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"Contents : - Table of Contents by Author - Acronym Guide - Next-Generation OSS for Next-Generation Services - OSS with the Network as a Killer Application - Testing Tomorrow's Needs Today - Business-to-Business E-Commerce for Telecom Carriers - OSS Challenges for CLECs - Evolving the Intelligent-Agent Technology - Architectural and OSS Changes for Entering the Real-Time World - OSS for the Next Millennium:The New Killer Ann - The Complexity Opportunity - The Carrier's View: A Reality Check - OSS Is Not a Killer App - How Next-Ceneration Technologies Will Alter the OSS Landscape - Enabling an Interactive Value Proposition for Communications Providers - Business Costs Placing Emphasis on OSS Features - Delivering On-Demand IP Service with Guaranteed Service Quality - IN as a Stepping Stone to IP - The Real Benefits of IP Telephony - Delivering QoS in IP Networks - Enabling Carrier Settlements in Multiservice IP Networks:A Case Study - Embracing the IP World - XML for Services - IP from a Service Provider's Perspective - Automating Customer Care - Guaranteeing Quality of Service with Next-Generation Equipment - Flow-Through Provisioning:Keeping Up with Changes to Your Trading-Partner Interfaces - Customer Focus a Main Part of E-Business Success - Data Quality for the Internet Age - Testing Challenges for ADSL Networks - Today's IP-VPN Quality of Service - Delivering Services to Customers:Becoming an E-Telco - Delivering and Profiting from End-to-End Quality of Service - Service Management for Effective Service Delivery - Keys to Open-System Integration - OSSs and Data Warehouses:Working Together - Moving towards a Modular, Open OSS Framework Architecture for Maximum Flexibility and Scalability - Examining a New Network from an ILEC Perspective - Network Management of Multi-Technology Networks - Coordinating OSS Integration and Interconnection for End-to-End Fulfillment of Customer Orders - Business-to-Business Challenges of the Unbundled Local Loop - Unking Legacy RTUs into a TMN Environment - Solutions to BSS and OSS Interaction - Facilitating End-to-End OSS Integration and Management:An Interconnection Perspective - Mediation: Building Bridges or Building Walls? - Achieving Integrated Management for Multi-Vendor, Multi-Technology Transport networks - optimal Infrastructure for the Managed Network:Benefits of the Object Model - OSS Vendors: Challenges and Methodologies - Problems Associated with the Loss of NPA/NXX Intelligence - Application Service Framework - 3G service Modeling and Optimization - outsourcing: The Bind That Ties - using TMN and TOM in a Converged Network - The Foundation of Next-Gen Networks: In-Memory Database Technology - Component Technology for Developing Network-Management GUIs - An Architecture for Warehousing and Accessing Usage Data - Rules-Based Mediation Engine The Enabler for Open OSS"

"In recent years there has been a marked increase in the number of labor disputes,particularly disputes involving individual cases,an increase reflected in a corresponding increase both in the number having resort to the One-stop worker consultation corners in prefectural Labor bureaus and also in cases brough before the civil court....."

"Contents : - Table of Contents by Author - Acronym Guide - Making DSL Profitable: A Financial Analysis - Wholesale versus Retail: A Comparison of CLEC-DSL Business Models - International Telecom Market Opportunities and Trends - Using a Strong Brand through Retail and E-Commerce to Expand the DSL Market - The ISP Experience in Today's DSL Marketplace - Wholesale versus Retail Model for CLECs - VoDSL: Challenges in the Partnership Model - Automating Loop Management - Mass-Market Solutions for DSL Deployment - Driven Deployment in the New Millennium - ADSL Welcome to the Suburbs! - Enabling Effective DSL Deployment - The Building Blocks of Broadband - Managing for Explosive Digital Subscriber Line Growth - Delivery of ADSL Services in DLC Environments - The Future of Digital Subscriber Line - Lessons learned in Deploying Voice over DSL - DSL Mass Deployment: What You Don't Know Can Hurt You - Deployment Challenges and Solutions - loop-Management Processes for Efficient Customer Activation - DSL Deployment: The ISP Perspective - The Future Broadband Home - Challenges of the Digital loop Carrier - Practical Issues of Delivering Services inside the Customer Premises - Provisioning Broadband Services over DSL - Automated, End-to-End DSL Provisioning: From Loop Qualification to the Backhaul Network - DSL's Effect on ILEC Network Architecture - Connecting to the Network - DSL: A Last-Mile Technology - Access Issues in the Local Loop - Internet via Satellite - Integrated Software-on-Silicon Solutions for Next-Generation DSL CPE - Digital Subscriber Line Fault Localization - i-SLAM: The Next-Generation, IP-Aware, IP-Smart, Intelligent DSLAM - Fiber-to-the-Home Market Trial - Traffic Aggregation and Multiple Application Selection - Residential Broadband: The Move from How It Gets There to What Gets There - Plug-and-Play DSL - Internet Age: Going from Plug and Pray to Plug and Play - Residential Gateways: New Applications for High-Speed Premises Networking - Getting to Plug-and-Play DSL An SBC Perspective - SelectPlay: Software over Broadband on Demand - Moving toward Plug-and-Play DSL - Always-On DSL Requires Always-On Provisioning - G.shdsl and ETSI SDSL Multirate Symmetric DSLs - HDSL2 Standards Compliance and Interoperability - DSL Spectrum Management - The Interoperability Problem "

