Reviewerly/exHarmony huggingface dataset:
Optical Flow-Based Stereo Visual Odometry With Dynamic Object Detection

Autonomous vehicles (AVs) play an important role in the next-generation intelligent transportation system (ITS), which requires AVs to have the ability of rapid decision-making and control. As a part of ITS, simultaneous localization and mapping (SLAM) technology is the basis for AVs to operate in a complex and unknown environment. In this article, a stereo vision odometry method in a dynamic environment is proposed, which can not only effectively overcome the influence of dynamic objects but also detect the position of dynamic objects. An optical flow filtering algorithm based on the quantitative histogram (QH) and the optical flow angle histogram (OFAH) of the feature points is proposed to obtain dynamic points. Furthermore, a multifeature fusion mechanism is used to perform binary segmentation and get the bounding box of dynamic object. Experiments show that the proposed method can improve the accuracy of pose estimation and detect moving objects in a dynamic environment.
Usefulness Analysis of a Video-Based Local Resource Promotion Website

In recent years, the revitalization of local commerce and industry has been recognized as an important issue in Japan. While various activities are being promoted, the promotion of local resources through the Internet, such as the construction of websites, is considered to be an effective measure. In particular, because young people will be the leaders of the next generation of local communities, and because trends are often created by young people, web promotion for young people is considered to be essential. However, building a website with a rich design that attracts attention requires a great deal of money for the region. In addition, the cost and effort to update the information on the website and to continuously disseminate the information is also necessary. Therefore, promoting local resources using a website is not easy. In fact, there are many websites of local shopping malls and local governments that have stopped updating their websites even though they were created. Therefore, establishing a system that enables local residents to easily implement continuous Web promotion would be necessary. In this study, we focus on the information transmission method using video media, which is supported mainly by young people, and aim to analyze the usefulness of this method for the promotion of local resources.
Effective Identification of Spam Jobs Postings Using Employer Defined Linguistic Feature

With businesses expanding rapidly due to the integration of the internet. Hiring excellent candidates has become really essential for businesses. This has made hiring candidates online very common which has also consequently increased job scams on online job portals which critically puts the privacy of applicants at stake. Pertaining to this in this paper, we investigate the performance of various machine learning algorithms to identify job scams on online job portals based on employer defined linguistic feature. This approach would significantly help job portals to identify scam posting and make the process of identification cost effective, accurate and fast. Our proposed work achieved an accuracy of 98.679% and contributed significantly to the existing knowledge in the domain.
NFC-based Mobile Application for Student Attendance in Institution of Higher Learning

Manual student attendance taking has become a tedious and time-consuming task for lecturers in Institution of Higher Learning (IHL). When pandemic is becoming endemic, more countries are opening their borders for international students retuning to campus and attending in-person classes. While lecturers circulate the manual attendance list to students in class, the list may be a contaminated object. According to WHO, a person could catch COVID-19 by touching the contaminated objects and then touching their mouth, eyes, or nose. In order to address these problems, a NFC-based mobile application is proposed. The proposed method is making use of NFC technology in the mobile devices. Students just need to tap the lecturers’ NFC-enabled mobile devices while entering the lecture hall, then student attendance are recorded automatically in the database. The findings show that the proposed method improve students’ attendance taking more efficiently in terms of reducing time and efforts. At the same time, reduce chances of getting COVID-19 by avoid touching on possible contaminated objects during attendance taking in class. In this study, constraint of the proposed method is unstable and low coverage of WiFi or mobile network in lecture hall. The constraints disable the attendance taking to be completed in stipulated time. This constraint needs to be addressed in future work.
Online Booking Sports Facilities in the Republic of Equatorial Guinea

The purpose of this study is to provide a solution to a problem that affects people that lives in Equatorial Guinea when it comes to booking sports facilities. The study is about the development of a website to book sports facilities online in Equatorial Guinea. Since the advent of the internet, human beings have been developing ideas that can be beneficial, ideas that can help people in their daily lives. This project aims to improve the lives of sports lovers when booking, and at the same time, it will also help Equatorial Guinea to control its sports infrastructures. Base on the interviews and surveys made to collect different opinions from users this system will help people book sports facilities, check the availability of sports facilities, register, edit profiles, all these tasks will be conducted online. By implementing an automated system of this size, the country will improve the quality of its services and will get a good name for implementing innovative ideas at the technology level. users would not have to use paper to make reservations, much less wait for long queues, so that users will enjoy a fully automated system.
Analyzing Process of Distance Learning Using Resilience Engineering and Qualitative Approach

