Reviewerly/exHarmony huggingface dataset:
Integrating Background and General Knowledge for Dialogue Generation

The traditional sequence-to-sequence model generates responses that are smooth but empty in their content. Background-based dialogue is one solution that uses the context’s unstructured knowledge to generate informative responses. The key point of background-based dialogue is knowledge extraction, but some conversations have poor performance in knowledge selection due to insufficient information. At the same time, to improve the satisfaction of the responses, this paper can enhance the amount of conversational knowledge while allowing the model to carry some emotional awareness by selecting external knowledge sources with emotional information. In this paper, we introduce the CEC model, which utilizes graph attention and a double-matching matrix for the selection of external and background knowledge. The generation process is conducted within each decoding step, considering the selected knowledge’s content. We conduct experiments on the Holl-E dataset. According to the experimental results, our model CEC outperforms the previous model in terms of performance.
Incorporating Syntactic Knowledge and Position Information for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a subtask of fine-grained sentiment analysis that focuses on analysing the sentiment polarity of aspect terms in a given sentence. Previous studies have extracted sentiment interaction information between aspect and target sentiment words with a dependency syntax parse tree (DSPT). However, those models relied excessively on DSPT and thus did not perform well in identifying local context information. Moreover, modelling syntactic information equivalently with complex dependency information may introduce noise and degrade the model’s performance. Therefore, we incorporate sentence constituent information into DSPT such that the model can learn the association information not only within a sentence but also between remote words. Furthermore, we also capture the sentiment interaction information between aspect terms based on their distance in the context to reduce internal noise and the effect of irrelevant words.
Pengaruh Motivasi Instrinsik dan Motivasi Ekstrinsik Terhadap Proses Pembelajaran Mahasiswa Universitas Pendidikan Indonesia

The purposes of the research we conducted were (1) to analyze how much influence the result of intrinsic motivation has on the learning process of indonesian university of education students (2) to analyze how much influence the result of extrinsic motivation has on the learning process of indonesian university of education students and (3 ) to analyze how much influence is generated from both, namely from intrinsic motivation and extrinsic motivation on the learning process of indonesian university of education students. This research was conducted using qualitative methods with collection techniques through interviews, distributing questionnaires and questionnaires. The data analysis technique used is the research instrument test including: validation test and reliability test, based on the research we have done we get the results that (1) intrinsic motivation greatly influences the learning process of indonesian university of education students because when someone has an interest in learning within themselves then he will have the drive to achieve the expected goals. (2) extrinsic motivation also greatly influences the learning process of indonesian university of education students because when someone occupies a comfortable learning environment, a stronger enthusiasm for learning will arise as well. And (3) it can be concluded that intrinsic motivation and extrinsic motivation greatly influence the learning process of indonesian university of education students and the two also have a very close relationship because when a person only has intrinsic motivation and is not balanced with a good learning environment, the learning outcomes obtained will decrease.
Pengembangan Media Pembelajaran Interaktif Berbasis Ispring Suite Pada Mata Pelajaran Matematika Materi Bangun Datar Kelas III Di SDN Mangunharjo 1 Kota Probolinggo

Currently there are many learning media using the internet, ranging from simple to complex, one of which is the Ispring Suite. Ispring suite can be used as an application, HTML5, record video, combine images, as well as a quiz maker. It is hoped that with the ispring suite-based interactive learning media it is hoped that it can help teachers and students understand the flat shape material. The research method used is Research and Development (R &amp; D) proposed by Borg &amp; Gall. In its application, there are ten steps in the implementation of development according to Borg &amp; Gall. However, due to limited manpower, time and costs, the researchers simplified it into 5 development models by taking into account the main focus on the selected stages, namely research reviews and small-scale research. The following are the 5 stages, namely Preliminary Study, Design of interactive learning, product development, Validation and limited trials and Revision of the final product in development. Based on the results of the assessment of learning media experts, it can be concluded that the media is said to be very feasible but with revisions. Suggestions or input from the validator is that the bad songs should be removed during the presentation of the material, the font should be enlarged, as well as images that do not need to be deleted or minimized so as not to distract the students' focus.
Face Image Recognition Algorithm Based on Label Complementation

