Reviewerly/exHarmony huggingface dataset:
A Wise Matrix Factorization Model for Image Representation

The paper presents matrix factorization frameworks on the complex domain to get both intuitive features and high performance in image representation tasks. The real data matrix is transformed to a complex number first which allows exploiting a robust dissimilarity measure based on the Euler representation of complex numbers. Wirtinger’s calculus is used to compute the derivative of the cost function. The gradient descent method is utilized to solve complex optimization problems. We show that these frameworks provide feature extraction ability to face recognition models for enhanced performance. Experiments on two scenarios for face recognition including holistic face, and key points occluded face demonstrate that the proposed method of complex matrix factorization provides more faithful basis factors and consistently better recognition results as compared to standard real matrix factorization models.
Work-in-Progress: Towards a Smaller than Grain Stream Cipher: Optimized FPGA Implementations of Fruit-80

Fruit-80, an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource constrained devices in the Internet of Things. In this paper, we propose area and speed optimization architectures of Fruit-80 on FPGAs. The area optimization architecture reuses NFSR&LFSR feedback functions and achieves the most suitable ratio of look-up-tables and flip-flops. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. In conclusion, the optimal throughput-to-area ratio of the speed optimization architecture is better than that of Grain v1. The area optimization architecture occupies only 35 slices on Xilinx Spartan-3 FPGA, smaller than that of Grain and other common stream ciphers. To the best of our knowledge, this result sets a new record of the minimum area in lightweight cipher implementations on FPGA.
Quasi-Monostatic Antenna Displacement in Radar Target Simulation

Radar target simulators (RTS) have recently drawn much attention in research and commercial development, as they are capable of performing over-the-air validation tests under laboratory conditions by generating virtual radar echoes that are perceived as targets by a radar under test (RuT). The estimated angle of arrival (AoA) of such a virtual target is determined by the physical position of the particular RTS channel that creates it, which must therefore be considered when planning the setup. A single channel employs two antennas, one for the reception and the other for the re-transmission of the incoming radar signal. The antennas are positioned close together, but still spatially separated, thus an RTS channel can be considered quasi-monostatic, which causes non-negligible inaccuracies in the angle simulation. In this paper, the authors examine the analytical implications of this systemic deficiency on the angle estimation, which provides support for the design and setup of angle-simulating RTS systems. The mathematical derivations developed are verified by measurement.
Utilizing Machine Learning Algorithms to Detect Emotions from Tweets

Emotion detection has been a popular topic for a long time. The need to develop and improve the technologies has grown throughout the year. This is mostly because of the vast implementation in marketing, education, security, healthcare, and other fields. Emotion can be detected variously, from gestures, text, speech, facial expressions, and others. Today, the emotion detection technology is still not perfect, even with the huge access to textual and visual data. This is mostly because of the complexity of human emotions that can’t be easily captured by a machine. Therefore, a reliable method is required in order to create an accurate emotion detection system. A few of the popular machine learning methods that are implemented for emotion detection are Support Vector Machine, Naïve Bayes, and Logistic Regression. From our experiment, SVM is considered to be the best methods that have demonstrated high accuracy, especially on a quality data set.
Applicability Evaluation of Task Clustering Method Using Co-occurrence of Operations

Regarding RPA, it is necessary to investigate the flow of the target work, and there is a method to classify the operation log into the units of work by using the operator's operation log as an input and focusing on the co-occurrence of the operation. However, the nature of the logs to which this technique is applicable is not known. In this paper, we applied this technique to different kinds of operational logs to discover the applicability of this technique. As a result, this method was not possible to classify operational logs into the unit of task when tasks of long and short operational procedures were mixed in the data.
Effective Edge Server Placement for Efficient Federated Clustering

Recently, research on federated clustering has been actively studied to improve the performance of federated learning to solve the non-i.i.d issue. Federated clustering makes clusters with members who has similar characteristics of data which is used as inputs of federated learning, and each cluster trains an artificial intelligence model in a federated manner. However, if distances between members of a cluster configured through federated clustering is long in a network, the overhead related to federated learning becomes larger than expected and it may be lose the network cost benefits of federated learning. In this paper, we propose a DTW(Dynamic Time Warping) based federated clustering and MIP(Mixed Integer Programming)-based edge server placement in order to reduce the network overhead of federated learning caused by federated clustering under non-i.i.d setting.
An NLP-Based Model for the Dispatching Problem of Network Operation

