Reviewerly/exHarmony huggingface dataset:
Identifying Top-k Peaks Using an Extended Particle Swarm Optimization Algorithm with Re-diversification Mechanism

We propose a modified particle swarm optimization (PSO) algorithm to identify multiple optima (top-k peaks) in descending order, rather than just a single optimum value. With advances in computer technology and robotics, autonomous machines are used in applications such as search and rescue after a disaster. Survivors typically have only a limited amount of time to live, so finding them and directing rescuers to them are both time-critical. One way of rescuing more survivors more quickly is to deploy a number of aerial drones in advance and, after the disaster, use them to scan the area and identify the locations where survivors are most likely to be found. Thus, we first model such a situation by using mixture bivariate normal distributions with randomized means and identify individual drones as particle agents. Then, we propose top-k PSO, which an extension of the conventional Clerk-Kennedy PSO algorithm, to locate the top k peaks efficiently with high probability by remembering a list of global optima and introducing a strategy to increase the diversity in swarms to improve exploration. We conducted extensive experiments to evaluate top-k PSO by comparing its results with those produced by the baseline methods, canonical PSO, Clerk-Kennedy PSO, and NichePSO. Our experimental results indicate that the proposed PSO can find multiple peaks with higher probabilities than the baseline methods in various environments.
Algorithms for estimation of comic speakers considering reading order of frames and texts

Machine learning methods in recent years have focused on multimodal input and cross-modal tasks, and they are used as approaches to problems in various domains. Associating comic texts and characters using these approaches is informative for commercial activities such as speech synthesis and automatic translation of texts. In this study, we address the task of associating a text with a speaker in comics. It is challenging to correspond between them because these are not self-evidently attached, and few studies have attempted. These previous studies have less considered the continuity of comics such as narrative flow or contextual information. We assume that considering the continuity of comics is effective for speaker estimation. This paper proposes algorithms for estimating the reading order of frames or texts, and it also proposes methods for estimating speakers based on these orders. As a result, our proposed method improves accuracy compared to previous methods. Consideration of the frame order is an effective clue to the comic speaker estimation.
Data Augmentation for Question Answering Using Transformer-based VAE with Negative Sampling

In this paper, we propose a method to improve the accuracy of extracting appropriate question-answer pairs using generated questions with negative sampling. The base question-answering system that extracts similar questions for input queries is constructed on a Sentence-BERT model to carry out pairwised-ranking between questions of question-answer data and the input queries. The key issue of improving the question answering system is how we can prepare the enough size and variety of training examples. The Sentence-BERT model is trained on positive and negative pairs of extended questions generated by a Transformer-based Variational Autoencoder as well as human. Experimental results show that performance of retrieving appropriate questions for input queries is improved when the Sentence-BERT model is trained with the negative samples that are most similar to the positive examples.
Automatic Generation of Algorithm Procedures using General Pre-trained Models

To create programs, it is essential to have a logical thinking skill that breaks down program purposes into the necessary processes. Until now, we had developed procedure learning systems that cultivate logical thinking namely ability to think with algorithms. In problem creation for procedure learning systems, we need to create a large quantity of procedures that are broken down into necessary smaller problem statements. In this research, we propose a method of generating program procedures from problem statements using a general pre-trained learning model.
Extraction Method for Important Words as a Viewer’s Reaction Arousal Factor from YouTube - Transcription

We present a novel extraction method for important words as a viewer’s reaction arousal factor from YouTube transcription. The proposed method analyses the content of social media posts. Further, it extracts important words that are likely or unlikely to evoke reactions from the viewers. In this study, we analyze the subtitles and the statistical data obtained from YouTube videos. Further, a database is created consisting of the extracted words that are likely or unlikely to evoke reactions. The method consists of obtaining the subtitle data and the statistical data from YouTube, building a database, and constructing a machine learning model to classify them. This is followed by the local interpretation of the model to extract the aforementioned words. The experimental results showed that the machine learning model was effective using the created database.
Automatic assessment of student understanding for estimateing test score