"Semakin tinggi penetrasi penggunaan Internet seseorang, maka akan semakin berpotensi terkena Gangguan Adiksi Internet (GAI) yang dapat berdampak buruk pada status kesehatan mental penggunanya. Mayoritas penduduk Indonesia telah menggunakan layanan Internet selama 2 sampai 3 tahun dengan penggunaan rata-rata di atas 8 jamperhari. Hal tersebut menunjukkan penggunaan Internet dan potensi dampaknya pada kesehatan mental di Indonesia penting untuk diperhatikan sedini mungkin. Penelitian lain menunjukkan bahwa tingkat kesehatan mental yang dialami seseorang dapat mempengaruhi perilaku penggunaan Internetnya, sehingga menyebabkan munculnya keinginan yang tidak terkendali dan berlebihan dalam pengaksesan Internet. Secara tidak langsung, hal tersebut menyatakan bahwa kesehatan mental seseorang juga dapat diamati melalui tingkah laku serta kebiasaan seseorang dalam menggunakan Internet. Prediksi GAI dan gangguan kesehatan mental mahasiswa UI dilakukan dengan menggunakan algoritma pemelajaran mesin Support Vector Machine (SVM) berdasarkan perilaku penggunaan Internet yang dilakukan. Sampel diambil dari mahasiswa UI rumpun Ilmu Saintek (Ilmu Komputer, Teknik, dan MIPA). Data yang diambil adalah riwayat penulusuran halaman website yang diakses oleh mahasiswa dan hasil kuesioner Internet addiction test (IAT) dan General Health Questionnaire (GHQ-12). Riwayat penelusuran website dijadikan himpunan fitur yang merepresentasikan perilaku penggunaan Internet responden, sedangkan hasil skor kuesioner IAT dan GHQ-12 digunakan untuk menjadi ground truth atau label pada dataset. Tahapan preprocessing yang dilakukan adalah metode Synthetic Minority Over-Sampling Technique (SMOTE) untuk mengatasi ketidak seimbangan persebaran data pada kelas data yang digunakan. Metode SVM selanjutnya dibandingkan dengan performa lainnya seperti Decision Tree dan k-Nearest Neighbor (kNN). Untuk meningkatkan performa akurasinya, peneliti menggunakan metode grid search untuk mendapatkan parameter terbaik. Proses validasi dilakukan menggunakan cross-validation pada metode grid search. Hasil yang didapatkan menunjukkan bahwa performa akurasi tertinggi pada SVM untuk memprediksi GAI adalah 88% pada dataset kedua. Saat dilakukan perbandingan hasil dengan metode pemelajaran mesin Decision Tree dan kNN, didapatkan performa nilai akurasi tertinggi dicapai pada metode Decision Tree dengan nilai akurasi sebesar 96%. Sedangkan untuk prediksi gangguan kesehatan mental, metode SVM mendapatkan nilai performa akurasi tertinggi sebesar 71% pada dataset gabungan. Saat dilakukan perbandingan hasil performa akurasi dengan DecisionTree dan kNN, didapatkan nilai performa akurasi tertinggi dicapai pada metode kNN sebesar 72%. Hasil penelitian ini menunjukkan bahwa metode grid search meningkatkan performa SVM, Decision Tree, dan kNN karena adanya perubahan nilai parameter.

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Excessive internet usage lead to potential Internet Addiction Disorders (IAD) which affect user`s mental health. The mayority of Indonesian people have been used Internet services for 2 until 3 years in their lives with an average use of above 8 hours per day. It shows that an increase of internet usage has a positive potential impact to an increase in mental disorder. Other research shows that the level of mental health experienced by a person can influence his Internet usage behavior, thus causing an uncontrolled and excessive desire to access the Internet. It could be concluded that the mental health can also be observed through one`s behavior and habits in using the Internet. This study predicts the internet addiction disorder (IAD) and mental health disorder status of UI students by using machine learning based on Support vector Machine (SVM) algorithm. This study used behaviour of internet usage for the input. Samples used in this study were taken from Universitas Indonesia`s students with Science and Technology background. The data collection period was set before and after the exam period. Data collected in this study included history of website accessed by students and questionnaires based on Internet addiction test (IAT) and General Health Questionnaire (GHQ-12). Student`s website history would be used as feature data set that represent user internet usage behavior, while the IAT and GHQ-12 questionnaires results were used as the label. The preprocessing stage was carried out using Synthetic Minority Over-Sampling Technique (SMOTE) method to overcome the imbalance of data distribution in class used. Then, student`s website history would be analyzed using machine learning based on SVM algorithm to predict IAT and mental health status. This study also compared other algorithms such as Decision Tree and k-Nearest Neighbor (kNN). The optimization of machine learning model was conducted using grid search method to obtain the best

parameters. The validation of the model would be carried out using the cross-validation obtained from grid search method. Based on the results obtained, it shows that the highest accuracy for predicting internet addiction was obtained from SVM algorithm with 88% accuracy for the second dataset. Comparison with other models showed that Decision Tree obtained the highest accuracy value of 96% for predicting internet addiction. For the prediction of mental health disorder, SVM algorithm obtained the highest accuracy than Decision Tree or kNN. The SVM algorithm can predict with accuracy of 71% with combined dataset. When comparing the accuracy result with the accuracy of Decision Tree and kNN, the highest accuracy value of 72% was achieved by kNN method. The optimal value of accuracy is obtained when the grid search method is performed. The results of this study indicate that the grid search method has succeeded in improving the performance of SVM, Decision Tree, and kNN due to parameter value changes."