It is difficult for Telecom to decide whether dispatch the engineer to fix the network problem when the network management system can not figure out the detail of the problem. Some network problems may not derive from the network devices because those problems may trigger by other factors (e.g., an external power source failure, weather issue). However, the user would report that they can not use the network service and Telecom have to decide whether ask the engineer to go out to deal with the problem. If the engineer goes out to handle this problem but figures out there is nothing to do, it would waste their time and it means that this wrong decision would increase the Operating Expenses (OPEX). In this study, we would like to use the text data that the users report to Telecom to try to describe the failure from their perspective. The purpose is that we would like to take the data as a Natural Language Processing problem (NLP) by using BERT model to check that is how it can improve this problem. Though the result showing the model can get better performance seems to not significant improvement on this problem, we conduct the preliminary experiment on the real-world data to show that it is feasible of using only the text data to help Telecom to reduce the cost of network operation.
Verification of Unanswerable Questions in the Question Answering System using Sentence-BERT and Cosine Similarity

With the growing quantity of human-machine interaction in modern times, the need for a question answering system is also felt to improve the quality of human-machine interaction. One part of the question answering study is reading comprehension where the system must be able to determine the correct answer from the given document and state if there is no answer in the document. ELECTRA as previous research is state-of-the-art on the SQuAD 2.0. However, this research can still be developed again because ELECTRA on the SQuAD 2.0 dev dataset still makes 1,287 answers errors in determining whether the question is answerable or unanswerable. This study proposes a modification to ELECTRA in verifying answers by adding similarity parameters. Sentence-BERT which is a state-of-the-art in sentence embedding is used to get the sentence similarity between context and prediction that have been converted into declarative sentences using the rule-based method. The experimental results show that our method improve the performance of exact match and F1 scores of ELECTRA.
Utilizing Open Vocabulary Word Vector for Detecting Errors in Decoded Speech Transcripts

Annotating newly collected audio data requires substantial resources and time. Employing existing pretrained automatic speech recognition (ASR) decoder to initially perform decoding on the data can significantly reduce the resources and time. Nevertheless, the decoding will introduce some errors to the transcripts. It is therefore beneficial to have an error detection model to detect which words are erroneous. Our proposed error detection model incorporates “intrinsic” property of the decoded words, in form of open-vocabulary word-vector, modifying an existing network architecture. The model is trained and optimized on the ASR decoding result of existing annotated audio data sets. Evaluation of the model using another distinct data set achieves a Matthews correlation coefficient (MCC) of 0.5846. The model can be employed further to accelerate the annotation process by predicting the decoding errors, which can be corrected either manually or automatically.
Container Migration for Distributed Deep Learning Training Scheduling in Kubernetes

Distributed deep learning training is one of the most frequently used machine learning methods because it can speed up the training process and solve memory problems caused by overly complex models. One of the platforms used to run distributed deep learning training is Kubeflow, which runs on top of Kubernetes. Kubernetes is an orchestration tool that makes it easy to run and manage a containerized workload in a computer cluster. Some schedulers have been proposed for scheduling distributed deep learning training in Kubernetes, such as DRAGON, which runs on Kubeflow and focuses on locality awareness and gang scheduling capabilities, and SpeCon, which focuses on container migration capability. In DRAGON, the Locality Aware Task Placement strategy is used to place tasks from a training job into as few nodes as possible to minimize network overhead. As a result, a node may perform more tasks than other nodes, resulting in an uneven distribution of resource usage. To solve the problem, we proposed to combine DRAGON’s and SpeCon’s approaches to make a scheduler with container migration and gang scheduling capabilities. We use container migration to send training tasks in containers from one node to a better node without repeating the development of the training process so that the distribution of resource usage can be evenly distributed. Compared to the DRAGON scheduler, our approach successfully reduced the total elapsed time by 55.692% without any drawback in accuracy.
A Retrieval-and-Classification Approach for Fact-checking Grounded by Assembly Minutes

In this paper, we propose a method of the fact-verification task, which is one of the subtasks evaluated in the NTCIR-16 QA Lab-PoliInfo-3 task. The fact verification subtask aims at determining whether a given claim is actually said in a given assembly minutes, and if so, locating its corresponding sentences in the minutes as its evidence. Our proposed method consists of two steps, passage retrieval and textual entailment. In the passage retrieval step, it retrieves a relevant passage using the claim as a query. In the textual entailment step, it determines whether the claim entails the retrieved passage by employing a classifier. We experimentally compared two types of the passage and two IR metrics for the passage retrieval, and three classifiers for textual entailment. The best performed system was evaluated in the NTCIR-16 and achieved the highest score in the formal evaluation.
Abstractive Approach for Agenda Extraction from Assembly Minutes