Due to the COVID situation, it has become impossible to continue the conventional face-to-face classes and distance learning has become popular. The development and implementation of distance learning materials and scenarios that utilize ICT technology are similar to the development and operation of software systems. We will explore the methodology for effectively developing and implementing distance learning materials and scenarios by using software engineering methods such as process and modeling technology. We try to formalize implicit knowledge and skills in designing and implementing distance learning materials and scenarios by using process improvement methodologies. In addition to the conventional software engineering approach, we will use the methods of qualitative approach and resilience engineering, as people are the main implementing bodies of classes even with the advancement of ICT and their behaviors are sometimes non-deterministic and flexible when the situation changes. We explain our approach with actual interview data and models for distance learning, showing how we use these data and models in the process.
A Proposal for Creating Congruent Content of Different Media Types by Interactive Evolutionary Computation

We enjoy multiple media content in our daily lives, and using congruent content composed of different media types is ideal. However, obtaining congruent media content is difficult. Interactive Evolutionary Computation (IEC) is known as an effective approach to obtain good solutions for each user. However, conventional IECs treat single media types. This study proposes new Interactive Evolutionary Computation that searches the congruent media content in terms of a good combination of different types of media content. In the proposed IEC, the solution candidate contains different media types as its variables. A system was constructed by employing a genetic algorithm to investigate the effectiveness of the IEC with experiment. The target of creation was the congruent content composed of music and scent. Twelve participants evaluated a set of musical melodies and scents repeatedly. As results, a trend of increase in the mean fitness and significant increase in maximum fitness were observed in the search experiment. However, enough increase in the fitness was not observed in the evaluation experiment.
A Bottom-Up Enumeration Algorithm of Minimal Generators without Support Counting for Compressing Negative Association Rules

Negative association rules are valuable and essential for expressing various latent properties which hide in big data. The set of valid negative association rules, however, always becomes so huge, thus an effective compression method of negative rules is quite important. Minimal generators are important concepts for compressing the set of valid negative rules. In this paper, we study a bottom-up efficient algorithms for enumerating minimal generators from given closed itemsets. So far, support calculations have always been a bottleneck in data mining calculations. Therefore, we here propose a novel enumeration algorithm which does not use any explicit support computation. We show experimental results for evaluating our bottom-up enumeration algorithm, where the algorithm performs well in terms of memory consumption compared to previous algorithms.
MinpakuBERT: A Language Model for Understanding Cultural Properties in Museum

In this paper, we propose a BERT model for understanding cultural properties in museum. BERT is a language model used in natural language processing. Generally, BERT is pre-trained on a large corpus and then fine-tuned on a specific task. Recently, additional learning for BERT has been reported to improve task performance in fine tuning. Therefore, we propose a BERT model for understanding cultural properties in museum (MinpakuBERT) by additional learning a BERT model using document data from the National Museum of Ethnology. To evaluate the performance of MinpakuBERT, we compared the performance of MinpakuBERT and pre-trained BERT by fine-tuning them on two tasks related to cultural properties. The two tasks for comparison were classification tasks using the OCM (Outline of Cultural Materials) label for function and the OWC (Outline of World Cultures) label for region. As a result, the performance of MinpakuBERT is higher than the pre-trained BERT model in the two tasks. In addition, the learning speed in fine tuning was also faster for MinpakuBERT than for the pre-trained BERT. The MinpakuBERT was published on a model-sharing website and made available as a language resource for anyone to use.
Improved Response Generation Consistency in Multiturn Dialog

The consistency of response generation in multi-turn dialog must be improved. The existing VHDER model, that extends the RNN model to dialog generation, does not distinguish turns. Thus, in this study, we improve it by adding implementing a User-RNN that corresponds to a speaker who conveys information every odd and even turn. In addition, the global vibrational transformer model, which applies the CVAE, an advanced autoencoder, enables diverse response generation. However, latent variables sampled from the entire context are diluted by speaker characteristics, which degrades the consistency of generated responses. In this study, we employed speaker-specific latent variables to generate speaker-consistent responses.
Deepfake Detection on Videos Based on Ratio Images