&lt;p&gt;In face image recognition, labels play a fairly important role in recognition and classification, and rich and perfect labels can greatly improve the accuracy rate. However, it is almost impossible for the labels in the image to be recognized to describe the image completely and accurately. At the same time, the data obtained when feature extraction is performed on an image inevitably extracts a large amount of redundant and useless information at the same time, which affects the generalization performance of the model. Accordingly, we propose a face image recognition algorithm based on label completion in multi label learning. First, the SVD algorithm is used to remove redundant and useless information from the features of the original data by dimensionality reduction operation to obtain simplified sample attribute information, and the label completion algorithm is used to supplement the labels of the images using the extracted feature information. Finally the obtained label data as complete as possible is put into the extreme learning machine to construct the face recognition model and give the prediction results of the images. Experiments on the ORL dataset demonstrate that the algorithm can achieve good recognition results.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Public Integrity Verification for Cloud Storage with Efficient Key-update

&lt;p&gt;To improve the security of the data on cloud storage, numbers of data integrity auditing schemes have been proposed in the past several years. However, there only a few schemes considered the security challenge that the user’s key is exposed unknowingly which is very likely to happen in real-life. To cope with the problem, we propose a public data integrity auditing scheme for cloud storage with efficient key updating. In our scheme, the user’s key is updated periodically to resist the risk of key exposure. Meanwhile, the authentication tags of blocks are updated simultaneously with the key updating so as to guarantee&amp;nbsp;the data integrity can be verified normally. The algorithm of key updating in our scheme is very efficient which only needs a hash operation while previous schemes need two or three exponentiation operations. Moreover, the workload of tag updating is undertaken by cloud servers with a re-tag-key which reduces the burden of users and improves the efficiency of the scheme. The communication cost of the scheme is also reduced greatly, for instance, the information size in &amp;lsquo;re-key&amp;rsquo; step is decreased from two group members to one. Furthermore, we give the formal security model of our scheme and prove the security under the CDH assumption. The experimental results show that our proposal is efficient and feasible.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Optimize the Performance of the Neural Network by using a Mini Dataset Processing Method

&lt;p&gt;In the case of traditional methods such as network models and algorithms are highly open source and highly bound to hardware, data processing has become an important method to optimize the performance of neural networks. In this paper, we combine traditional data processing methods and propose a method based on the mini dataset which is strictly randomly divided within the training process; and takes the calculation results of the cross-entropy loss function as the measurement standard, by comparing the mini dataset, screening, and processing to optimize the deep neural network. Using this method, each iteration training can obtain a relatively optimal result, and the optimization effects of each time are integrated to optimize the results of each epoch. Finally, in order to verify the effectiveness and applicability of this data processing method, experiments are carried out on MNIST, HAGRID, and CIFAR-10 datasets to compare the effects of using this method and not using this method under different hyper-parameters, and finally, the effectiveness of this data processing method is verified. Finally, we summarize the advantages and limitations of this method and look forward to the future improvement direction of this method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Hybrid FCSR Based Stream Cipher for Secure Communications in IoT

&lt;p&gt;Linear Feedback Shift Register (LFSR) is the basic hardware of stream cipher, and Feedback with Carry Shift Register (FCSR) is the nonlinear analogues of LFSR. FCSR is a feedback architecture to generate long pseudorandom sequence. In this paper, we study the characteristics of FCSRs combined with nonlinear circuits such as Dawson&amp;rsquo;s Summation Generator (DSG), lp-Geffe generator and etc. Then we proposed a hybrid FCSR applying DSG and lp-Geffe generator as nonlinear combining elements to increase the period and the linear complexity of the output sequence. In addition, we further investigate the period, linear complexity, randomness, and use known attacks to verify the security strength of the proposed keystream generator. The pass rates of the proposed scheme are 100% for FIPS PUB 140-1 random tests, and at least 98% for SP800-22 random test, respectively.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Knowledge Graph Construction Method for Software Project Based on CAJP

&lt;p&gt;In recent years, there has been increasing interest in using knowledge graphs (KGs) to help stakeholders organize and better understand the connections between various artifacts during software development. However, extracting entities and relationships automatically and accurately in open-source projects is still a challenge. Therefore, an efficient method called Concise Annotated JavaParser (CAJP) has been proposed to support these extraction activities, which are vitally important for KG construction. The experimental result shows that CAJP improves the accuracy and type of entity extraction and ensures the accuracy of relationship exaction. Moreover, an intelligent question-and-answer (Q&amp;A) system is designed to visualize and verify the quality of the KGs constructed from six open-source projects. Overall, the software project-oriented KG provides developers a valuable and intuitive way to access and understand project information.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Development of Prose Appreciation Teaching Materials Based on Experiential Learning

