page titles and descriptions. In this research, we proposed a Convolution Neural Network (CNN) architecture that generates few parameters. The training and evaluation set was conducted on the web page dataset provided by DMOZ. As a result, the proposed CNN architecture with the number of N (Dropout + 1D Convolution + ReLU activation) equal to 1 achieves the best validation accuracy. It achieves 79.51% with only generates 825,061 parameters. The proposed CNN architecture achieved outperformed performance on the accuracy of the five other technologies in the state-of-the-art.
RIS-GAN: Self-Supervised GANs via Recovering Initial State of Subimages

Self-supervised methods play a vital role in representation learning without the need for annotated data and learn feature representations where data itself provides supervision. However, it is still challenging for the discriminator to learn reliable representations. To address this issue, we propose the self-supervised GANs via recovering initial state of subimages (RIS-GAN). Recovering initial state (RIS) task requires the network to recover the original state information of each sub-part of a composite image. Specifically, the network should predict the rotation angle and the initial position of subimages, and also predict which original image each subimage belongs to. To recover these information, the network should capture intra-image and inter-image information and extract high-similarity features between those patches belonging to the same original images. Besides, we propose local-discriminator, to improve the ability of the discriminator to judge global features from local patch features. To accomplish the above task, our proposed model should extract multi-level information to improve the understanding of image structure and content, and the ability to mine valuable information from image patches. We experimentally demonstrate the state-of-the-art performance of our RIS-GAN with respect to other self-supervised GAN methods on CIFAR-10, ImageNet 32 × 32, CelebA, and STL-10 datasets.
Implementasi Augmented Reality dalam Mata Pelajaran Pekerjaan Dasar Teknik Mesin di SMK Negeri 1 Sumatera Barat

Students' understanding of science is limited because the teacher's lecturing teaching method. This method makes students uninterested in following the learning process. This research is aiming to see what extent the Augmented Reality(AR) applicationcan influence students' learning perceptions. In this research, total sampling method was used with a case study approach. Data was collected through classroom observations, interviews with students, and content analysis of student responses to the use of AR as a learning media. The collected data was analyzed thematically to identify patterns of students' perceptions regarding the benefits, constraints, and responses to using AR. Results from 35 students showed that the use of AR in Mechanical Engineering Basic Work subject received positive responses with 92.5% in scores. Students report that using AR increases interest and engagement in learning, and helps them understand technical concepts better. However, several obstacles such as device limitations and initial training in the use of the technology were also identified. This research emphasizes the importance of integrating AR technology in mechanical engineering education at the vocational school level. These findings provide a vision of the impact of using AR in practical and technical learning, and provide a basis for the development of more better learning strategies. Overall, this study provides valuable insights into students' perceptions of using AR in the context of mechanical engineering education. The implications of these findings can help educators and curriculum developers to design learning experiences that are more engaging and effective in preparing students for future technical challenges.
Application of Automatic Collection of Real-time Data Based on Clustering Algorithm in Training Assessment

For the sports field, the training process and teaching are both complicated sports processes. According to the characteristics and processes of sports training, the movements in sports training can be shown to students and sports teachers again, thus playing a role in sports training. In this paper, a sports training data assessment model based on clustering algorithm is put forward, which combines the data management equation and the response time of data collection to realize the automatic collection of real-time sports training data. Through the simulation application effect of the model, its rationality is tested, thus improving the existing decision support system. Applying fuzzy clustering analysis to the analysis of sports training assessment data can reflect the diversity of group thinking more objectively. By improving the clustering algorithm, the efficiency of clustering analysis is improved.
Photonic Convolution Engine Based on Phase-Change Materials and Stochastic Computing

The last wave of AI developments sparked a global surge in computing resources allocated to neural network models. Even though such models solve complex problems, their mathematical foundations are simple, with the multiply-accumulate (MAC) operation standing out as one of the most important. However, improvements in traditional CMOS technologies fail to match the ever-increasing performance requirements of AI applications, therefore new technologies, as well as disruptive computing architectures must be explored. In this paper, we propose a novel in-memory implementation of a MAC operator based on stochastic computing and optical phase-change memories (oPCMs), leveraging their proven non-volatility and multi-level capabilities to achieve convolution. We show that resorting to the stochastic computing paradigm allows one to exploit the dynamic mechanisms of oPCMs to naturally compute and store MAC results with less noise sensitivity. Under similar conditions, we demonstrate an improvement of up to $64\times$ and $10\times$ in the applications that we evaluated.
Simulation Algorithm of Virtual Reality Urban Road Traffic Flow Based on Particle Swarm Optimization

With the continuous improvement of people's daily living standard and the acceleration of urbanization, smart city transportation planning and smart city planning have been promoted. Relevant departments are committed to creating a healthy, stable and sustainable urban planning scheme, providing more quality and comprehensive services for people's daily life and social improvement. At present, in the improvement of smart city planning in China, urban transportation planning is a vital foundation to ensure the sound improvement of smart cities, and the transportation planning of smart cities has vital significance for the coordination of smart city planning. In this paper, particle swarm optimization (PSO) algorithm is adopted, and according to the traffic flow message of major road intersections and sections, real-time dynamic prediction is made by appropriate methods, which provides basic basis for travelers to provide the best driving route, balance traffic flow, optimize traffic administration scheme and improve traffic control. From the study in this paper, it is concluded that the algorithm in this paper is effective, and it is suitable to be widely put into practice.
Design-Space Exploration of Multiplier Approximation in CNNs

