Reviewerly/exHarmony huggingface dataset:
A Novel and Lightweight Real-Time Continuous Motion Gesture Recognition Algorithm for Smartphones

Advancement in smartphones has facilitated the investigation of new modalities of human-machine interaction, including communication through touch, voice, and gestures.In-depth, the researchers examined the problem of recognizing distinct gestures (surface, hand, and motion).However, the gesture recognition algorithm pitches discontinuity while the user performs the subsequent continuous gesture.The discontinuity may occur due to the selection of a delimiter to differentiate between successive motions or the employment of a complex algorithm to boost the accuracy of gesture detection, which takes significant time to recognize the gesture before a user may enter the next gesture.Further, gesture recognition based on template matching, machine learning models, and neural networks requires a lot of storage space, processing resources, or both, which are resource-intensive for smartphones.This research proposes a novel Axis-Point Continuous Motion Gesture (APCMG) recognition algorithm that uses accelerometer sensor data to recognizes continuous motion gestures in real time.The algorithm has low computational complexity and easily implemented on resource-constrained devices with minimal computing cost, memory, and energy.The prime objective of the APCMG is to find the start and end of a gesture from a continuous stream of accelerometer sensor data and recognize the gesture in real-time.To demonstrate the APCMG efficacy, the experimental simulation of the Android application for dialing a phone number is considered.The App acknowledges 12 continuous gestures corresponding to 0 to 9 number, delete, and calls termination.The experimental simulations collected 7500 gestures samples from the 25 volunteers.The algorithm efficiently recognizes isolated and continuous gestures with 95% and 94% accuracy, respectively.The proposed algorithm efficiently recognizes isolated and continuous gestures with minimal energy consumption.
Neutral Face Learning and Progressive Fusion Synthesis Network for NIR-VIS Face Recognition

To meet the strong demand for deploying face recognition systems in low-light scenarios, the Near-InfraRed and VISible (NIR-VIS) face recognition task is receiving increasing attention. However, heterogeneous faces have the characteristics of heterogeneity and non-neutrality. Heterogeneity refers to the fact that the matching images are in different modalities, and non-neutrality means that the matching images are significantly different in pose, expression, lighting, etc. Both situations pose challenges for NIR-VIS face matching. To address this problem, we propose a novel Neutral face Learning and Progressive Fusion synthesis (NLPF) network to disentangle the latent attributes of heterogeneous faces and learn neutral face representations. Our approach naturally integrates Identity-related Neutral face Learning (INL) and Attribute Progressive Fusion (APF) into a joint framework. Firstly, INL eliminates modal variations and residual variations by guiding the network to learn homogeneous neutral face feature representations, which tackles the challenge of heterogeneity and non-neutrality by mapping cross-modal images to a common neutral representation subspace. Besides, APF is presented to perform the disentanglement and reintegration of identity-related features, modality-related features and residual features in a progressive fusion manner, which helps to further purify identity-related features. Comprehensive evaluations are carried out on three mainstream NIR-VIS datasets to verify the robustness and effectiveness of the NLPF model. In particular, NLPF has competitive recognition performance on LAMP-HQ, the most challenging NIR-VIS dataset so far.
A Cylindrical Convolution Network for Dense Top-View Semantic Segmentation with LiDAR Point Clouds

Accurate semantic scene understanding of the surrounding environment is a challenge for autonomous driving systems. Recent LiDA- R-based semantic segmentation methods mainly focus on predicting point-wise semantic classes, which cannot be directly used before the further densification process. In this paper, we propose a cylindrical convolution network for dense semantic understanding in the top-view LiDAR data representation. 3D LiDAR point clouds are divided into cylindrical partitions before feeding to the network, where semantic segmentation is conducted in the cylindrical representation. Then a cylinder-to-BEV transformation module is introduced to obtain sparse semantic feature maps in the top view. In the end, we propose a modified encoder-decoder network to get the dense semantic estimations. Experimental results on the SemanticKITTI and nuScenes-LidarSeg datasets show that our method outperforms the state-of-the-art methods with a large margin.
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection

Salient object detection (SOD) has been in the spotlight recently, yet has been studied less for high-resolution (HR) images. Unfortunately, HR images and their pixel-level annotations are certainly more labor-intensive and time-consuming compared to low-resolution (LR) images and annotations. Therefore, we propose an image pyramid-based SOD framework, Inverse Saliency Pyramid Reconstruction Network (InSPyReNet), for HR prediction without any of HR datasets. We design InSPyReNet to produce a strict image pyramid structure of saliency map, which enables to ensemble multiple results with pyramid-based image blending. For HR prediction, we design a pyramid blending method which synthesizes two different image pyramids from a pair of LR and HR scale from the same image to overcome effective receptive field (ERF) discrepancy. Our extensive evaluations on public LR and HR SOD benchmarks demonstrate that InSPyReNet surpasses the State-of-the-Art (SotA) methods on various SOD metrics and boundary accuracy.
Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation

Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new semantic classes. A major challenge in CiSS is overcoming the effects of catastrophic forgetting, which describes the sudden drop of accuracy on previously learned classes after the model is trained on a new set of classes. Despite latest advances in mitigating catastrophic forgetting, the underlying causes of forgetting specifically in CiSS are not well understood. Therefore, in a set of experiments and representational analyses, we demonstrate that the semantic shift of the background class and a bias towards new classes are the major causes of forgetting in CiSS. Furthermore, we show that both causes mostly manifest themselves in deeper classification layers of the network, while the early layers of the model are not affected. Finally, we demonstrate how both causes are effectively mitigated utilizing the information contained in the background, with the help of knowledge distillation and an unbiased cross-entropy loss.
Tracking Small and Fast Moving Objects: A Benchmark

With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for Tracking Small and Fast Moving Objects. This benchmark aims to encourage research in developing novel and accurate methods for this challenging task particularly. TSFMO consists of 250 sequences with about 50k frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box. To the best of our knowledge, TSFMO is the first benchmark dedicated to tracking small and fast moving objects, especially connected to sports. To understand how existing methods perform and to provide comparison for future research on TSFMO, we extensively evaluate 20 state-of-the-art trackers on the benchmark. The evaluation results exhibit that more effort are required to improve tracking small and fast moving objects. Moreover, to encourage future research, we proposed a novel tracker S-KeepTrack which surpasses all 20 evaluated approaches. By releasing TSFMO, we expect to facilitate future researches and applications of tracking small and fast moving objects. The TSFMO and evaluation results as well as S-KeepTrack are available at https://github.com/CodeOfGithub/S-KeepTrack .
Query Expansion Based On User Requirements Clustering for Finding Feature Location

Feature Location is an approach of how to determine the code area based on high level software artifacts. Use Case Scenarios (UCS) are requirements documents in software artifacts that contain many words. A sentence in a UCS is sometimes described by a sentence in other UCS, so capture the relationship among UCS may be advantageous in finding feature locations. The research contribution is how to find feature locations better by making an expansion to query based on UCS clustering. The method was clustering the UCS using k-medoids clustering and index the source code using latent dirichlet allocation. The results was 56.8% for the best recall rate.
Privacy Preserving Electronic Health Record with Consortium Blockchain

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, Science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance.
Usability Evaluation of Office Stationery Procurement Service and Management System Using System Usability Scale

One of the factors that influence the success of a website is usability. The higher the level of usability of a website, the more the website is accepted by users and vice versa. In the Regional Office of the Directorate General of Taxes of North Sulawesi, Central Sulawesi, Gorontalo, and North Maluku, there is an application called Office Stationery Procurement Management and Equipment Services (LALAMPA). LALAMPA is an application that aims to cut the duration of service and simplify the process of requesting office stationery, can display information on the availability of office stationery in real-time, and can monitor the distribution of office stationery. To determine the feasibility of the application offered for use, this research proposes measuring the usability level of the application using the System Usability Scale. The System Usability Scale has ten statements that serve as evaluation tools and consist of three assessment indicators, namely acceptability, grade scale, and adjective rating. Each statement serves as a measure of the usability level of the application. A questionnaire containing 10 statements from the System Usability Scale method was distributed to 25 respondents who are users of the LALAMPA application. The usability evaluation of the LALAMPA application using the System Usability Scale method concluded that the application received a B+ grade for usability because the SUS score was 78.4. The interpretation results of the approach based on the nature (Adjective), this LALAMPA application is included in the “Good” category, and the level of acceptability is included in the “Acceptable” category, which indicates that this application can be widely accepted by users.
Realtime Recognition of Handwritten Lontara Makassar Characters Using Yolov5 Algorithm