Prediction of the level of student understanding to learning is one of crucial issues in education. The level of understating is usually estimated by simulation test scores from the assumption that there is a strong relationship between the level of understanding and a simulation test score. Meanwhile, instructors or teachers often assess student understanding by observing their learning status and use the assessment scores to give the students feedback. However, since the assessment scores to student understanding are obtained subjectively, the distribution of the understanding scores tends to be biased toward extremely high scores; consequently the assumption of the relationship to simulation test scores would not be held. In this paper, we propose a method to automatically rate student understanding scores based on instructors’ observation reports using supervised learning method.In order to evaluate our proposed method, we conducted experiments on real data provided by a cram school. In the experiments, we estimate simulation test scores on a students’ 60-point math exam using student understanding scores rated by our proposed method and those manually rated by instructors for comparison. The results of experiments show that the MAE between the understanding scores rated by our proposed method and those manually rated by the instructors were 7.81 and 8.73, respectively, which indicates that our proposed model significantly improve rating of the level of student understanding to learning in mathematics.
Meaning of the Clusters on Dimensionality Reduction by Word Clustering

In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not clear.We proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the cluster.In this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset.
Pseudo data acquisition using machine translation and simile identification

The simile is a kind of figurative language. It expresses the target of the figurative language by using some typical phrases such as "like". It is important to distinguish whether the sentence is a simile or a literal for understanding a sentence. However, a large amount of data is required to generate a classifier by machine learning. Moreover, creating the dataset is costly. In this paper, we propose a pseudo dataset acquisition method for simile identification. We first construct a dataset of simile and literal sentences using machine translation. This process automatically generates pseudo simile and literal instances from three types of corpora. Then, we apply some machine learning approaches to the simile identification task. We compare Support Vector Machine, Naive Bayes, and BERT in the experiment. The experimental result shows the validity of the pseudo dataset as compared with a simple baseline. For the fine-tuning of BERT, our large pseudo training data were more effective than a small manual training data.
Using the Scatter of Opinions to Predict Responses to Tweets

The importance of social media, such as Twitter and Instagram, has increased as a result of COVID-19: with social media, users can easily communicate with other people. Accordingly, social media have produced both new problems and benefits; these have occasionally occurred with upsurges in reactions owing to the huge numbers of social media followers. "Flaming" is a negative aspect of an upsurge: it is a rapid increase in blame, harassment, and insults directed at a person. Conversely, it is possible to use an upsurge to identify popular topics or trends, which is a positive aspect. Predicting the reactions to a social media post is currently very difficult; however, the aim is to prevent the above problems and provide support by forecasting upsurges at an early stage. We observed that such upsurges were affected by the scatter of opinions in the responses. If there are two groups of conflicting opinions, the original social media post tended to receive both sets of opinions. Toward predicting the reactions to a social media post, this study investigate the relationship between the upsurge and scatter of opinions. We quantify the scatter of opinions in the responses, which we term the scatter of replies. We enter that scatter into multiple regression analysis by means of clustering, and we evaluated our reaction prediction method. We found that for most settings, the correlation coefficient between the predicted and actual values exceeded 0.6, which is a moderate level of correlation.
Risk Assessment Quantification for Remote Learning Based on Lecture Type

The rapid development of ICT has promoted various approaches to remote learning. By surveying and analyzing the actual situation in other countries (the U.S., the UK, Denmark, etc.) and the implementation methods and systems of advanced initiatives, measures to improve the quality of higher education have been studied. With the advent of COVID-19 in 2020, teleworking has been implemented in companies and remote learning in universities and other educational institutions to control the spread of infection. University classes, which are traditionally conducted face-to-face, are increasingly being taught remotely, but there are many risks and issues inherent in this method. These risks have caused anxiety and dissatisfaction among both students and faculty, and in some cases have prevented the smooth implementation of classes at a distance. This paper proposes and evaluates specific countermeasures by conducting a risk assessment of remote learning for universities. This will contribute to safe and secure remote learning in the new normal era. Specifically, risk assessments were conducted for two main types of remote learning formats: on-demand and live-streaming. Then, on the basis of the results, we proposed countermeasures such as the enhancement of environmental facilities (for the on-demand type) and privacy-conscious countermeasures (for the live-streaming type). We also clarified the effectiveness of the proposed measures by comparing the risk values before and after their implementation.
Modality estimation methods in internal training reflection texts using BERT

