Reviewerly/exHarmony huggingface dataset:
PUBLIC POLICY ANALYSIS ON DISASTER THREAT DUE TO GEO-ENVIRONMENTAL CONDITION OF TUGURARA RIVER IN TERNATE CITY, NORTH MALUKU PROVINCE

This research was conducted to produce a public policy model to anticipate floods in Ternate City due to changes in the geo-environmental conditions of the Tugurara River based on geological analysis results. These model intended to help the local government in determining the policy to deal with the Tugurara River disaster threat. Analysis of geo-environmental conditions was conducted using statistical methods obtained from previous studies - furthermore, the theory of public policy analysis in determining the appropriate policy for handling the risk that will occur. Results revealed that runoff water correlates with extensive changes of the Tugurara River area. These area change becomes more because of runoff water discharge amount erode the rock on the river wall and cause the river wall as well as the presenting any material river wall along the river as a catastrophic outburst flood lava on a residential area three villages in 2011 (this condition is particular compare than another river in North Maluku or others). Therefore changes Tugurara River area, occurred very significant. The river engineering can refer to the flood prevention model that has been applied to the Jeneberang River in South Sulawesi by building Sabo Dam along the river, adapted to the particular characteristics of the Tugurara River. Therefore, the critical formulation of public policy alternative carried out by the Ternate City government is infrastructure development, community involvement, and networking development. After the public policy is determined, then further research can be conduct on the evaluation and comprehensive analysis of policy implementation.
Modeling Heterogeneous Influences for Point-of-Interest Recommendation in Location-Based Social Networks

The huge amount of heterogeneous information in location-based social networks (LBSNs) creates great challenges for POI recommendation. User check-in behavior exhibits two properties, diversity and imbalance. To effectively model both properties, we propose an Aspect-aware Geo-Social Matrix Factorization (AGS-MF) approach to exploit various factors in a unified manner for more effective POI recommendation. Specifically, we first construct a novel knowledge graph (KG), named as Aspect-aware Geo-Social Influence Graph (AGS-IG), to unify multiple influential factors by integrating the heterogeneous information about users, POIs and aspects from reviews. We design an efficient meta-path based random walk to discover relevant neighbors of each user and POI based on multiple influential factors. The extracted neighbors are further incorporated into AGS-MF with automatically learned personalized weights for each user and POI. By doing so, both diversity and imbalance can be modeled for better capturing the characteristics of users and POIs. Experimental results on several real-world datasets demonstrate that AGS-MF outperforms state-of-the-art methods.
Toward Supporting Unplanned Dynamic Changes of Service-Based Business Processes

Service-oriented architecture (SOA) has become an application development paradigm widely recognized both in academia and in industry.Although SOA-based applications may vertically implement business processes through the composition of loosely coupled services, they have to face frequent changes, such as unavailability of service (due to the uncontrollable, dynamic, and distributed environments) or dynamic replacement of service (due to specific user requirements).This indicates that the service compositions are expected to be adaptable enough to cater to such changing situations.In this paper, we propose an approach to supporting unplanned dynamic changes of service compositions by combining variability management and dynamic binding.The proposed approach introduces the concept of abstract proxy services in a variability-supporting service composition language, namely VxBPEL, and provides a mechanism to support variation design and dynamic binding for unplanned changes at run time.To realize the proposed approach, we have developed a service composition engine that supports abstract proxy services and their run-time replacement via service discovery or user intervention.Finally, a case study has been conducted to demonstrate the feasibility of the proposed approach and quantify the performance overhead resulting from runtime variability management and dynamic binding.The experimental results show that the proposed approach overcomes the limitation of imperative variability-based approaches in handling unplanned dynamic changes and further enhances the dynamic adaptability of VxBPEL-based service compositions.
A Systematic Review of SCRUM in Software Development

