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"Denecke’s objective is to introduce the multiple possibilities and facets of surveillance and its applications. She first introduces the task of surveillance and provides an overview on surveillance in various domains. Next, the various information sources that are available and could already be used by surveillance systems are summarized. In the main part of the book, her focus is on unstructured data as a source for surveillance. An overview on existing methods as well as methods to be developed in order to process this kind of data with respect to surveillance is presented. As an example application, she introduces disease surveillance using Web 2.0, including corresponding methods and challenges to be addressed. The book closes with remarks on new possibilities for surveillance gained by recent developments of the Internet and mobile communication, and with an outline of future challenges."

"Ilmu pengetahuan merupakan sumber penting dalam sesebuah organisasi yang memberi kesan kepada sasaran pencapaian organisasi. Realiti ini menjelaskan kepentingan mengenal pasti aset-aset pengetahuan dan menangani jurang pengetahuan bagi menjamin aktiviti perkongsian pengetahuan adalah konsisten dan berterusan dalam sesebuah organisasi. Pejabat lanjutan merupakan organisasi perkongsian pengetahuan yang unik di mana melibatkan aktiviti perkongsian ilmu antara institusi dan komuniti demi mencapai visi dan misi institusi penggajian tinggi yang terlibat. Aktiviti perkongsian pengetahuan oleh Pusat Pembangunan Komuniti Lasallian (LCDC) yang dinaungi oleh Universiti De La Salle-Dasmarinas (DLSU-D) telah dianalisis. Hasil analisis yang telah dilakukan telah mengenal pasti jurang-jurang yang memberi cabaran dalam mencapai usaha-usaha pembangunan komuniti. Kajian ini memfokuskan dua sekitaran penyelidikan iaitu DLSU-D dan anggota-anggota organisasi bukan kerajaan (NGO) serta melibatkan dua set peserta iaitu warga fakulti LCDC dan anggota-anggota NGO dari tahun akademik 1997-1998 dan 2009-1010. Data kuantitatif dan kualitatif dalam mengenal pasti jurang-jurang diperoleh melalui audit pengetahuan yang menggunakan kaedah soal selidik, perbincangan kumpulan fokus, temu bual informan penting dan analisis dokumen. Hasil kajian juga telah mewujudkan satu kerangka yang dapat membantu institusi pengajian tinggi lain dalam menganalisis amalan penciiptaan dan perkongsian pengetahuan."

"Social work as a profession and academic discipline has long centered women and issues of concern to women, such as reproductive rights, labor rights, equal rights, violence and poverty. In fact, the social work profession was started by and maintained in large part by women and has been home to several generations of feminists starting with recognized first wave feminists. This wide-ranging volume both maps the contemporary landscape of feminist social work research, and offers a deep engagement with critical and third wave feminisms in social work research. Showcasing the breadth and depth of exemplary social work feminist research, the editors argue that social work's unique focus on praxis, daily proximities to privilege and oppression, concern with social change and engagement with participatory forms of inquiry place social workers in a unique position to both learn from and contribute to broader social science and humanities discourse associated with feminist research. The authors attend here to their specific claims of feminisms, articulate deep engagement with theory, address the problematic use of binaries, and engage with issues associated with methods that are consistently of interest to feminist researchers, such as power and authority, ethics, reflexivity, praxis and difference. Comprehensive and containing an international selection of contributions, Feminisms in Social Work Research is an important reference for all social work researchers with an interest in critical perspectives"

"ABSTRACTBackpropagation neural network merupakan salah satu algoritme machine learning yang mengizinkan sebuah mesin untuk melakukan pembelajaran dari sekumpulan data, sehingga tidak perlu diprogram secara eksplisit. Namun, backpropagation neural network yang baik memerlukan proses pembelajaran dengan waktu lama dengan data dalam jumlah banyak. Penelitian ini akan merancang sebuah program backpropagation neural network yang dapat dieksekusi secara paralel untuk mendapatkan waktu eksekusi yang lebih cepat. Pembuatan program ini dilakukan menggunakan OpenMP API dalam bahasa pemrograman C. Hasil pengujian membuktikan bahwa adanya pengurangan waktu eksekusi, yakni secara berurutan sebesar 2,2653 detik dan 0,5838 detik untuk masing-masing mesin pengujian yang digunakan, untuk pertambahan setiap jumlah thread yang bekerja pada program. Namun, program masih memiliki skalabilitas yang kurang bagus dikarenakan oleh terjadinya fenomena false sharing pada program. Program memiliki sifat kenaikan waktu eksekusi linier sebesar 0,9263 detik untuk setiap pertambahan jumlah sampel input. Hal ini dikarenakan oleh pertambahan jumlah sampel hanya menambah jumlah data yang harus diproses program saja. Sedangkan, program memiliki sifat kenaikan waktu eksponensial sebesar e0,0103 detik untuk setiap pertambahan jumlah dimensi sampel input. Hal ini dikarenakan oleh pertambahan jumlah dimensi tidak hanya menambah jumlah data yang harus diproses saja, melainkan juga menambah sejumlah variabel yang bekerja pada program yang menimbulkan pertambahan komputasi pada setiap sampel input.

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ABSTRACTBackpropagation neural networks is one of many machine learning algorithms that allows a machine to do a learning process from a set of data, instead of programming it explicitly. However, a good backpropagation neural network program needs a lot amount of learning time and involves huge amount of data. This experiment made a backpropagation neural network program that can be executed in parallel fashion in order to reduce its execution time using OpenMP API in C programming language. The program rsquo s test results show that there are 2.2653 and 0.5838 second execution time decreases, each corresponds to each testing machine, for every thread added to the program. However, the program rsquo s scalability is not good enough due to false sharing phenomenon that appeared in time of execution. Program has a 0.9263 second linear execution time increase for every input samples added to the program. This is because of the addition will only effect on how much data the program needs to process. However, the program has an e0.0103 second exponential execution time increase for every input sample rsquo s feature added. This is because of the addition will not only effect on how much data that needs to be processed, but also generate some additional variables involved inside program which affects the computational process of each input sample."

"This book provides a novel solution for existing challenges in wireless body sensor networks (WBAN) such as network lifetime, fault tolerant approaches, reliability, security, and privacy. The contributors first discuss emerging trends of WBAN in the present health care system. They then provide possible solutions to challenges inherent in WBANs. Finally, they discuss results in working environments. Topics include communication protocols of implanted, wearable and nano body sensor networks; energy harvesting methodologies and experimentation for WBAN; reliability analysis and fault tolerant architecture for WBAN; and handling network failure during critical duration. The contributors consist of researchers and practitioners in WBAN around the world."