Reviewerly/exHarmony huggingface dataset:
Professional Bottlenecks and Targeted Suggestions for Young Teachers in Vocational Colleges

This paper analyzes the professional bottlenecks limiting the career development of young teachers in vocational colleges, including insufficient teaching skills, limited research capacity, and restricted professional development and career progression. Targeted recommendations involve fostering young teachers’ teaching skills and research capabilities through mentorship, training, research participation and skills competition guidance. Addressing these bottlenecks effectively develops young teachers’ competencies, contributing innovative ideas and practices to improve vocational education quality, cementing their role as the next generation of vocational educators.
Pelatihan Penggunaan Mendeley Reference Dalam Penulisan Karya Ilmiah

Writing scientific papers is an essential foundation in the academic sphere, requiring researchers, students, and academics to manage, refer to, and cite various sources that support their arguments and findings. Emerging as a solution to this challenge, Mendeley Reference has the potential to change the way writers manage references in the context of higher education. This Community Service Activity (PKM) aims to provide a more comprehensive understanding to students, especially those in the 7th semester of the PGSD major at the Teaching and Education Faculty of Buton Muslim University, regarding the benefits of using the Mendeley application in writing scientific assignments. This PKM is carried out through three stages: preparation, implementation, and evaluation. The evaluation results showed that most participants had a good understanding, but some required further training. The positive impact of the training can be seen in the academic and professional development of the participants. Therefore, as a follow-up, practical exercises will be intensified, increased structured discussions between participants and presenters, and gathering input from participants for future improvements. Overall, this training significantly contributes to providing students with essential skills in scientific writing and better preparation for future academic and professional challenges.
Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data

Pretrained task-optimized convolutional neural networks are commonly used to predict brain responses to visual stimuli. Yet, they contain biases introduced by their training dataset and task objective (e.g. classification). Recent large-scale visual neuroimaging datasets have opened the avenue towards training modern convolutional neural networks with the objective of directly predicting brain responses measured with human neuroimaging data, which allows overcoming these biases. Here, we used the THINGS and the Natural Scenes Datasets – both massive functional MRI datasets acquired during the presentation of object photographs – to identify a suitable neural network architecture from the machine learning community from a set of candidate architectures (ResNet50, VGG-16, CORnet-S, and others) for predicting responses of individual regions in high-level visual cortex. Careful optimization of these networks yielded voxel-wise encoding models with high correlations, significantly surpassing state-of-the-art encoding performances of task-optimized models based on the same architectures. Treating these brain-optimized networks as in-silico models of ROIs in visual cortex, a sensitivity analysis based on passing millions of images through the network and a GAN-based synthesis of preferred images revealed the expected sensitivity of FFA, PPA, and EBA for faces, places, and body parts, respectively. Our results furthermore revealed novel selectivity, such as close-range pebble patterns in FFA and horizontal and perspectival lines in PPA. Together, these findings demonstrate the feasibility of training common neural network architectures on available massive neuroimaging datasets and provide novel insight into representations underlying human vision.
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization

Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e.g., multi-agent meta-learning and multi-agent reinforcement learning) over peer-to-peer edge networks. However, to work with the limited computation and communication capabilities of edge networks, a major challenge in developing decentralized bilevel optimization techniques is to lower sample and communication complexities. This motivates us to develop a new decentralized bilevel optimization called DIAMOND (decentralized single-timescale stochastic approximation with momentum and gradient-tracking). The contributions of this paper are as follows: i) our DIAMOND algorithm adopts a single-loop structure rather than following the natural double-loop structure of bilevel optimization, which offers low computation and implementation complexity; ii) compared to existing approaches, the DIAMOND algorithm does not require any full gradient evaluations, which further reduces both sample and computational complexities; iii) through a careful integration of momentum information and gradient tracking techniques, we show that the DIAMOND algorithm enjoys $\mathcal{O}\left( {{ \in ^{ - 3/2}}} \right)$ in sample and communication complexities for achieving an ϵ-stationary solution, both of which are independent of the dataset sizes and significantly outperform existing works. Extensive experiments also verify our theoretical findings.
Optimum Regularization Parameter C in Support Vector Machine (SVM) Binary Classification

