Reviewerly/exHarmony huggingface dataset:
Autonomous scene exploration using experience enhancement

Autonomously exploring and mapping unknown indoor scenes is a prerequisite for many robotic tasks. Deep reinforcement learning-based methods can teach a robot to leverage structural regularities of indoor scenes via interaction with the environment. Therefore, an efficient and robust exploration strategy is assured. The existing approaches either directly control the robot's motions, which leads to long decision sequences and necessitates an enormous number of training samples, or indirectly specify a long-term goal but do not guarantee the goal's reachability, which hinders the training as well. To overcome these problems, we propose a novel autonomous scene exploration method, using an experience enhancement algorithm to accelerate policy training and generate highly efficient exploratory targets. We first incorporate an off-policy reinforcement learning algorithm and the experience replay buffer mechanism. Then, a global exploration policy is used to specify a long-term goal. Next, we take advantage of an incremental heuristic pathfinding algorithm to plan a collision-avoidance path to the long-term goal. When the robot follows the path, we divide it into several segments and evaluate the subpath rewards, which help further improve existing experiences. Finally, the experiences are filtered based on the temporal difference error and stored in the experience replay buffer. The proposed method can extract correct behaviors from past failures and designate accessible long-term goals. Through various experiments, we validated that our approach can effectively improve the training efficiency of the global exploration policy and the final performance of the overall system.
Enhancement of calculation accuracy of the integrated photonic tensor flow processer by global optical power allocation

We present a global optical power allocation architecture, which can enhance the calculation accuracy of the integrated photonic tensor flow processor (PTFP). By adjusting the optical power splitting ratio according to the weight value and loss of each calculating unit, this architecture can efficiently use optical power so that the signal-to-noise ratio of the PTFP is enhanced. In the case of considering the on-chip optical delay line and spectral loss, the calculation accuracy measured in the experiment is enhanced by more than 1 bit compared with the fixed optical power allocation architecture.
A Straggler Identification Model for Large-Scale Distributed Computing Systems Using Machine Learning

Nowadays, Large-Scale Distributed Computing Systems has become crucial for storing, processing, and analyzing massive datasets. Apache Spark endorses a general and efficient programming model for large-scale data processing called Resilient Distributed Dataset (RDD). However, the incidence of stragglers is one of the major issues with the Spark cluster. It results in performance deterioration because a task on a system takes abnormal time to finish execution. In this paper, a straggler identification model for distributed environments using machine learning is proposed. This model employs a several spark parameters extracted by the execution of various types and large scale jobs on to assist in identifying the stragglers. In addition, the proposed model applies machine learning approaches to Spark log to learn various kinds of job execution features. The performance of the introduced model is evaluated across various real-world benchmark datasets using default apache spark across diverse CPU, I/O, and mixed workloads. Furthermore, we have empirically shown that Logistic Regression outperforms and can achieve average accuracy of 90% for straggler identification with comparison to other competitive models.
AraFranco: An Arabic Word Embedding Model for Representing Franco-Arabic User-Generated Text

Social media platforms are now widely used for a variety of personal and business activities. Natural Language Processing (NLP) can be used to extract meaningful insights from user-generated content on social media, such as posts or chat conversations. Analyzing social media text has some challenges when working with a language like Arabic, because of its morphological nature, limited resources, and different spoken dialects. Another recent challenge related to the Arabic language is the use of different typing styles. A commonly used typing style related to the Arabic language in social networks is the Franco-Arabic Typing Style (FATS). It uses a combination of Latin characters and numerals to express different Arabic words. In this paper, “AraFranco”, a new word-embedding model capable of learning and understanding the context of Franco-Arabic (FA) user-generated text, is proposed. It is based on the well-known FastText word embedding model, and it has been trained and tuned using a large corpus of FA tweets. The generated word embeddings are used for the training of a deep learning classifier (Bi-GRU) in a multi-label emotion classification task. The evaluation of the proposed AraFranco model on such task showed performance results of 0.46, 0.57, 0.68, 0.61, and 0.63 for the various measures: Jaccard score, precision, recall, macro F1, and micro F1 respectively, which are considered a good contribution to the recent studies of Arabic NLP.
MOOC Video Classification Using Natural Language Processing and Machine Learning Model

