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"Assessing trustworthiness of social media posts is increasingly important, as the number of online users and activities grows. Current deploying assessment systems measure post trustworthiness as credibility. However, they measure the credibility of all posts, indiscriminately. The credibility concept was intended for news types of posts. Labeling other types of posts with credibility scores may confuse the users. Previous notable works envisioned filtering out non-newsworthy posts before credibility assessment as a key factor towards a more efficient credibility system. Thus, we propose to implement a topic-based supervised learning approach that uses Term Frequency-Interim Document Frequency (TF-IDF) and cosine similarity for filtering out the posts that do not need credibility assessment. Our experimental results show that about 70% of the proposed filtering suggestions are agreed by the users. Such results support the notion of newsworthiness, introduced in the pioneering work of credibility assessment. The topic-based supervised learning approach is shown to provide a viable social network filter."

"Facial detection is a crucial stage in the facial recognition process. Misclassification during the facial detection process will impact recognitionresults. In this research, windowing system facial detection using the Gaborkernel filter and the fast Fourier transform was proposed. The training setimages, for both facial and non-facial images, were processed to obtain thelocal features by using the Gabor kernel filter and the fast Fourier transform.The local features were measured using probabilistic learning vectorquantization. In this process, facial and non-facial features were classifiedusing label 1 and -1. The proposed method was evaluated using facial and non-facial image testing sets, whichwere taken from the MIT+CMU image database. Thetesting images were enhanced first before the detection process using fourdifferent enhancement methods: histogram equalization, adaptive histogram equalization,contrast limited adaptive histogram equalization, and the single-scale retinexmethod. The detection results demonstrated that the highest average accuracywas 83.44%."

"The cloud is an outstandingplatform to deal with functionally equivalent services which are exponentiallyincreasing day-by-day. The selection of servicesto meet the client requirements is a subtle task. The services can be selected byranking all the candidate services using their network and non-network Quality-of-Service (QoS) parameters, which is formulated as a NP hard optimization problem. In this paper, weproposed a linear discriminant analysis (LDA) based a four level matching model for service selection based on QoS parameters,which includes description matching of a service, matchmaking phase, LDA-based QoS matching and ranking. The LDA-serviceselection agent is deployed on each cloud to classify services into classes andrank the services based on the aggregate QoS value of each service. Finally,the test results show the efficiency in service selection with minimaldiscovery overhead, significant reduction in the computation time and thenumber of candidate services to be considered."

"Assessing trustworthiness of social media posts is increasingly important, as the number of online users and activities grows. Current deploying assessment systems measure post trustworthiness as credibility. However, they measure the credibility of all posts, indiscriminately. The credibility concept was intended for news types of posts. Labeling other types of posts with credibility scores may confuse the users. Previous notable works envisioned filtering out non-newsworthy posts before credibility assessment as a key factor towards a more efficient credibility system. Thus, we propose to implement a topic-based supervised learning approach that uses Term Frequency-Interim Document Frequency (TF-IDF) and cosine similarity for filtering out the posts that do not need credibility assessment. Our experimental results show that about 70% of the proposed filtering suggestions are agreed by the users. Such results support the notion of newsworthiness, introduced in the pioneering work of credibility assessment. The topic-based supervised learning approach is shown to provide a viable social network filter."

"In any heterogeneous system, porting reconfigurable computing is often a highperformance platform for a broad range of computationally challenging issues.However, efficiently utilizing the maximum potential of these reconfigurablesystems is a difficult job without understanding their performancecharacteristics. This work proposes an analytic performance model using PetriNets (PN) for a Reconfigurable OR1200 (ROR1200) soft-core processor with modelvalidation and verifications. By modeling the ROR1200 system using Petri Nets,both behavioral and structural properties existing in parallel systems wereanalyzed. The Bound Level Analysis with respect to the dependency level of datais also performed on Soft Core Processors (SCP) like the ROR1200, the OR1200and the MicroBlaze."

"ABSTRAKPerkembangan teknologi dan transformasi sosial-ekonomi telah menyebabkan bisnis jaringan seluler 5G mengalami perubahan, sehingga jaringan seluler 5G diharapkan dapat mengirim informasi dengan cepat dan mendukung kasus penggunaan yang banyak bermunculan dari berbagai aplikasi. Salah satu kasus penggunaan pada jaringan 5G adalah massive Machine Type Communication (MTC). Salah satu aplikasi massive MTC adalah jaringan sensor nirkabel (JSN). Tantangan bagi jaringan seluler 5G ini adalah bagaimana memodelkan arsitektur/topologi untuk mendukung JSN dan bagaimana mengatasi masalah efisiensi konsumsi energi di JSN. Untuk menjawab tantangan ini, maka diterapkan sistem HAP yang terintegrasi JSN dan menggunakan protokol routing Low Energy Adaptive Clustering Hierarchy. Sistem HAP dirancang untuk digunakan di ketinggian 20 km dengan topologi tanpa dan dengan clustering, menggunakan 1.000 node sensor. Sistem ini telah disimulasikan dengan menggunakan MATLAB. Simulasi dilakukan untuk melihat konsumsi energi, jumlah node yang mati dan rata-rata total paket yang dikirim ke HAP untuk topologi tanpa dan dengan clustering. Dari serangkaian simulasi, terlihat bahwa topologi dengan clustering dapat mengurangi konsumsi energi dan jumlah node yang mati, sekaligus meningkatkan total paket yang dikirimkan ke HAP."

"An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration."

"Every organization today needs a mission statement as the saying goes. Many consider mission statement s as to be important to bunsine success...."

"Time synchronization is a vital process in wireless sensor networks, where energy sources are highly limited. In this work, we propose a broadcast-based skew correction technique that will improve both the accuracy and the lifetime of any time synchronization protocol that only corrects time offsets among sensor nodes. Using time information transmitted periodically by the root node, each client node can compensate its software clock frequency in real time after an initial offset correction. The experimental results show that a clock skew below 0.05 us/s can be consistently achieved with appropriate compensation parameters after the correction process is stabilized."

"The main objective of this paper is to review and examine the effects of the selected collision parameter values on the characteristics of collision energy in several ship collision scenarios. The benchmarking particulars were taken from the ship-to-ship collision case on Sunda Strait in 2014, while the collision process was modeled with non-linear simulations using the finite element (FE) method to determine the predicted damage and internal energy in the collision process. In the first phase, the verified model for the numerical simulation was built based on the benchmarks and other previous findings by researchers. The study of parameters during the collision process was performed in a later stage. The location of the target point in the vertical axis and the effect of the collision angle were the primary main focuses. The characteristics of damage and energy tendencies will be presented. One remarkable findings was that the structure of the car deck showed a better resistance and was more difficult to destroy than the other proposed locations. The angle position between the two objects during the impact process significantly contributed to the damage pattern on the side hull. The final results also indicated that the cross-section of the target point?s location influenced the observed parameters."