Reviewerly/exHarmony huggingface dataset:
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations

Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator. However, the assumption limits the usage of imitation learning, especially when collecting demonstrations for the imitator is difficult. In this paper, we study out-of-dynamics imitation learning (OOD-IL), which relaxes the assumption to that the demonstrator and the imitator have the same state spaces but could have different action spaces and dynamics. OOD-IL enables imitation learning to utilize demonstrations from a wide range of demonstrators but introduces a new challenge: some demonstrations cannot be achieved by the imitator due to the different dynamics. Prior works try to filter out such demonstrations by feasibility measurements, but ignore the fact that the demonstrations exhibit a multimodal distribution since the different demonstrators may take different policies in different dynamics. We develop a better transferability measurement to tackle this newly-emerged challenge. We firstly design a novel sequence-based contrastive clustering algorithm to cluster demonstrations from the same mode to avoid the mutual interference of demonstrations from different modes, and then learn the transferability of each demonstration with an adversarial-learning based algorithm in each cluster. Experiment results on several MuJoCo environments, a driving environment, and a simulated robot environment show that the proposed transferability measurement more accurately finds and down-weights non-transferable demonstrations and outperforms prior works on the final imitation learning performance. We show the videos of our experiment results on our website.
Deepdense: Enabling Node Embedding to Dense Subgraph Mining

Dense subgraphs convey important information and insights about a graph structure. This explains why dense subgraph mining is a problem of key interest that arises in several tasks and applications such as graph visualization, graph summarization, graph clustering, and complex network analysis. It is a hard problem that has been intensively addressed by the data mining community.\\In this paper, we propose a deep learning approach that enumerates all occurrences of dense subgraphs in a graph without any constraints or limitations on their size. More precisely, we enrich exiting structural node embedding with extra information, computed on node neighborhoods, to effectively capture their belonging to dense subgraphs. We evaluate our approach on several datasets to attest its efficiency on two main applications: graph summarization and graph clustering.
Accurate and Real-Time Vehicle Detection in Uav Imagery Based on Small-Size and Improved Yolov5

Nowadays, Unmanned Aerial Vehicles (UAVs) have become useful for various civil applications, such as smart parking, where real-time vehicle detection and classification is one of the key tasks. There are many challenges in detecting vehicles including small size objects and the variety in the UAV’s altitude and angle. As classic object detection solutions have limitations in confronting these challenges, recent methods are developed based on convolutional neural networks and their ability in effective feature learning. Due to the computational complexity in these networks and the need for accurate and real-time object detection, balancing the accuracy and inference speed is obligatory. This paper aims to propose an accurate and real-time vehicle detection network based on the successful YOLOv5 object detection model. This is done by improving the structure of the model, adding attention mechanism and using an adaptive bounding box regression loss function. Also, considering the need for real time inference speed, depth and width of the model are balanced, while using ghost convolution in the Neck unit facilitate accuracy and inference speed balancing even more. The proposed method is evaluated on three different urban UAV imagery datasets, VisDrone, CARPK and VAID, specifically intended for civil applications. Comparing the obtained results from the proposed method with YOLOv5 baseline models, it achieved 3.52% higher mAP50 and 207.15% FPS than YOLOv5X on VisDrone dataset, while it is much smaller in size and GFLOPS. Totally, the proposed network outcomes show how the applied structural and conceptual modifications can upgrade the YOLO family towards being small in size, high in accuracy and fast in inference speed.
Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification

With the good performance of deep learning algorithms in the field of computer vision (CV), the convolutional neural network (CNN) architecture has become a main backbone of the computer vision task. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. However, due to the limitation of computing power, deep learning algorithms are usually not available on mobile devices. This paper proposes a lightweight convolutional neural network, TripleNet, which can operate easily on Raspberry Pi. Adopted from the concept of block connections in ThreshNet, the newly proposed network model compresses and accelerates the network model, reduces the amount of parameters of the network, and shortens the inference time of each image while ensuring the accuracy. Our proposed TripleNet and other state-of-the-art (SOTA) neural networks perform image classification experiments with the CIFAR-10 and SVHN datasets on Raspberry Pi. The experimental results show that, compared with GhostNet, MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time of TripleNet per image is shortened by 15%, 16%, 17%, 24%, and 30%, respectively. The detail codes of this work are available at https://github.com/RuiyangJu/TripleNet.
Wfsc-Tacotron2: Chinese Dialect Speech Synthesis Based on Word Frame Speech Similarity Coding and Tacotron2