In-house training includes opportunities for employees to reflect on the training through the writing of written reflections. Its purpose is to create opportunities to look back and think about "how the content learned in the training could be utilised in subsequent work," and to allow employees to discover their own issues and set future action goals. In addition, it is possible to measure the effectiveness of the training, and the planners can also discover issues in the training itself, such as the appropriateness of the content, which is useful for improving future training programs. However, there has been little research on these employee reflection texts, especially text analysis using natural language processing (NLP), and there is a need for methods to find useful uses for the texts. By taking a NLP approach to the accumulated data of reflective statements, it is possible to estimate the degree of understanding of the training content and to grasp the degree of growth of employees over time. In addition, there is a potential for proposing training content suited to each employee and improving the efficiency of training as a further development of this approach. As a first step towards the realization of these possibilities, this study proposes automatic modality estimation methods focusing on sentence-final expressions in retrospective self-reflection sentences. Modality in Japanese includes the speaker’s subjective semantic content, which indicates the speaker’s judgement of facts and his/her attitude towards speech and communication to the listener. Therefore, we believe that the modality part of self-reflection sentences on training texts contains important information that leads to the understanding of each employee’s behavior and intentions. Experimental results show that the proposed methods enables modality estimation with higher accuracy than manually formulated rules based on sentence structure. The results also show the possibility of developing the proposed methods to be applied to a deeper analysis approach to reflection sentences.
Cross-Domain Event Participant Prediction with Public Event Description

Event participant prediction is an important problem when planning future events. Previous works have found that cold-start recommendation techniques can be used to solve the problem effectively. However, we consider the case of a newly started domain where training data for learning the recommendation model is limited. We propose to use a support domain data that has a similar user behavior model to help improve the training process. Our assumption does not involve linked users across domains, but uses public event description as the bridge. We show how such data can be combined with the target domain data under such assumptions. Experimental evaluation with real-world event participation datasets shows that adding a support domain data with our method does steadily improve prediction accuracy in the target domain.
Light Weight Cryptographic Algorithms- A Comparison for IoT Sensor Data

Transactions done online everyday are generating terabytes of information data.Securing this information over the internet is a challenging task.Cryptography plays a major role in securing this data and providing confidentiality to authenticated and genuine users.Various parameters such as encryption time, decryption time, memory consumption, weakness and strengths are to be evaluated before choosing the right algorithm.IoT devices are constrained in their resources.Light weight cryptographic algorithms are meant for such resource constrained devices.This paper gives a comparative study of few light weight cryptographic algorithms.This paper also discusses the encryption and decryption of data collected from IoT sensor and their performance evaluation
Enhancing Relation Extraction by Using Shortest Dependency Paths Between Entities with Pre-trained Language Models

Relation Extraction (RE) is the task of finding the relation between entities in a plain text. As the length of the sentences increases, finding the relation becomes more challenging. The shortest dependency path (SDP) between two entities, obtained by traversing the terms in the dependency tree of a sentence, provides a view focused on the entities by pruning noisy words. In the supervised form of the relation extraction task, Relation Classification, the state-of-the-art methods generally integrate a pre-trained language model (PLM) into their approaches. However, none of them incorporates the shortest dependency paths to the best of our knowledge.This paper investigates the effects of using shortest dependency paths with pre-trained language models by taking the R-BERT relation classification model as the baseline and building upon it. Our novel approach enhances the baseline model by adding the sequence representation of the shortest dependency path between entities, collected from PLMs, as an additional embedding. In the experiments, we evaluated the proposed model’s performance for each combination of SDPs generated from Stanford, HPSG, and LAL dependency parsers with BERT and XLNet PLMs in two datasets, SemEval-2010 Task 8 and TACRED. We improved the baseline model by absolute 1.41% and 3.60% scores, increasing the rankings of the model from 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> to 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and from 18 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> to 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> in SemEval-2010 Task 8 and TACRED, respectively.
Single Stage Facial Recognition based on YOLOv5

