Reviewerly/exHarmony huggingface dataset:
A Scale-Reductive Pooling with Majority-Take-All for Salient Object Detection

With the rapid development of hardware and related technologies, salient object detection based on deep learning methods has become one of the popular research topics in computer vision applications. For the detection focused on the integrity of salient objects, edge accuracy of objects is one of the important indicators in the evaluation of visual saliency detection. However, in deep learning-based methods, complex networks and large amounts of data are usually required to achieve good boundary accuracy. To solve this issue, a scale-reductive pooling approach with clustering-based majority-take-all strategy is proposed in this paper. According to the experimental results, we show that the prediction results are improved with reasonable quantity of superpixels.
Multiclass Classification for Air Quality In Jakarta Using Support Vector Machine and Multi-Layer Perceptron Classifier

Air is one of the necessities of living things. Therefore, it is necessary to have good air quality. Air pollution can cause many negative impacts on life. Therefore, it is important to know the air quality in an area. Jakarta is one of the cities with poor air quality in Indonesia and the world. During the Covid-19 pandemic, the government implemented a large-scale social restriction policy, the impact of this policy was better air quality. But now it has started to back to normal, then it is important to control air quality. There are 5 locations to measure air quality in Jakarta. The results of the independence test between the location of air quality measurements and critical variables on air pollution indicate a relationship between the two variables. Moreover, there are differences in air quality before Covid-19 and during Covid-19 based on the results of the t-test. Air quality classification was carried out in this research using machine learning methods. Because there are several levels of air quality, the classification uses a multiclass classification. Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) Classifier are used in this research. Because based on the results of literature reviews, both methods produce high accuracy. The results of this research showed a comparison of the two methods. The comparison showed that the SVM method is better than the MLP Classifier.
A Walk-through Type Authentication System Design via Gaze Detection and Color Recognition

With the development of IoT technology, more and more devices can provide the function of detecting the user’s gaze. Especially, as COVVID-19 spreads around the world, there is a growing demand for non-contact type of authentication method, entering passwords by users' fixation became a viable option. Therefore, based on the using of glasses type eye-tracking device, in this study we focused on the detection of user’s eye movement and tried to propose a walk-through type of authentication system design by color recognition. Through a set of preparatory experiments, we tested the usability of the designed system, and found the effect of light setting on the accuracy of the experiment. The results of this study will be useful in providing fast and convenient user authentication services and provide guidance for the future authentication system development.
Algorithms for estimating the parameters of factorisation machines

Since the introduction of factorisation machines in 2010, it became a popular prediction technique amongst machine learners who applied the method with success in several data science challenges such as Kaggle or KDD Cup. Despite these successes, factorisation machines are not often considered as a modelling technique in business, partly because large companies prefer tried and tested software for model implementation. Popular modelling techniques for prediction problems, such as generalised linear models, neural networks, and classification and regression trees, have been implemented in commercial software such as SAS which is widely used by banks, insurance, pharmaceutical and telecommunication companies. To popularise the use of factorisation machines in business, we implement algorithms for fitting factorisation machines in SAS. These algorithms minimise two loss functions, namely the weighted sum of squared errors and the weighted sum of absolute deviations using coordinate descent and nonlinear programming procedures. Using a simulation study, the above-mentioned routines are tested in terms of accuracy and efficiency. The prediction power of factorisation machines is then illustrated by analysing two data sets. Keywords: Factorisation machines, Fitting algorithms, Parameter estimation
A design of cloud computing resource lifecycle management framework based on micro-services

With the popularization and improvement of cloud computing, there are many problems in the life cycle management of cloud computing, such as the dispersion of interaction and the inconsistency of data, which lead to the difficulties of system maintenance and upgrade, and the problems of data independence. In this paper, we analyze the functions in the life cycle of cloud computing resources. The ontology based semantic theory is used to integrate heterogeneous data among modules. Micro-service architecture is used to control all modules.
A Flexible Agent Architecture in SPADE

For some years now, multi-agent systems have demonstrated their potential to be used as a base technology to address the development of distributed, intelligent and autonomous systems. However, existing agent platforms still require a higher degree of flexibility in order to effectively combine decision-making processes into the same system. In this context, the main goal of this paper is to improve flexibility in the development of behavior-based agents, by means of introducing the Flexible Agent Architecture (FAA). The FAA defines agents in such a way that each agent may integrate and combine different types of reasoning processes (for example, procedural-based and BDI processes) as behaviors. This definition remarkably empowers the global behavior of the agent, by allowing it to combine different, more or less deliberative techniques in its decision making process, in a transparent and flexible way. In addition, the paper presents a full implementation of the proposal in a real agent middleware called SPADE.
LinGBM: A Performance Benchmark for Approaches to Build GraphQL Servers