As “living cultural fossils”, dialects play crucial role in local culture for continued life of any linguistic form of expression and communication. Some of Chinese dialects, nevertheless, are leaving the historical stage and thus effective synthesis of Chinese dialects promotes local communication and preserves Chinese dialects. Due to its linguistic difference from Latin languages and Chinese characters are ideographs, and building a pronunciation dictionary to convert Chinese characters into phonemes is essential for Chinese speech synthesis, which requires linguistic expertise and time consuming. Therefore, we propose a method WFSC-Tacotron2 exploiting word frame speech similarity to auto-encode pronunciation rules for Chinese characters. So that no prior linguistic knowledge required to construct WFSC-Tacotron2 without manually building a pronunciation dictionary. As result, we synthesize Chinese dialect speech using premise of small sample size (5 hours or less) without pronunciation dictionary. The novel method can also achieve 7% improvement in MOS with pinyin inputs better than Tacotron2 on Chinese mandarin dataset. The MOS values of these dialects were 3.92, 3.74 and 3.88, respectively on Cantonese, Hefei and Hunan dialects synthesizing using WFSC-Tacotron2. In addition, the method allows to synthesize thousands of different Chinese dialects with lower sample size and without pronunciation dictionary.
Development of an Online Matching System (OMS) for Studying in the Graduate Program

Database management systems are essential for achieving competitiveness, enabling organizations to formulate proactive strategies matching their target audience effectively. Thus, this study aimed to develop an online matching system (OMS) for studying in the Graduate Program at Srinakharinwirot University. In this case, we used the System Development Life Cycle (SDLC), a conceptual model that includes system planning, analysis, design, development, and implementation as well as operation. System efficiency evaluation by three experts and satisfaction with system evaluation by 60 users evaluated the system performance of the research instruments. According to the findings, the OMS for studying in the Graduate Program received an excellent rating (X̅ = 4.72), and the overall satisfaction of using this database system was high (X̅ = 4.28). The OMS for studying in the Graduate Program is critical toward value creation that suits the requirements of the students and enhances marketability to reach the target effectively.
AI-POWERED APPS TO ENHANCE NOVICE NEWSREADERS’ ENGLISH PRONUNCIATION

Among the goals that Communication students, who are ESP learners, desire to achieve is to become professional newsreaders who can deliver the news in both Indonesian and English. These novice newsreaders with little to no knowledge about news delivery in English often face problems with English pronunciation. This study aims to help forty-one novice newsreaders, who are students of the Islamic Broadcasting and Communication Department, with their English pronunciation using AI-powered apps and to reveal their perceptions of the apps. The students were involved in one cycle of Classroom Action Research (CAR), where they had to make a video of them reading English news before using the apps as the pre-test and another video after the lecturer taught them how to use the apps as the post-test. The researcher distributed a questionnaire via Google Form after the post-test to complete the data. The results show that AI-powered apps can enhance novice newsreaders’ English pronunciation, especially in the aspects of Accuracy, Key Words, Chunking and Pausing, Intonation, as well as Sounds and Vocal Features; yet, Intonation becomes the lowest improved aspect. Also, the respondents believed that the apps help them with their future job as newsreaders, are easy to use, and give instant feedbacks, which are necessary for independent learning and suitable for ESP learners. The ELSA app rose to the top of the list of the respondents’ favorites. Finally, it is recommended that future researchers carry out comparable studies that focus on one or two particular English sounds evaluated across multiple cycles.
Undergraduates’ Satisfaction with Zoom e-learning Application: Empirical Evidence from Management Undergraduates

E-learning is developing as the new paradigm of modern education. The global e-learning business is growing at a 35.6 percent annual rate and still, there are failures. The success of e-learning systems is predicted mainly by user satisfaction. However, factors derived from users’ satisfaction with using zoom for e-learning are still underexplored. Therefore, the purpose of this study is to explore the factors that might impact undergraduate satisfaction regarding the use of zoom applications for e-learning. A theoretical foundation was laid for the study from empirical findings based on that conceptual framework was developed. Data was collected from 213 Management undergraduates of the University of Ruhuna by using a questionnaire survey. Results of the study indicated that perceived quality, perceived value, and technology self-efficacy positively impact undergraduates’ satisfaction with using the Zoom application for e-learning. Further, the study found that perceived utility has a significant negative impact on undergraduates’ e-learning satisfaction by using Zoom. The study was conducted from a student's point of view and implies their actual desires for e-learning specifically the Zoom application. Based on the findings stakeholders can pay attention to taking necessary actions to fulfil students’ needs and guiding them to gain higher satisfaction with e-learning and finally it will lead to the overall success of implemented e-learning system.
Review: Collins, Luke C. (2019). Corpus linguistics for online communication: A guide for research. Routledge.

