Reviewerly/exHarmony huggingface dataset:
A New Chaotic Mixer Design Based on the Delta Robot and Its Experimental Studies

In this study, a new chaotic mixer based on the Delta robot was designed and produced which had been controlled with Arduino Uno card and MATLAB. First of all, chaotic mixing systems with different dynamic properties were chosen for the chaotic mixing process. Then, by solving the chaotic systems selected in the MATLAB with the Runge Kutta 45 (RK45) numerical solution algorithm, the results in the integer format were obtained. The obtained chaotic time-series results were transformed into 3-dimensional position information for the servomotors used in the mixer with the algorithm developed in MATLAB. The supervision was provided to ensure that the newly designed chaotic mixer was pacing chaotically in x, y, and z coordinates by transferring the chaotic position information to the Arduino Uno R3 card via USB 2.0. With the software developed in MATLAB, the performances of 7 diversified chaotic systems’ trajectories and circular motion trajectories were compared over the numerical simulation orbital distribution ratio (ODR). In the final stage, in a solid-liquid mixture type, at the selected constant mixing time, experimental studies were performed where homogeneity and orbital distribution ratio (ODR) parameters were compared by using 7 diversified chaotic systems. The designed and produced chaotic mixer can also be used in experimental studies of certain liquid-liquid mixture types. It is thought that this prototype presented in the article will serve the aim of developing new chaotic mixer systems and algorithms to derive more homogeneous mixtures in a shorter time.
Blockchain-Enabled Data-Sharing Scheme for Consumer IoT Applications

A staggering number of consumer Internet-of-Things devices are being deployed in various application scenarios, and massive data will be generated per day. How to achieve a secure and efficient data-sharing scheme for the consumer IoT applications is a huge challenge for us. The traditional cloud-based IoT has the dilemma of prolonged communication delay and privacy leakage. With the application of 5G technology, edge computing can effectively alleviate these problems. However, it cannot meet the higher security requirements for the data sources’ authenticity and information reliability. By combining the blockchain and smart contracts technology, this article proposes a distributed, efficient, and secure data-sharing scheme centered on consumer IoT devices. This architecture consists of four layers: 1) IoT devices layer, 2) edge storage layer, 3) blockchain network layer, and 4) application services layer. We design smart contracts based on the attributed based access control and the searchable encryption algorithm, including device retrieval contract, policy management contract, and authorization verification contract. Through the implementation of simulated experiments, we prove that our proposed architecture can satisfy the large-scale data access requests and bring a tolerable level of communication overhead. The proposed framework is one of the few attempts to leverage the edge computing and blockchain technologies to support IoT data sharing.
Modelling Learners in Adaptive Educational Systems: A Multivariate Glicko-based Approach

The Elo rating system has been recognised as an effective method for modelling students and items within adaptive educational systems. A common characteristic across Elo-based learner models is that they are not sensitive to the lag time between two consecutive interactions of a student within the system. Implicitly, this characteristic assumes that students do not learn or forget between two consecutive interactions. However, this assumption seems insufficient in the context of adaptive learning systems where students could have improved their mastery through practising outside of the system or that their mastery may be declined due to forgetting. In this paper, we extend the existing works on the use of rating systems for modelling learners in adaptive educational systems by proposing a new learner model called MV-Glicko that builds on the Glicko rating system. MV-Glicko is sensitive to the lag time between two consecutive interactions of a student within the system and models it as a parameter that captures the confidence of the system in the current inferred rating. We apply MV-Glicko on three public data sets and three data sets obtained from an adaptive learning system and provide evidence that MV-Glicko outperforms other conventional models in estimating students' knowledge mastery.
Using Mixed Reality (MR) to Improve On-Site Design Experience in Community Planning

In recent years, designing in existing environments has been consistently emphasized in community planning. However, practicing such on-site design is not easy for designers, because the current technical conditions do not allow virtual design objects into real environments for 3D visualization and interaction. Thus, designers’ intuitive design perceptions, accurate design judgments, and convenient design decisions are hardly supported. This paper explores the possibilities of using mixed reality (MR) technology to improve designers’ on-site design experiences in community planning. For this, we introduced an MR design support system (MR-DSS) for the interactive on-site 3D visualization of virtual design objects. With the MR-DSS, we performed a design experiment with sixteen participants in a typical on-site design scene of community planning. The results showed that the MR technology could provide designers with intuitive design perceptions, accurate design judgments, and convenient design decisions, thus effectively improving their on-site design experiences.
SC-RPN: A Strong Correlation Learning Framework for Region Proposal