GraphQL is a popular new approach to build Web APIs that enable clients to retrieve exactly the data they need. Given the growing number of tools and techniques for building GraphQL servers, there is an increasing need for comparing how particular approaches or techniques affect the performance of a GraphQL server. To this end, we present LinGBM, a GraphQL performance benchmark to experimentally study the performance achieved by various approaches for creating a GraphQL server. In this paper, we discuss the design considerations of the benchmark and describe its main components (data schema; query templates; performance metrics). Thereafter, we present experimental results obtained by applying the benchmark in two different use cases, which demonstrate the broad applicability of LinGBM.
Designated-Verifier Linkable Ring Signatures with Unconditional Anonymity

In this work we propose Designated-Verifier Linkable Ring Signatures with unconditional anonymity, a cryptographic primitive that protects the privacy of signers in two different ways. First, it allows signers to hide inside a ring (an anonymity set of signers), which they can create by collecting a set of public keys all of which must be used for verification. Second, it allows a designated entity to simulate signatures, thus making it difficult for an adversary to deduce a signer’s identity from the content of the exchanged messages. To the best of our knowledge, our scheme is the first to combine linkable ring signatures with designated-verifier signatures, achieving unconditional anonymity without compromising any other security property of these schemes.
Blockchain-based traceability in food supply chains: requirements and challenges

While traceability has become an important aspect in managing safety-sensitive supply chains, blockchain arose as an innovative technology with great application potentials in this area. This research determines the requirements of the main stakeholders along the supply chain from agriculture to retail with regard to a blockchain-based traceability system. The resulting specification framework includes usage requirements formulated as data classes, technical requirements regarding data access, storage and processing, and interoperability requirements necessary to assure digital communication and permanent operability. General application guidelines for digital information systems are derived, and the blockchain technology is evaluated with regard to applicability. A distinction between public and sensitive data appears to be fundamental, while a two-part architecture involving a public permissioned blockchain network is proposed as a basis for a digital traceability system in the food industry.
Research on UAVs Collision Avoidance with Multiple Dynamic Obstacles

In a complex dynamic environment, the method for UAV to avoid multiple dynamic obstacles one by one is not efficient. This paper adjusts the existing single obstacle collision avoidance method, and based on the velocity obstacle method, proposes a collision avoidance algorithm that adjusts the direction and speed of multiple dynamic obstacles at the same time. First, within the range of UAV identification, analyze the direction, speed, and position information of all dynamic obstacles, and determine whether there is a flight conflict between UAV and dynamic obstacles according to the principle of speed obstacle method; The set relationship between the parameters between the UAV and the obstacle is used to derive a conflict resolution strategy that meets the requirements of collision avoidance, and the optimal solution is obtained through the cost function; Finally, use Cubic B-splines for path smoothing operation. In the dynamic obstacle scene, the algorithm proposed in this paper is more flexible and efficient.
Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors

Human activity recognition (HAR) remains a difficult challenge in human-computer interaction (HCI). The Internet of Healthcare Things (IoHT) and other technologies are expected to be used primarily in conjunction with HAR to support healthcare and elder care. In HAR research, lower limb movement recognition is a challenging research topic that can be applied to the daily care of the elderly, fragile, and disabled. Due to recent advances in deep learning, high-level autonomous feature extraction has become feasible, which is used to increase HAR efficiency. Deep learning approaches have also been used for sensor-based HAR in various domains. This study presents a novel method that uses convolutional neural networks (CNNs) with different kernel dimensions, referred to as multi-resolution CNNs, to detect high-level features at various resolutions. A publicly available benchmark dataset called HARTH was used to evaluate the recognition performance to collect acceleration data of the lower limb movements of 22 participants. The experimental results show that the proposed approach improves the F1 score and achieves a higher score of 94.76%.
Classification of Public Opinion on Vaccine Administration Using Convolutional Neural Network

