Reviewerly/exHarmony huggingface dataset:
The Implementation of KWL (Know, Want to Know, and Learned) to Improve Students’ Reading Comprehension

ABSTRACTThis study focused on enhancing students’ reading comprehension through the implementation of K-W-L (know, want to know, and learned) strategy at the secondary level. The purpose of K-W-L strategy is to offer construction for stimulating and building students’ schemata, to create a determination of their reading, and to conclude what they acknowledged before. This research applied Classroom Action Research based on the model suggested by Taggart and Kemmis (1998), consisting of five stages: preliminary study, planning, implementing, observing, and reflecting. The students were asked what they know about the topic, what they wanted to know about the topic and then what they learned from the text after they had read the written text. Based on the findings of the study, this K-W-L strategy trained students to be brave to present in front of the class to share their ideas with their friends, and it also assured other students to keep on listening and appreciating their friends’ comprehension of the text being studied. It is, therefore, strongly suggested that the English teachers at Junior High School use the K-W-L strategy to improve students’ reading comprehension. ABSTRAKPenelitian ini berfokus pada peningkatan pemahaman membaca siswa melalui pengimplementasian strategi KWL (know, want to know, and learned). Tujuan dari strategi KWL ini adalah untuk menstimulasi dan membangun skemata siswa, menciptakan determinasi dalam membaca, dan menggunakan informasi yang telah mereka miliki sebelumnya. Penelitian ini menggunakan penelitian tindakan kelas berdasarkan model yang disarankan oleh Taggart dan Kemmis, yang terdiri darilima tahap, yakni, studi pendahuluan, perencanaan, pengimplementasian, pengobservasian, dan refleksi. Para siswa diberi pertanyaan terkait apa yang mereka ketahui tentang topik tersebut, apa yang mereka ingin ketahui terkait topik tersebut, dan apa yang telah mereka peroleh setelah mereka membaca teks yang diberi. Berdasarkan hasil penelitian, strategi KWL ini melatih siswa untuk memiliki keberanian dalam berekspresi di depan kelas dan membagikan ide dan opini mereka kepada teman-temannya. Selain itu, strategi ini juga melatih siswa untuk mengapresiasi pemahaman yang dimiliki oleh teman yang sedang melakukan presentasi di depan kelas. Oleh karena itu, peneliti menyarankan guru Bahasa inggris di tingkat sekoah menengah pertama untuk menerapkan strategi KWL untuk meningkatkan pemahaman membaca siswa. How to Cite: Sholeh, A., Rosalina, N. E., Weganofa, R. (2020). The Implementation of KWL (Know, Want to Know, and Learned) to Improve Students’ Reading Comprehension. IJEE (Indonesian Journal of English Education), 7(1), 22-31. doi:10.15408/ijee.v7i1.15541
Arabic Offensive Language Detection with Attention-based Deep Neural Networks

In this paper, we tackle the problem of offensive language and hate speech detection. We proposed our methods for data preprocessing and balancing, and then we presented our Convolutional Neural Network (CNN) and bidirectional Gated Recurrent Unit (GRU) models used. After that, we augmented these models with attention layer. The best results achieved was using the Bidirectional Gated Recurrent Unit augmented with attention layer (Bi-GRU_ATT). Keywords: Abusive Language, Text Mining, Arabic Language, Social Media Mining, Deep Learning, Convolutional Neural Network, Gated Recurrent Unit, Attention Mechanism, Machine Learning.
Learning to play against any mixture of opponents

Intuitively, experience playing against one mixture of opponents in a given domain should be relevant for a different mixture in the same domain. If the mixture changes, ideally we would not have to train from scratch, but rather could transfer what we have learned to construct a policy to play against the new mixture. We propose a transfer learning method, Q-Mixing , that starts by learning Q -values against each pure-strategy opponent. Then a Q -value for any distribution of opponent strategies is approximated by appropriately averaging the separately learned Q -values. From these components, we construct policies against all opponent mixtures without any further training. We empirically validate Q-Mixing in two environments: a simple grid-world soccer environment, and a social dilemma game. Our experiments find that Q-Mixing can successfully transfer knowledge across any mixture of opponents. Next, we consider the use of observations during play to update the believed distribution of opponents. We introduce an opponent policy classifier—trained reusing Q-learning data—and use the classifier results to refine the mixing of Q -values. Q-Mixing augmented with the opponent policy classifier performs better, with higher variance, than training directly against a mixed-strategy opponent.
Application of Location-Controlled Mobile Attendance Recording System in College Classroom Teaching

