Reviewerly/exHarmony huggingface dataset:
Design and Development SIOPIK Application

Office of Archives and Library of Surakarta City Government or Dinas Kearsipan dan Perpustakaan Pemerintah Kota Surakarta (Disarpus) is a government agency responsible for regional archives and libraries. Employee performance appraisal is the primary step and data to determine the quality of employee performance. So far, employee performance appraisal at the Surakarta City Archives and Library Office is still done manually, not using an information system, so it often causes some fundamental problems. These problems include data validity, security, calculation errors, and ease of activity approval. Based on these problems, an information system is needed to solve the existing problems, namely the design and manufacture of a performance information system. We call this application the SiOPIK Application (Sistem Informasi Penilaian Kinerja). The objectives of this SiOPIK application solution are: 1) Design and create an information system for evaluating employee performance at the Surakarta City Archives and Library Office. 2) Making an information system that agencies can use to determine employee performance. 3) As consideration for continuing the agency employee contract. From other publications. The abstract must be explains something that has been found in research
A Novel Approach for Static Video Content Summarization using Shot Segmentation and k-means Clustering

Due to the widespread availability of the Internet and video-capturing mobile devices, thousands of videos are uploaded and downloaded per second. The use of mobile cameras for capturing videos has increased a lot; it creates a new problem in handling these complicated video objects in terms of storage and processing. An efficient way in this situation is to shorten the original video into abstract summaries. These short summaries will give freedom to the user as per their interest. The proposed video summarization technique identifies only significant frames (i.e., keyframes) and produces static storyboards using shot segments and clusters—these extracted keyframes aid in creating video summaries for the abstraction and summarising of video summaries. Here, a static video content summarization is implemented as a problem of choosing significant keyframes and producing a visual static storyboard to describe the entire video content with less redundancy and good quality summary. This research proposes a novel clustering method to identify the set of static video frames, mainly keyframes and defines the novel way of creating static video storyboards. The experimentation is done using Open Video Project (OVP) videos. The performance of the video summarization is tested in terms of percentage accuracy. The set of keyframes obtained by the proposed system is validated using the OVP storyboards as ground truth; the results of the proposed shot and clustering-based keyframes extraction technique for static video content summarization are closer to the OVP ground truth and are improved than the Delaunay Triangulation video summaries.
Evaluation and Comparison of Yarn Schedulers in the Science and Technology Services Resource Pool System

In order to develop the science and technology service industry in Central Plains Urban Agglomeration, as well as to accelerate inter-regional coordination and cooperation, a regional resource pool was built. Science and technology service resources here are numerous, complex and diverse, and contain large amounts of data. Hadoop-2.9.2 could organize and analyze these big data, and achieve high-speed computing and storage. Considering that the concurrent demand of some scarce resources is relatively large, the system needs scheduling capabilities. Yarn is used to provide resource scheduling, multi-tenancy, and linear-scale storage. In experiments, we evaluated all of the provided schedulers, FIFO Scheduler, Capacity Scheduler and Fair Scheduler, and chose one of them as the actual scheduler.
Improving Optical Flow Inference for Video Colorization

Recent video colorization methods optimize correspondence estimation and information propagation in an end-to-end manner. However, they usually suffer from loss of fidelity due to the inaccurate inference of correspondence measurement. In this paper, we propose a post-training optimization (PTO) strategy to refine correspondence measurement in the end-to-end optimized framework. The proposed PTO strategy introduces a pseudo loss function to well approximate the target loss and guide the direction of updates. We further develop a video colorization method that incorporates PTO and optical flow to guarantee high-fidelity colorized frames in theory. Experimental results demonstrate that the proposed method achieves state-of-the-art PSNR performance in video colorization on the DAVIS dataset and common test sequences for video coding. Furthermore, the proposed method can be employed into video compression and achieves competitive rate-distortion performance with the recent High Efficiency Video Coding (HEVC) standard.
Planning Large-scale Object Rearrangement Using Deep Reinforcement Learning

