Reviewerly/exHarmony huggingface dataset:
KGDet: Keypoint-Guided Fashion Detection

Locating and classifying clothes, usually referred to as clothing detection, is a fundamental task in fashion analysis. Motivated by the strong structural characteristics of clothes, we pursue a detection method enhanced by clothing keypoints, which is a compact and effective representation of structures. To incorporate the keypoint cues into clothing detection, we design a simple yet effective Keypoint-Guided clothing Detector, named KGDet. Such a detector can fully utilize information provided by keypoints with the following two aspects: i) integrating local features around keypoints to benefit both classification and regression; ii) generating accurate bounding boxes from keypoints. To effectively incorporate local features , two alternative modules are proposed. One is a multi-column keypoint-encoding-based feature aggregation module; the other is a keypoint-selection-based feature aggregation module. With either of the above modules as a bridge, a cascade strategy is introduced to refine detection performance progressively. Thanks to the keypoints, our KGDet obtains superior performance on the DeepFashion2 dataset and the FLD dataset with high efficiency.
STATE OF THE ART IN DENSE IMAGE MATCHING COST COMPUTATION FOR HIGH-RESOLUTION SATELLITE STEREO

Abstract. Large-scale Digital Surface Model (DSM) generated with high-resolution satellite images (HRSI) are comparable, cheaper, and more accessible when comparing to Light Detection and Ranging (LiDAR) data and aerial remotely sensed images. Several photogrammetric commercial/open-source software packages are being developed for satellite image-based 3D reconstruction, in which, most of them adopt a modified version of Semi-Global Matching (SGM) algorithm for dense image matching. With the continuous development of matching cost computation methods, the existing methods can be divided into classical (low-level) and learning-based algorithms (non-end-to-end learning and end-to-end learning methods). On Middlebury and KITTI datasets, learning-based algorithms has shown their superiority compared to SGM derived methods. In this context, we assume that matching cost is the key factor of DIM. This paper reviews and evaluates Census Transform, and MC-CNN on a WorldView-3 typical city scene satellite stereo images on the premise that the overall SGM framework remains unchanged, providing a preliminary comparison for academic and industrial. We first compute the cost valume of these two methods, obtains the final DSM after semi-global optimization, and compares their gemetric accuracy with the corresponding LiDAR derived ground truth. We presented our comparison and findings in the experimental section.
Improved DeepLab v3+ with Metadata Extraction for Small Object Detection in Intelligent Visual Surveillance Systems

A surveillance system deploys multiple cameras to monitor a wide area in real time to detect abnormal situations such as a crime scene, traffic accident, and natural disaster. An Increased number of cameras results in the same number of monitors, which makes human decisions or automatic decisions difficult. To solve the problem, a smart surveillance scheme has recently been proposed. The smart surveillance system automatically detects an object and provides an alarm to a surveillant. In this paper, we present a metadata extraction method for object-based video summary. The proposed method adopts deep learning-based object detection and background elimination to correctly estimate an object region. Finally, metadata extraction is performed on the estimated object information. The proposed metadata consists of the representative color, size, aspect ratio, and patch of an object. The proposed method can extract reliable metadata without motion features in both static and dynamic cameras. The proposed method can be applied to various object detection areas using complex metadata.
Locally Linear Embedding Based on Seagull Optimization Algorithm with T-distribution Parameters

With the development of machine learning and artificial intelligence, manifold structure is widely studied because it can extract relatively simple structure from high dimensional data. Locally linear embedding (LLE) has attracted more and more attention with the idea of algebraic structure. However, due to the constraints of locally linear embedding, there are computational problems such as tedious, sparse and unstructured. In this paper, we propose a seagull optimization algorithm with t-distribution parameters for global optimizer, which avoids the trap of falling into local minima and can simply and accurately find the optimal parameters.
Media PowerPoint dalam Pembelajaran

Media is a learning tool that every teacher needs to pay attention to when they want to carry out the teaching and learning process because if there is no development in the use of media in learning, the teaching and learning process will be monotonous and unpleasant, even students tend to be lazy to take part in learning. if this happens it is certain that the failure of the teaching and learning process has failed. Facing the digital era 4.0, a teacher must be really able to use various kinds of multimedia in learning, teachers are not allowed to only use student worksheets when teaching. In this era, teachers should use technology-based learning media such as the internet or use Microsoft PowerPoint as a medium when they want to deliver material to students.
An Effective Model for Fraud Risk Management in Mitigating Telecommunication Fraud Incidences