Modern day facial recognition typically consists of four sequential stages: detection, alignment, representation and classification. Facial recognition and object detection are among the most widely researched fields in machine learning. However, they are often seen as entirely separate tasks. I propose a model that can handle facial recognition in just one stage, thus significantly improving the recognition speed. For this purpose, I have used the object detection model YOLOv5. YOLO, short for You Only Look Once, is known for being the fastest state-of-the-art object detection algorithm. With some key modifications to it’s design and loss function I have used it to train a model that can recognize faces from images containing one or more faces. For the experiment, I have used a custom dataset consisting of 283 images. The dataset has a mixture of individual and group photos, with some individuals repeating in some of the images. A total of 4 faces have been trained as individual classes in the modified YOLOv5 algorithm. When testing on new images, there is a significant increase in computation speed compared to other state-of-the-art facial recognition technologies that would be used for the same task, while having a marginal drop in accuracy. These are promising results that have potential applications in attendance systems and surveillance, for cases where the set of faces to be searched or recognized are known in advance.
Feasibility Analysis of Path Planning Algorithms

Path planning, which aims to find the most suitable route between source and destination, is a challenging problem in mobile robotics. Although there are several study ideas on the path planning challenges of mobile robots in the literature, there is no approach that evaluates path quality qualitatively and quantitatively. It is significant to choose the optimal path by analyzing the properties of the path and the planning algorithm for effectiveness and efficiency. In this paper, we proposed a feasibility analysis to evaluate the quality of the path planning algorithm from various perspectives. This paper defines criteria for path planning algorithms and investigates traceability, smoothness, and shortness factors. Furthermore, by using these test results, a novel artificial neural network-based method has been proposed to enhance path planning algorithms without altering them.
Multi-Resolution Inter-Level Refinement (MR-ILR) Architecture for Anomaly Prediction in IoT Data

We develop a novel architecture called "Multi-Resolution Inter-Level Refinement (MR-ILR)" architecture for anomaly prediction in Internet of Things (IoT) data. Our architecture is comprised of three modules: First, the Inter-Level OR module takes the logical OR of the vector that represents the past anomaly states in IoT data and represents the occurrence of an anomaly state at increasingly coarse resolutions. Second, a Multi-Layer Perceptron (MLP) predicts the occurrence of anomaly states at any given resolution. Third, the anomaly predictions at successive resolutions are combined in Refiner modules to produce more accurate anomaly predictions. Our architecture provides the flexibility to produce anomaly predictions at distinct temporal resolutions. We compare the performance of our MR-ILR architecture against MLP and Long Short-Term Memory (LSTM) benchmark models. The results show that our architecture significantly outperforms both of these benchmark models with respect to the F1-score. This work represents an important advance in solving the challenging problem of anomaly prediction in IoT data and has the potential to be applied to a much wider range of problems that target anomaly prediction.
An Interacting Multiple Model Correntropy Kalman Filter Approach for Unmanned Aerial Vehicle Localisation

This paper compares a conventional interacting multiple model Kalman filter (IMM-KF) filter and an interacting multiple models with maximum correntropy Kalman filter (IMM-MCKF). A nonlinear UAV dynamics model was used to compare these two methods. The compared filters estimated the position of the UAV under the noise distribution. Although KF has reliable accuracy, MCKF has got better results under non-Gaussian or mixed distributions. At this point, these filters have been investigated under maneuver and non-maneuver motion, and it is known that better advantages will be shown when both filters are used in the IMM. These filters have been compared under non-Gaussian distributions, and the Student’s-T distribution has been selected as a non-Gaussian type. The performance validation and testing stages are carried out with variable degrees of freedom, and scaling matrix factors for the Student’s-T distributions have been used. Results from simulation tests from 3000 independent Monte-Carlo runs are presented. In these experiments, UAV models and UAV trajectory results have been used.
An Unsupervised Topic-driven New York Times Recommendation System

Recommendation systems are viral tools that make users’ lives easier in terms of decisions and actions. In the online news portals, users are interested in specific domains and want to take a deep dive into the hottest topics of the moment. In this context, a content-based recommendation system helps identify the most relevant articles, satisfying their current needs and curiosities.The proposed New York Times recommendation system aims to suggest news articles for the New York Times readers, using an unsupervised machine learning-based approach. The k-Means clustering algorithm detects clusters of articles grouped based on similar topics or subjects. Then, the k Nearest Neighbors (kNN) algorithm is applied to determine the most similar articles to the target one based on the previously computed clusters of topics.The good results obtained for both the clustering and the recommendation processes in the conducted numerical experiments show that the proposed system is an excellent solution to the information overload problem in the online news portals.