Collaborative Govenance in Preserving the Malay Culture of Riau

The Riau Government is committed to preserving the Riau Malay Culture as stated in the Riau Vision 2025. The current facts show that there is a severe weakening of the Malay Culture in Riau Province. This can be seen from the ethics, grammar, Fashion, culinary, art, and Malay traditions that the Riau Malay community is starting to leave. The purpose of this study was to determine collaborative governance in the effort to preserve the Riau Malay Culture. The research method used is qualitative. The results of this study indicated that the Riau Government is not committed to achieving its vision, which is reflected in government policies. Lack of a roadmap for a cultural vision so that a network structure between stakeholders was not formed, the master plan owned by each stakeholder was not integrated, and was not interdependent, did not have a standard measure that describes procedures and authority in action, there was no function of joint decision-making and sharing of responsibilities, and there was no communication and flow of information between stakeholders so that collaborative governance in the preservation of Riau Malay culture was not carried out

Pemberdayaan Usaha Mikro Kecil Menengah (UMKM) Cendawan House Kelurahan Tebing Tinggi Okura Kota Pekanbaru

Usaha Mikro Kecil Menengah (UMKM) memiliki peranan penting dalam perekonomian negara, baik dari sisi penciptaan lapangan kerja maupun dari sisi jumlah usaha. UMKM Cendawan House memiliki kendala dalam aktivitas usahanya berupa belum dimilikinya pembukuan yang baik, struktur, dan pembagian tugas sehingga berdampak pada tingkat produktivitas dan pemasaran produk yang dihasilkan. Berdasarkan latar belakang tersebut maka dilaksanakan kegiatan pengabdian dengan mitra pengabdian yakni UMKM Cendawan House yang berada di Kelurahan Tebing Tinggi Okura, Kecamatan Rumbai Pesisir, Kota Pekanbaru, Provinsi Riau. Kegiatan pengabdian yang dilakukan yakni pemberdayaan melalui sosialisasi manajemen usaha untuk mengatasi masalah manajemen UMKM Cendawan House. Hasil dari kegiatan ini adalah mitra memiliki pembukuan yang baik, struktur, dan pembagian tugas serta wewenang yang jelas yang kemudian juga berdampak positif terhadap peningkatan produksi dan pemesaran produk yang dihasilkan. Dari kegiatan ini disimpulkan bahwa permasalahan yang dihadapi mitra dapat diselesaikan setelah pelaksanaan kegiatan ini

Explainable YouTube Video Identification Using Sufficient Input Subsets

Neural network models are black boxes in nature. The mechanics behind these black boxes are practically unexplainable. Having the insight into patterns identified by these algorithms can help unravel important properties of the subject in query. These artificial intelligence based algorithms are used in every domain for prediction. This research focuses on patterns formed in network traffic that can be leveraged to identify videos streaming over the network. The proposed work uses a sufficient input subset (SIS) model on two separate video identification techniques to understand and explain the patterns detected by the techniques. The first technique creates the fingerprints of videos on a period-based algorithm to handle variable bitrate inconsistencies. These fingerprints are passed to a convolutional Neural Network (CNN) for pattern recognition. The second technique is based on traffic pattern plot identification that creates a graph of packet size with respect to time for each stream before passing that to a CNN as an image. For model explainability, a sufficient input subset (SIS) model is used to identify features that are sufficient to reach the same prediction under a certain threshold of confidence by the model. The generated SIS of each input sample is clustered using DBSCAN, K-Means, and cosine-based Hierarchical clustering. The clustered SIS highlight the common patterns for each class. The SIS patterns learnt by each model of three individual videos are discussed. Furthermore, these patterns are used to investigate misclassification and provide a rationale behind it to justify the working of the classifier model

E-Ensemble: A Novel Ensemble Classifier for Encrypted Video Identification

In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such reasons advocate for the need to track users on the internet. However, with the introduction of the secure socket layer (SSL) and transport layer security (TLS), it has become difficult to analyze traffic. In addition, dynamic adaptive streaming over HTTP (DASH), which creates abnormalities due to the variable-bitrate (VBR) encoding, makes it difficult for researchers to identify videos in internet traffic. The default quality settings in browsers automatically adjust the quality of streaming videos depending on the network load. These auto-quality settings also increase the challenge in video detection. This paper presents a novel ensemble classifier, E-Ensemble, which overcomes the abnormalities in video identification in encrypted network traffic. To achieve this, three different classifiers are combined by using two different combinations of classifiers: the hard-level and soft-level combinations. To verify the performance of the proposed classifier, the classifiers were trained on a video dataset collected over one month and tested on a separate video dataset captured over 20 days at a different date and time. The soft-level combination of classifiers showed more stable results in handling abnormalities in the dataset than those of the hard-level combination. Furthermore, the soft-level classifier combination technique outperformed the hard-level combination with a high accuracy of 81.81%, even in the auto-quality mode. © 2022 by the authors

Explainable YouTube Video Identification Using Sufficient Input Subsets

Neural network models are black boxes in nature. The mechanics behind these black boxes are practically unexplainable. Having the insight into patterns identified by these algorithms can help unravel important properties of the subject in query. These artificial intelligence based algorithms are used in every domain for prediction. This research focuses on patterns formed in network traffic that can be leveraged to identify videos streaming over the network. The proposed work uses a sufficient input subset (SIS) model on two separate video identification techniques to understand and explain the patterns detected by the techniques. The first technique creates the fingerprints of videos on a period-based algorithm to handle variable bitrate inconsistencies. These fingerprints are passed to a convolutional Neural Network (CNN) for pattern recognition. The second technique is based on traffic pattern plot identification that creates a graph of packet size with respect to time for each stream before passing that to a CNN as an image. For model explainability, a sufficient input subset (SIS) model is used to identify features that are sufficient to reach the same prediction under a certain threshold of confidence by the model. The generated SIS of each input sample is clustered using DBSCAN, K-Means, and cosine-based Hierarchical clustering. The clustered SIS highlight the common patterns for each class. The SIS patterns learnt by each model of three individual videos are discussed. Furthermore, these patterns are used to investigate misclassification and provide a rationale behind it to justify the working of the classifier model. © 2013 IEEE