Scrum is a part of agile software development which also called incremental software development model. Scrum methodology commonly used to overcome uncertain condition and tight deadline. Scrum consists of three roles, they are product owner, development team, and scrum master. Scrum can be implemented on many areas such as development of education mobile application, integration of document management system, strengthen IT development centers, and many others. The successful of Scrum implementation depends on the skill and knowledge of the team members. The Scrum also has several risk factors and therefore needs risk management approaches. A comparison between agile and waterfall model is also discussed. The Scrum performance can also be tested with certain ways.Â Scrum used to make sure that all parts of the software can be delivered on time. The other benefits, scrum can make creative and productive workplace, and scrum can also increase business value.
Multi Information Fusion Network for Saliency Quality Assessment

Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.
High secure sequence design in frequency hopping communications

Frequency-hopping (FH) technique is widely used in high-secure communications by exploiting its capabilities of mitigating interference and confidentiality. However, electronic attacks in wireless systems become more and more rigorous, which poses huge challenges to the use of the number theory based and chaos theory assisted sequences. The structure of the FH sequence directly affects the performance of FH communication systems. In this paper, the novel FH sequence generation scheme is proposed with the aid of the so-called Government Standard (GOST) algorithm, which achieves a promising balance between efficiency and security. Moreover, the security performance of the proposed algorithm is analyzed, which reveals that it is more resistant to impossible differential attacks than the widely-used Data Encryption Standard (DES) algorithm. The numerical results show that the FH sequences generated by the GOST algorithm significantly outperform the ones generated by the DES algorithm and chaotic theory in terms of the randomness and complexity.
Design and Evaluation of Outlier Detection Based on Semantic Condensed Nearest Neighbor

Abstract Social media contain abundant information about the events or news occurring all over the world. Social media growth has a greater impact on various domains like marketing, e-commerce, health care, e-governance, and politics, etc. Currently, Twitter was developed as one of the social media platforms, and now, it is one of the most popular social media platforms. There are 1 billion user’s profiles and millions of active users, who post tweets daily. In this research, buzz detection in social media was carried out by the semantic approach using the condensed nearest neighbor (SACNN). The Twitter and Tom’s Hardware data are stored in the UC Irvine Machine Learning Repository, and this dataset is used in this research for outlier detection. The min–max normalization technique is applied to the social media dataset, and additionally, missing values were replaced by the normalized value. The condensed nearest neighbor (CNN) is used for semantic analysis of the database, and based on the optimized value provided by the proposed method, the threshold is calculated. The threshold value is used to classify buzz and non-buzz discussions in the social media database. The result showed that the SACNN achieved 99% of accuracy, and relative error is less than the existing methods.
Complete and incomplete state estimation via the simultaneous unsharp measurement of two incompatible qubit operators

We consider the simultaneous coupling of a qubit system to two qubit probes, designed to measure noncommuting qubit observables when working in isolation. While the single meter model usually corresponds to an unsharp measurement of a qubit operator, the double meter model usually describes an informationally complete measurement. The double meter positive operator measurement can be characterized by three vectors associated with the space of Hermitian tracesless observables of the system. The dimension of the linear space generated by these vectors (Bloch rank) is equal to the number of independent operators (or the number of independent Bloch-vector components) which can be estimated. $\mathbb{S}$, the subspace of parameters associated with informationally incomplete measurements (IICs), is characterized either analytically or numerically, and their Bloch rank is used to classify the IICs. Bloch rank of the ``simultaneous measurement'' of two Pauli matrices is shown to be 2 if the isolated measurements are projective and 3 if they are weak; however, two-meter weak measurements are expected to converge more slowly than their single-meter counterparts.
SIV: a structural integrity verification approach of cloud components with enhanced privacy

Private data leakage is a threat to current integrity verification schemes of cloud components. To address this issue, this work proposes a privacy-enhancing Structural Integrity Verification (SIV) approach. It is made up of three processes: proof organization, proof transformation, and integrity judgement. By introducing a Merkle tree technique, the integrity of a constituent part of a cloud component on a node is represented by a root value. The value is then masked to cipher texts in proof transformation. With the masked proofs, a structural feature is extracted and validated in an integrity judgement by a third-party verification provider. The integrity of the cloud component is visually displayed in the output result matrix. If there are abnormities, the corrupted constituent parts can be located. Integrity is verified through the encrypted masked proofs. All raw proofs containing sensitive information stay on their original nodes, thus minimizing the attack surface of the proof data, and eliminating the risk of leaking private data at the source. Although some computations are added, the experimental results show that the time overhead is within acceptable bounds.
Wind Wave Prediction by using Autoregressive Integrated Moving Average model : Case Study in Jakarta Bay

