Reviewerly/exHarmony huggingface dataset:
Quantitative class association rule-based approach to lecturer career promotion recommendation

This paper introduces an effective method for recommendation of career promotion for educators in universities. Given an educator with an activity profile currently in a certain career state, the system is able to recommend the next career state that is most suitable for the educator by learning the pattern of historical activities of other educators. The system also recommends activities and their volume that the educators should take in order to achieve the career state. Patterns of activities and their volume were obtained using the quantitative class-association rule mining method that classifies quantified activities to a next-state class. The experiment using educator career data taken from Indonesian universities produced several recommendations that were somewhat contrary to the opinions of experts about educators who can make a career leap, an indication that expert subjectivity was more dominant than a statistically more reliable recommendation system in making decisions.
Improved Approximation for Longest Common Subsequence over Small Alphabets.

This paper investigates the approximability of the Longest Common Subsequence (LCS) problem. The fastest algorithm for solving the LCS problem exactly runs in essentially quadratic time in the length of the input, and it is known that under the Strong Exponential Time Hypothesis the quadratic running time cannot be beaten. There are no such limitations for the approximate computation of the LCS however, except in some limited scenarios. There is also a scarcity of approximation algorithms. When the two given strings are over an alphabet of size k, returning the subsequence formed by the most frequent symbol occurring in both strings achieves a 1/k approximation for the LCS. It is an open problem whether a better than 1/k approximation can be achieved in truly subquadratic time (O(n^{2-δ}) time for constant δ > 0). A recent result [Rubinstein and Song SODA'2020] showed that a 1/2+e approximation for the LCS over a binary alphabet is possible in truly subquadratic time, provided the input strings have the same length. In this paper we show that if a 1/2+e approximation (for e > 0) is achievable for binary LCS in truly subquadratic time when the input strings can be unequal, then for every constant k, there is a truly subquadratic time algorithm that achieves a 1/k+δ approximation for k-ary alphabet LCS for some δ > 0. Thus the binary case is the hardest. We also show that for every constant k, if one is given two strings of equal length over a k-ary alphabet, one can obtain a 1/k+e approximation for some constant e > 0 in truly subquadratic time, thus extending the Rubinstein and Song result to all alphabets of constant size.
A Cloud-Based Robot Framework for Indoor Object Identification Using Unsupervised Segmentation Technique and Convolution Neural Network (CNN)

Nowadays autonomous indoor mobile robot is getting more attention in many application areas. A cloud-based multi-robot framework provides highspeed data processing and inter robot’s communication efficiently for the indoor mobile robot system. However, efficient detection and recognition of the environmental objects are vital issues for indoor mobile robots. Thus, this paper proposes a cloud-based multi-robot system, where Indoor objects are detected using a new unsupervised object segmentation model and object identification using cloud-based Convolutional Neural Networks (CNN) model. In the object segmentation model, a segmentation algorithm is developed with the combination of Canny edge detection, Floodfill, and BoundingBox image processing technique for efficiently segmenting the objects of the indoor environment. After detecting objects, a cloud-based CNN model with SoftMax classifier is used for classifying objects. Besides, an iterative learning is introduced in our proposed model for identifying unknown objects. Some indoor images captured by the camera are used to test the proposed system. To validate the proposed model, a benchmarked object image dataset from an open resource repository is used in this paper to train the CNN model. The model shows good object detection and identification result and the cloud-based framework enhance the usability of the proposed system.
An Almost Optimal Edit Distance Oracle

We consider the problem of preprocessing two strings S and T, of lengths m and n, respectively, in order to be able to efficiently answer the following queries: Given positions i,j in S and positions a,b in T, return the optimal alignment score of S[i..j] and T[a..b]. Let N = mn. We present an oracle with preprocessing time N^{1+o(1)} and space N^{1+o(1)} that answers queries in log^{2+o(1)}N time. In other words, we show that we can efficiently query for the alignment score of every pair of substrings after preprocessing the input for almost the same time it takes to compute just the alignment of S and T. Our oracle uses ideas from our distance oracle for planar graphs [STOC 2019] and exploits the special structure of the alignment graph. Conditioned on popular hardness conjectures, this result is optimal up to subpolynomial factors. Our results apply to both edit distance and longest common subsequence (LCS).The best previously known oracle with construction time and size 𝒪(N) has slow Ω(√N) query time [Sakai, TCS 2019], and the one with size N^{1+o(1)} and query time log^{2+o(1)}N (using a planar graph distance oracle) has slow Ω(N^{3/2}) construction time [Long & Pettie, SODA 2021]. We improve both approaches by roughly a √ N factor.
Hybrid Statistical Machine Translation for English-Myanmar: UTYCC Submission to WAT-2021