In the process of college classroom teaching activities, it is necessary to record the attendance of the students. The traditional methods are through the manual way naming statistics, need to take up a lot of time, affecting the normal classroom teaching activities. For the social practice classes, online classrooms, large-scale lectures and other such teaching activities, traditional attendance records methods apparently can't meet the education requirements. In this paper, a mobile attendance record system based position controllable classroom instruction is presented. Students will submit the attendance and location information through the attendance record APP. The teachers can set the attendance location to control the learners must be to the specified location for attendance records or remote attendance records. The experimental results showed that the system is flexible and straightforward, which can save attendance and statistic time, improve teaching efficiency and meet the needs of attendance record in scene teaching activities.
An Effective Variable Transformation Strategy in Multitasking Evolutionary Algorithms

Multitasking evolutionary algorithm (MTEA), which solves multiple optimization tasks simultaneously in a single run, has received considerable attention in the community of evolutionary computation, and several algorithms have been proposed in the literature. Unfortunately, knowledge transfer between constituent tasks may cause negative effect on algorithm performance, especially when the optimal solutions of all tasks are in different locations of the unified search space. To address this issue, an effective variable transformation strategy and the corresponding inverse transformation are proposed in multitasking optimization scenario. After using variable transformation strategy, the estimated optimal solutions of all tasks are both near the center point of the unified search space. More importantly, this strategy can enhance the task similarity, and then the effectiveness of knowledge transfer will probably be positive in this case, which can help us to improve the algorithm performance. Keeping this in mind, a multitasking evolutionary algorithm (named MTDE-VT) is realized as an instance by embedding the proposed variable transformation strategy into multitasking differential evolution. In MTDE-VT, the individuals in the original population are first transformed into new locations by the variable transformation strategy. Once the offspring is generated in the transformed unified search space, it must be transformed back to the original unified search space. The statistical analysis of experimental results on some multitasking optimization benchmark problems illustrates the superiority of the proposed MTDE-VT algorithm in terms of solution accuracy and robustness. Furthermore, the basic principle and the good parameter combination are also provided based on massive simulated data.
Interpreting Multivariate Interactions in DNNs

This paper aims to explain deep neural networks (DNNs) from the perspectiveof multivariate interactions. In this paper, we define and quantify thesignificance of interactions among multiple input variables of the DNN. Inputvariables with strong interactions usually form a coalition and reflectprototype features, which are memorized and used by the DNN for inference. Wedefine the significance of interactions based on the Shapley value, which isdesigned to assign the attribution value of each input variable to theinference. We have conducted experiments with various DNNs. Experimentalresults have demonstrated the effectiveness of the proposed method.
MEMTONIC: A Neuromorphic Accelerator for Energy Efficient Deep Learning

Most deep learning accelerators in the literature focus only on improving the design of inference phase. We propose a novel photonics-based backpropagation accelerator for high performance deep learning training. The proposed MEMTONIC architecture is a first-of-its-kind memristor-integrated photonics-based deep learning architecture for end-to-end training and prediction. We evaluate the architecture using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves at least 35× acceleration in training time, 31× improvement in computational efficiency, and 45× energy savings compared to the state-of-the-art designs, without any loss of accuracy.
Seeking Accessible Physiological Metrics to Detect Cybersickness in VR

Virtual reality is predicted to change the way we use technologies in education. However, currently restricting the lack of integration is high cost, unconvincing learning data, complexity of the technologies, and persistently, cybersickness. There are a number of theories as to the causes of cybersickness, but none are infallible. Moreover, many of the evaluation methods and empirical studies are highly specialized physiological analyses requiring sophisticated measuring equipment. Such studies can be difficult to prepare and present unnatural conditions for users engaged in a VR experience. In this research the Empatica E4 wearable device and its ecosystem were utilized to record physiological metrics of heart rate variability and electrodermal activity during customized computer-based and VR tasks to detect the onset of cybersickness. Although inconclusive, the metrics of NNMean, SDNN, RMSSD in HRV data, and SCR width and Peak EDA in EDA data are proposed for further analysis as potential indicators of cybersickness.
Blockchain components and concepts

The blockchain architecture consists of various components like peer network, smart contract, membership, events, ledger, system integration, wallet, and system management. Several actors such as developer, user, architect, regulator, operator, membership services, etc. are present in a blockchain that provides a solution to a blockchain-based business network. Each actor and component present in a blockchain plays a major role to make the network more secure and reliable. In this chapter, we have described the functionality of blockchain components and concepts.
Low-altitude protection technology of anti-UAVs based on multisource detection information fusion