MOOC opens up the doors for universal access to education remotely and serves as a constructive approach to acquire formal education informally by negating the traditional practices. In recent years, the number of MOOC video resources has increased exponentially. Therefore, the need is a fully automated system that would be proficient enough to store, analyze and manage such an immensity of videos while sustaining the quality in response. An automatic classification/prediction of videos is a challenging and complex aspect, although supervised machine learning can effectively achieve this task in an effective way. Many applications use text classification to categorize documents like, e.g. spam filtering, email routing, sentiment analysis, etc. In this study, we present a clever and adaptive technique for autonomous classification of MOOC videos transcription using natural language processing and machine learning model. Our approach can predict the category of a targeted video; the data mining algorithms such as SVM, Random Forest, and Naive Bayesian will be engaged to organize the MOOC videos. Experiments reveal that our approach outperformed other approaches in the field of transcription classification and supervised learning.
Comparative Analysis of Object Detection Methods in Computer Vision for Low-Performance Computers Towards Smart Lighting Systems

Nowadays the object detection research based on machine learning techniques is focused on improving the accuracy and the detection speed of a given algorithm. However, most of such approaches assume substantial amount of computational resources available to the algorithm to make it fairly efficient. Therefore, a vast majority of them are hardly feasible on low-powered and less-capable embedded IoT devices, where the object detection tasks are equally common and usually even more challenging—considering a huge diversity in environmental conditions, camera positions and resolutions, outdoor illuminations and deployments in both urban and offline remote environments. This paper presents a comparative research of different machine-learning object detection approaches targeted specifically towards the low-performance embedded computers in a context of smart street lighting applications. The comparison includes real and synthetic datasets preprocessed for a given detection method, conducted on a Raspberry Pi SoC platform.
A Proposed Model for Fake Media Detection Using Deep Learning Techniques

Recent advances in visual media technology have led to new tools for processing and, more importantly, generating multimedia content. Modern AI-based technologies, in particular, have made it simple to create highly realistic manipulated videos. These synthetic videos, called Deep Fakes, can pose a serious threat to damage the reputation of public bodies or to shape public opinion about a particular event. For this, being able to identify this type of false information becomes essential. To extract features at the frame level, the system employs a convolutional neural network (CNN). These skills are then used to train a Long short-term memory (LSTM) that learns to classify whether a video has been manipulated or not. We ran tests on two datasets to assess the quality of the model. DeepFake Detection Dataset (DFDC) with the model receives 0.968 AUC and 0.937 F1score, compared to SOTA model, which receives 0.972 AUC and 0.906 F1score, and the CelebDF dataset, which receives 0.988 AUC and 0.9628 F1score.
Weakly Supervised Localization of the Abnormal Regions in Breast Cancer X-Ray Images Using Patches Classification

We present an approach of a Weakly Supervised Localization of the Abnormal Regions in Breast Cancer X-Ray images using patches classification. We use the breast cancer X-Ray image dataset provided by radiologists of the Hanoi Medical University Hospital to generating patches with Normal, Benign and Malignant categories. After that we train the ResNet 34 model on the breast cancer X-Ray patches dataset for classifying Normal, Benign and Malignant categories. Our network achieves a macAUC of 0.817139. Finally, we apply this model to calculate heatmaps for visualization of Benign and Malignant areas in the Breast Cancer X-Ray images.
Image Caption Generator with a Combination Between Convolutional Neural Network and Long Short-Term Memory

Automatically describing the content of an image holds an essential role in numerous applications. Some practical applications include providing more accurate and detailed images or videos in scenarios like information search by image or video surveillance systems. The technique generates picture captions that are generally semantically informative and grammatically accurate by acquiring image and caption pairings. Humans use natural languages to describe scenes because they are short and compact. On the other hand, machine vision systems characterize the scene by capturing an image that is a two-dimensional array. The concept is to combine the image and captions into one area and then map from the image to the sentences. This study proposes a merge model to combine the image vector and the partial caption. It can be implemented through three steps: processing the sequence from the text, extracting the feature vector from the image, and decoding the output by concatenating the above two layers. Besides that, to evaluate model performance, we generate multiple sentences using Beam Search and BLEU. The experiments show that the method can generate captions with relatively accurate content and less training memory. We use the Flickr8k dataset consisting of 8000 images paired with five different captions, which provide precise descriptions of the salient entities and events. For training, we use 6000 images, 1000 for test and 1000 for development, while Flickr8k text includes text files describing train set, test set. The best results were obtained when testing the model with BLEU-1, Greedy, and Beam Search with k $$=$$ 5 or k $$=$$ 7 all over 60 in BLEU scores.
Towards developing energy efficient algorithms to discover partial periodic patterns in big temporal databases