Prediction of wind wave is highly needed to support safe navigation, especially for ship. Besides that, loading and unloading activities in a harbour, as well as for design purpose of coastal and offshore structures, data of prediction of wave height are needed. Based on its nature, the wind wave has random behaviour that is highly depending on behaviour of wind as the main driving force. In this paper, we propose a prediction method for wind wave by using Autoregressive Integrated Moving Average or ARIMA. To obtain historical data of wind wave, we perform wave simulation by using a phase-averaged wave model SWAN (Simulating Wave Near Shore). From the simulation, time series of wind wave is obtained. The prediction of wind wave is performed to calculate forecast of 24 hours ahead. Here, we perform wind wave prediction in a location in Jakarta Bay, Indonesia. We perform several combination of ARIMA model to obtain best fit model for wind wave prediction in the location in Jakarta Bay. Results of prediction show that ARIMA model give an accurate prediction especially for short term prediction.
Genetic Resampling Particle Filter with Randomly Delayed Measurements and Its Application to Line-of-Sight Rate Estimation

To deal with randomly delayed measurements and glint noise, a novel recursive filter referred as a randomly delayed genetic resampling particle filter (RD-GRPF) is proposed in this paper. By making use of Bernoulli random variables, the measurement model is modified to describe the random delay. Then, the weight update equation is reformulated based on this model. To avoid the particle degeneration and sample impoverishment that always arise in the application of a particle filter, the genetic resampling method is utilized to resample the particles. Then, the RD-GRPF is obtained. Therefore, the filter proposed not only copes with randomly delayed measurements, it also keeps the advantage of the standard particle filter, which ensures good performance in the case of non-Gaussian noise. In addition, the RD-GRPF proposed is applied to line-of-sight (LOS) rate estimation, the model for which is also presented in this paper. A simulation was conducted and the results demonstrate the superiority of the RD-GRPF.
Prediction the data consumption for power demands by elman neural network

The load forecasting consider as part important in power system operation. The exact prediction for power demand is important for planning how much need extra power generation to cover extra load to keep without happen shutdown. Neural networks stay frequently designed for modeling dynamic processes. The Multi-Layer Perceptron (MLP) with Radial Basis Functions (RBF) network is static approximations used fewer frequently in the discrete-time domain. In this paper proposed predict method for daily peak load by Elman Neural Network (ENN) with using data power demand for 2 years collected from National Control Center (NCC) and comparing the result. The result show the proposal is evaluated and followed the power demand.
THE ANALYSIS OF TEACHING WRITING TO ENGLISH YOUNG LEARNERS (EYL) THROUGH A MOVIE: AN ICT PERSPECTIVE

This research has a purpose about using a movie to improve the learners’, especially English Young Learners’, writing skills. Movie, as a part of the development of technology, can be used to improve the learners’ writing skill. The participants of this research are English Young Learners (EYL) in of the English Course at Gading Serpong, Tangerang. Descriptive qualitative was used as the method of the research and the English Young Learners’ (EYL) story paper are used as the instruments of the research. The results of the research are: (1) Most learners were excited when they are asked to watch a movie; (2) Learners who have good writing skill could retell the story well, and (3) Learners who are not really master in writing, got some difficulties in retelling the story in written.
Collaborative Evolutionary Reinforcement Learning.

Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters. One reason is that most approaches use a noisy version of their operating policy to explore - thereby limiting the range of exploration. In this paper, we introduce Collaborative Evolutionary Reinforcement Learning (CERL), a scalable framework that comprises a portfolio of policies that simultaneously explore and exploit diverse regions of the solution space. A collection of learners - typically proven algorithms like TD3 - optimize over varying time-horizons leading to this diverse portfolio. All learners contribute to and use a shared replay buffer to achieve greater sample efficiency. Computational resources are dynamically distributed to favor the best learners as a form of online algorithm selection. Neuroevolution binds this entire process to generate a single emergent learner that exceeds the capabilities of any individual learner. Experiments in a range of continuous control benchmarks demonstrate that the emergent learner significantly outperforms its composite learners while remaining overall more sample-efficient - notably solving the Mujoco Humanoid benchmark where all of its composite learners (TD3) fail entirely in isolation.
Klasifikasi Jeruk Nipis Terhadap Tingkat Kematangan Buah Berdasarkan Fitur Warna Menggunakan K-Nearest Neighbor

In the process of classification of lime fruit previously done manually using the human eye is a very difficult thing to do. This is proven by being inconsistent and subjective, causing a low level of accuracy. Sometimes there are also differences of opinion from the human eye to one another. Therefore, to increase the level of accuracy and reduce the subjectivity of the human eye, this study proposes the K-Nearest Neighbor algorithm to classify the maturity level of lime based on the skin color of the lime. In this study, the K values used were 1, 3, 5, 7 and 9 to test the search for Euclidean distance and cityblock distance distances on images with pixel sizes of 512x512, 256x256 and 128x128. In the prerosesing stage, the extraction feature process uses mean RGB. The research that has been done proves that with Euclidean distance distance k = 3 and k = 7 has a percentage value of 92% and the cityblock distance distance k = 1 and k = 3 has a percentage value of 88%. Based on the level of accuracy possessed, the color feature k = 3 shows the best k value in the classification of the maturity level of the lime fruit.
Temporal Graph Regression via Structure-Aware Intrinsic Representation Learning

Temporal graph regression is a frequently encountered research problem in many studies of graph analytics. A temporal graph is a sequence of attributed graphs where node features and target variables change over time, but network structure stays constant. The task of temporal graph regression is to predict the target variables associated with nodes at future time-points given historical snapshots of the graph. Existing methods tackle this problem mostly by conducting structured regression for all target variables. However, those methods have limited performance due to redundant information. Although several techniques have been proposed recently to learn lower dimensional embedding for the target space, the problem of how to effectively exploit the structure of the temporal graph in such embeddings is still unsolved. Other recent works only study node embedding of the stationary graphs only, and this is not applicable to temporal attributed graphs.In this paper, we introduced a Structure-Aware Intrinsic Representation Learning model (SAIRL) to jointly learn lower dimensional embeddings of the target space and feature space via structure-aware graph abstraction and feature-aware target embedding learning. To solve this problem, we have developed a derivative-free block coordinate descent algorithm with closed-form solutions. To characterize the quality of embedding-based learned with SAIRL, we conducted extensive experiments on a variety of different real-world temporal graphs. The results indicate that the proposed method can be more accurate than the state-of-the-art embedding learning methods, regardless of regressors.MSC codestemporal graphregressionnetwork embeedingrepresentation learning
CONFLICT MANAGEMENT OF SCIENTIFIC PROJECT

Human resource management in project activities requires managers at various levels to continuously monitor and control in order to prevent, avoid and reduce the occurrence of conflict situations. According to the results of the analysis of scientific work, it was concluded that in most cases the successful implementation of any project and meeting the needs of its stakeholders depends on the professional and competence abilities, skills and abilities of the project team, as well as the leadership qualities of the project manager and his ability to avoid or prevent conflicts. A new component of project management methodology is proposed, in particular, conflict management research project, which includes the processes of planning project conflict management, conflict identification, conflict analysis, conflict response planning, conflict response implementation and conflict monitoring. The implementation of these processes will allow the project manager and his team to take on a conscious and controllable risk regarding the implementation of the project in order to create value. Conflict within each research project exists on two levels, in particular: individual conflicts that can affect the achievement of one or several project goals, and general conflicts that arise due to a combination of individual conflicts, stresses, problems, etc., and can influence the implementation of the entire project. The project manager must clearly understand the consequences of destructive conflicts and how to improve the impact of constructive conflicts on the project implementation. It is concluded that conflicts can occur throughout the entire life cycle of a research project, therefore project conflict management processes should be implemented continuously from the very beginning of project planning to its successful completion and meeting the goals of stakeholders that are put in front of the project.