&lt;p&gt;Support Vector Machines (SVMs) are widely used learning algorithms for data classification. In machine learning algorithms, such as the SVM approach, one or more parameters control the smoothness of the solution and are required to be tuned for the optimum solution. Such parameters are called regularization parameters, which are critical in building robust and accurate algorithms to prevent overfitting and underfitting. In SVM, the regularization parameter, denoted by C, regularizes the training loss of misclassified data. Traditionally, the value of C is first set to one, and if after training the data, misclassifications are observed, C is tuned by K-fold Cross-Validation (CV) method, which is a time- consuming process. This thesis aims to rigorously analyze and study the behavior of the C value in SVM. The analysis shows that for the case of a linearly separable dataset, setting the value of C to one does not always provide the optimum solution, and in addition, it is shown that there exists a Minimum Acceptance Value (MAV) for C as a function of Separability and Scatteredness (S&amp;S). S&amp;S is a new notion that is defined in this thesis, inspired by the Signal-to-Noise ratio (SNR) definition and is shown to be a critical parameter in the analysis of SVM classifiers. The study is further extended for the case of linearly non-separable dataset, and it has shown that a lookup table based on the analysis of bias-variance tradeoff (BVB C-Table) provides the optimum value of C, which not only outperforms but also is much faster than, the existing k-fold CV. For example, in a simple binary classification scenario, a typical k-fold cross-validation can take more than two hours, whereas the proposed method requires only a couple of minutes in a python-based environment. Due to its efficiency, the proposed method of choosing the regularization parameter enables online binary classification and will have potential benefits in One-vs-All and One-vs-One SVM classification. &lt;/p&gt;
Sales Application on Distro Souplay

The development of the business world is now inseparable from the support of technology, especially computer technology and information because in increasingly modern economic activities are needed the existence of computer technology to support and facilitate all economic activities in order to achieve these goals. With the use of computers as a tool in Distro Souplay does not mean without problems, there are still troublesome things and obstacles to create a comfortable and efficient atmosphere while in Distro suoplay. The accumulation of irregular goods data. The slow search for data needed when ordering an item of goods Adds knowledge and insights, especially about the sale of goods and implements the science obtained in lectures into the business world.
Low-light image enhancement via adaptive frequency decomposition network

Abstract Images captured in low light conditions suffer from low visibility, blurred details and strong noise, resulting in unpleasant visual appearance and poor performance of high level visual tasks. To address these problems, existing approaches have attempted to enhance the visibility of low-light images using convolutional neural networks (CNN). However, due to the insufficient consideration of the characteristics of the information of different frequency layers in the image, most of them yield blurry details and amplified noise. In this work, to fully extract and utilize these information, we proposed a novel Adaptive Frequency Decomposition Network (AFDNet) for low-light image enhancement. An Adaptive Frequency Decomposition (AFD) module is designed to adaptively extract low and high frequency information of different granularities. Specifically, the low-frequency information is employed for contrast enhancement and noise suppression in low-scale space and high-frequency information is for detail restoration in high-scale space. Meanwhile, a new frequency loss function are proposed to guarantee AFDNet’s recovery capability for different frequency information. Extensive experiments on various publicly available datasets show that AFDNet outperforms the existing state-of-the-art methods both quantitatively and visually. In addition, our results showed that the performance of the face detection can be effectively improved by using AFDNet as pre-processing.
Application for Selling Cuisine at Padang X Restaurant Based on Web

This Padang X restaurant was founded about 2 years ago, and is located on Jl. Pangeran No.136 Bekasi. This location is chosen because it is an area surrounded by offices such as village offices, sub-districts, and banks. So this place is a strategic place to be established restaurants. To provide good service to consumers, Padang X restaurant needs to know what is expected from consumers. In addition, X restaurant also needs to pay attention to the services provided to consumers so that if consumers are satisfied with the service they have received, consumers will still choose restaurants in Sabana Minang compared to other restaurants. So, the quality of service services is very important to note because it is closely related to consumer satisfaction Tekhnik data collection by looking directly at the survey to the field and dealing directly with activities that are directly involved in the sale of padang X restaurants.
Web-Based Melviana Health Clinic Information System

In today's globalization era, nscience and technology are developing very rapidly, especially information technology. Human need for everything is required to be more efficient, for example it is very clear from the development of information technology, the work that originally still uses a lot of manual systems at this time has begun to decrease, because it began to practice to a computerized system. Because with a computerized process any work will be easier to do. In the process of developing data and information needs that are growing longer, has encouraged better handling of data and information so that each element can be implemented optimally the author to get information relevant to the topic and problems that will or are being researched. The information can be obtained from scientific books, research reports, yearbooks and written sources both printed and electronic.
Heterogeneous Evolution Network Embedding with Temporal Extension for Intelligent Tutoring Systems