Deepfake detection comes as a countermeasure to identify fake media content to reduce its harmful implications. Most detection approaches rely on identifying specific artifacts that can quickly become obsolete due to the fast advance in facial forgery methods. Some facial manipulation detection methods use temporal information to classify the video as real or fake. These methods mainly rely on 3D CNN architectures or two-stream networks using frame and video features. Our method not only considers temporal aspects, but it comes from a different perspective: extracting features that can account for inter-frame changes on a video. Inspired by the concept of ratio images, we extract features based on the ratio between adjacent frames for the face and its background. The experimental evaluation showed better results in intra- and cross-dataset tests on FaceForensics++ (FF++) and CelebDF datasets compared to the state-of-the-art deepfake detection approaches in the assessment with seen and unseen facial manipulation methods, as well as in seen and unseen video settings. In the intra-dataset experiment, the model resulted in an AUC of 100% for both CelebDF and FF++ datasets. In the cross dataset experiment, the model resulted in an AUC of 98% when trained with CelebDF and tested with FF++ and 86% when trained with FF++ and tested with CelebDF.
Accurate Image Cutout Using Automatically Generated Trimaps

Image cutout has been widely used in various image processing applications. Image cutout techniques can be divided into semi-automatic and automatic methods. Semi-automatic methods require user interaction for precise cutout of target objects. Automatic methods adopt machine-learning approaches which do not require any user interaction. In this paper, we proposed a new cutout method which combines the two types of methods for obtaining precise cutout results without any user interaction. The proposed method, first, roughly extracts an object region using a machine-learning approach. Next, using the extracted result instead of user input for a semi-automatic method, the proposed method obtains a precise cutout result. From experiments using COCO dataset and AIM-500 dataset, we confirmed that the proposed method can precisely extract object regions without any user interaction.
Hyperheuristic Method Based on Deep Reinforcement Learning

For solving combinatorial optimization problems, methods in which a candidate solution is iteratively updated are often used. One of the problems in the iterative methods is how to update the candidate solution, and it is not easy to design an appropriate update method. To solve the problem, hyperheuristics have been proposed. They can find a near-optimal solution by using multiple update methods and automatically selecting an appropriate update method, often combined with an existing optimization algorithm such as evolutionary computation. On the other hand, deep reinforcement learning has recently attracted attention due to its ability to solve large-scale and complicated problems. This paper proposes a hyperheuristic method introducing deep reinforcement learning to automatically find the appropriate update method. As a case study, we apply the proposed method to a drone delivery problem and evaluate its performance.
Topic Model Analysis of Japanese Microblog Profile Text

Analyzing the data in SNS has played a very important role in marketing and other areas. In these analyses, it is expected that the content of posts and user profiles can be used together to further increase accuracy. For this reason, this paper classifies profiles into topic categories and clarifies what kind of content the profiles consist of. Topic model analysis revealed some characteristics of the profile texts, such as some profile texts describing male idols and some profile texts telling the persona of the twitter user.
Interpretable Predictive Results in Classification of Waka Poets

In this paper, we present our proposed method for providing interpretable predictive results in the classification of waka poets. Waka is a type of traditional Japanese poetry. Waka poetry reflects the mind of waka poets. By extracting taste and feeling from waka poetry, it is possible to reveal aesthetic notions in waka poets. This work focuses on waka poetry composed by the Sanjurokkasen included in the Nijuichidaishu. We thus interpret the predicted results in the classification of waka poets by LIME. The number of waka poetry data varies from poet to poet. Therefore, we adopt Naive Bayes as the classification algorithm in this work, which is expected to have high accuracy even when the number of data is small. Using LIME enables interpretation of results in the classification of waka poets; it is possible to provide rationale words for the classification results. The study of waka poetry from the point of view of data science contributes to the development of literary study by providing objective verification of hypotheses and new findings such as previously undiscovered expressions and interpretations.
Segmental Distance between Rooted Labeled Caterpillars

A segmental distance for rooted labeled unordered trees is the edit distance such that the insertion and the deletion in the edit operations are allowed to either the root or the leaves. In this paper, we design the algorithm of computing the segmental distance for rooted labeled caterpillars in O(n+h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> σ) time under the unit cost function, where n is the maximum number of vertices, h is the maximum height and σ is the number of labels, whereas the problem of computing the segmental distance for rooted labeled unordered trees is MAX SNP-hard.
Collocation Extraction Method Using Mutual Information Contents