Crucial information that can empower the users to achieve socioeconomic development lies hidden in big temporal databases. Previous studies employed partial periodic pattern mining techniques to find all regularly occurring patterns in the data. Unfortunately, these techniques consumes too much energy as they often produce too many patterns most of which may be uninteresting to the user. This paper tackles this problem by introducing two new types of patterns, namely maximal partial periodic patterns and closed partial periodic patterns patterns, which represent a concise set of all partial periodic patterns that may exist in the data. Two depth-first search algorithms were also described to find the desired patterns. Experimental results demonstrate that our algorithms are both runtime, memory, and energy efficient. The usefulness of our patterns was also demonstrated with a case-study on air pollution data.
Evaluating the vulnerability of ISO 19100 and S-100 standards through visual approaches

This paper introduces a graph-based visualization system for representing the vulnerabilities of an ecosystem of norms. Norm dependencies are crawled and extracted from reference normative documents. A series of interactive graphs and chord-based visualisations support the identification and valuations of norm interdependencies and clusters, as well as potential vulnerability propagation paths. The whole approach is applied to ISO 19100 standard series and S-100 geographical and electronic navigation chart standards.
PEMBELAJARAN KOSAKATA BAHASA INDONESIA BAGI PENUTUR ASING (BIPA) LEVEL A-1 DENGAN MENGGUNAKAN PERMAINAN KARTU KATA DAN KARTU GAMBAR

Indonesian for Foreign Speakers or BIPA is a branch of basic Indonesian linguistic teaching that focuses on foreigners as learners. These learners prepare themselves to learn Indonesian formally for educational or work needs. In this study, the students consisted of eight female and male students, Universitas Pendidikan Indonesia who came from South Korea. They are BIPA level A-1 learners or beginners with educational and work purposes. The focus of the material in this research is learning Indonesian vocabulary BIPA level A-1 by using word card games and picture cards. Vocabulary is fundamental in learning a new language. The higher one's vocabulary, the more communicative one's language skills will be. to maximize this learning process, word card game techniques and picture cards were used which were created by the teacher as well as the researcher. Data collection techniques used are tests, questionnaires, and interviews. The results of this study indicate that students are very enthusiastic and interested in learning vocabulary using word cards and picture card game techniques. Based on the results of the data obtained, it was found that there was a significant increase in test scores between the pretest and posttest. The test presented is a multiple-choice test with 10 questions with 5 alternative answers. Then, the mean of the questionnaire data shows a strong category with a score of 78.7. Questionnaires were distributed to students using a Likert Scale with 10 questions and 5 alternative categories of answers. This significant increase in posttest scores is relevant to the interviews that have been conducted. The students hope that this kind of learning technique will continue to be improved by BIPA teachers.
EVALUASI PELAKSANAAN PELATIHAN JARAK JAUH TEKNOLOGI INFORMASI DAN KOMUNIKASI TINGKAT MADRASAH TSANAWIYAH ANGKATAN 2 (GURU MATEMATIKA) TAHUN 2022

This research entitled is the Evaluation of the Implementation of Information and Communication Technology Distance Training for Madrasah Tsanawiyah Class 2 (Mathematics Teachers). The purpase of this study is to evaluate the implementation of the distance training seen from the documents in the Medan Religious Education and Training Center Learning Management System (LMS). From the results of the document research and data processing taken from the Bdk Medan LMS, it's found that the participants’ attitude assessment given by the committee was with an average value of 83.25 with “good” qualifications, for knowledge assessment with an average value of 79.45 with “competent” qualifications and the skills provided by widyaiswara are with an average value of 80.73 with "competent" qualifications. From the results of the overall assessment, i,e the participants' attitudes, knowledge and skills assessmentof, the participants passed with a score of 92.00 to 100.00 as many as 2 people with “very competent” qualifications, in the range of 84.00 to 91.99 as many as 15 people with “competent” qualifications, in the range of 76 ,00 to 83.99 as many as 9 people with “quite competent” qualifications, while those who did not pass were assessed as less than 76.00 as many as 4 people with “less competent” qualifications. The evaluation of the organizers assessed by participants has an average aspect value of 94.02 with “very satisfactory” qualifications. Finally, the widyaiswara assessment was assessed by participants with an average value of 93.11 with “very satisfactory” qualifications.
Monitoring, Detecting and Early Warning of Forest Fires using Blockchain in Wireless Sensor Network