Graph embedding (GE) aims to acquire low-dimensional node representations while maintaining the graph’s structural and semantic attributes. Intelligent tutoring systems (ITS) signify a noteworthy achievement in the fusion of AI and education. Utilizing GE to model ITS can elevate their performance in predictive and annotation tasks. Current GE techniques, whether applied to heterogeneous or dynamic graphs, struggle to efficiently model ITS data. The GEs within ITS should retain their semidynamic, independent, and smooth characteristics. This article introduces a heterogeneous evolution network (HEN) for illustrating entities and relations within an ITS. Additionally, we introduce a temporal extension graph neural network (TEGNN) to model both evolving and static nodes within the HEN. In the TEGNN framework, dynamic nodes are initially improved over time through temporal extension (TE), providing an accurate depiction of each learner’s implicit state at each time step. Subsequently, we propose a stochastic temporal pooling (STP) strategy to estimate the embedding sets of all evolving nodes. This effectively enhances model efficiency and usability. Following this, a heterogeneous aggregation network is devised to proficiently extract heterogeneous features from the HEN. This network employs both node-level and relation-level attention mechanisms to craft aggregated node features. To emphasize the superiority of TEGNN, we perform experiments on several real ITS datasets and show that our method significantly outperforms the state-of-the-art approaches. The experiments validate that TE serves as an efficient framework for modeling temporal information in GE, and STP not only accelerates the training process but also enhances the resultant accuracy.
Satisfaction of Postgraduate Students With Hybrid Flipped Learning to Develop Their Mathematical Competencies

The objective of the research is to assess the effectiveness of flipped learning in the development of mathematical competencies of postgraduate students to determine the level of satisfaction achieved during academic training in hybrid modality. The methodological approach was a quantitative a descriptive scope. The questionnaire was the instrument used with a cross-sectional design. The sample was 32 postgraduate students. The results are that hybrid flipped learning promotes the usefulness of the contents; integrates through discussions, practical cases or problems what has been learned for work performance. The mathematical competencies with the highest level of development are the use of mathematical terminology; use digital tools for the presentation of the strategy. The relevance of the research is to contribute about the effectiveness and use of flipped learning with the support of digital tools to promote quality mathematical training.
Chaotic Color Image Encryption Based on Eight-Base DNA-Level Permutation and Diffusion

Images, as a crucial information carrier in the era of big data, are constantly generated, stored, and transmitted. Determining how to guarantee the security of images is a hot topic in the information security community. Image encryption is a simple and direct approach for this purpose. In order to cope with this issue, we propose a novel scheme based on eight-base DNA-level permutation and diffusion, termed as EDPD, for color image encryption in this paper. The proposed EDPD integrates secure hash algorithm-512 (SHA-512), a four-dimensional hyperchaotic system, and eight-base DNA-level permutation and diffusion that conducts on one-dimensional sequences and three-dimensional cubes. To be more specific, the EDPD has four main stages. First, four initial values for the proposed chaotic system are generated from plaintext color images using SHA-512, and a four-dimensional hyperchaotic system is constructed using the initial values and control parameters. Second, a hyperchaotic sequence is generated from the four-dimensional hyperchaotic system for consequent encryption operations. Third, multiple permutation and diffusion operations are conducted on different dimensions with dynamic eight-base DNA-level encoding and algebraic operation rules determined via the hyperchaotic sequence. Finally, DNA decoding is performed in order to obtain the cipher images. Experimental results from some common testing images verify that the EDPD has excellent performance in color image encryption and can resist various attacks.
Exploring The Influence of Virtual Reality on Interpersonal Communication: A Novel Study In Communication Science

This study aims to investigate the influence of virtual reality on interpersonal communication, focusing on aspects that have never been studied before in communication science. In an increasingly advanced digital age, virtual reality has become a phenomenon that attracts the attention of many people. However, there is still a lack of research that comprehensively reveals its impact on interpersonal communication. The research method used in this study is a combination of qualitative and quantitative approaches. Through the use of surveys and in-depth interviews, we obtain data that includes individual perceptions and experiences related to interpersonal communication in a virtual reality environment. Our respondents consisted of participants who were experienced in the use of virtual reality and those who had never used the technology before. Our results suggest that the use of virtual reality has the potential to influence various aspects of interpersonal communication, including emotional expression, nonverbal recognition, and levels of interpersonal closeness. These discoveries provide new insights into how virtual reality can enrich our communication experience in different contexts. This research makes an important contribution to the development of communication science, especially in understanding the role of new technologies such as virtual reality in shaping interpersonal interactions. The implications of this research could be used to develop practical guidelines for the use of virtual reality in interpersonal communication, as well as inform the design and development of better technologies in the future.
How object segmentation and perceptual grouping emerge in noisy variational autoencoders