Corpus Linguistics for Online Communication: A Guide for Research, published by Routledge, is a practical handbook for corpus-based online communication research. This volume introduces corpus-based online communication research methods for a variety of research topics with empirical case studies. The volume blends theoretical progress with practical application, making it a useful resource for researchers and scholars in online communication and related fields.
Verification Of Student Diplomas Based On Qr Code

&lt;p&gt;Diploma verification is still done manually. During this time, the diploma is verified by displaying the original diploma. Because the campus does not match the diploma with the existing archives, diploma falsification is possible. IAIN Bukittinggi uses manual methods to verify the authenticity of diplomas. The goal of this research is to create a diploma verification system based on QR CODE to verify the authenticity of diplomas. Research and Development is the type of research employed (R&amp;amp;D). The system development model employs a waterfall approach and the System Development Life Cycle (SDLC). The validity test results are valid, with an average of 0.90. The average practicality test result is 92, indicating that the product is very practical. And the effectiveness test results with an average of 0.90, which is very high.&lt;/p&gt;
Emotional Intelligence And Polite Character Of Elementary School Students: A Correlation Studies

The moral and mental degradation of students indicates low emotional intelligence in various countries, including Indonesia, with indicators of loss of manners, bad language, bullying, brawling, etc. This study aimed to determine the influence of students’ emotional intelligence levels on the manners character of 5th-grade students of Madrasah Ibtidaiyah al-Islam Balesari, Magelang, Indonesia. The study used a quantitative approach with a quasi-experimental method and a nonequivalent control group design. Sampling was done by a random sampling technique with a purposive sampling type. Data were collected through observation, questionnaires, and documentation with simple linear regression analysis assisted by SPSS 16 software for Windows. The results show that the average value of emotional intelligence in the experimental class is 79.4% (high-level category), while the control class is 72% (medium-level). On the other hand, the character of manners in the experimental class is 79% (very good category), and in the control class is 72% (sufficient category). The results indicate that there is an influence of students’ emotional intelligence on their manners and characters. The results of a simple regression test in the experimental class are Y = 11.354.–0.861 with a significant value of 0.00. The significance value is &lt;0.05 (0.00&lt;0.05), then Ha is accepted and Ho is rejected. The results of a simple regression test in the control class are Y = 17,323- 0.760 with a significant value of 0.00, the significance value &lt;0.05 (0.00 &lt;0.05), then Ha is accepted and Ho is rejected. The research findings concluded that the higher the level of emotional intelligence, the higher the manners of character, and vice versa. Further research needs to be conducted to explore the influence of emotional intelligence on the students’ character as a whole
PENGEMBANGAN MUTU PENGELOLAAN PROGRAM STUDI BERBASIS MONITORING ANALYTIC TOOLS MENUJU AKREDITASI UNGGUL PROGRAM STUDI MATEMATIKA FST UIN SYARIF HIDAYATULLAH

This paper discusses Monitoring Analytics Tools (MAT) to analyze the management quality of the Mathematics Study Program Faculty of Science and Technology UIN Syarif Hidayatullah Jakarta. Study program management based on Monitoring Analytics Tools (MAT) is one of strategy for building a study program management system based on data-driven and automated analytics. This method is handy in improving the quality of study programs so that they are more measurable, targeted, effective, and efficient in providing excellent services and developments. Monitoring Analytics Tools (MAT) in this study focused on two management quality developments of the study program, namely Monitoring Analytics Tools (MAT) for Lecturer Quality Development in the Learning Process through a Lecturer Evaluation Survey by Students and Monitoring Analytics Tools (MAT) for the Graduate Development through Graduate User Surveys. The results show that the responsiveness dimension (punctuality in learning activities) has the lowest satisfaction index for Lecturer Quality Development. Meanwhile, for the quality of graduates, the dimensions of specific skills (presentation and writing report skills) are the lowest satisfaction index.
Clarity of self-concept and psychological well-being of teachers of general secondary education institutions

