Reviewerly/exHarmony huggingface dataset:
Improving the Performance of the Extractive Text Summarization by a Novel Topic Modeling and Sentence Embedding Technique using SBERT

Given the limitations of human reading abilities and the massive amount of text data available in modern times, there is a need for an automatic text summarization system. One automatic text summarization method that produces a satisfactory summary is extractive text summarization based on density peaks clustering. Previous research that applied this method has become state-of-the-art for the DUC 2004 dataset. However, there is still an opportunity for further development, specifically by applying the artificial neural network-based sentence embedding technique to replace the embedding vector space model and LDA topic modeling that was previously used. This research proposes a cluster-based automatic text summarization system using Sentence-BERT (SBERT) to perform sentence embedding and topic modeling processes to improve the summarization technique proposed by previous research. SBERT was chosen because it has state-of-the-art performance on sentence embedding tasks, so it is expected to represent the semantic meaning of sentences better than the techniques used in previous studies. This research is the first research that applied SBERT for text summarization. This research also proposes several improvements for the sentence selection techniques used in previous studies. Based on the assessment using the ROUGE toolkit, the text summarization system built in this study succeeded in creating a better summary than the previous research.
Full-Reference Video Quality Assessment Based on Spatiotemporal Visual Sensitivity

Video streaming services have become one of the important businesses of network service providers. Accurately predicting video perceptual quality score can help providing high-quality video services. Many video quality assessment (VQA) methods were trying to simulate human visual system (HVS) to get a better performance. In this paper, we proposed a full-reference video quality assessment (FR-VQA) method named DeepVQA-FBSA. Our method is based on spatiotemporal visual sensitivity. It firstly uses a convolutional neural network (CNN) to obtain the visual sensitivity maps of frames according to the input spatiotemporal information. Then visual sensitivity maps are used to obtain the perceptual features of every frame which we called frame-level features in this paper. The frame-level features are then feed into a Feature Based Self-attention (FBSA) module to fusion to the video-level features and used to predict the video quality score. The experimental results showed that the predicted results of our method have great consistency with the subjective evaluation results.
The managing the University Digital Transformation based on Big Data

The study examines the process of managing the digital transformation of the university, which uses analytical tools. They arise when implementing the strategy of transition from the classical form of the university to the smart one. The objects of digital transformation of the university (data, university processes, user interfaces and people) are defined. It is shown that in a smart university, the management decision-making process should be based on approaches and technologies based on Big Data. This article also presents a conceptual vision of the analytical subsystem of intelligent university management. It shows an algorithm for using digital analytics in making management decisions based on Big Data. The directions and tools for analyzing the digital traces of the key stakeholders of the university (teachers, students, employees and management) are highlighted. The mathematical apparatus for supporting the management of the university digital transformation, taking into consideration the features of the educational environment, is proposed. The scientific novelty of the research lies in the development of models and methods of decision-making in the university’s digital transformation management system, taking into account the universality of the management process, as well as the degree of its uncertainty. The proposed models can be applied to manage the digital transformation of the university and the transition to the concept of intellectual education. The experience of managing the digital transformation of the university using Big Data analysis tools on the example of Russian universities is considered.
Teacher's ICT Skills and Readiness of Integrating Augmented Reality in Education

In recent years, the ministry of education has adopted policies aimed at enhancing the consistency and efficacy of the teaching and learning process. In the midst of the Covid-19 pandemic, the implementation of different alternatives to the current teaching and learning mechanism is extremely crucial. With the migration of the current education online, AR is viewed to be a suitable and relevant alternative to be implemented. However, study on teacher readiness to adopt AR in education is still absent. Therefore, there is a need to investigate teacher's ICT skills and their readiness in deploying AR in teaching and learning. This research uses quantitative study and survey as the research methodology with 96 teachers involved as a participant. The data analysis is performed using descriptive analysis with the help of SPSS software. The findings showed that teachers in this research show a minimum to moderate level of expertise skilled in handling different ICT aspects. In addition, the research has also shown that teachers in this study show less familiarity with AR technology. The significance of this study is able to provide a set of empirical results that is beneficial for further education planning by the government.
Smart Web-based Advertising System using Eye-tracking Technology