Prose appreciation is a mandatory course taken by prospective primary school teacher education students in the first semester with a load of 2 credits. This research was carried out to develop Prose Appreciation teaching materials based on an experiential learning approach and to determine the validity and practicality of Prose Appreciation teaching materials based on an experiential learning approach. This research uses an R&amp;amp;D (research and development) approach, namely the Dick and Carey model. The data collection techniques used were interviews and questionnaires. Data analysis techniques used were interviews and questionnaires conducted qualitatively and quantitatively. The research results show that the Prose Appreciation teaching lesson based on the Experiential Learning approach is valid and practical for use in lectures. The linguistic validation results show that the Prose Appreciation teaching material is very valid for use with a score of 0.89. The validation results of the presentation and graphics obtained a figure of 0.77 in the valid category. Meanwhile, the content/material expert assessment obtained a figure of 0.87, which is in the valid category. Furthermore, the small group trial showed that the Prose Appreciation teaching material was practical to use and the large group trial showed that the Prose Appreciation teaching material was very practical to use.
Towards Conceptualization Of A Prototype For Quantum Database: A Complete Ecosystem

This study proposes a conceptualization of a prototype And a possibility to converge classical database and fully quantum database. This study mostly identifies the gap between this classical and quantum database and proposes a prototype that can be implemented in future products. It is a way that can be used in future industrial product development on hybrid quantum computers. The existing concept used to consider oracle as a black box in this study opens up the possibility for the quantum industry to develop the QASAM module so that we can create a fully quantum database instead of using a classical database as BlackBox.As the Toffoli gate is basically an effective NAND gate it is possible to run any algorithm theoretically in quantum computers. So we will propose a logical design for memory management for the quantum database, security enhancement model, Quantum Recovery Manager &amp; automatic storage management model, and more for the quantum database which will ensure the quantum advantages. In this study, we will also explain the Quantum Vector Database as well as the possibility of improvement in duality quantum computing. It opens up a new scope, possibilities, and research areas in a new approach for quantum databases and duality quantum computing.
Online Shoppers' Purchase Intention using Ensemble Learning Approach

The customer’s purchase intention can be predicted by analyzing the history of the customers. In this study, we have analyzed the data of online shoppers for building a better prediction model to predict their purchase intention. Initially exploratory data analysis is performed on the dataset. We have used different ensemble algorithms such as Random Forest, Gradient Boosting, XGBoost and LightGBM to predict whether a customer, visiting the website of an online shop, will end up with a purchase or not. Later we have performed ensemble methods to boost up the performance of the algorithms using SMOTE to overcome class imbalance. Lastly model performance evaluation is done using parameter tuning. Study has shown that XGBoost model predicts with 89.97 % accuracy on imbalanced data, whereas Random Forest displays 93% accuracy after using SMOTE to predict the customer’s purchase intention. Moreover, XGBoost shows the highest accuracy, which is 93.54% after parameter tuning. Execution time of the models is also observed in the study.
Dynamic Hand Gesture Recognition for Indian Sign Language using Integrated CNN-LSTM Architecture

Human Centered Computing is an emerging research field that aims to understand human behavior. Dynamic hand gesture recognition is one of the most recent, challenging and appealing application in this field. We have proposed one vision based system to recognize dynamic hand gestures for Indian Sign Language (ISL) in this paper. The system is built by using a unified architecture formed by combining Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). In order to hit the shortage of a huge labeled hand gesture dataset, we have created two different CNN by retraining a well known image classification networks GoogLeNet and VGG16 using transfer learning. Frames of gesture videos are transformed into features vectors using these CNNs. As these videos are prearranged series of image frames, LSTM model have been used to join with the fully-connected layer of CNN. We have evaluated the system on three different datasets consisting of color videos with 11, 64 and 8 classes. During experiments it is found that the proposed CNN-LSTM architecture using GoogLeNet is fast and efficient having capability to achieve very high recognition rates of 93.18%, 97.50%, and 96.65% on the three datasets respectively.
Investigation of Available Datasets and Techniques for Visual Question Answering

Visual Question Answering (VQA) is an emerging AI research problem that combines computer vision, natural language processing, knowledge representation &amp; reasoning (KR). Given image and question related to the image as input, it requires analysis of visual components of the image, type of question, and common sense or general knowledge to predict the right answer. VQA is useful in different real-time applications like blind person assistance, autonomous driving, solving trivial tasks like spotting empty tables in hotels, parks, or picnic places, etc. Since its introduction in 2014, many researchers have worked and applied different techniques for Visual question answering. Also, different datasets have been introduced. This paper presents an overview of available datasets and evaluation metrices used in the VQA area. Further paper presents different techniques used in the VQA domain. Techniques are categorized based on the mechanism used. Based on the detailed discussion and performance comparison we discuss various challenges in the VQA domain and provide directions for future work.
Mendeteksi Indikasi Kecurangan Laporan Keuangan dengan Metode F-Score Menggunakan Model Fraud Pentagon

