Reviewerly/exHarmony huggingface dataset:
An Exploratory Study of the Educational Utilization of Web-based Tools in Design Problem-Solving Process - Focused on Miro tool -

For creative design thinking, a series of actions to understand problems, search for solutions, and select alternatives should be systematized. To do this, we need to provide a strategic method that helps students reframe their problem-solving in a new context. The purpose of this study is to explore the applicability of web tools that students can use positively in the early stages of concept design. A remote collaborative design scenario was developed as an experimental study and an experiment was conducted with architecture students using a web-based application called Miro. After the experiment, questionnaires and interviews were conducted, and the analysis results are as follows. First, the students who performed the design task in Miro had a very high sense of co-presence and social presence with other team members. Presence is an important variable that affects effective learning in an online education environment, and learners’ experiences through Miro were very positive. Miro is expected to be advantageous for remote collaboration as a space-based collaboration tool because it provides a sense of presence. Second, it was found that Miro has the potential to support the problem-solving process such as set-up goal, problem reinterpretation, engagement, and cognitive synchronization. Miro supports co-evolution, in which learners constantly repeat the process of recognizing problems and searching for solutions. Third, Miro provides a unified workspace that supports joint sketching among remote team members. If students use Miro to brainstorm in the concept design stage, it is expected that they will lead a project kick-off process effectively. This study is significant in that it overcomes the limitations of traditional studio class-centered design education and attempts the possibility of a new design education suitable for the changing era.
Research on the Content Validity of English Reading Test in Chinese High School Entrance Examination Based on Curriculum Standards -A Case Study of English Test Papers in Jilin Provincial High School Entrance Examination (2017-2021)

Based on the 〈English Curriculum Standards for Compulsory Education〉 and Bacheman and Palmer’s task-characteristic framework, this paper adopted literature method and educational statistical method on the content validity of English reading test in Jilin Provincial High School Entrance Examination (2017-2021) from two dimensions of discourse input and expected response. The results showed that although the content validity of English reading test is high and meets the requirements of the 〈English Curriculum Standards for Compulsory Education〉 to a certain extent, there are still some problems in the test paper in the following five aspects: (1) the proportion of expository essay and narrative essay is relatively high, and the proportion of practical essay is the lowest; (2) the order of the texts is unreasonable; (3) on the whole, the texts are all not difficult; (4) the feature of “discourse” is not highlighted at the level of test points; (5) the examination to reading skills is not balanced. Based on the above research results, this paper provided some suggestions to the test-makers and middle school English teachers respectively.
Algebraic Tensor Product

This chapter presents a unified view of algebraic tensor productsAlgebraic tensor product wherein the so-called universal property is taken as an axiomatic starting point, rather than as a theorem for a specific realization, and this approach leads to an abstract notion of algebraic tensor products of linear spaces. The concrete standard forms will be shown in the next chapter to be interpretations of the axiomatic formulation. Tensor products are axiomatically defined as follows.
Mechanism System to Reduce the Pendulum Effect of a Load for Delivery Drone

Unmanned aerial vehicles (UAVs), also referred to as "drones," are becoming more widely accepted as a typical form of transportation for a range of purposes in the logistics sector. Drones are anticipated to outperform traditional vehicles in a number of ways, including with a constant, quick speed, travel is straightforward, there is no exposure to traffic, and there is no requirement for physical transportation infrastructure. When an aircraftis in a sideslip, these surfaces generate sideward lift forces, which is known as the pendulum effect. The keel effect, also known as the "Pendulum Effect," occurs when the effect of sideways forces above the center of gravity in producing a rolling moment is increased by having a lower center of gravity. The load shift, also known as the pendulum effect, reduces flight efficiency and may unbalance the UAV, resulting in catastrophic failure. The purpose of this project is to construct a mechanism system toreduce the pendulum effect of a load for the delivery drone and to investigate the numbers of pendulum sway with the different types of loads. The mechanism for this project was constructed by using a 3D printer. The experiment process was used to carry out this project. This project's variables included various types of loads, string length, and the number of sways. The data obtained in this project are important to determine which design is the best for the delivery drone. Unfortunately, the data from the experiment gave the same result for all designs. For future studies try to come up with new concepts for how to construct the mechanism system to reduce the pendulum effect of a load for delivery drone and compare all the concepts.
Author’s Age and Gender Prediction on Hotel Review Using Machine Learning Techniques