Vaccine is one of the interesting discussions that are often found in various timelines, both from social media and local scope. The covid vaccine is a topic that is often discussed because the virus is still not over until now. Many researches are discussing how to stop this virus and produce various types of vaccines that are claimed to be able to stop cases of Corona Virus infection. Astrazeneca and Sinovac are two of several types of vaccines circulating in several countries and have a good reputation even though it differ from one another. Indonesia as one of the countries with the highest death rate due to Covid-19 with a total daily death of ±2000 people make Indonesia vulnerable to the level of transmission. To overcome this problem, Indonesia uses Astrazeneca and Sinovac as an effort to prevent deaths from Covid-19. This study focuses on comparing public opinion responses to both types of Astrazeneca and Sinovac vaccines using data collected via Twitter with the text mining technique. The data obtained then is preprocessed so that the data can be used properly for further processing so as to produce the appropriate model. This study used the Convolutional Neural Network (CNN) method in the training process which has a good reputation in making deep learning models. From the process that has been passed with 2000 tweets from Indonesian users, 1347 tweets were ready to be used in the training process and resulted in 59.5% positive responses and 40.5% negative responses for the Astrazeneca vaccine. In addition, for the Sinovac vaccine, 53.3% of positive responses and 46.7% of negative responses were obtained.
Social Network Analysis of Campaign Mobilization of Marketplace on Twitter

This study presents a visualization and analysis of social network for campaign mobilization conducted by marketplace Shopee and Lazada. The aim of this study is to gain insight on user interaction as well as to examine the comparison of hashtags used in campaign mobilization on social media. Hashtag has been used as part of digital activism which allows the user generated content (UCG). Marketplace Shopee and Lazada also employ digital activism by conducting Harbolnas (online shopping festival) campaign to boost customers engagement and purchase intention. The case investigated is the hashtag #Shopee1111BigSale and #Lazada1111. This paper used social network analysis by using big data from Twitter obtained by Twitter Archiving Google Sheets (TAGS) application. The data is then processed using Gephi. The result showed that #Shopee1111BigSale was more successful to create mobilization. In terms of network properties and network centrality, Shopee also has more influential actor that disseminate information in Twitter faster and more widely
Predicting Bug-Fixing Time: DistilBERT Versus Google BERT

The problem of bug-fixing time can be treated as a supervised text categorization task in Natural Language Processing. In recent years, following the use of deep learning also in the field of Natural Language Processing, pre-trained contextualized representations of words have become widespread. One of the most used pre-trained language representations models is named Google BERT (hereinafter, for brevity, BERT). BERT uses a self-attention mechanism that allows learning the bidirectional context representation of a word in a sentence, which constitutes one of the main advantages over the previously proposed solutions. However, due to the large size of BERT, it is difficult for it to put it into production. To address this issue, a smaller, faster, cheaper and lighter version of BERT, named DistilBERT, has been introduced at the end of 2019. This paper compares the efficacy of BERT and DistilBERT, combined with the Logistic Regression, in predicting bug-fixing time from bug reports of a large-scale open-source software project, LiveCode. In the experimentation carried out, DistilBERT retains almost 100% of its language understanding capabilities and, in the best case, it is 63.28% faster than BERT. Moreover, with a not time-consuming tuning of the C parameter in Logistic Regression, the DistilBERT provides an accuracy value even better than BERT.
Improving color homogeneity measure in superpixel segmentation assessment

The quality of a superpixel segmentation may consider accuracy in delineation, shape compactness, and color homogeneity. Among several existing measures, Explained Variation (EV) and Intra-cluster Variation (IV) seem to be the only ones focusing on color homogeneity. However, EV ignores color differences inside superpixels while IV reduces penalization by averaging those differences. This work proposes a superpixel color descriptor to measure color homogeneity when comparing superpixel algorithms. Our RGB-cube Bucket Descriptor (RBD) is a compact representation of the most relevant colors in each superpixel. Color homogeneity is measured based on differences between pixel color and its closest color in RBD and the color differences inside RBD. We call it Similarity between Image and Reconstruction from Superpixels (SIRS) since, substituting each pixel color by its closest color in RBD, one obtains an image reconstruction. A high-quality superpixel segmentation (c) (d) should then present a reconstruction similar to the original image. Experiments on three datasets show that SIRS can better distinguish segmentation algorithms according to color homogeneity than EV (the most popular measure). The results also show that SIRS is more robust to slight color variations due to luminosity than EV.
Reservoir release forecasting by artificial neural network at Pa Sak Jolasid Dam

Due to floods in the past year The Pa Sak River Basin is therefore another watershed source that causes widespread damage to life and property. An interesting predictive study of the dam release is therefore an experimental idea. Using data recorded at the beginning of '64 using artificial neural networks techniques, compared with a support vector regression model. Estimated for satisfactory results by an ANN precision model R2 approximately 0.88513 RMSE approximately 8.044 and the SVR model R2 result. Approx. 0.43369 RMSE Approx. 18.484.
The Investigating Water Infiltration Conditions Caused by Annual Urban Flooding Using Integrated Remote Sensing and Geographic Information Systems