In terms of the hardware design, approximation offers not only low power and compact footprint benefits but also accelerated outcomes. Quantization of the data format is one of the approximation methods aimed at improving hardware efficiency. However, quantization when applied to the datapath design reduces the computational accuracy and also the control over the expected precision. Besides, the quantized format applies to all layers in the Neural Network (NN) leading to a cumulative accuracy drop. Placing Approximate Multipliers (AM) to perform feature extraction in the NN model allows tighter control of the approximation along the datapath flow, and reaps a fine balance between model accuracy and hardware benefits. However, most of the literature study characterizes individual AMs and has not examined the compounded effect of the AM design when employed for convolutional layers in the NN model. This paper aims to close this gap by adopting three categories of AMs available in the literature: i) Evolutionary algorithm generated 36 AMs referred to as Apprx-A, ii) 20 Approximate compressor-based multipliers referred to as Apprx-B, and iii) 8 Positive and Negative Error distributed AMs referred to as Apprx-C, and performing an exhaustive design-space exploration of these individual multipliers with exact multipliers over three convolutional neural networks (CNNs) that are trained to classify images from the popular datasets - CIFAR-10, MNIST, and Fashion MNIST, separately. Hardware characteristics and classification accuracy for the convolutional layers built from these AMs as against retaining it with the exact computation over multiple layers of the network model is reported. The CNN accelerator designs equipped with accuracy and hardware parameters aid in choosing the best Pareto-optimal solution as per the given requirements. This work is an attempt to characterize and realize hardware-aware CNN accelerator designs, which is a step towards establishing an NN architecture search framework for the underlying hardware.
Domain-Oriented Transformation Method for Big Data Analysis Process Model

In order to ensure data mining analysis and knowledge discovery in the context of multi-disciplinary fields, big data analysis takes into account domain complexity, data analysis ease of use, and efficient execution. The domain-driven big data analysis process modeling is proposed to guide the construction and implementation of big data analysis process models. The big data analysis process is divided into a domain-oriented and platform-oriented two-layer model, and the transformation between the two is automatically done through the model-driven model mapping method. The domain-oriented analysis model is defined from the perspective of domain business, allowing users to focus on the analysis logic itself during the big data analysis process construction phase, without concern for the implementation details of specific algorithms. The platform-oriented analysis model is defined from the perspective of calculation and execution, so that the big data analysis process makes full use of the platform's computing resources, storage resources, and algorithm resources during the execution phase, the process execution efficiency and algorithm calculation speed are improved. Finally, the Hadoop platform as the underlying execution platform is taken as an example to illustrate the transformation process of the domain-oriented big data analysis process model.
Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy Image Segmentation

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. Visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.
Ergonomic Work Station Design In Internet Rental Services At Warnet Bina Boyolali

Responding to the rapid progress of the internet and the large public need for internet service users, many internet cafes have been opened. Many internet cafes in designing their work stations are less ergonomic so that it often causes complaints for users. Therefore, it is necessary to conduct research on how to design an ergonomic work station in order to provide comfort for internet rental service users. The research was conducted at the Bina Boyolali internet cafe with the object of research regarding the size of computer tables and chairs. Computer desks and chairs are facilities provided at Bina's internet cafe. The tables and chairs used by the internet cafe operators felt uncomfortable, which was caused by the sitting position which eventually caused fatigue in the limbs. The research process begins with collecting data through a Nordic Body Map questionnaire to find out whether visitors are comfortable with the conditions of the existing work station. From the questionnaires distributed to 60 respondents, it can be seen that 1.32% of visitors felt very comfortable with the size of the table and chair size, the location of the board and the distance from the monitor to the eyes. Visitors who feel comfortable are 19.34%, those who feel quite comfortable are 42.63%, those who feel less comfortable are 34.41% and feel uncomfortable are 2.31%. This shows that the Bina Boyolali internet cafe work station is still less ergonomic so that many visitors feel uncomfortable. From the questionnaires distributed to 60 respondents, it can be seen that 1.32% of visitors felt very comfortable with the size of the table and chair size, the location of the board and the distance from the monitor to the eyes. Visitors who feel comfortable are 19.34%, those who feel quite comfortable are 42.63%, those who feel less comfortable are 34.41% and feel uncomfortable are 2.31%. This shows that the Bina Boyolali internet cafe work station is still less ergonomic so that many visitors feel uncomfortable. From the questionnaires distributed to 60 respondents, it can be seen that 1.32% of visitors felt very comfortable with the size of the table and chair size, the location of the board and the distance from the monitor to the eyes. Visitors who feel comfortable are 19.34%, those who feel quite comfortable are 42.63%, those who feel less comfortable are 34.41% and feel uncomfortable are 2.31%. This shows that the Bina Boyolali internet cafe work station is still less ergonomic so that many visitors feel uncomfortable.
PetSavers research