Fraud in financial reports can occur because there is pressure on agents, thereby encouraging them to commit fraudulent acts whose aim is to show that the company is performing well. This research aims to test the fraud pentagon theory in detecting indications of fraudulent financial statements. Testing was carried out on mining companies registered on the IDX during the 2016 - 2020 period. Sample selection was carried out using a purposive sampling technique with a final sample of 21 mining companies. Data analysis in this study used multiple linear regression analysis. The research results show that external pressure has a negative effect on detecting indications of financial report fraud and ineffective monitoring has a positive effect on detecting indications of financial report fraud. This research provides implications for all parties who need information to provide an assessment of the opportunities for indications of financial statement fraud in mining companies by considering the factors that influence it.&#x0D; Keywords: Fraud Pentagon Theory; Fraudulent Financial Reporting; Mining Compines
Graph Model for Traffic Control in Surakarta

Surakarta City is the most populous city in Central Java that has 64 traffic signals to regulate traffic. Drivers' travel time in Surakarta City is heavily influenced by the amount of time they spend waiting for their turn to move at each traffic signals they pass. A time setting for traffic signal changes needs to be designed to optimize each driver's travel time on several routes in Surakarta City. Once the traffic has been researched and modeled, a time setting can be created. This study shows a graph model that represent roadways in Surakarta. The Dijkstra algorithm was used in this study to optimize the routes represented in the graph by finding all the shortest routes between two traffic signals points.
Clustering-Based Numerosity Reduction for Cloud Workload Forecasting

Finding smaller versions of large datasets that preserve the same characteristics as the original ones is becoming a central problem in Machine Learning, especially when computational resources are limited, and there is a need to reduce energy consumption. In this paper, we apply clustering techniques for wisely selecting a subset of datasets for training models for time series prediction of future workload in cloud computing. We train Bayesian Neural Networks (BNNs) and state-of-the-art probabilistic models to predict machine-level future resource demand distribution and evaluate them on unseen data from virtual machines in the Google Cloud data centre. Experiments show that selecting the training data via clustering approaches such as Self Organising Maps allows the model to achieve the same accuracy in less than half the time, requiring less than half the datasets rather than selecting more data at random. Moreover, BNNs can capture uncertainty aspects that can better inform scheduling decisions, which state-of-the-art time series forecasting methods cannot do. All the considered models achieve prediction time performance suitable for real-world scenarios.
The impact of Classical Education techniques, drilling and teacher-led discovery, on speaking accuracy in 8th-grade students

This research paper examines the effectiveness of Classical Education in the teaching-learning process of English in a private institution in Cuenca. The primary objective of this action research piece is to analyze the learning progress of the students in teacher-centered lessons in which drilling, and teacher-led discovery techniques were used. The research employs a qualitative approach given that it is the report of an action research study carried out in two cycles in the context in which the researcher is currently working. After implementing a diagnostic, a need was encountered and the previously mentioned techniques (i.e. drilling and teacher-led discovery) were implemented to see whether or not they could be useful. Data were collected by combining data collection tools such as semi-structured observation, rubrics, diagnostic tests, and summative assessment. Through the implementation of two classical education-derived techniques mentioned before, it was found that 73.68% of students successfully addressed challenges in conjugating the simple present tense affirmatively in speaking exercises, with 39.47% demonstrating proficiency in negative conjugations during freer oral practice.
MACHINE LEARNING FOR E-COMMERCE FRAUD DETECTION

The study examines the effectiveness, challenges, and best machine learning algorithms for detecting e-commerce fraud. This study uses a systematic literature review to evaluate the effectiveness of machine learning-based e-commerce fraud detection, identify challenges, and identify the most effective techniques. The study examinedliterature extracted from the ScienceDirect, Emeralds, Wiley, and Springer databases, identifying 29 publications from recognized journals from 2012–2022, filtered using limitations and quality assessment criteria, and assessing paper eligibility. This study reveals that machine learning significantly enhances the accuracy of detecting e-commerce fraud. Yet, there are a number of issues that need to be resolved before machine learning can be utilized to detect e-commerce fraud. Poorer-quality data distribution is the biggest challenge in detecting e-commerce fraud. In order to determine the best machine learning strategy, the model's accuracy was also evaluated, and it was discovered that random forests performed the best in terms of accuracy. This study increases theoretical contributions as a continuation of previous research relevant to the concept of machine learning in detecting fraud in e-commerce. Then, based on the random forest's greater precision, it provides practical advice to e-commerce firms as a basis for decision-makers to find a suitable machine learning technique for fraud detection.