Nowadays, handwriting pattern recognition has developed rapidly, including handwritten letters for ancient characters. The machine can be trained to read each letter. The use of lontara letters in South Sulawesi still exists today. To introduce lontara letters interactively, the author tries to research real-time reading of handwritten lontara letters to support these expectations. The results of this study succeeded in making this and getting satisfactory results with the evaluation value of precision rate (P) of 95.2%, recall rate (R) of 95.2%, and mean average precision (mAP) of 95.9%.
Comparison Vector Autoregressive and Long Short Term Memory for forecasting Air Pollution Index In Jakarta

The Air Pollutant Index (API) is a number without units that describes the condition of ambient air quality in a certain location. API monitoring is carried out based on meteorological data affecting ambient air concentrations. On the SILIKA DKI Jakarta website, there is no feature to see ISPU predictions even though this feature is useful for the community. The benefit of predicting ISPU is that people can anticipate early air quality conditions that will occur, including air pollution. In the time series study, a comparison of multivariate time series problems was carried out on ISPU parameters that were correlated with each other. Conventional methods such as Vector Autoregressive. The VAR model is a development of the Autoregressive (AR) model where more than one endogenous variable is used in the VAR model. Artificial Intelligence forecasting methods such as Long Short-Term Memory can be used to forecast multivariate time series. The purpose of this research is to compare the best method between VAR and LSTM in forecasting the parameter index of air pollutant standards in Jakarta. Air pollutant standard index data comes from the Jakarta environmental service from January 2021 to December 2021. These two methods are compared using the lowest RMSE value so that the best method is obtained. The results showed that the LSTM model had the best RMSE value for the prediction of pm10, which was 24.482, and pm25, which was 22.504. Modeling for multivariate time series can be done by conventional methods such as VAR. However, in this study, the deep learning algorithm, namely long short-term memory, is the best method that can be used to solve multivariate time series cases. In addition, the LSTM method is a method that is easier to implement because it does not require parameter estimation like the VAR model.
Face Expression Recognition with Local Ternary Pattern Images using Convolutional Neural Network and Extreme Learning Machine

Facial Expression Recognition (FER) performed computationally is an exciting task to explore in computer vision. Some methods have been proposed to handle variants of illumination in FER. Based on research, Local Ternary Pattern (LTP) as a feature extractor can handle the variation of lights. However, LTP is a traditional feature extractor that needs to be processed manually. Unlike LTP, Convolution Neural Network (CNN) architecture has an automatic feature extractor. Therefore, this study proposes LTP images as input into CNN architecture to handle light variations and keep the feature extraction automatically. Afterward, in the classification layer, Extreme Learning Machine (ELM) is employed as a classifier to improve the training speed of the original CNN classifier. The proposed model performance for the KDEF dataset with 10-fold cross-validation yields an accuracy of 85.51%
A Hybrid Evaluation Index Approach in Optimizing Single Tuition Fee Cluster Validity

The grouping of the socio-economic level of new students at the time of registration at public universities is a problem faced by all state universities. Identifying the right group will have an impact on the students and the university. The quality of the results of a valid grouping will give a sense of fairness to the parents of students in paying tuition fees. On the other hand, the university also expects that the results of a valid grouping will contribute to optimal revenue. This study aims to evaluate the cluster structure of a single tuition fee at the State University of Surabaya. The existing cluster structure is compared with the results of grouping using nine clustering methods, namely K-Mean, Hierarchical, BIRCH, DBSCAN, Mini Batch K-Mean, Mean Shift, OPTICS, Spectral Clustering, and Mixture Gaussian. The proposed evaluation method is a combination of three evaluation concepts, namely internal validity (Silhouette-Index), external validity (Rand Index), and the percentage conformity value to the expected income factor (Revenue-Index). These three indicators are then calculated as the average value for each clustering method as Hybrid-Index. The highest Hybrid-Index is shown by the Mini Batch K-Mean algorithm, with an average value of 0.6420, so the Mini Batch K-Mean algorithm can be recommended as a method for grouping single tuition fees.
Context-Aware Recommendation System Survey: Recommendation When Adding Contextual Information