Advertisements are all around us. In this paper smart web-based advertising system is developed using eye-tracking technology. User interface and database are developed for human-machine interaction in order to enable consumer to purchase a product via QR code from a range of products and to enable admin to analyze the attention of potential customers collected by gaze monitoring using eye-tracking camera. The tracking system can provide the advertiser information about the user's interest in a particular product. Graphical representations and analytics enable setting thresholds for alarming the both sides about the product popularity. The system for smart advertising provides the foundation for further advanced decision making based on users' attention.
An autoencoder-based representation for noise reduction in distant supervision of relation extraction

Distant Supervision is an approach that allows automatic labeling of instances. This approach has been used in Relation Extraction. Still, the main challenge of this task is handling instances with noisy labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). The approaches reported in the literature addressed this problem by employing noise-tolerant classifiers. However, if a noise reduction stage is introduced before the classification step, this increases the macro precision values. This paper proposes an Adversarial Autoencoders-based approach for obtaining a new representation that allows noise reduction in Distant Supervision. The representation obtained using Adversarial Autoencoders minimize the intra-cluster distance concerning pre-trained embeddings and classic Autoencoders. Experiments demonstrated that in the noise-reduced datasets, the macro precision values obtained over the original dataset are similar using fewer instances considering the same classifier. For example, in one of the noise-reduced datasets, the macro precision was improved approximately 2.32% using 77% of the original instances. This suggests the validity of using Adversarial Autoencoders to obtain well-suited representations for noise reduction. Also, the proposed approach maintains the macro precision values concerning the original dataset and reduces the total instances needed for classification.
Real-time and automatic map stitching through aerial images from UAV

Abstract Real-time aerial map stitching through aerial images had been done through many different methods. One of the popular methods was a features-based algorithm to detect features and to match the features of two and more images to produce a map. There are several feature-based methods such as ORB, SIFT, SURF, KAZE, AKAZE and BRISK. These methods detect features and compute homography matrix from matched features to stitch images. The aim for this project is to further optimize the existing image stitching algorithm such that it will be possible to run in real-time as the UAV capture images while airborne. First, we propose to use a matrix multiplication method to replace a singular value decomposition method in the RANSAC algorithm. Next, we propose to change the workflow to detect the image features to increase the map stitching rate. The proposed algorithm was implemented and tested with an online aerial image dataset which contain 100 images with the resolution of 640 × 480. We have successfully achieved the result of 1.45 Hz update rate compared to original image stitching algorithm that runs at 0.69 Hz. The improvement shown in our proposed improved algorithm are more than two folds in terms of computational resources. The method introduced in this paper was successful speed up the process time for the program to process map stitching.
Refactoring Java Loops to Streams Automatically

Java, starting from version 8, adopted the functional paradigm, comprising lambda expressions, streams, functional interfaces, etc. This paper proposes an approach to automatically refactor for loops to streams. In our approach, code implementing for loops is checked against a set of preconditions and matched to proposed templates associating a for loop to a proper equivalent stream-based code. Then, matching loops can be replaced with the equivalent code, according to the recognised template, embedding selected statements extracted from the original version. The approach has been validated on several open source projects, showing that the proposed templates are effective and transformations preserve the behaviour.
Prediction Model for the Tenant's Potential Failure from Business Incubation Process during COVID-19 Period Using Supervised Learning

The COVID-19 has brought the world to a new economic crisis. Hence, it creates a disruption that causes the shutdown of many businesses. Business Incubator is also responsible for their tenant business sustainability, especially during this pandemic period. Analyzing the incubation parameter process can be the initial study to collect the relevant information to determine how each tenant is successful. Using the information, building the prediction model in the classification problem for the potential failure of the tenant can be initiated. The works involve the data mining analysis for 207 tenant's track record data from 6 university business incubators in Bali Province, Indonesia. The selected data as the features are the intensity of tenants joining the mentoring program, the level of financial literacy, and the amount of capital or saving. The prediction applies cost-sensitive classifiers combined with machine learning algorithms in WEKA. Based on the model analysis from the precision, recall, F-Measure, and AUC-ROC, the Random Forest model has the highest accuracy, followed by KNN, SVM, MLP, and Naïve Bayes as the lowest. The AUC-ROC from the algorithms models sequentially: (1) 0.934, (2) 0.930, (3) 0.890, (4) 0.859, and (5) 0.846. The features are too few to generalize if they can be used to build a robust supervised learning model to predict the potential failure of the tenant incubation process, but at least based on the observation, the three features have a direct implication. University Business Incubator can consider the three features as the primary points to give their main attention.
DRIFT: A visual analytic tool for scientific literature exploration based on textual and image content