Humans and many newborn animals are able to effortlessly perceive objects and to segment them from other objects and the background, and a long-standing debate concerns the question of whether object segmentation is necessary for object recognition. While deep neural networks (DNNs) are state-of-the-art models of object recognition and representation, their performance in segmentation tasks is generally worse than in recognition tasks. For this reason, it is often believed that object segmentation and recognition are separate mechanisms of visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer - for example, when coding for a face, VAEs may empirically code for mouths and eyes independently. Specifically, we show that VAEs can be made to segment objects without any additional finetuning or downstream training. This segmentation is achieved with a procedure that we call the latent space noise trick: by perturbing the activity of the bottleneck units with activity-independent noise, and recurrently recording and clustering decoder outputs in response to these small changes, the model is able to segment and bind separate features together. We demonstrate that VAEs can group elements in a human-like fashion, are robust to occlusions, and produce illusory contours in simple stimuli. Furthermore, the model generalizes to the naturalistic setting of faces, producing meaningful subpart and figure-ground segmentation without ever having been trained on segmentation. For the first time, we show that learning to faithfully represent stimuli can be generally extended to segmentation using the same model backbone architecture without any additional training.
Oculomotor “laziness” constrains fixation selection in real-world tasks

Introduction: People coordinate their eye, head, and body movements to gather information from a dynamic environment while maximizing reward and minimizing biomechanical and energetic costs. Such natural behavior is not possible in a laboratory setting where the head and body are usually restrained and the tasks and stimuli used often lack ecological validity. Therefore, it’s unclear to what extent the principles of fixation selection derived from lab studies, such as inhibition-of-return (IOR), apply in a real-world setting. Results: Participants performed nine real-world tasks, including driving, working at a desk, grocery shopping, and building a lego set (N recordings, 169). We measured fixations using a Tobii Pro Glasses 2 mobile eye tracker. We found that spatial and temporal IOR were absent in all tasks. Instead, participants most often re-fixated on what they had just viewed. Saccade latencies were shorter preceding re-fixations than forward saccades. We propose that participants orient their heads toward objects of interest and then minimize eccentric fixations in order to conserve oculomotor effort. Correspondingly, we observed center biases in the distributions of fixation location and duration (relative to the head orientation). A model that generates scanpaths by randomly sampling these distributions reproduced the spatial and temporal return phenomena seen in the data. The model also explained the existence of distinct 3-fixation sequences of durations for forward and return saccades. The amount of the orbit used in a given task traded off with fixation duration, consistent with an oculomotor cost that interacts with task demands. Conclusion: In naturalistic settings, people orient their heads toward objects of interest and then repeatedly return their gaze to those objects, fixating longer than at other locations. Conservation of oculomotor effort (“laziness”) explains these behaviors parsimoniously, demonstrating that oculomotor costs fundamentally shape how people extract and act on relevant visual information from the environment.
Fast and Accurate Indonesian QnA Chatbot Using Bag-of-Words and Deep-Learning For Car Repair Shop Customer Service

A chatbot is a software that simulates human conversation through a text chat. Chatbot is a complex task and recent approaches to Indonesian chatbot have low accuracy and are slow because it needs high resources. Chatbots are expected to be fast and accurate especially in business settings so that they can increase customer satisfaction. However, the currently available approach for Indonesian chatbots only has low to medium accuracy and high response time. This research aims to build a fast and accurate chatbot by using Bag-of-Words and Deep-Learning approach applied to a car repair shop customer service. Sixteen different intents with a set of their possible queries were used as the training dataset. The approach for this chatbot is by using a text classification task where intents will be the target classes and the queries are the text to classify. The chatbot response then is based on the recognized intent. The deep learning model for the text classification was built by using Keras and the chatbot application was built using the Flask framework in Python. Results showed that the model is capable of giving 100% accuracy in predicting users’ intents so that the chatbot can give the appropriate responses and the response time is near zero milliseconds. This result implies that developers who aim to build fast and accurate chatbot software can use the combination of bag-of-words and deep-learning approaches. Several suggestions are presented to increase the probability of the chatbot’s success when released to the general public.