Automatic agenda extraction from minutes is useful for quickly grasping the contents discussed in the meeting. Extracted agendas can also be used as indexes for information retrieval. We previously proposed an extractive approach for this task, but our analysis revealed that a considerable number of human-created agenda expression did not appear as it is in the minutes. Therefore, in this paper, we propose an abstractive approach for this task. For each pair of question and answer segments, we apply the transformer-based sequence-to-sequence model to generate an agenda expression. We also incorporate a copy mechanism to the model. Our experimental evaluation showed that the proposed approach created more agendas similar to the human-created ones than extractive baseline in terms of ROUGE. We also conducted human evaluation and found that the proposed abstractive approach outperformed our previous extractive approach.
Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization

The universal-set naive Bayes classifier (UNB) [1], defined using likelihood ratios (LRs), was proposed to address imbalanced classification problems. However, the LR estimator used in the UNB overestimates LRs for low-frequency data, degrading the classification performance. Our previous study [2] proposed an effective LR estimator even for low-frequency data. This estimator uses regularization to suppress the overestimation, but we did not consider imbalanced data. In this paper, we integrated the estimator with the UNB. Our experiments with imbalanced data showed that our proposed classifier effectively adjusts the classification scores according to the class balance using regularization parameters and improves the classification performance.
A response generation method of chat-bot system using input formatting and reference resolution

Recently, the demand for chatting with dialogue systems has been increasing. However, one of the significant problems of chat dialog systems is that they cannot respond appropriately to users’ spontaneous speech since systems often respond inconsistently to past topics due to their weak dialog history management. To avoid this problem, the system tends to generate conservative, simple and short speech for consistent conversation, which leads to a boring dialogue. In this study, we constructed a chat dialog system that compensates for the lack of content of input sentences by two components. The first is an attention-based Japanese Text-to-Text Transfer Transformer(T5) model fine-tuned to spoken-to-written language conversion tasks for input formatting. The second is Centering Theory for the reference resolution. Then, we used a Japanese GPT-2-based response generation model adapted to the written language domain. Input shaping allows the dialogue content before the last three utterances to be stored in pronouns and generate responses. As a result, the system could output a response with richer content than the results without using our method and, according to the 27 evaluators, was able to generate more natural dialogue than without the method.
Lexical Complexity Prediction using Deep Learning with Sentence and Word Features

Lexical complexity predictions are one of the critical Natural Language Understanding (NLU) tasks to be solved. Currently, the best approach to this task is to use contextual pre-trained embeddings and deep learning. In order to get better performance, this research focuses on adding word and sentence features to the contextual pre-trained embedding-based model and ensemble the model using a stacking method. Based on the experiments, using the word and sentence features is proven to improve the model’s performance and the stacking results. The results of the best stacking model managed to rank first in subtask 1 with a Pearson value of 0.7887. In subtask 2, we managed to rank second with a Pearson score of 0.8590. Based on further analysis, the characteristics of the built model tend to predict the complexity of words or phrases that are rarely used higher than words or phrases that are often used.
Implicit language information replacing method in Japanese encoder–decode ASR model

Recent years, automatic speech recognition (ASR) tasks often use language models as an adjunct to ASR models. The density ratio approach (DRA) is one of several language model integration methods. It is known that Japanese has a much larger number of characters than the alphabet language, and that there are variations in reading with homonyms and the same characters. It was unclear whether the “implicit language information” of character-based encoder–decoder ASR model using beam search algorithm is approximated by the external language model. In our experiments, We have applied an DRA to a Japanese encoder–decoder ASR model to reduce character error rate (CER) in cross-domain scenarios. Cross-domain CERs were calculated for the Japanese academic presentation speech (APS) corpus and the Japanese simulated presentation speech (SPS) corpus. This method achieved a relative error reduction of 11.0% and 22.5% with the RNN and Transformer models compared to the shallow fusion. To investigate the applicability of different speaking styles to different domains, We also conducted an experiment to replace the “implicit language information” inside the CSJ ASR model with Mainichi Shimbun language model. For the JNAS task, the DRA achieved a relative error reduction of 7.3% compared to the shallow fusion method.
Multi-Label Classification of Indonesian Financial Risk News using Transformer-based Multi-Task Learning