Current state-of-the-art two-stage detectors heavily rely on region proposals to guide the accurate detection for objects. In previous region proposal approaches, the interaction between different functional modules is correlated weakly, which limits or decreases the performance of region proposal approaches. In this paper, we propose a novel two-stage strong correlation learning framework, abbreviated as SC-RPN, which aims to set up stronger relationship among different modules in the region proposal task. Firstly, we propose a Light-weight IoU-Mask branch to predict intersection-over-union (IoU) mask and refine region classification scores as well, it is used to prevent high-quality region proposals from being filtered. Furthermore, a sampling strategy named Size-Aware Dynamic Sampling (SADS) is proposed to ensure sampling consistency between different stages. In addition, point-based representation is exploited to generate region proposals with stronger fitting ability. Without bells and whistles, SC-RPN achieves AR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1000</sub> 14.5% higher than that of Region Proposal Network (RPN), surpassing all the existing region proposal approaches. We also integrate SC-RPN into Fast R-CNN and Faster R-CNN to test its effectiveness on object detection task, the experimental results achieve a gain of 3.2% and 3.8% in terms of mAP compared to the original ones.
Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search

Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to first perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. The depth of the transformed quantum circuits is minimized by utilizing the Monte Carlo tree search (MCTS) to perform qubit routing by making it both construct each action and search over the space of all actions. It is aided in performing these tasks by a Graph neural network that evaluates the value function and action probabilities for each state. Along with this, we propose a new method of adding mutex-lock like variables in our state representation which helps factor in the parallelization of the scheduled operations, thereby pruning the depth of the output circuit. Overall, our procedure (referred to as QRoute) performs qubit routing in a hardware agnostic manner, and it outperforms other available qubit routing implementations on various circuit benchmarks.
Binocular stereo matching algorithm based on MST cost aggregation

<abstract> For common binocular stereo matching algorithms in computer vision, it is not easy to obtain high precision and high matching speed at the same time. In this paper, an improved binocular stereo matching algorithm based on Minimum Spanning Tree (MST) cost aggregation is proposed. Firstly, the performance of the parallel algorithm can be improved by reducing the height of the tree. Then, an improved Root to Leaf (L2R) cost aggregation algorithm is proposed. By combining stereo matching technology with parallel computing technology, the above method can realize synchronous parallel computing at the algorithm level. Experimental results show that the improved algorithm has high accuracy and high matching speed for binocular stereo vision. </abstract>
HCI education of choice

This article is from the perspective of an Egyptian HCI educator who explores "designing" inclusive designers, and how decolonial thinking can address inclusion in HCI education as one possible critical lens.
Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images.Splicing and copy-move are the most common types of passive digital image forgeries.Several approaches have been proposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously.However, a more efficient method is still in demand to meet the day-to-day challenges to detect these forgeries at the same time.So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binary pattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously.The extra parameter i.e. fractional parameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively.Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and spliced images.Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas in the image.Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE, and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively.To validate the effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms of ROC, precision, recall, F1 score, F2 score, and accuracy.Moreover, the robustness of the proposed work is tested under rotation attack and better results are attained in comparison to the existing techniques.
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis

Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask.
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation

A recent topic of research in natural language generation has been the development of automatic response generation modules that can automatically respond to a user's utterance in an empathetic manner.Previous research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences.However, the outputs by these models may be inconsistent.We employ multitask learning to predict the emotion label and to generate a viable response for a given utterance using a common encoder with multiple decoders.Our proposed encoder-decoder model consists of a self-attention based encoder and a decoder with dot product attention mechanism to generate response with a specified emotion.We use the focal loss to handle imbalanced data distribution, and utilize the consistency loss to allow coherent decoding by the decoders.Human evaluation reveals that our model produces more emotionally pertinent responses.In addition, our model outperforms multiple strong baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses.Agent 1 Do you like wearing hats?It has so many functions. CuriousAgent 2 I don't like them on myself but I know a lot of people that can pull them off. NeutralAgent 1 Yes me as well.In the military hats denote a nationality, branch of service, rank or regiment. CuriousAgent 2 Yes.I love hats!I have a wide variety of hats
Neural Explicit Factor Model Based on Item Features for Recommendation Systems

In recent years, recommendation systems based on collaborative filtering (CF) have achieved a high performance.Most of the existing recommendation systems use similarity measures to determine the suitability of items for users based on latent factor models (LFM).However, these recommendation systems reduce the explainability of recommendations and hide the reasons for recommending specific items.As a result, users tend to distrust the recommendation results.To address this problem, we propose the neural explicit factor model (NEFM).Based on the user-item rating matrix, we propose adding both user-feature attention matrix and an item-feature quality matrix to improve the explainability of user and item vectors.In addition, a feedforward neural network and a one-dimensional convolutional neural network extract features from user, item and the item-feature vector.Finally, a prediction layer performs the inner product of user data, item data, and item features.Experiments on the MovieLens and Yahoo Movies datasets validate the proposed model, and comparisons with similar recommendation models show the higher accuracy and explainability of our proposal.
Object Recognition using TensorFlow