In this paper we describe our submissions to WAT-2021(Nakazawa et al., 2021) for English-to-Myanmar language (Burmese) task.Our team, ID: "YCC-MT1", focused on bringing transliteration knowledge to the decoder without changing the model.We manually extracted the transliteration word/phrase pairs from the ALT corpus and applying XML markup feature of Moses decoder (i.e.-xml-input exclusive, -xml-input inclusive).We demonstrate that hybrid translation technique can significantly improve (around 6 BLEU scores) the baseline of three well-known "Phrase-based SMT", "Operation Sequence Model" and "Hierarchical Phrase-based SMT".Moreover, this simple hybrid method achieved the second highest results among the submitted MT systems for English-to-Myanmar WAT2021 translation share task accord-
Live Forensics on GPS inactive Smartphone

Google is known to still track the user's location despite the GPS settings and location history in smartphone settings has been turned off by the user. This requires special handling to prove the location on smartphones with inactive GPS and view its Location History previously used by user. The research investigates if Google is still recording its user data location. Live Forensic requires data from the running system or volatile data which is usually found in Random Access Memory (RAM) or transit on the network. Investigations are carried out using a Google account with a method used by live forensics to obtain results from the location history. Smartphones have been checked manually through data backup through custom recovery that has been installed. When checking the backup filesystem, turned out that no location data is stored. Therefore, researchers conducted an analysis on the Google Account which was analyzed using a forensic tool to analyze cloud services to obtain location data results. The results of the analysis carried out obtained a similarity in location from 8-days investigations. Google can still find the location of smartphones with GPS disabled, but the location results are not accurate. Google can store user location data via cellular networks, Wi-Fi, and sensors to help estimate the user's location. The process of extracting the results from the google maps log using a Google account will be analyzed using the Elcomsoft Cloud eXplorer and Oxygen Forensic Cloud Extractor so that the log location results are still available by Google.
Radar Signal Detection Applying Deep Learning to the Correlation Between Adjacent Windows in Time Domain

펄스 레이더 신호는 시간 영역에서 동일한 펄스가 반복된다는 특성을 갖는다. 이전 연구에서 이에 근거하여 시간 영역에서 이웃한 관측 창 신호 사이에 원형 컨벌루션을 구하고, 그 결과의 최대 절대값을 검정 통계량으로 사용하는 레이더 신호를 검출하는 방안을 제시한 바가^_@span style=color:#cccccc ^_# 있다. 본 논문에서는 좀 더 향상된 성능을 확보하기 위한 방안으로 이웃한 관측창 신호 사이의 원형 컨벌루션에 심층 학습(deep l^_@/span^_#^_@span style=color:#999999 ^_# ... ^_@/span^_#^_@a href=javascript:; onclick=nodeUse('NODE10579334|147003') style=color:#999999;font-size:14px;text-decoration:underline; ^_#전체 초록 보기^_@/a^_#
BiQuAD: Towards QA based on deeper text understanding

Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question.Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained.We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion.The dataset consist of 4, 190 closed-domain texts and a total of 99, 149 question-answer pairs.The texts are synthetically generated soccer match reports that verbalize the main events of each match.All texts are accompanied by a structured Datalog program that represents a (logical) model of its information.We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset.In particular, transformer based state-of-theart models achieve F 1 -scores of only 39.0.We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.
Trusted-Maximizers Entropy Search for Efï¬cient Bayesian Optimization

Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require several approximations or simplifying assumptions (without clearly understanding their effects on the BO performance) and/or their generalization to batch BO is computationally unwieldy, especially with an increasing batch size. To alleviate these issues, this paper presents a novel trusted-maximizers entropy search (TES) acquisition function: It measures how much an input query contributes to the information gain on the maximizer over a i¬nite set of trusted maximizers, i.e., inputs optimizing functions that are sampled from the Gaussian process posterior belief of the objective function. Evaluating TES requires either only a stochastic approximation with sampling or a deterministic approximation with expectation propagation, both of which are investigated and empirically evaluated using synthetic benchmark objective functions and real-world optimization problems, e.g., hyperparameter tuning of a convolutional neural network and synthesizing â€˜physically realizableâ€™ faces to fool a black-box face recognition system. Though TES can naturally be generalized to a batch variant with either approximation, the latter is amenable to be scaled to a much larger batch size in our experiments.
RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction

We present the technical report of the system called RS GV at SemEval-2021 Task 1 on complexity prediction of English words.RS GV is a neural network using hand-crafted linguistic features in combination with character and word embeddings to predict the target words' complexity.For the generation of the handcrafted features, we set the target words in relation to their senses.RS GV predicts the complexity well of biomedical terms but it has problems with the complexity prediction of very complex and very simple target words.
A CONCEPTUAL MODEL AND PROCESS MANAGEMENT METHOD OF THE PLANNING AND MONITORING OF THE WORKLOAD IN THE EDUCATIONAL ENVIRONMENT

The article formulates the aims of HEI's activities, as well as approaches to managing all actions that ensure the achievement of the stated aims. The process approach is defined as the main one in the university management system. It is shown that the main directions of improving the activities of the university are the transition to the application of the process approach to the management of HEI and its informatization. On this basis, it is necessary to develop new, more modern university management systems that meet the contemporary requirements for the governance of multifunctional facilities and implement them in the form of process systems. We propose a conceptual model of the implementation of the functions to plan the educational process. The effects on planning functions are presented and the interrelation of these functions in the traditional form of planning is described. The tasks are formulated that need to be solved for modeling and optimizing business processes of planning and monitoring the academic workload to create an effective information technology for automating the functions of information processing. We propose a structural model for the implementation of information and procedural components of IT for planning and monitoring the educational environment. The implementation of this model in software provides the calculation of the optimal planned workload of students and teachers, considering the conditions in which the educational process is implemented.
ES-JUST at SemEval-2021 Task 7: Detecting and Rating Humor and Offensive Text Using Deep Learning

This research presents the work of the team's ES-JUST at semEval-2021 task 7 for detecting and rating humor and offensive text using deep learning.The team evaluates several approaches (i.e.BERT (Devlin et al., 2018), Roberta (Liu et al., 2019), XLM-Roberta (Conneau et al., 2019), and BERT embedding + Bi-LSTM) that employ in four sub-tasks.The first sub-task deal with whether the text is humorous or not.The second sub-task is the degree of humor in the text if the first sub-task is humorous.The third sub-task represents the text is controversial or not if it is humorous.While in the last task is the degree of an offensive in the text.However, Roberta pre-trained model outperforms other approaches and score the highest in all sub-tasks.We rank on the leader board at the evaluation phase are 26, 26, 25, and 9 through 0.9564 F-score, 0.5709 RMSE, 0.4888 F-score, and 0.4467 RMSE results, respectively, for each of the first, second, third, and fourth sub-task, respectively.
CICM’21 Systems Entries

This consolidated paper gives an overview of new tools and improvements of existing tools in the CICM domain that occurred since the last CICM conference.
Development of an AR Training Construction System Using Embedded Information in a Real Environment

In recent years, emergency drills have been held in important facilities such as nuclear facilities. The problem with these drills is that the number of emergency scenarios is limited. In this study, we have developed an augmented reality training system that allows users to easily create scenarios by simply assembling and placing blocks representing the components of the scenario in the real-world environment. The system is capable of simulating various scenarios, involving the interaction between the trainee and the real-world environment. A subjective experiment was conducted at a nuclear facility to evaluate the usability of the system and the flexibility about scenario creation. The results showed that the users can construct an AR training environment easily in a shorter time than the conventional text-based programming.
Factor Analysis of Supporting Air Conditioning Practicum Activity in Vocational Education

Vocational education has specificity in the composition of practical learning that is more dominant than theoretical learning.The optimal implementation of practicum needs to be supported by various related factors.The purpose of this research is to find out the factors that support air conditioning practicum in vocational education.The quantitative approach was used in this study and the questionnaire was used to collect research data consisting of: laboratories, tools and practicum materials, lecturers/instructors, laboratory assistants/technicians, laboratory regulations, practicum schedules, learning resources, practicum worksheets, and practicum evaluation tools.The populations in this study were students of refrigeration and air conditioning study programs at a vocational education institution in Bandung, Indonesia.All data processing and statistical analysis uses the IBM Statistics SPSS version 21.0.The results showed that there were relationships between a number of variables that were independent of each other.Nine independent variables were identified at the beginning of the study as a source of support for practicum and then grouped into two new factors.Both of these factors have a correlation greater than 0.5, so it can be concluded that it is appropriate to summarize the nine variables analyzed.Based on the Rotated component matrix, it is known that the practicum evaluation tool variable has the highest correlation, this shows that the evaluation of student practicum performance is very important to be a reference for completing competency and planning for practicum activities in the future.
Indoor Path Finding and Simulation for Smart Wheelchairs