Object rearrangement is about moving a set of objects from an initial state to a goal state through task and motion planning. Existing methods either show poor scalability in number of objects they can handle, or do not generalize well across situations, or need explicit running buffers to avoid collisions during placements. In this paper, we propose a deep-RL based task planning method to solve large-scale object rearrangement problems. Given the source and target state of objects in the form of images, our method determines a collision-free object movement plan. Our method produces a feasible plan in discrete-continuous action space where picking the selected objects are discrete actions followed by a set of continuous actions to place the object. We propose a novel hierarchical dense reward structure to train our deep-RL network to make our method more sample efficient using the AI2Thor simulator. We show that our method works well on unseen publicly available datasets and on a publicly available simulation environment such as Pybullet thereby demonstrating the superiority of our method in terms of generalizability. To the best of our knowledge, our method is the first one that demonstrates the rearrangement across different scenarios from 2D surfaces such as tabletops to 3D rooms with a large number of objects and without any explicit need of buffer space.
Telugu Paraphrase Detection Using Siamese Network

Paraphrasing methods will generate, identify or extract the phrases or sentences that convey the same meaning. In this work, given a pair of sentences, they are labeled as Paraphrased or Non-paraphrased. The ability to detect similar sentences is essential for several applications like text summarization, question answering, and plagiarism detection. Simple Telugu sentences are employed to create a Telugu paraphrase dataset; after paraphrasing, they are labeled as Paraphrased pair or non-paraphrased pair, respectively. iNLTK word embedding has been used with the Siamese network for calculating the similarity score between the pairs of sentences. And different deep learning architectures like the average scoring, Long Short-Term Memory (LSTM,) and Bidirectional Long Short-Term Memory (Bi-LSTM) are implemented for obtaining the sentence embedding. Their comparative study has been presented in this work.
Enhancing Cooperation Between South Korea and the EU in the Fight Against Cybercrime

Cybercrime is one of the representative transnational crimes occurring across national jurisdictions and has become an international challenge with the Internet reaching developing countries since 2000. Since an effective response to cybercrime requires an enhanced level of global cooperation, South Korea has achieved a certain level of success through international cooperation. However, there is still a long way to succeed: participation of South Korea to the Convention on Cybercrime has been delayed, and cooperation with the EU has lagged behind compared to progress with the US and other Asian countries. This chapter reviews South Korea’s international cooperation framework on cybercrime and presents four solutions to promote EU-South Korea cooperation: (1) joining the Convention on Cybercrime of the Council of Europe, (2) establishing a working group on joint investigation in cybercrime, (3) implementing cybercrime capacity building projects in developing countries, (4) expanding cooperation on digital forensics technology.
Cooperative Online Path Planning for UAVs Based on DMPC and Improved Grey Wolf Optimizer

Online path planning for multiple fixed-wing UAVs in 3D aerial space is a complex optimization problem with high dimension and multiple constraints, which requires efficient solving methods. In this paper, distributed model predictive control (DMPC) method is adopted to accomplish online path planning for multiple UAVs. In order to reduce the computation time of DMPC, an improved grey wolf optimizer (GWO) is designed where pheromone factors are incorporated into GWO to strengthen the exploration ability of the original GWO. In addition, pheromones are optimally selected based on the hierarchy of wolves to guide subsequent iterations so that the convergence speed of the optimizer can be increased. Simulation examples show the superiorities of the proposed method by comparing with various existing optimizers.
A Noise Clustering-induced Robust Adaptive Network-based Fuzzy Inference System for Classification

Adaptive Network-based Fuzzy Inference System (ANFIS) is a neural network-based model for fuzzy inference system (FIS) and can be a promising approach for explainable neural networks. In this paper, robustification of ANFIS is considered from the classification application viewpoint, where the noise rejection mechanism is introduced induced by noise fuzzy clustering. In real world classification tasks, we often suffer from unreliable class labels, where some objects have incorrect class labels and should be rejected from classifier construction. Noise fuzzy clustering proposed by Davé is an extension of fuzzy c-means (FCM) to robust clustering, where an additional noise cluster works for absorbing noise objects. Because the noise clustering scheme with single cluster cases can be identified with robust least square estimation, the robustifying mechanism has been applied to several least square-type data analyses. In this paper, the noise clustering scheme is introduced into the ANFIS classification model, where non-noise fuzzy memberships are utilized such that the additional noise cluster absorbs noise objects and the ANFIS classifier is robustly constructed by rejecting noise objects. The minimization of the membership-weighted least square objective function and the estimation of non-noise fuzzy memberships are iteratively implemented until convergence. The characteristics of the proposed method are demonstrated through numerical experiments using real world benchmark datasets.
PREF: Predictability Regularized Neural Motion Fields

Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods while requiring no prior knowledge of the scene.
An Image Enhancement Filtering Algorithm for Speckle Patterns

Noise reduction is one of the most exciting problems in speckle pattern. We present an Image Enhancement Filtering Algorithm on experimental speckle correlation fringes and speckle image of cone respectively. In the algorithm, adaptively automatic threshold and gradient of the detecting pixel is calculated according to the mean value of the 3 × 3 area pixels around the detecting pixel and the human vision system. The results show that this technique is capable of significantly improving the quality patterns and enhancing the contrast with the edge of the speckle image, as well as preserving more detailed information of the cone.
A Social Network Approach for Automated Generation of YouTube Playlists

Video online sharing and social media platform YouTube has more than a billion monthly users, where videos continue to be uploaded at a high rate per minute. With so many videos being created every minute, the viewers have to choose from this pool of videos available on the platform. And not only the viewers but even the content creators also aim to maximize their presence on YouTube, and having playlists on their channels can turn out to be a real help in sorting such a huge number of videos. Creation of the playlists manually can be a very tedious task, and hence, the paper proposes to automate the process of the creation of playlists for a channel. The research is performed on multiple YouTube channels with more than 100 videos. The solution involves the creation of the Social Network Graph for the channel with the help of the NLP framework. Four different clustering algorithms, namely, K-Means++, EM-GMM, Mean-Shift, DBSCAN, and OPTICS algorithms, are also performed on the network graphs. A comparative analysis is performed on the results of the clustering algorithms to determine the best algorithm that provides the most useful playlist distribution of the videos. The approach concludes with the results showing that k-means++ outperformed the other algorithms on the basis of the Silhouette and Calinski Harabasz scores.
A Systematic Review on the Relationship Between Artificial Intelligence Techniques and Knowledge Management Processes

Knowledge management (KM) processes and artificial intelligence (AI) techniques, two sides of the same coin, define the ontologies and rules required for knowledge-based systems. However, a systematic review on the relationship between AI techniques and KM processes is neglected. Therefore, this systematic review aims to identify the common AI techniques and their relationships with KM processes. Out of 20,834 articles identified, 29 studies met the eligibility criteria and were thoroughly analyzed. The main results showed that support vector machine (SVM), decision trees, logistic regression, and artificial neural network (ANN) were the most frequent AI techniques used in the extant literature. The results also indicated a significant relationship between the knowledge creation process and data mining techniques. In contrast, only a few studies examined the relationship between knowledge storage and AI techniques. Further, none of the analyzed studies examined the relationship between AI techniques and knowledge protection. This systematic review is believed to be a useful resource for scholars and practitioners interested in gaining more insights into the challenges and opportunities associated with AI techniques and their relationships with KM processes.
Merge and Shrink Abstractions for Temporal Planning

Temporal planning is a hard problem that requires good heuristic and memoization strategies to solve efficiently. Merge-and-shrink abstractions have been shown to serve as effective heuristics for classical planning, but they have not yet been applied to temporal planning. Currently, it is still unclear how to implement merge-and-shrink in the temporal domain and how effective the method is in this setting. In this paper we propose a method to compute merge-and-shrink abstractions for temporal planning, applicable to both partial- and total-order temporal planners. The method relies on pre-computing heuristics as formulas of temporal variables that are evaluated at search time, and it allows to use standard shrinking strategies and label reduction. Compared to state-of-the-art Relaxed Planning Graph heuristics, we show that the method leads to improvements in coverage, computation time, and number of explored nodes to solve optimal problems, as well as leading to improvements in unsolvability-proving of problems with deadlines.
Imbalance Data Classification Method Based on Improved SMOTE Algorithm and Granular Computing