Multi-label classification is one of the task groups in text classification, which can predict more than one class in the text. A commonly used approach for multi-label task is binary relevance technique which usually needs large amounts of resources and suffers from loss of knowledge on characteristics of other labels in data during training. In this study, we propose to implement multi-task learning during Transformers finetune on binary relevance data, which aims to reduce resource consumption by utilizing parameter sharing and increase performance by obtaining other label characteristics during training. Our experiments had shown that our method outperformed vanilla multi-label finetune. Using 1599 instances of training data and 400 instances of testing data with 16 classes, our method achieved.8817 for classification and achieved.9083 in F1 for classification of 3 sampling classes.
Developing COVID-19 Information validation system Using Natural language inference

In the recent COVID-19 pandemic outbreak, a lot of information about the disease spread without being able to validate its truth. In this work, we contribute to develop a COVID-19 information validation system, the truth of which refers to scientific articles. The system consists of two main modules, namely the fact-finding module and the sentence comparison module. The fact-finding module looks for relevant facts to the validated information, while the sentence comparison module leverages Natural Language Inference (NLI) task by using deep learning to compare validated information and facts relevant to that information. This work focuses on building the NLI model, with RoBERTa-Large trained on SNLI and MultiNLI dataset. The model was evaluated on SNLI dev and test set and Multi NLI dev set (matched and mismatched), with median accuracy of 0.931, 0.925, 0.9031, and 0.9042, respectively. The model was also evaluated on stress test for NLI with an accuracy of 0.7466 on the competence test, 0.8776 on the noise test, and 0.6884 on the distraction test. We also created our own NLI dataset for further evaluation, which is created based on rumors and circulating information about COVID-19 and facts from the CORD-19 dataset, containing 100 sentence pairs of premise and hypothesis. Model evaluation on this dataset resulting in an accuracy of 0.84, which means the model already works well on sentences from the CORD-19 dataset.
Conservative Likelihood Ratio Estimator for Infrequent Data Slightly above a Frequency Threshold

A naive likelihood ratio (LR) estimation using the observed frequencies of events can overestimate LRs for infrequent data. One approach to avoid this problem is to use a frequency threshold and set the estimates to zero for frequencies below the threshold. This approach eliminates the computation of some estimates, thereby making practical tasks using LRs more efficient. However, it still overestimates LRs for low frequencies near the threshold. This study proposes a conservative estimator for low frequencies, slightly above the threshold. Our experiment used LRs to predict the occurrence contexts of named entities from a corpus. The experimental results demonstrate that our estimator improves the prediction accuracy while maintaining efficiency in the context prediction task.
Indonesian AMR-to-Text Generation by Language Model Fine-tuning

Research related to generating text from Abstract Meaning Representation (AMR) for Indonesian Language is still a few and only using simple method (e.g., a heuristic approach that only generates bag of words from AMR). Therefore, a better method is needed for Indonesian AMR-to-text generation. Methods based on fine-tuning pretrained language models have become state-of-the-art for AMR-to-text generation. However, most of AMR-to-text generation research only focus on generating English text from AMR. In this paper, we develop a model for Indonesian AMR-to-text generation by fine-tuning a pretrained language model. We evaluate IndoT5, IndoBART, and mT5 on Indonesian AMR-to-text generation. We also observe the effect of adding supervised task adaptation and tree-level embedding on the model performance. Finally, we use our AMR-to-text generation model to improve the previous Indonesian AMR-based summarization system.
A bi-directional shortest path calculation speed up technique for RDBMS

In Relational Database Management Systems (RDBMS) the representation and storage of graph datasets is achieved by using two data tables. One representing the nodes’ data and another for the edges’ data of the graph. Using this model every search process in a big data volume requires multiple joins of large data tables, which significantly reduces their performance. The increasing use of graph datasets in various computer science fields such as (big data, social and computer networks, networks, Semantic Web, knowledge graphs, biological networks, and road networks) creates the need to research methods that will improve the performance of RDBMS when managing this type of data. In this paper, unlike most of the solutions proposed in the literature to date, a purely relational approach is presented using only the native RDBMS query analyzer and not either aggregate functions not implemented in all RDBMSs or an additional plugin layer that analyzes and processes the graph queries in order to translate them into the corresponding SQL versions. With the proposed solution we proved that by designing a bi-directional BFS algorithm that passes through properly selected intermediate pivot points, it is possible to query graph datasets significantly larger than those a complex SQL query can handle, with no major overhead.