The recommendation system (RecSys) helps users provide a referral by filtering information based on the relationship between interests and needs. Several methods and approaches in the SR domain, such as CBF, CF, and hybrid, continue to be developed in looking for recommendations relevant to user interests. Recent research suggests that adding contextual information can significantly increase the accuracy of RecSys. However, adding contextual information to RecSys also raises new problems, such as increasingly sparse data. The context-aware recommender system (CARS) is a solution to overcome the problem of changing user preferences by entering contextual information into the recommendation method. In this survey paper, we try to define and classify the types of contextual information. Contextual attributes appropriate for the domain under discussion can be captured using our definition and classification of contextual information. In addition, we also describe and summarize the modifications made by researchers to the model. We also describe the evaluation method and dataset that can be used in the domain of CARS. Finally, we present future research in three schemes at the end of this survey paper.
Multi-Label Classification of Film Genres Based on Synopsis Using Support Vector Machine, Logistic Regression and Naïve Bayes Algorithms

Movies were still a very popular means of entertainment. The current distribution of internet users causes a large amount of movie data to be created and distributed online. The emergence of movie streaming services makes consumers very interested in using automatic film genre classification. In this study, a multi-label film genre classification will be carried out based on an English synopsis. Data were collected from the Internet Movie Database (IMDb) website. The amount of data used in this study was 10,432 lines of data obtained using scraping techniques on June 7, 2022. Researchers divided the dataset labels into 18 labels representing each genre. Feature extraction using TF-IDF and Stemming. The multi-label classification algorithm used is the Support Vector Machine, Logistic Regression, and Naive Bayes Algorithms. Optimal parameter search using GridSearch of each algorithm. The optimum result in this study was obtained f1-score value of 0.58 using the SVM algorithm with TF-IDF feature extraction with stemming dataset, followed by NB with the f1-score value of 0.48 and LR with an f1-score value of 0.43.
ITTS Mart: Mobile Application Design using Lean-UX

This paper describes a study on developing a mobile-based trading application prototype. The Telkom Surabaya Institute of Technology has a roof used for research on hydroponics and aquaponics. These activities produce excellent quality products such as fish, vegetables, and fruits. These products have been sold to the academic community and the public via WhatsApp and have yet to be dealt with optimally. Because of these problems, this research wants to utilize mobile platforms to sell hydroponic and aquaponic products so that they can be marketed optimally and accessed by many people. This study uses the Lean UX method with usability testing. The Lean UX method can complete the development of the ITTS Mart mobile platform more quickly by collaborating between functionalities so that it focuses on building prototypes through declaring assumptions, creating minimum viable products, running experiments and feedback and research. The study evaluated the design results to determine the level of usability following the perspective of potential ITTS Mart users. The preliminary research results show that the most significant gap is web appearance, so this research focuses on that dimension. In this study, the test results showed a significant difference at the 95% confidence level; only one variable, namely response time, indicated that respondents were satisfied with the reaction time or speed of the ITTS Mart. In addition to the variable response time, it shows a significant difference between expectations and experience when using the ITTS Mart application. From these results, there needs to be further research that focuses on six variables that indicate the presence of respondents' dissatisfaction with ITTS Mart. ITTS Mart developers should focus on improving applications at the level of trust in the application, application page views, application user experience, application-related government regulations, and the ability to carry out transactions as the primary function.
Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms

Hyperparameters are the foundation for optimizing the way machine learning algorithms supposed to learn. It is essential to have the optimal hyperparameter values for any learning algorithms. However, tuning hyperparameters can be difficult because it requires a rule of thumb and how to adjust them appropriately. This paper compares hyperparameter tuning approaches in various machine learning algorithms. Simulation results discussed six machine learning algorithms deployment, which is: Decision Tree, Gaussian Naive Bayes, Random Forest, LightGBM, Catboost, and XGBoost. For each mentioned algorithm, six hyperparameter tuning methods (random search, grid search, Bayesian Optimization, Genetic Algorithm, SHERPA, and Optuna) are embedded to evaluate their efficiency. For each test, the value range that is specified are same throughout the algorithm. The advantages of having this method are that the time taken to find the best hyperparameter value is more controlled. The objective of this research is to learn the necessity of hyperparameter tuning, and if that is the case, what hyperparameter tuning methods is the best for each algorithm. The result of this research is that Random Forest without the aid of hyperparameter tuning achieved the best performance. The Future research will be able to forgo evaluating all hyperparameter tuning methods simultaneously after identifying the most efficient way for six machine learning algorithms.