Nowadays, unmanned aerial vehicles (UAVs) have achieved massive improvement, which brings great convenience and advantage. Meanwhile, threats posed by them may damage public security and personal safety. This article proposes an architecture of intelligent anti-UAVs low-altitude defense system. To address the key problem of discovering UAVs, research based on multisensor information fusion is carried out. Firstly, to solve the problem of probing suspicious targets, a fusion method is designed, which combines radar and photoelectric information. Subsequently, single shot multibox detector model is introduced to identify UAV from photoelectric images. Moreover, improved spatially regularized discriminative correlation filters algorithm is used to elevate real-time and stability performance of system. Finally, experimental platform is constructed to demonstrate the effectiveness of the method. Results show better performance in range, accuracy, and success rate of surveillance.
Human Social Feedback for Efficient Interactive Reinforcement Agent Learning

As a branch of reinforcement learning, interactive reinforcement learning mainly studies the interaction process between humans and agents, allowing agents to learn from the intentions of human users and adapt to their preferences. In most of the current studies, human users need to intentionally provide explicit feedback via pressing keyboard buttons or mouse clicks. However, in our paper, we proposed an interactive reinforcement learning method that facilitates an agent to learn from human social signals - facial feedback via a ordinary camera and gestural feedback via a leap motion sensor. Our method provides a natural way for ordinary people to train agents how to perform a task according to their preferences. We tested our method in two reinforcement learning benchmarking domains - LoopMaze and Tetris, and compared to the state of the art - the TAMER framework. Our experimental results show that when learning from facial feedback the recognition of which is very low, the TAMER agent can get a similar performance to that of learning from keypress feedback with slightly more feedback. When learning from gestural feedback with a more accurate recognition, the TAMER agent can obtain a similar performance to that of learning from keypress feedback with much less feedback received. Moreover, our results indicate that the recognition error of facial feedback has a large effect on the agent performance in the beginning training process than in the later training stage. Finally, our results indicate that with enough recognition accuracy, human social signals can effectively improve the learning efficiency of agents with less human feedback.
Code Free Bot: An easy way to jumpstart your chatbot!

Advancement in technology and innovation in teaching such as chatbot and extended reality can be daunting for teachers but as an educator, we need to leverage on these advancements to respond to the changes and challenges in the teaching and learning landscape. There are a number of tools available for teachers to use to overcome the challenges, and one of them is the application of artificial intelligence (AI) but creating a chatbot requires complex computer programming skills, and it is usually built from scratch to fit the intended educational purpose. This practice makes it difficult for teachers to adapt existing systems or to attempt in creating a similar version. In this workshop, we will be sharing our experiences gained from developing various chatbots for higher education using a commercial platform that can jumpstart your chatbot.
Design of mine safety dynamic diagnosis system based on cloud computing and internet of things technology

A dynamic diagnosis system of mine safety based on multi-data fusion is designed by combining the advantages of cloud computing and the Internet of things. The system’s framework is built with a three-tier model, and the construction of the cloud computing service platform provides essential support for mass data storage, processing, and security diagnosis reasoning. Simultaneously, using the relational database SQL Server 2017 and the object-oriented language C# to complete the design of an expert knowledge base and reasoning mechanism, and establish the diagnostic scoring system in the way of weighted sum. Therefore, based on the logical matching and reasoning between the collected data and the safety rules, the dynamic diagnosis of mine safety is realized.
Optimization of artificial CNN based on swarm intelligence algorithm

Aiming at the precocious convergence, low search accuracy and easy divergence of most particle swarm optimizations with velocity terms, a particle swarm optimization (IWPSO) with random inertia weights and quantization is proposed. First, the inertia weights are obeyed to be distributed randomly, and the learning factors are adjusted asynchronously to optimize the parameters in BP network. Secondly, BP network is trained using the IWPSO algorithm based on the sample data. Finally, simulation experiments prove that the algorithm has significantly improved search speed, convergence accuracy, and stability compared with existing improved algorithms. Due to the characteristics of IWPSO algorithm, the BP neural network optimized by IWPSO has better global convergence performance and is an efficient particle swarm optimization.
Automatic Assignment of Emergency Vehicles in Response to Sensors-based Generated Alarms in Smart City Scenarios