The new systems obtained by adding computer support to battery powered wheelchairs are called smart wheelchairs. Today, there are various map providers and navigation systems that a mobile vehicle can use to get from one location to another. However, these map providers do not support indoor spaces and satellite-aided global positioning systems cannot provide solutions in indoor environments. In this study, by using the pre-mapping of interior spaces and the use of appropriate path finding algorithms, individuals can reach their desired location in an environment that they do not know exactly, via smart wheelchairs. The designed interface software provides the path was determined with the jump point search, one of the shortest path finding algorithms, and the system was simulated with the simulation software.
d-BTAI: The Dynamic-Binary Tree Based Anomaly Identification Algorithm for Industrial Systems

Many of the existing approaches to anomaly detection are based upon supervised learning and heavily dependent on training datasets. However, anomalies rarely occur in most industrial systems. Hence it is challenging to retrieve a training dataset labeled with true anomalies. Therefore, this motivates us to investigate such scenarios where it is arduous to get labeled data for anomalies. This paper has proposed a clustering-based recursive anomaly detection algorithm; dynamic-Binary Tree Anomaly Identifier (d-BTAI). d-BTAI has been applied on industrial devices since anomalies in large industrial devices can incur massive losses. The algorithm has experimented on various publicly available industrial datasets such as Cloudwatch, Yahoo, and Backblaze. d-BTAI has attained a higher Area under the ROC curve (AUC) in comparison with Isolation Forest (iForest), One Class Support Vector Machine (OCSVM), and Elliptic Envelope. The higher Negative Predictive Value (NPV) and specificity value demonstrate the algorithm’s efficacy on multiple datasets.
Educational Problems and Improvement on Ulleungdo Islands in Korea

Islands have ecological value and play roles such as maritime boundaries and territorial protection, but their economic outlook is not bright, and above all, improving residential conditions is difficult due to their aging and declining populations. Education on islands is worse than some of their other underdeveloped sectors. Therefore, this study investigated the current status of education and improvement plans for the island of Ulleungdo, South Korea. According to a survey of 327 of its residents, school education played an important role in finding employment, and the most frequent response was that the number of experts in charge of education on Ulleungdo was insufficient. The measures suggested for improving lifelong education were developing and distributing programs and strengthening the professionalism of educators. To solve the educational problems on Ulleungdo, we suggest the following: develop educational programs for parents, offer career experiences for youths and young adults, establish Ulleung County People’s University, and support learning clubs.
Subword Semantic Hashing for Intent Classification in Turkish Language ChatBots

In this study, the issue of intent classification in frequently asked questions for chatbots is discussed. There are many studies on this subject. However, most of the studies were conducted for the English language, and the issue of improving the intent classification results for small data sets has not been adequately examined. For this purpose, frequently asked questions data were used with subword semantic hashing method and its effects in Turkish and English languages were examined. According to the results, we observe an increase in classification performance of the subword semantic hashing method in Turkish language and the improvement is significantly higher compared to the English language. In addition, the classification performance results of pretrained the subword semantic hashing is better than the results of neural network models such as CLIP and FastText.
Quality is in the Salient Region of the Image

The ultimate goal in any proposed Image Quality Metrics (IQMs) is to accurately predict the subjective quality scores given by observers. In the case of most IQMs the quality score is calculated by pooling the quality scores from what is referred to as a quality map of an image. While different pooling methods have been proposed, most such approaches use various types of a weighting average over the quality map to calculate the image quality score. One such approach is to use saliency maps as a weighting factor in our pooling process. Such an approach will result in giving a higher weight to the salient regions of the image. In this work we study if we can evaluate the quality of an image by only calculating the quality of the most salient region in the image. Such an approach could possibly reduce the computational time and power needed for image quality assessment. Results show that in most cases, depending on the saliency calculation method used, we can improve the accuracy of IQMs by simply calculating the quality of a region in the image which covers as low as 20% of the salient energy.
Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter

Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.