Author’s Profile (AP) may only be displayed as an article, similar to text collection of material, and must differentiate between gender, age, education, occupation, local language, and relative personality traits. In several information-related fields, including security, forensics, and marketing, and medicine, AP prediction is a significant issue. For instance, it is important to comprehend who wrote the harassing communication. In essence, from a marketing perspective, businesses will get to know one another through examining items and websites on the internet. Accordingly, they will direct their efforts towards a certain gender or age restriction based on the kind of individuals who comment on their products. Recently many approaches have been presented many techniques to automatically detect user age and gender from the language which is based on text, documents, or comments on social media. The purpose of this research is to classify age (18–24, 25–34, 35–49, 50–64, and 65–70) and gender (male, female) from a PAN 2014 Hotel Reviews dataset of the English language. The usage of six machine learning models is the main emphasis of this work, including the methods of Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT) and K-Nearest Neighbors (KNN).
Attribution of Adversarial Attacks via Multi-task Learning

Deep neural networks (DNNs) can be easily fooled by adversarial examples during inference phase when attackers add imperceptible perturbations to original examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., attack algorithm, victim model and hyperparameter. Existing works transfer AAP into a single-label classification task and ignore the relationship among above three signatures. Actually, there exists owner-member relationship between attack algorithm and hyperparameter, which means hyperparameter recognition relies on the result of attack algorithm classification. Besides, the value of hyperparameter is continuous, hence hyperparameter recognition should be regarded as a regression task. As a result, AAP should be considered as a multi-task learning problem rather than a single-label classification problem or a single-task learning problem. To deal with above problems, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize above three signatures simultaneously. It takes the relationship between attack algorithm and the corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework.
Quantized Disentangled Representations for Object-Centric Visual Tasks

Recently, the pre-quantization of image features into discrete latent variables has helped to achieve remarkable results in image modeling. In this paper, we propose a method to learn discrete latent variables applied to object-centric tasks. We assign objects to slots which are represented as vectors generated by sampling from non-overlapping sets of low-dimensional discrete variables. We empirically demonstrate that embeddings from the learned discrete latent spaces have the disentanglement property. We use set prediction and object discovery as downstream tasks. The model achieves state-of-the-art results on the CLEVR dataset among a class of object-centric methods for the set prediction task. We also demonstrate manipulation of individual objects in a scene with controllable image generation in the object discovery setting.
Workshop#5 Sentiment Analysis and Text Classification with LSTM Using spaCy (Hour 9–10)

NLTK and spaCy are two major NLP Python implementation tools for basic text processing, N-gram modelling, POS tagging, and semantic analysis introduced in last four workshops. Workshop 5 will explore how to position these NLP implementation techniques into two important NLP applications: text classification and sentiment analysis. TensorFlow and Kera are two vital components to implement Long-Short Term Memory networks (LSTM networks), a commonly used Recurrent Neural Networks (RNN) on machine learning especially in NLP applications. This workshop will: (1) study text classification concepts in NLP and how spaCy NLP pipeline works on text classifier training; (2) use movie reviews as a problem domain to demonstrate how to implement sentiment analysis with spaCy; (3) introduce Artificial Neural Networks (ANN) concepts, TensorFlow and Kera technologies; (4) introduce sequential modelling scheme with LSTM technology using movie reviews domain as example to integrate these technologies for text classification and movie sentiment analysis.
Knowledge Distillation via Information Matching

Knowledge distillation can enhance network generalization by guiding a smaller student network to learn from a more complex teacher network. The challenge lies in maximizing the performance of the student network under the supervision of the teacher network. Currently, the feature-based distillation approach utilizes the middle-layer features of the teacher network to improve the performance of the student network. However, this approach lacks a measure to evaluate the content of the information present in the intermediate layers of both the teacher and student networks, which leads to a distillation mismatch of features and damages the student’s performance. In this study, we propose a new feature distillation method to solve this problem. We measure the information content in the intermediate layers of the teacher and student networks based on the receptive fields of corresponding features. Subsequently, the suitable number and locations of transmission features are decided based on information content, effectively alleviating the risk of information mismatch during distillation. Our experimental results demonstrate that the proposed method significantly improves the performance of the student network.
Generative Plug and Play: Posterior Sampling for Inverse Problems