Dr Silke Salavati, small animal internal medicine specialist, and Holly Grenville, Royal (Dick) School of Veterinary Studies undergraduate 5th year veterinary student, report on their BSAVA PetSavers-funded research which was carried out as a student research project: Feasibility of using a point-of-care analyser for faecal calprotectin (fCAL) to diagnose and differentiate intestinal disease in cats.
Context-aware Information Extraction from Multi-thread Business Conversations

This paper primarily focuses on developing an end-to-end solution which can process multi-threaded conversations and perform information extraction (IE) specific to a domain and intended business task. The challenges of IE in a conversation are a) context understanding, which consists of two elements: topic and sense of expression and b) establishing context flow. Since the target is free-flow dialogue, understanding the change in contexts is crucial. In this research, we attempt to build a solution that can infer and connect these contexts and reflect the same in the extracted information, taking care of things like negotiations. The proposed approach has three main steps; The first step is domain-dependent which performs topic classification at the sentence level. The second step is domain-independent, and it categorizes sentences into different semantic classes, to understand the conversation flow and parse it into multiple conversation threads. In the final step, we carry out morphological parsing to extract the target value, utilizing the predicted sentence class labels along with the conversation flow. A buyer-seller chat conversation is taken as the sample domain and the target IE is towards information for purchase order generation.
Trust-Supporting Design Elements as Signals for AI-Based Chatbots in Customer Service

In the present study, different trust factors regarding customers' perceptions of their intention to interact with or without trust-supporting design elements as signals (stimuli) in an artificial intelligence (AI)-based chatbot in customer service are identified. Based on 199 publications, a research model is derived for identifying and evaluating various variables influencing customers' views of their intention to interact with or without trust-supporting design elements as signals (stimuli) in AI-based chatbots in customer service. The research approach of the study model includes the influencing variables of perceived security and traceability, perceived social presence, and trust. A survey with 158 survey participants is used to empirically evaluate the model developed. One of the main findings of this research study is that perceived security and comprehensibility have a significant influence on the usage intention of an AI-based chatbot with trust-supporting design elements as signals (stimuli) in customer service.
Research on Multimodal Visual Saliency Detection Based on BP Neural Network Algorithm

When the number of pictures or video signals to be processed by the computer reaches hundreds of millions, the useless energy consumed will be greatly increased. Therefore, how to make the computer automatically pay attention to and only deal with the areas of human interest is of great significance to improve the efficiency of computer vision tasks. In this paper, the research of multimodal visual saliency detection based on BPNN (BP neural network) algorithm is carried out. Establish a BPNN algorithm based on GA (genetic algorithm) optimization. Using GA to roughly search out a certain weight range, and taking the weight at this time as the initial weight of BPNN can improve the shortcomings of BPNN, such as easily falling into local minimum, slow convergence speed and causing oscillation effect. The research results show that the proposed method has excellent performance in multimodal visual saliency detection, with precision, recall and F value reaching 0.8207, 0.8455 and 0.7935 respectively. The conclusion shows that this model can detect prominent targets more accurately than other methods, which proves the effectiveness of the algorithm and can adaptively fuse visible and thermal infrared information.
Texting and tutoring: Short-term K-3 reading interventions during the pandemic

AbstractIn the midst of the pandemic, we implemented and investigated short-term interventions to support K-3 reading. Specifically, we studied a text messaging program that provided caregivers with tips on supporting K-3 reading. Additionally, we studied combining the text messaging program for caregivers with one-on-one remote tutoring for their children. Survey data suggested that caregivers in the text messaging program gained self-efficacy, though they did not show gains in implementing home literacy activities or communicating with their children’s teacher. Assessment data suggested that students whose caregivers participated in the text messaging program did not make gains regardless of whether they received tutoring. We offer implications and directions for future research in the discussion.Keywords: Tutoringliteracyelementary Notes1 The main error was that all students were intended to receive an automated text at the start of each week, but initially due to a coding error, only students who were also receiving tutoring received that automated text. This resulted in all students in the text-only condition missing two texts during the first two weeks.
Design of Usability Information in Web Page Interaction Based on Constrained Clustering Algorithm

The rapid development of computer networks. And promoted the generation of websites. The website is a platform for enterprises or individuals to display their image on the network. It is a way for visitors to understand enterprises or individuals. How to make their own websites stand out has also become a concern of more and more people. User centered design is the main development trend of web design in the future. User demand is the direction of website design. In the process of website design, it is first necessary to investigate user demand and carry out website usability design with user demand as the core. This paper focuses on the analysis of the short video content of Douyin beauty makeup bloggers based on social network analysis. The occurrence of constraints will complicate the distribution of data members in the clustering process, and greatly affect the clustering results. The process of adding restrictive conditions set by users to clustering can also be regarded as adding knowledge to unsupervised classification learning and becoming a semi supervised classification method. After research, the algorithm in this paper has achieved remarkable results and is suitable for wide use.