Exploring digital libraries of scientific articles is an essential task for any research community. The typical approach is to query the articles’ data based on keywords and manually inspect the resulting list of documents to identify which papers are of interest. Besides being time-consuming, such a manual inspection is quite limited, as it can hardly provide an overview of articles with similar topics or subjects. Moreover, accomplishing queries based on content other than keywords is rarely doable, impairing finding documents with similar images. In this paper, we propose a visual analytic methodology for exploring and analyzing scientific document collections that consider the content of scientific documents, including images. The proposed approach relies on a combination of Content-Based Image Retrieval (CBIR) and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Additionally, we enable visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We show the effectiveness of our methodology through two case studies and a user evaluation, which attest to the usefulness of the proposed framework in exploring scientific document collections.
Prediction of Forest Fire using Hybrid SOM-AdaBoost Method

Abstract Prediction of the occurrences of forest fire has become interest of various research studies for instances, it is found that the hybrid method based on clustering using fuzzy c-means before doing the classification approach will improve the performance of prediction than directly apply the classification approach. In this study, we will consider the new hybrid approach between clustering based on Self Organizing Map (SOM) approach and classification using Boosting (AdaBoost) approach. Our empirical analysis shows using the same public data set, which has been used in several previous studies, the performance of hybrid SOM-AdaBoost will outperforms other methods in literatures.
Cross the Chasm: Scalable Privacy-Preserving Federated Learning against Poisoning Attack

Privacy protection and defense against poisoning attack and are two critical problems hindering the proliferation of federated learning (FL). However, they are two inherently contrary issues. For constructing a privacy-preserving FL, solutions tend to transform the original information (e.g., gradient information) to be indistinguishable. Nevertheless, to defend against poisoning attacks is required to identify the abnormal information via the distinguishability. Therefore, it is really a challenge to handle these two issues simultaneously under a unified framework. In this paper, we build a bridge between them, proposing a scalable privacy-preserving federated learning (SPPFL) against poisoning attacks. To be specific, based on the the technology of secure multi-party computation (MPC), we construct a secure framework to protect users’ privacy during the training process, while punishing poisoners via the method of distance evaluation. Besides, we implement extensive experiments to illustrate the performance of our scheme.
An Adaptive Real-time Gesture Detection Method Using EMG and IMU Series for Robot Control

With the development of technology, people constantly put forward new requirements to the level and quality of human-computer interaction. In view of the intuitive and natural characteristics of sign language, gesture has become an important means of human-computer interaction. Therefore, a dynamic gesture recognition system which could adaptively recognize multiple gestures is the main development trend of gesture recognition system in the future. Though vision based dynamic gesture recognition system has always been one of the hotspots in the current scientific research field, its vulnerability to interference of light and obstacles is still a significate weakness that need to be overcome before utilizing in the real scenario. On this account, the current research constructs an actual time gesture recognition system fusing the physiological signal including Inertial Measurement Unit (IMU) and Electromyography (EMG), combined with adaptability design through self-adjusting threshold. Together, by evaluating experiment in 5 days, the adaptive classifier successfully identified 91.95% of 360 gestures with the latency of 10.89 ms, and the robots correctly responded to 88.61% of gestures in control dictionary.
A Comparison of Data Balancing Techniques for Credit Card Fraud Detection using Neural Network

This research study has employed an unequalled credit card dataset and evaluated several approaches of data balancing by using a basic neural network-based model with only one hidden layer of size 32. Even with a simplified design, the supervised machine-learning model has produced optimal results in contrast to sample-based approaches for the over-sampling of data balance. The proposed model based on random over-sampling performs better than other two model based on over-sampling approaches (SMOTE, SMOTETomek).
Deep Learning in Automatic Fingerprint Identification

The development of fingerprint identification as a computer application technology is closely related to the new technology of computer science. The artificial intelligence technology, especially the image technology based on deep learning, has opened a new mode of fingerprint identification algorithm. In this paper, we divide the development of artificial intelligence in fingerprint field into three stages, and analyze the development trend of the second stage. The fingerprint identification technology based on deep learning uses image features instead of traditional minutiae feature, which changes the cognition of fingerprint recognition in the field of forensic science. This study investigate the application mode and typical methods of deep learning technology in fingerprint identification, give the technical schemes based on deep learning, and put forward several key technologies such as image processing and dimensionality reduction. The existing DNN models that can be used for fingerprint identification are introduced, such as convolutional neural network and auto-encoder network. The results show that the performance of artificial intelligence fingerprint identification algorithm is better than the traditional algorithm in many indicators.
A Mobile Application For The Visually Handicapped