Over the past decade, Plug-and-Play (PnP) [1], [2] has become a popular method for reconstructing images using a modular framework consisting of a forward and prior model. The great strength of PnP is that an image denoiser can be used as a prior model while the forward model can be implemented using more traditional physics-based approaches. However, a limitation of PnP is that it reconstructs only a single deterministic image.In this paper, we introduce Generative Plug-and-Play (GPnP), a generalization of PnP to sample from the posterior distribution. As with PnP, GPnP has a modular framework using a physics-based forward model and an image denoising prior model. However, in GPnP these models are extended to become proximal generators, which sample from associated distributions. GPnP applies these proximal generators in alternation to produce samples from the posterior. We present experimental simulations using the well-known BM3D denoiser [3]. Our results demonstrate that the GPnP method is robust, easy to implement, and produces intuitively reasonable samples from the posterior for sparse interpolation and tomographic reconstruction. Code to accompany this paper is available at https://github.com/gbuzzard/generative-pnp-allerton.
Deep Learning Based Cryptocurrency Real Time Price Predication

Cryptocurrency is an intangible form of digital currency based on blockchain technology and become an alternative to the traditional exchange of currency. It is decentralized in nature which makes it more flexible to use as it eliminates the role of banks, cryptocurrency cannot be affected by the government crises and has less devaluation. In recent years, it is obvious that the popularity of cryptocurrencies have been increasing for a little while. It frequently participated in companies and stock industries thus becoming a critical element. The accurate prediction will assist investors to make the right choice but the uncertainty and dynamism makes the price prediction difficult. Deep Learning techniques have been used progressively in this area, due to its flexibility of features and ability to provide high quality of recommendations which is based on interaction with a neural network. The properties which make neural architecture effective are end-to-end differentiable and provide learning biases to input data type. In this paper, deep learning models are used for cryptocurrency price prediction using LSTM (Long Short Term Memory), Bidirectional LSTM and Gated Recurrent Unit (GRU). The trained model focuses on accurately prediction of prices by maintaining high accuracy. The experiment is done on a real time dataset. RMSE and MAPR are used as evaluation metrics for the models. The comparison between the models shows that the performance of GRU is best among them as it has lowest MAPE and RMSE that indicates that the accuracy of the model is best and considered as a good model fit for prediction.
Interactive Gaming Experience in VR Integrated with Machine Learning

The COVID-19 pandemic has led to a boost in the virtual reality industry. Virtual games and environment has been blooming but it is still in their nascent stage as it is not accessible and less available to the general public. VR headsets and gears deter people away from the usage of such technology as it could become unaffordable. In this paper, we proposed a method to make the virtual reality experience more accessible and available. The method proposed uses the Unity game engine to create various scenes and avatars, as well as the Barracuda library to control the avatar. We also used the PoseNet model to recognize the user’s and professional’s poses, as well as cosine similarity and Dynamic Time Wrapping to grade the users’ accuracy. The article also includes a live test with a trainee to demonstrate the implementation of the research. This takes into account multiple perspectives (front, side, and rear view).
Patch management planning - towards one-to-one policy

Software security is a key component in protecting an enterprise’s digital and operational infrastructure. Time from vulnerability disclosure to exploitation is decreasing and a large number of vulnerabilities are being exploited before security teams have implemented patches or other mitigation methods. To achieve a sufficient level of software security, timely patching is critical. However, failure to recognize patching as a necessary business cost is extremely common at many levels of organizations. The lack of proper patch management strategy is apparent. In order to help organizations to improve their patch management planning we surveyed the latest academic publications, vulnerability reports and the latest NIST special publication 800-40 on patch management planning and focused on enriching the provided recommendations for organizations, to improve their management of threats.
Height Restriction Bar Detection via Deformable YOLOv5s

In recent years, there are more and more traffic accidents caused by the height restriction bars, as current vehicles are becoming taller and taller. To avoid such traffic accidents, automatic height measurement system for height restriction bars is urgently required. To accurately measure the height, the location of the height restriction bar should be detected first. However, to the best of our knowledge, there are no professional algorithm and relevant dataset for height restriction bar detection. Therefore, in this work, we elaborately collect a novel dataset for height restriction bars, named as HRB22, which comprises more than 8600 images covering various types of height restriction bars in different natural scenes. This dataset could serve as a catalyst for promoting many vision tasks, e.g., segmentation and height estimation, etc. As we know, the height restriction bar is usually thin and has long boundary. Therefore, a deformable module is proposed based on the modulated deformable convolution to facilitate the learning of these features. In order to satisfy the real-time detection requirement for driving, the proposed feature learning module is integrated into the backbone of YOLOv5s. A variety of experiments and ablation studies are conducted to demonstrate the effectiveness of the proposed dataset and deformable module. In addition, the proposed method is tested on a common object detection dataset MS-COCO, which illustrates the generalization of the proposed method.
Pelatihan Computer Assisted Test dalam Rangka Menghadapi Tes CPNS 2021 di Kota Pontianak