In this paper, we obtained nouns and verbs in dependencies from a corpus and extracted collocations using a statistical measure, mutual information content. We compared the extracted collocations with the NINJAL (Japanese corpus published by the National Institute for Japanese Language and Linguistics). This comparison was based on whether the collocation was a correct idiomatic expression and whether it existed in the NINJAL corpus. As a result, we could estimate collocations with a certain degree of accuracy.
Joint Compression of Document Identifiers and Term Frequencies via Dense Unary Codes

Compressing posting lists in large-scale search engines for efficiency improvement conventionally involves encoding document identifiers and term frequencies separately. In this work, we adopt dense unary codes in favor of joint encoding and evaluate them in terms of compression ratio along with two other techniques on eight document collections having different characteristics as to term frequency distribution. As a result, it has been observed that dense unary codes yield higher compression ratios particularly in short-text collections, where it is more unlikely for sparse terms to have high frequency values per document, as well as retaining a simple decoding mechanism.
Automatic detection of kidneys on abdominal CT images using Aggregate Channel Features

Accurate detection of kidney regions in abdominal CT images makes it easier to detect formations such as cysts, lesions, and stones in the kidneys. In this study, the Aggregate Channel Features (ACF) algorithm, which is a machine learning method, is used for automatic detection of the kidneys. Negative samples are automatically taken from the images during the learning process. The ACF obtained are formed alternately and repeatedly for N steps using the AdaBoost classifier. At each step negative samples are removed and collected with the previous ones. The confusion matrix and k-fold cross-correlation methods are used to test the performance of the study. The data set fragmented according to k-fold is trained according to the location information of the labeled objects using the ACF. Recall, precision, and F1 scores gleaned from the confusion matrix are used in performance analysis. The results show that the proposed method can successfully detect kidney regions.
AlphaBluff: An AI-Powered Heads-Up No-Limit Texas Hold’em Poker Video Game

Complex games require disparate behaviors in order to be solved, giving space to researchers to study AI model behaviors in various settings. At the same time, the video game industry benefits by incorporating these models in their games for delivering realistic and challenging gameplay experience to users. However, there is a well-known difficulty of implementing and training efficient models in complex games for entertainment purposes. In this paper, we report on our approach to overcome this challenge and ultimately develop AlphaBluff, a Heads-Up No-Limit Texas Hold’em (HUNL) Poker variation video game developed in the Unity game engine, in which human players can play against trained AI opponents. Initially we trained different state-of-the-art AI models and analyzed their individual performance scores in a custom HUNL environment, as well as their performance against each other. AlphaBluff was developed with the goal of producing a professional-level poker video game that includes cutting-edge AI opponents, which, to our knowledge has never been developed before. Using data gathered by gameplay sessions from beta testers, we performed a statistical analysis and concluded that our models have a high win rate against human players. An adaptation of our system can further enrich this unique gameplay experience by combining these models, data and statistical reports, in order to develop an efficient player-opponent matchmaking mechanism.
Social Media Post Impact Prediction using Computer Vision and Natural Language Processing

Millions of people use Twitter every month, which makes it one of the most popular social networks worldwide. Currently, there is an enormous scope market with the potential to be optimized to increase Twitter posts’ popularity and engagement. In this paper, we present a method of predicting the number of likes a given post will receive. We introduce a deep learning model and training procedure that uses both computer vision and natural language processing to reach high accuracy when shown new data. Considered use-cases will show the situations in which our system behaves well and the situations in which we do not yet have a solution to improve the current results.
Fast Algorithm for Big Data Summarization with Knapsack and Partition Matroid Constraints

As an effective tool to extract representative summary from big data, data summarization is often cast into a submodular maximization problem. Although submodular maximization problem has a long research history, and many related algorithms have been born, these algorithms often have high computational complexity and are difficult to apply to the field of big data. Therefore, in recent years, research on low-time complexity algorithms has attracted extensive attention. In this paper, we mainly focus on the non-monotone submodular maximization problem under the setting of a knapsack and a partition matroid constraints. To solve it, we design a practical, effective and efficient algorithm called FASKP, that can achieve an approximation ratio of near 7.2 + ϵ using near linear runing time. As far as we know, the FASKP algorithm achieves the best approximate guarantee compared to existing algorithms with low-time complexity. Furthermore, we demonstrate how to apply FASKP in three real data summarization applications: image summarization (10K images), movie recommendation (11K movies), and revenue maximization on social networks (Youtube). Experimental results in real scenarios show that, compared with existing algorithms, the FASKP algorithm can consistently obtain the highest utility, which validates its superiority.