In the context of Malaysia, the most common types of cyber attacks are denial of service, intrusion attempts, spam, vulnerability report, fraud, malicious code, and content-related attacks as reported by Cyber Security Malaysia. However, since 2011, cyber attacks such as intrusion attempts, denial of service and spam are decreasing steadily as a result of security measures that have been taken by companies or internet users. Nevertheless, cyber attacks which are steadily increasing are cyber harassment, intrusion and fraud. Approximately 5,328 fraud incidences were reported in 2011 while in 2020, the number of cases rose to 7,593 cases. Out of these cyber attacks in Malaysia, the most proliferating ones have been identified as fraud incidences. Thus, it is highly needed to come up with and propose the best fraud risk management strategy to handle fraud incidences among internet users and internet providers through the utilization of business intelligence tools, and quality enhancement via quality systems in place, quality information, and quality users. With these tools in hand, it is hoped that the proposed model will serve as a framework to mitigate and/or prevent the occurrence of fraud incidences.
A NOVEL FEATURE SELECTION WITH FUZZY DEEP NEURAL NETWORK FOR ATTACK DETECTION IN BIG DATA ENVIRONMENT

In recent times, the massive quantity of data and its continual expansion have transformed the significance of information security and data analysis systems for Big Data.An intrusion detection system (IDS) is commonly employed to monitor and analyze data for the detection of intrusions in the network.The conventional IDS models are not adequate to handle the high volume, variety, and speed of big data.This paper presents a new quantum brain storm optimization (QBSO) based feature selection with fuzzy deep neural network (FDNN), called QBSO-FDNN model for IDS in big data environment.The proposed model enables to detection of intrusions in the big data environment.The presented model initially performs preprocessing to enhance the quality of the big data.Also, to reduce the computational complexity, QBSO algorithm is applied to elect an optimal set of features.The choice of optimal features by the QBSO algorithm helps to boost the detection performance.Besides, FDNN model is applied as a classification model for identifying the occurrence of intrusions in the network.An extensive set of simulations was carried out to highlight the results on benchmark dataset.The resultant experimental values showcased the superior performance of the QBSO-FNN model with the detection accuracy of 98.90%.
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training

Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel dataset.To break the corpus bottleneck, in this paper we aim to improve context-aware NMT by taking the advantage of the availability of both large-scale sentence-level parallel dataset and source-side monolingual documents. 1To this end, we propose two pre-training tasks.One learns to translate a sentence from source language to target language on the sentencelevel parallel dataset while the other learns to translate a document from deliberately noised to original on the monolingual documents.Importantly, the two pre-training tasks are jointly and simultaneously learned via the same model, thereafter fine-tuned on scalelimited parallel documents from both sentencelevel and document-level perspectives.Experimental results on four translation tasks show that our approach significantly improves translation performance.One nice property of our approach is that the fine-tuned model can be used to translate both sentences and documents.
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning

Service manual documents are crucial to the engineering company as they provide guidelines and knowledge to service engineers.However, it has become inconvenient and inefficient for service engineers to retrieve specific knowledge from documents due to the complexity of resources.In this research, we propose an automated knowledge mining and document classification system with novel multi-model transfer learning approaches.Particularly, the classification performance of the system has been improved with three effective techniques: fine-tuning, pruning, and multi-model method.The fine-tuning technique optimizes a pre-trained BERT model by adding a feed-forward neural network layer and the pruning technique is used to retrain the BERT model with new data.The multi-model method initializes and trains multiple BERT models to overcome the randomness of data ordering during the fine-tuning process.In the first iteration of the training process, multiple BERT models are being trained simultaneously.The best model is then selected for the next phase of the training process with another two iterations and the training processes for other BERT models will be terminated.The performance of the proposed system has been evaluated by comparing with two robust baseline methods, BERT and BERT-CNN.Experimental results on a widely used Corpus of Linguistic Acceptability (CoLA) dataset have shown that the proposed techniques perform better than these baseline methods in terms of accuracy and MCC score.
Sound and space: learning from artistic practice

3D Audio is often associated with the technical ability to represent 360o sound fields through loudspeaker arrays or headphones. There are numerous technical challenges involved in making 3D-Audio experiences perceptually plausible, be it in the representation of realistic acoustic situations or the delivery of abstract sonic environments. Despite the ability of any given system or technique to deliver spatial audio, sound design is key to engaged spatial listening. Spatial approaches to sound design can be traced back to a wide range of musical practices which consider space as a key factor. Aural spatiality has been an important element of music history since, if not before, Giovanni Gabrielli’s Cori Spezzati with the distribution of choral forces around an audience in the 1600’s. The exploration of space as an element in music creation intensified significantly in the middle of the 20th century, resulting in the development of numerous strategies for creating what are, effectively, immersive sound experiences. These strategies can provide important blueprints for current 3D-Audio development and this chapter outlines paradigms emerging from music creation which can translate into current challenges in designing sound for VR/AR/MR, gaming and 360° recording formats.
[Cross-subject electroencephalogram emotion recognition based on maximum classifier discrepancy].