In 2021, the CPNS acceptance will again be carried out by the government. As a profession that is favored by the community, careful preparation of course needs to be done in various ways, one of which is by participating in problem-solving training activities and debriefing from the presenters. In this activity, the presenters are experienced experts so they are expected to motivate participants and foster enthusiasm in the face of CPNS acceptance later. In this Community Service participants will get tricks in facing the test and perform a simulation of working on CAT questions that have been prepared and then evaluate the results of the simulation work.
A systematic review and research contributions on aspect-based sentiment analysis using twitter data

Recently, Aspect-based Sentiment Analysis (ABSA) is considered a more demanding research topic that tries to discover the sentiment of particular aspects of the text. The key issue of this model is to discover the significant contexts for diverse aspects in an accurate manner. There will be variation among the sentiment of a few contexts based on their aspect, which stands as another challenging point that puts off the high performance. The major intent of this paper is to plan an analysis of ABSA using twitter data. The review is concentrated on a detailed analysis of diverse models performing the ABSA. Here, the main challenges and drawbacks based on ABSA baseline approaches are analyzed from the past 10 years’ references. Moreover, this review will also focus on analyzing different tools, and different data utilized by each contribution. Additionally, diverse machine learning is categorized according to their existence. This survey also points out the performance metrics and best performance values to validate the effectiveness of entire contributions. Finally, it highlights the challenges and research gaps to be addressed in modeling and learning about effectual, competent, and vigorous deep-learning algorithms for ABSA and pays attention to new directions for effective future research.
Enhancing Teachers' Proficiency in Implementing Augmented Reality Technology as Interactive Learning Media

Creativity is critical in education, especially for teachers in crafting instructional media that captivates and engages students' interest in comprehending the subject matter. Each student possesses distinct interests and enthusiasms towards the instructional press, allowing educators to innovate in devising captivating learning resources. One form of instructional media that offers a unique experience is augmented reality (AR) based media, which delivers individualized audio-visual interactions and experiences. In this research, we harness the Assemblr Edu platform, an AR and VR-based instructional media tool, within a community engagement workshop held at SMPN 3 Batusangkar, involving participation from educators of the Natural Sciences Teachers' Subject Study Group (MGMP IPA) in Tanah Datar Regency. The research aims to enhance the creative aptitude of educators in designing instructional media using the augmented reality technology of Assemblr Edu. Employing a quantitative approach, we gauge the extent of success among educators in crafting interactive instructional media using the Assemblr Edu platform, yielding augmented reality instructional media accessible through Android smartphones. The research findings underscore that Assemblr Edu, integrated with augmented reality technology, remarkably stimulates students' interest, amplifies their learning motivation, and assists them in comprehending subject matter while efficiently tackling assignments
Prediction of Dengue Fever in Coastal Areas of North Sumatera (Kuala Namu and Belawan) With Random Forest and Support Vector Machine (SVM) Methods

Dengue Fever is a really infectious disease. This disease may cause death. The lack of health facilities in several regions can increase the number of cases and death. Thus, a proper prevention is needed so the number of cases can be decreased and the spread of the fever can be prevented especially in remote area like the coast area of North Sumatera. Because of this, a system that can predict the number of cases based on several parameters is needed to prevent the spread of fever in several areas, using Random Forest dan Support Vector Machine method. Both methods have different forecast results but the number is close to the actual number of cases. Random Forest can predict more accurate with MSE value at 43.
Text Analytics

This chapter covers text analytics definitions, how to get started with text analytics, examples and approaches, and a case study. The chapter gives examples of existing text analytics applications to show the wide range of real-world implications. Finally, as a guide to text analytics and the book, we give a process road map. Chapter 2 (How to Get Started with Text Analytics) briefly explains the Analyse Your Data, Use BI Tools to Understand Your Data and Final Words. Chapter 3 (Examples and Methods for Text Analytics) explains various Text Analytics Approaches 1: Word Spotting Text Analytics Approach 2. Manual Rules Text Analytics Approach 3. Text Categorization Approach 4: Topic Modelling Approach and 5. Thematic Analysis. Applications of Word Spotting Text Analytics Approach, Manual Rules, Text Categorization Approach, Topic Modelling Approach and Thematic Analysis are discussed with real-time examples. Chapter 4 discusses the case study, the following real-time application, Word Cloud Explorer, to illustrate its analytic capabilities.