The adoption of sensors-based monitoring systems supported by Internet of Things technologies has opened new possibilities for data retrieving and processing in urban areas. Among such possibilities, emergencies management is expected to play an important role in how modern cities will evolve, reducing the negative impacts of critical events and improving the quality of life perceived by their inhabitants. Actually, when an emergency is detected and alerted, emergency vehicles, notably ambulances, fire trucks, police cars and transit agents vehicles, should be quickly assigned to respond to that situation, as soon as possible. In this context, we propose a dynamic algorithm to automatically assign emergency vehicles in smart city scenarios, exploiting for that a sensors-based emergency detection system to provide emergency alerts. The proposed algorithm can then be used to quickly assign a number of emergency vehicles in the first moments of an emergency, which can potentially save lives and improve existing crisis management applications in smart cities.
Speech Sentiment and Customer Satisfaction Estimation in Socialbot Conversations

For an interactive agent, such as task-oriented spoken dialog systems or chatbots, measuring and adapting to Customer Satisfaction (CSAT) is critical in order to understand user perception of an agent's behavior and increase user engagement and retention. However, an agent often relies on explicit customer feedback for measuring CSAT. Such explicit feedback may result in potential distraction to users and it can be challenging to capture continuously changing user's satisfaction. To address this challenge, we present a new approach to automatically estimate CSAT using acoustic and lexical information in the Alexa Prize Socialbot data. We first explore the relationship between CSAT and sentiment scores at both the utterance and conversation level. We then investigate static and temporal modeling methods that use estimated sentiment scores as a mid-level representation. The results show that the sentiment scores, particularly valence and satisfaction, are correlated with CSAT. We also demonstrate that our proposed temporal modeling approach for estimating CSAT achieves competitive performance, relative to static baselines as well as human performance. This work provides insights into open domain social conversations between real users and socialbots, and the use of both acoustic and lexical information for understanding the relationship between CSAT and sentiment scores.
Revisiting Ontologies of Units of Measure for Harmonising Quantity Values – A Use Case

Processing quantity values in industry applications is often arduous and costly due to different systems of units and countless naming and formatting conventions. As semantic technology promises to alleviate the situation, we consider using existing ontologies of units of measure to improve data processing capabilities of a cloud-based semantic platform for operating digital twins of real industry assets. We analyse two well-known ontologies: OM, the Ontology of units of Measure, and QUDT, the Quantities, Units, Dimensions and Types ontology. These ontologies are excellent resources, but do not meet all our requirements. We discuss suitable modelling choices for representing quantities, dimensions and units of measure and we outline the process we followed to adapt relevant definitions from OM and QUDT into a new ontology of units of measure that better meets our needs. Compared with the alternative of manually creating the ontology from scratch, the development and maintenance costs and duration were reduced significantly. We believe this approach will achieve similar benefits in other ontology engineering efforts.
Self-Paced Deep Reinforcement Learning

Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical successes, an open question in CRL is how to automatically generate a curriculum for a given reinforcement learning (RL) agent, avoiding manual design. In this paper, we propose an answer by interpreting the curriculum generation as an inference problem, where distributions over tasks are progressively learned to approach the target task. This approach leads to an automatic curriculum generation, whose pace is controlled by the agent, with solid theoretical motivation and easily integrated with deep RL algorithms. In the conducted experiments, the curricula generated with the proposed algorithm significantly improve learning performance across several environments and deep RL algorithms, matching or outperforming state-of-the-art existing CRL algorithms.
Double-layer affective visual question answering network

Visual Question Answering (VQA) has attracted much attention recently in both natural language processing and computer vision communities, as it offers insight into the relationships between two relevant sources of information. Tremendous advances are seen in the field of VQA due to the success of deep learning. Based upon advances and improvements, the Affective Visual Question Answering Network (AVQAN) enriches the understanding and analysis of VQA models by making use of the emotional information contained in the images to produce sensitive answers, while maintaining the same level of accuracy as ordinary VQA baseline models. It is a reasonably new task to integrate the emotional information contained in the images into VQA. However, it is challenging to separate questionguided-attention from mood-guided-attention due to the concatenation of the question words and the mood labels in AVQAN. Also, it is believed that this type of concatenation is harmful to the performance of the model. To mitigate such an effect, we propose the Double-Layer Affective Visual Question Answering Network (DAVQAN) that divides the task of generating emotional answers in VQA into two simpler subtasks: the generation of non-emotional responses and the production of mood labels, and two independent layers are utilized to tackle these subtasks. Comparative experimentation conducted on a preprocessed dataset to performance comparison shows that the overall performance of DAVQAN is 7.6% higher than AVQAN, demonstrating the effectiveness of the proposed model.We also introduce more advanced word embedding method and more fine-grained image feature extractor into AVQAN and DAVQAN to further improve their performance and obtain better results than their original models, which proves that VQA integrated with affective computing can improve the performance of the whole model by improving these two modules just like the general VQA.