Flood disasters always hit densely populated urban areas during the rainy season. The causes of flooding that will examine in this scientific article are the condition of water infiltration into the soil. The case study was conducted in the urban area of Denpasar, Bali, Indonesia. Remote sensing data derived from various satellite images i.e., Sentinel-2 (BSI and NDVI extraction), Alos Palsar Imagery (slope extraction), CHIRPS (annual rainfall), and soil texture by laboratory analysis. Acquisition of remote sensing data using a Cloud Computing platform named Google Earth Engine (GEE). Data analysis using weighted overlay with ArcGIS 10.8 and threshold classification using natural breaks (Jenks). Denpasar City has the potential for water infiltration is good to very critical conditions. The correlation of the water infiltration map was carried out by comparing flood events in Denpasar City. The correlation results show (R2 = 0.84), (r = 0.916), (RMSE = 0.138), and p-value &lt;0.05, these values indicate very high relation. Flood events often occur in zones with very critical water infiltration with high building density and low vegetated land cover. The condition of water infiltration critical to very critical category, spatially at the proportion of land cover vegetation &lt; 1% and built-up area &gt; 37%.&#x0D;
OAK4XAI: Model Towards Out-of-Box eXplainable Artificial Intelligence for Digital Agriculture

Recent machine learning approaches have been effective in Artificial Intelligence (AI) applications. They produce robust results with a high level of accuracy. However, most of these techniques do not provide human-understandable explanations for supporting their results and decisions. They usually act as black boxes, and it is not easy to understand how decisions have been made. Explainable Artificial Intelligence (XAI), which has received much interest recently, tries to provide human-understandable explanations for decision-making and trained AI models. For instance, in digital agriculture, related domains often present peculiar or input features with no link to background knowledge. The application of the data mining process on agricultural data leads to results (knowledge), which are difficult to explain. In this paper, we propose a knowledge map model and an ontology design as an XAI framework (OAK4XAI) to deal with this issue. The framework does not only consider the data analysis part of the process, but it takes into account the semantics aspect of the domain knowledge via an ontology and a knowledge map model, provided as modules of the framework. Many ongoing XAI studies aim to provide accurate and verbalizable accounts for how given feature values contribute to model decisions. The proposed approach, however, focuses on providing consistent information and definitions of concepts, algorithms, and values involved in the data mining models. We built an Agriculture Computing Ontology (AgriComO) to explain the knowledge mined in agriculture. AgriComO has a well-designed structure and includes a wide range of concepts and transformations suitable for agriculture and computing domains.
Robust Spectral Based Compression of Hyperspectral Images using LSTM Autoencoders

The large size of hyperspectral images limits the applicable uses and necessitates effective compression methods. While there has been great success in a combined spectral and spatial compression approach, the reconstruction error rate for lossy compression methods with higher compression rates is still relatively large. Inspired by recent successes in spectral based deep learning for classification, we propose a spectral based Long Short Term Memory Autoencoder (LSTM-AE) to compress the spectral dimension alone. The obtained results show that not only can LSTM-AE achieve similar compression rates to existing methods, but also a large reduction in reconstruction error. We have also demonstrated the robustness of the approach in being able to generalize a single model for use in multiple scenes without being retrained. The model was also demonstrated to be successful in compressing unseen images at higher rates than existing methods that have trained on those images.
Multi-Vehicle Tracking and Speed Estimation Model using Deep Learning

Speed estimation of vehicles is one of the prime application of speed estimation of moving objects. The YOLOv5 model has proven to have a very good accuracy in detecting moving objects in real-time. The vehicles on the road are extracted from each frame of the video by running it through a custom YOLOv5 object detector. The YOLO model splits the frame into a grid and each grid detects a vehicle within itself. An instance identifier tracks the vehicle across the frames. The tracking algorithm computes deep features for every bounding box and utilizes the similarities within the deep features to identify and track the object. The pixel per meter metric has to adjusted based on perspective after which the speed of the vehicle can be estimated. Finally a comparison of our model metrics with the existing state of the art models is provided.
Methodology for supporting and making decisions on equipping the onboard equipment of a small spacecraft's motion control system

The paper offers an original method of decision support for choosing the composition of onboard equipment(configuration) motion control systems (MCS) of a small spacecraft (SSC) with a given layout scheme. The method is based on the algorithm of multi-criteria (Pareto) narrowing of the set of acceptable MCS implementation and the principle of interactive interaction with the decision-maker at the stage of system design, which potentially improves the quality and efficiency of the application of space systems being created.