Affective brain-computer interfaces (aBCIs) has important application value in the field of human-computer interaction. Electroencephalogram (EEG) has been widely concerned in the field of emotion recognition due to its advantages in time resolution, reliability and accuracy. However, the non-stationary characteristics and individual differences of EEG limit the generalization of emotion recognition model in different time and different subjects. In this paper, in order to realize the recognition of emotional states across different subjects and sessions, we proposed a new domain adaptation method, the maximum classifier difference for domain adversarial neural networks (MCD_DA). By establishing a neural network emotion recognition model, the shallow feature extractor was used to resist the domain classifier and the emotion classifier, respectively, so that the feature extractor could produce domain invariant expression, and train the decision boundary of classifier learning task specificity while realizing approximate joint distribution adaptation. The experimental results showed that the average classification accuracy of this method was 88.33% compared with 58.23% of the traditional general classifier. It improves the generalization ability of emotion brain-computer interface in practical application, and provides a new method for aBCIs to be used in practice.情感脑机接口在人机交互领域中具有重要的应用价值。脑电（EEG）由于在时间分辨率、可靠性和准确性方面具备的优势，在情绪识别领域受到广泛关注。然而，脑电的非平稳特性和个体差异限制了情绪识别模型在不同时间、不同受试者之间的泛化。为解决跨被试、跨时间情绪分类的问题，本文提出了最大分类器差异域对抗方法（MCD_ DA），通过建立神经网络情感识别模型，将浅层特征提取器分别对抗域分类器和情感分类器，进而使特征提取器产生域不变表达，在实现近似联合分布适配的同时训练分类器学习任务特异性的决策边界。实验结果表明，在进行跨被试情绪识别时，相较于传统通用分类器 58.23% 的平均分类准确率，该方法的平均分类准确率达到了 88.33%。本研究结果提高了情感脑机接口在实际应用中的泛化能力，为情感脑机接口走向实际应用提供了新的方法。.
OutFlip: Generating Examples for Unknown Intent Detection with Natural Language Attack

Out-of-domain (OOD) input detection is vital in a task-oriented dialogue system since the acceptance of unsupported inputs could lead to an incorrect response of the system.This paper proposes OutFlip, a method to generate outof-domain samples using only in-domain training dataset automatically.A white-box natural language attack method HotFlip is revised to generate out-of-domain samples instead of adversarial examples.Our evaluation results showed that integrating OutFlip-generated outof-domain samples into the training dataset could significantly improve an intent classification model's out-of-domain detection performance 1 .
Encrypted Network Traffic Classification Using a Geometric Learning Model

The increasing volume of encrypted traffic from emerging applications has made conventional traffic classification approaches ineffective and called for novel methods for identifying network flows. Recent machine learning and deep learning based approaches have been proposed yet many of them are severely limited by their feature selection and inherent neural network architecture. As network data by nature are of non-Euclidean distance space and carry abundant chronological relationship, we are inspired to utilize geometric deep learning that simultaneously takes into account packet raw bytes, metadata and packet relations for classifying encrypted network traffic. We validate our proposed graph neural network (GNN) models against the reference methods including convolutional neural networks (CNN) and recurrent neural networks (RNN) quantitatively and demonstrate that the proposed graph neural networks outperform the state of art.
Research Aspects of Machine Learning: Issues, Challenges, and Future Scope

This chapter discusses the “Research Aspects of Machine Learning: Issues, Challenges, and Future Scope” is to provide a mature approach to the basics of ML, which is one of the hot topics recently for the researchers. It presents the issues and challenges using machine learning (ML). Feature engineering is relevant to the selection of features, while feature engineering develops innovative features in an effort to enhance learning objectives; selection of features is intended to choose the features that are most appropriate. Documented data, or the original source data, is often used to identify a source of processing error since it explains all of the steps, transactions, and operations that invalid data has gone through, providing useful knowledge for machine learning. ML is indeed an attractive alternative, so the totally automated evolution of the process will enable the function to keep pace with the growing application increase in supply.
Transformation Approach of Open Web Data to Linked Open Data

A large amount of data available over the Web and, in particular, the open data have, generally, heterogeneous formats and are not machine-readable. One promising solution to overcome the problems of heterogeneity and automatic interpretation is the Linked Data initiative, which aims to provide unified practices for publishing and contextually to link data on the Web, by using World Wide Web Consortium standards and the Semantic Web technologies. LinkedIn data promote the Web’s transformation from a web of documents to a web of data, ensuring that machines and software agents can interpret the semantics of data correctly and therefore infer new facts and return relevant web data search results.&#x0D; This paper presents an automatic generic transformation approach that manipulates several input formats of open web data to linked open data. This work aims to participate actively in the movement of publishing data compliant with linked data principles.
Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms

Identification of proton and gamma plays an essential role in ultra-high energy gamma-ray astronomy with LHAASO-KM2A.In this work, two neural networks (deep neural networks (DNN) and graph neural networks (GNN)) are applied to distinguish proton and gamma in the LHAASO-KM2A simulation data.The receiver operating characteristic (ROC) curves are used to evaluate the quality of the model.Both KM2A-DNN and KM2A-GNN models give higher Area Under Curve (AUC) scores than the traditional baseline model.
Measuring the Inequality in Education: Educational Kuznets Curve

This study aims to measure the inequality of education.The study examines the pattern in the educational Lorenz curve as a way to investigate the impact of education experience, per capita income, and poverty on educational inequality.Using Banten Province secondary data series over 1996 -2016, the unbalanced panel unit root were tested for the educational Kuznets curve.The regression results shows that the educational Kuznets curve does not exist.While we found that the educational experience reduce the educational inequality, the per capita income and poverty were not significant.
The Renewed Role of Sweep Functions in Noisy Shortcuts to Adiabaticity

We study the robustness of different sweep protocols for accelerated adiabaticity following in the presence of static errors and of dissipative and dephasing phenomena. While in the noise-free case, counterdiabatic driving is, by definition, insensitive to the form of the original sweep function, this property may be lost when the quantum system is open. We indeed observe that, according to the decay and dephasing channels investigated here, the performance of the system becomes highly dependent on the sweep function. Our findings are relevant for the experimental implementation of robust shortcuts-to-adiabaticity techniques for the control of quantum systems.
Geochemical Appraisal on History and Evolution of Barail Sandstones of Zote-Ngur, Champhai District, Mizoram, India

Mizoram is part of Surma basin which later evolved into the present state of geological terrain due to Indo- Myanmar tectonic collision during the Oligocene period. The present work deals with geochemical characteristics of Barail sandstones exposed in Champhai area of eastern region in Mizoram. The major/minor oxides, trace elements and rare earth elements data are used to infer the geological history and evolution of the sandstone in the study area of Champhai. The petrographic study shows the presence of various detrital grains like quartz, lithic fragments, feldspar, chertz, mica, etc., which are cemented by siliceous and ferruginous materials. Geochemically, the sandstones indicate high wt% of SiO2, Al2O3 and MgO compared to Upper Continental Crust (UCC) while rest of the major oxides indicate low concentrations. The geochemical classification indicated the sandstones as litharenite and wacke. The chondrite normalised REE pattern shows the enrichment of HREE and depletion of LREE with negative Eu anomaly. The value of Eu/Eu*, La/Lu, La/Co, Th/Sc, Th/Co, Cr/Th and high ratio of LREE/HREE of Barail sandstone suggest felsic source rock. The analysis of paleoweathering history indicated moderate to intensive weathering in the provenance. Various tectonic discriminant function diagrams suggested Active Continental Margin settings.
Goodness-of-Fit Testing for Hölder Continuous Densities Under Local Differential Privacy

We address the problem of goodness-of-fit testing for Hölder continuous densities under local differential privacy constraints. We study minimax separation rates when only non-interactive privacy mechanisms are allowed to be used and when both non-interactive and sequentially interactive can be used for privatisation. We propose privacy mechanisms and associated testing procedures whose analysis enables us to obtain upper bounds on the minimax rates. These results are complemented with lower bounds. By comparing these bounds, we show that the proposed privacy mechanisms and tests are optimal up to at most a logarithmic factor for several choices of $$f_0$$ including densities from uniform, normal, Beta, Cauchy, Pareto, exponential distributions. In particular, we observe that the results are deteriorated in the private setting compared to the non-private one. Moreover, we show that sequentially interactive mechanisms improve upon the results obtained when considering only non-interactive privacy mechanisms.
Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.
SISTEM INFORMASI PEMBAYARAN KONTRAK PEKERJAAN BERBASIS WEB PADA DINAS PEKERJAAN UMUM DAN PENATAAN RUANG KABUAPATEN KEPULAUAN ARU

Abstract: This job payment information system is designed to improve the document creation system and payment archive management in the finance department of the public works and spatial planning department of the Aru Regency which still does not have a soft archive or E-Archive. This application processes data related to capital expenditures such as 3rd party data, official work packages, while the output is several payment documents, namely Payment Orders (SPM), Payment Payment Certificates (SPP), payment receipts and monthly payment realization reports. The method used in the design is the waterfall method, Keywords: Information System, Contract Payment, PUPR Service, Spm