Computers can apply vision technologies using cameras and artificial intelligence software to achieve image recognition and identify objects, places, and people. The objective of this project is to capture the image of an automobile as it drives by, identify its model and color, and determine its location, travel direction, and speed. This system can be used to assist law enforcement with vehicle identification in an emergency such as an Amber alert or to detect traffic infractions. For this purpose, we constructed, trained, and applied an object detection model using TensorFlow. First, an image capturing system was built using camera lenses (Raspberry Pi Camera V2-8) and Raspberry Pi (Raspberry Pi 4) small computers. Next, the computers were set up with a software application called TensorFlow. The system was trained to recognize an automobile's model and color by processing a variety of car images. Pictures of different cars were uploaded from Google images and resized highlighting the features of the vehicle. Finally, code was developed in Python to create a universal clock for each camera that recorded the detection time. Five trials were conducted using 2 automobiles available for testing. The cars were recognized by the model with 87 percent certainty in each of the 5 trials. That information was recorded on a table together with the time of capture and the location of the camera. The information from the table was used to successfully identify a specific car's location and speed, with a few limitations. Because of budget restrictions only two cameras were built and two models were used for training. The information from the cameras was not transmitted in real time because wifi or LTE capability are not available at this time. An extension of this research will include multiple cameras, multiple models and real time data transmission.
Human Exercise Posture Analysis based on Pose Estimation

Exercise is an indispensable activity in people's spare time. Proper and correct exercise can not only help people to get a healthy body, but also help to reduce pressure and relax their mood. However, the wrong way of exercising not only makes the exercise fall short of expectations, but also causes muscle damage. Therefore, we have developed a set of human exercise posture analysis and guidance software to analyze the user's exercise posture through the video captured by the camera. This software chooses OpenPose as the basic pose estimation network, and two methods are proposed to improve the model effect. In addition, referring to the professional movement specifications, we define two correct forms of common movements: squats and push-ups. The software firstly conducts the human body posture detection, obtains the coordinate information of key points, then carries on the exercise posture analysis, and gives relevant suggestions according to the definition of the correct exercise form.
Cyber Security Featuresfor National E-Learning Policy

This paper proposes cybersecurity features in the National e-Learning policy. Cybersecurity in the learning environment is becoming an issue that has been considered by the community. DePAN 1.0 and DePAN 2.0 policies have not been carefully planned in any related security concerns. Amongst security domains in e-learning are authentication and accountability, access control, and non-repudiation issues. However, as the functionality of e-learning is expanding, information must be actively protected in this bigger context to avoid the loss of its confidentiality, integrity, and availability. Therefore, the existing policy and guidelines on e-learning have been studied thoroughly. A very feasible study has been conducted on the existing literature and related works to e-learning and e-learning. The security threats are also reviewed in this paper. Based on the established e-Learning policy, therefore, the proposed security features are namely (CSF1) authentication and accountability, (CSF2) access control, (CSF3) protection of communication, and (CSF4) non-repudiation issues. The findings from this study can be added to the implementation of e-learning in the future.
Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation

Recall and ranking are two critical steps in personalized news recommendation.Most existing news recommender systems conduct personalized news recall and ranking separately with different models.However, maintaining multiple models leads to high computational cost and poses great challenges to meeting the online latency requirement of news recommender systems.In order to handle this problem, in this paper we propose UniRec, a unified method for recall and ranking in news recommendation.In our method, we first infer user embedding for ranking from the historical news click behaviors of a user using a user encoder model.Then we derive the user embedding for recall from the obtained user embedding for ranking by using it as the attention query to select a set of basis user embeddings which encode different general user interests and synthesize them into a user embedding for recall.The extensive experiments on benchmark dataset demonstrate that our method can improve both efficiency and effectiveness for recall and ranking in news recommendation.
Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot

In this study, Fuzzy Logic (FL) and Interval Type-2 FL (IT-2FL) controllers were applied to a mobile robot in order to determine which method facilitates navigation and enables the robot to overcome real-world uncertainties and track an optimal trajectory in a very short time. The robot under consideration is a non-holonomic unicycle mobile robot, represented by a kinematic model, evolving in two different environments. The first environment is barrier-free, and moving the robot from an initial to a target position requires the introduction of a single action module. Subsequently, the same problem was approached in an environment closer to reality, with objects hindering the robot's movement. This case requires another controller, called obstacle avoidance. This system allows the robot to reach autonomously a well-defined target by avoiding collision with obstacles. The robustness of the structures of the defined controllers is tested in Matlab simulations of the studied controllers. The results show that the IT-2FL controller performs better than the FL controller.
AN ANALYSIS OF ENGLISH LANGUAGE TEACHING ACTIVITIES DURING COVID-19 PANDEMIC AT SMP PURNAMA JAKARTA

This study was led to know the activities of English teaching during the COVID-19 pandemic at SMP Purnama Jakarta. This study utilized a qualitative descriptive method, while the information assortment methods utilized interviews, observation, and documentation. Interviews were led with an English teacher who teaches at SMP Purnama Jakarta, while polls were given to a few students at SMP Purnama Jakarta. The subjects of this investigation were an English teacher and a few students at SMP Purnama Jakarta. The outcomes show that learning during the pandemic is way different from the regular learning (in-person) that was done before the COVID-19 pandemic. The distinction that can be seen is that the instructional framework, which was initially up in-person, turned out to be totally internet or technology-based by utilizing Google Meet, Google Classrooms, WhatsApp, YouTube, and other applications.
Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.&#x0D;