The article presents the results of an empirical study of the clarity of the self-concept of teachers of general secondary education institutions as a degree of expressiveness, internal coherence and stability of the structural elements of the self-concept that make up its content. An insufficient level of clarity of self-concept was established for quite a large number of teachers. Attention is focused on the fact that people with low self-clarity have higher levels of anxiety, depression, emotional instability, a tendency to destructive types of reflection, and rumination than people with high self-concept clarity&#x0D; The gender-age and organizational-professional features of the clarity of the self-concept of teachers of general secondary education institutions are determined. In particular, it was established that male pedagogical workers are characterized by a higher level of self-concept clarity than women, although such differences level off with age. In addition, it was determined that the clarity of the self-concept is higher among teachers from lyceums and gymnasiums compared to their colleagues from traditional institutions of general secondary education, especially among those who hold managerial positions.The relationship between the clarity of teachers' self-concept and their psychological well-being was revealed. It was established that the higher the level of clarity of self-concept, the higher indicators of psychological well-being are characterized by the studied teachers.A conclusion was made about the expediency of developing the clarity of the self-concept of teachers of general secondary education institutions, which will contribute to their psychological well-being.
Parallel Encryption With Digit Arithmetic Of Covertext Model Using Duplicated Covertext

Steganography is the art and science of writing hidden messages or hiding messages in such a way that, apart from the sender and the recipient, no one knows or is aware that there is a secret message (it does not appear that there is a hidden message. Furthermore, public key cryptography is the type of cryptography that using two interrelated keys, namely the public and private keys. Cryptography and steganography techniques have an important role in securing information. Parallel Encryption with Digit Arithmetic of Covertext (PDAC) encryption model adopts cryptography and steganography for securing messages. The accuracy of the number of covertext in the PDAC affects whether the PDAC works. If the number of PDAC covertext does not match, then the encryption process will be imperfect. This study aims to close the PDAC gap. The proposed model adopts the repeated use of covertext. Covertext loops applied in PDAC can process information security. Another advantage obtained is that this modification makes it easier for users to use PDAC without having to meet the minimum covertext requirement.
Analysis of Covid-19 Spread in Palopo City Using DBSCAN Algorithms

Pandemic Covid-19 has spread rapidly, causing a severe health crisis worldwide, including Indonesia. As is well known, due to Indonesia’s diverse population, there are differences in the number of cases between cities. Therefore, a distribution clustering process is needed to develop a map of Covid-19 cases which aims to enable optimal handling of this pandemic. In this study, DBSCAN was used for the clustering process of Covid-19 spread on Palopo city. Clustering using the DBSCAN algorithm is needed to find out the clusters that are formed and the location of Covid-19 spread in certain areas. The result of clustering using DBSCAN show that the largest cluster has a total 0f 432 cases that occurred throughout the time period with an average of 15 cases per day. After visualization, the most distribution of Covid-19 was in the central area of Palopo City and concentrated in Wara District with 250 cases.
Tltd: Transfer Learning for Tabular Data

Nowadays, Deep Neural Networks (DNNs) have become effective for various machine learning tasks. DNNs are known to achieve high accuracy with unstructured data in which each data sample (e.g., image) consists of many raw features (e.g., pixels) of the same type. The effectiveness of this approach diminishes for structured (tabular) data. In most cases, decision tree-based models such as Random Forest (RF) or Gradient Boosting Decision Trees (GBDT) outperform DNNs. In addition, DNNs tend to perform poorly when the number of samples in the dataset is small. This paper introduces Transfer Learning for Tabular Data (TLTD) which utilizes a novel learning architecture designed to extract new features from structured datasets. Using the DNN’s learning capabilities on images, we convert the tabular data into images, then use the distillation technique to achieve better learning. We evaluated our approach with 25 structured datasets, and compared the outcomes to those of RF, eXtreme Gradient Boosting (XGBoost), and Tabnet. The results demonstrate the usefulness of the TLTD approach.
Cezsar: A Contrastive Embedding Method for Zero-Shot Action Recognition

This paper proposes a novel Zero-Shot Action Recognition (ZSAR) method based on contrastive learning. We aim to reduce the semantic gap and domain shift problems in ZSAR by learning a joint embedding space. This space is learned by aligning visual appearance with their corresponding description in natural language. Our motivation comes from recent works that reported impressive results by representing videos and labels with descriptive sentences embedded with pre-trained models on the paraphrasing task. We design an automatic negative sampling procedure to augment the training dataset and generate unpaired data, i.e., visual appearance and unrelated descriptions. Then, we train an encoding model to retain positive pairs as close as possible and negative ones as far as possible. Using our negative sampling procedure, we can overcome the data limitation to perform contrastive learning and train the model in a few hours in a conventional Graphics Processing Unit (GPU). The text encoding ability of our model allowed us to use semantic side information as WordNet definitions of recognized objects in scenes and descriptions from video captioning methods. Our results are state of the art on the UCF-101 and Kinetics-400 datasets under several split configurations. Our code is available at https://github.com/valterlej/cezsar.