Classification of imbalanced data is a problem to be solved in practical applications, and sampling algorithm is a more effective solution. The SMOTE algorithm is a classical oversampling algorithm that provides a strong guideline for dealing with imbalanced datasets, but there are still some limitations, such as blurring the boundary between positive class samples and negative class samples. To alleviate the limitations, in this study, a new oversampling method called KB-SMOTE(K-means++ Borderline-SMOTE) is proposed. The new algorithm first clusters the minority class, next synthesizes new samples between cluster center and borderline points in minority samples, which solves the problem of blurred class boundary of imbalanced datasets, avoids the interference of noisy samples and reduces the impact on the original dataset. Then, this paper uses KB-SMOTE as a data preprocessing algorithm, combined with a hypersphere information granular classifier to classify the imbalanced data. In the experimental part, the combined classifier of this paper is compared with other classification algorithms on several imbalanced datasets, and the effectiveness of the algorithm is verified.
Real-Time Lumen Detection for Autonomous Colonoscopy

Lumen detection and tracking in the large bowel is a key prerequisite step for autonomous navigation of endorobots for colonoscopy. Attempts at detecting and tracking the lumen so far have been made using optical flow and shape-from-shading techniques. In general, these methods are computationally expensive, and most are either not real-time nor tested on real devices. To this end, we present a deep learning-based approach for lumen localisation from colonoscopy videos. We avoid the need for extensive, costly annotations with a semi-supervised learning and a self-training scheme, whereby only a small subset of video frames is annotated. We develop an end-to-end pseudo-labelling semi-supervised approach incorporating a self-training scheme for colon lumen detection. Our approach reveals a competitive performance to the supervised baseline model with both objective and subjective evaluation metrics, while saving heavy labelling costs in terms of clinicians’ time. Our method for lumen detection runs at 60 ms per frame during the inference phase. Our experiments demonstrate the potential of our system in real-time environments, which contributes towards improving the automation of robotics colonoscopy.
Intra-hour PV Power Forecasting based on Multi-source Data and PSC-SVR Model

Accurate photovoltaic power forecasting (PVPF) can ensure the stability of grid operation and the reliability of economic dispatch. However, PV power generation is greatly affected by weather conditions, thus more external information needs to be combined to improve the forecasting performance. Based on the measured data and NWP data, this paper proposes a PSC-SVR model to forecast the intra-hour PV power. The model consists of PV physical model (PSC) and support vector regression (SVR). PSC converts the irradiance as a priori feature of the model, including calculating the sun position, irradiance separation and transposition model. SVR is used for the two-stage data fusion of NWP power modeling and power forecasting. This method was verified on 4 power stations in the public data set PVOD. When forecasting PV power in the next one hour, the average RMSE/MAE of all stations is 1.927 MW/1.167 MW, which decreases by 40%/55% compared with persistent model, 6%/7% compared with NWP correction forecasting method, 9%/13% and 32%/34% compared with the single measured data and single NWP data. The above results show the effectiveness of our proposed model in multi-source data fusion and PV power forecasting.
Substation Equipment Abnormity Detection Based on Improved YOLOv5

Substation equipment inspection is the basic work contents of equipment management and security. However, the substation equipment inspection is facing the contradiction between the rapid increase in the number of substation and the shortage of inspection personnel. We propose a deep learning-based method for detecting substation equipment abnormity to enhance the efficiency of operations. Based on YOLOv5, we add a small object detection head to solve the large scale variation of the equipment images. Besides, the K-means clustering algorithm is adopted to generate suitable anchors for training process, and we also perform data augmentation to increase the data complexity and numbers. Last, considering the overlapping area, center point distance and aspect ratio of the bounding box and ground truth, we use CloU loss function to replace the GIoU loss function. The experiments are executed on the industrial dataset, and the results reach 0.677 for ten types of equipment abnormities by the mAP. The experiment results show that the improved YOLOv5 algorithm is feasible and effective.
A Temperature-Modified Dynamic Embedded Topic Model

Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and Abstract dataset show that the proposed model – nicknamed DETM-tau after the temperature parameter – has been able to improve the model's perplexity and topic coherence for all datasets.