Forest fires cause economic and material losses and destroy the environment, causing many negative impacts on human habitats and forest ecosystems. The unpredictable threat of wildfires leads to climate change and the greenhouse effect. It is worth noting that up to 90% of forest fires occur mainly due to human activities [1], even due to tampering, vandalism, and hostile actions. Therefore, to minimize the destruction caused by forest fires as well as accurately identify the area where forest fires occur and support government agencies, forest rangers, and search and disaster relief forces, ... it is necessary to monitor, detect and warn of forest fires right from the early stage (early warning). This paper proposes to use static sensor nodes combined with mobile in wireless sensor network (WSN) to Forest fire Monitoring, Detection, and Early Warning - FMDW. At the same time, integrating Blockchain technology (BC) in WSN solves the problem of secure routing, and distributed data storage, and prevents destructive and tampering attacks. This also suggests new research directions on monitoring, detecting, warning, and protecting forests in countries using Blockchain security technology in WSN.
The Student Employee's English Language Skills at Denpasar Academy of Tourism

This research aimed to investigate the student employees' English language skills at Denpasar Academy of Tourism. Framed within a qualitative design, this study employed observation, survey, and interview as the methods with an observation sheet, a questionnaire, and an interview guide as the instruments to collect the data. It is expected that working students have the ability to communicate well in English. However, the results of the study showed that the working students’ English skills are moderate. Therefore, the students need to maximize their English skills to support them in the work field. Further, this study could be used as a reflection to improve the process of English teaching and learning by upgrading the curriculum and creating an English club.
Knowledge Organization Approach in Online Public Access Catalogues (OPAC) for Search and Navigation

Effective organization, discovery, and retrieval of resources have always been essential functions for any library. Libraries have implemented various new information systems along with traditional library systems such as federated search, webscale discovery, next-generation catalogue, etc. Faceted navigation, known as faceted search, faceted browsing, or guided navigation, has been applied in many e-commerce sites and dominates features in the interaction paradigm. Faceted approach to knowledge organization in searching and navigation features in the online catalogue supports users in finding the information needed in the most effective and efficient manner. This paper discusses faceted searching and navigation application in library Online Public Access Catalogues (OPAC).
Three levels of reproducible workflow remove barriers for archaeologists and increase accessibility

Reproducible research is being implemented at different speeds in different disciplines, and Archaeology is at the start of this journey. Reproducibility is the practice of reanalysing data by taking the same steps and producing the same or similar results. Enabling reproducibility is an important step to ensure research quality and validate interpretations. There are currently many barriers to moving towards reproducible research such as upskilling researchers in the practices, software and infrastructure needed to do reproducible research and concerns relating to opening up research such as how to share sensitive data.
Stability of spiking effect in membrane laser neurons on Si utilizing optical feedback

The stability of the ultrafast spiking effect in membrane III- V lasers on Si with optical feedback is experimentally evaluated for neuromorphic spiking neural network PICs. Stable operation, spiking thresholds, and refractory periods are obtained in two different lasers and at two different bias current regions.
The Application of Interactive Humanoid Robots in the History Education of Museums Under Artificial Intelligence

The purpose is to improve the application of museum robots in museum scenes, enhance the service capabilities of robots in museums, break tourists’ boring concepts of museum environment, manual explanation, services, etc., and promote tourists’ exhibition experience. A method for sentiment analysis of humanoid robots in museums is proposed by studying the transformation of museums with the help of artificial intelligence (AI) technology, as well as the function and significance of museums in history education. First, the function of museums in history education and the role of AI in constructing intelligent museums are described. Second, on account of the multimodal sentiment analysis method of speech and emotion, a scenario model of the visitor museum is established. An uncertain reasoning method for robot service tasks based on Multi-entity Bayesian network (MEBN) is also proposed. Finally, the proposed model is validated by experiments. The results show that compared with the recognition rates of Arousal and Valence dimensions, the consistency correlation coefficient value of the Kalman filter is higher. The Consistency Correlation Coefficient (CCC) value of the Arousal dimension is 0.703, and the CCC value of the Valence dimension is 0.766. Besides, in different tour times, the proportion of services that tourists want to be provided with varies in different emotional states. From time t1 to time t2, the proportion of tourists who want to hear explanations of cultural relics dropped by 11.5%, while the proportion of tourists who want to be provided with tea service increased by 24%. This indicates that when the Kalman filter algorithm performs continuous emotion recognition of a multimodal fusion, the final emotion recognition accuracy is higher, and emotion analysis can help humanoid robots to be more intelligent and humanized. The proposed sentiment analysis based on the multimodal analysis and MEBN’s uncertainty reasoning method for robot service tasks not only broadens the practical application field of intelligent robots under human–computer interaction technology but also has important research significance for the innovative education development of museum history education.