In this project, an application has been developed to facilitate the lives of visually impaired people and to offer them a better standard of living. This application aims to recognize the objects in front of the user in real time through the camera and announce these recognized objects to the user with the help of a voice assistant. In this way, visually impaired people will be aware of the obstacles in front of them in advance, and they will encounter less of a possible accident. At the same time, they will be able to easily access information about what the objects around them are. YOLOv2 and SDD algorithms were used as object recognition models in the developed project. YOLO (You Only Look Once) is an object recognition algorithm. It also gives the location information of the objects in the image. SSD (Single Shot MultiBox Detector) is an algorithm that uses a single deep network when detecting objects in the image. However, the tiny version of these models was used to improve performance. In this way, the dimensions of the objects are reduced and space is saved and the application works faster.
Performance Study of Kotlin and Java Program Considering Bytecode Instructions and JVM JIT Compiler

Java has been widely used in the information technology field such as smartphones and server-side software. Java language and its runtime environments, which is called the Java ecosystem, have been developed and improved for some decades. This has become a huge and sophisticated ecosystem. Therefore, optimizing software codes, such as bytecodes program, should be well matched for this eco-system. Recently, a new programing language, which is called Kotlin, based on JVM (Java virtual machine) was developed. Kotlin is a well-designed program language, but is newly proposed and may not be exhaustively optimized for this Java ecosystem. In this paper, we discuss the performances of programs that are written in Java and Kotlin languages based on this ecosystem implementation. We first evaluate the performances of the loop function, which is a basic function of programming languages, of these two languages, then show that the performance of Kotlin is less than that of Java in the case of subjectively typical and common description way. Also, we show that even the performance of Java is less in some cases. We seconds discuss the reason for these performance declines comparing the bytecodes that are output from compilers of these languages. We reveal a small difference in bytecode sequences causes a large performance change due to the optimizer of JIT (Just-in-time) compiler implementation. We third discuss a method for improving the performance by solving this issue. One of the easiest ways is translating a JVM bytecode file. Finally, we evaluate our performance improving method and show that the method can improve the performance by 33% in the largest case.
Fault Tolerant XY-YX Routing Algorithm Supporting Backtracking Strategy for NoC

With the continuous improvement of IC manufacturing technology, the interconnection scale of network on chip is expanding constantly, which puts forward higher requirements for system reliability. Based on the analysis of the NoC’s fault models, and the analysis of the fault-tolerant design method, this paper proposes a spatial redundancy technology to overcome the permanent or transient errors on links. If three links in the none-edge node fail at the same time, or two links in the edge node fail at the same time, we backtrack and switch the routing policy between XY routing and YX routing to bypass the failed node. The experimental results show that compared with the original XY-YX fault-tolerant routing algorithm, the network throughput is increased and the global average packet delay is reduced.
Sarcasm Detection in Politically Motivated Social Media Content

During the coronavirus pandemic, sarcasm was often used to insult, taunt, mock, and deride alternative points of views on social media platforms. Because sarcasm used in this form can promote hatred, incite violence and encourage people to abandon safety measures, it then becomes essential to separate such sarcastic content from volumes of social media feeds to understand the essence of what is being said. Prevalent sarcasm detection approaches that rely on hashtags such as #sarcasm or #irony for weak learning may not apply well because most people either do not use these hashtags in chaotic and passionate situations, or even worse sometimes they use these hashtags erroneously to tag non-sarcastic content. This paper proposes a comprehensive coding guide to label sarcastic social media content by integrating definitions from popular English language dictionaries. It applies this guide to label tweets collected following two antilockdown protests in Michigan. A suite of features that capture contextual, linguistic, social, sentiment, and auxiliary aspects are extracted from the labeled tweets. These features are used to train many common machine learning models, which can separate sarcastic from non-sarcastic tweets with a F1-score of 0.83. The approach is promising because it offers competitive accuracy in detecting sarcasm from data combined from two different contexts, without any guiding hashtags. Importance scores indicate that non-contextual features contribute about 57% to the detection, suggesting that sarcastic tendencies and their expressions may be innate to individuals, may transcend the context, and hence, offer clues for building portable classifiers for automated detection that are agnostic to the context.