TANGGUNG JAWAB NOTARIS TERHADAP RUSAKNYA MINUTA AKTA YANG DISIMPAN OLEH NOTARIS

Penelitian ini untuk mengetahui dan menganalisis bentuk tanggung jawab notaris terhadap rusaknya minuta akta dan limitasi tanggung jawab notaris Tujuan terhadap rusaknya minuta akta. Rumusan masalah Bagaimana bentuk tanggung jawab notaris terhadap rusaknya minuta akta? Bagaimana limitasi tanggung jawab notaris terhadap rusaknya minuta akta?. Metode penelitian ini yuridis normatif dengan menggunakan teori kewenangan, teori tanggung jawab dan teori kepastian hukum. Hasil penelitian bentuk pertanggungjawaban notaris terhadap minuta akta yang rusak secara administratif berupa pemberhentian sementara dari jabatannya dimaksudkan agar notaris tidak melaksanakan tugas jabatannya untuk sementara waktu, bentuk pertanggungjawbaan notaris terhadap minuta akta yang rusak secara perdata berupa penggantian biaya, ganti rugi dan bunga dan bentuk pertanggungjawaban secara pidana terhadap minuta akta yang rusak jika notaris terbukti melakukan pelanggaan dan dijatuhkan sanksi dapat dijadikan dasar notaris yang bersangkutan diberhentikan sementara dari jabatannya Pasal 9 ayat (1) UUJN atau diberhentikan dengan tidak hormat dari jabatannya Pasal 12 UUJN. Limitasi tanggung jawab notaris terhadap rusaknya minuta akta secara yuridis yaitu tidak memiliki batas daluwarsa dan berlaku seumur hidup dan limitasi tanggung jawab notaris berdasarkan umur biologis notaris yaitu sepanjang masih mempunyai wewenang untuk menjalankan tugas jabatannya sebagai notaris yaitu berumur 65 tahun sampai dengan umur 67 tahun. Saran dalam penelitian ini adalah perlu adanya peraturan tambahan di dalam UUJN mengenai permasalahan minuta akta yang rusak dan limitasi waktu untuk notaris pertanggungjawaban terhadap minuta akta yang rusak

Love Thy Neighbors: Image Annotation by Exploiting Image Metadata

Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors. Prior work typically models image metadata parametrically, in contrast, our nonparametric treatment allows our model to perform well even when the vocabulary of metadata changes between training and testing. We perform comprehensive experiments on the NUS-WIDE dataset, where we show that our model outperforms state-of-the-art methods for multilabel image annotation even when our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201

Localization of JPEG double compression through multi-domain convolutional neural networks

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.Comment: Accepted to CVPRW 2017, Workshop on Media Forensic

As Lilith

This is a review of As Lilith (2009)

Context-Aware Trajectory Prediction

Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might be perceived as points of attraction or obstacles. In this work, we present a new model for human trajectory prediction which is able to take advantage of both human-human and human-space interactions. The future trajectory of humans, are generated by observing their past positions and interactions with the surroundings. To this end, we propose a "context-aware" recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall. We evaluate our model on a public pedestrian datasets, and we contribute a new challenging dataset that collects videos of humans that navigate in a (real) crowded space such as a big museum. Results show that our approach can predict human trajectories better when compared to previous state-of-the-art forecasting models.Comment: Submitted to BMVC 201

The Oath

This is a review of The Oath (2010)

A Data-Driven Approach for Tag Refinement and Localization in Web Videos

Tagging of visual content is becoming more and more widespread as web-based services and social networks have popularized tagging functionalities among their users. These user-generated tags are used to ease browsing and exploration of media collections, e.g. using tag clouds, or to retrieve multimedia content. However, not all media are equally tagged by users. Using the current systems is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook. On the other hand, tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a method for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to keyframes. Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing. Given a keyframe, our method is able to select on the fly from these visual sources the training exemplars that should be the most relevant for this test sample, and proceeds to transfer labels across similar images. Compared to existing video tagging approaches that require training classifiers for each tag, our system has few parameters, is easy to implement and can deal with an open vocabulary scenario. We demonstrate the approach on tag refinement and localization on DUT-WEBV, a large dataset of web videos, and show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU

Am I Done? Predicting Action Progress in Videos

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets

EMG Analysis of Trunk Musculature following a Nine Hole Round of Golf: The Fatigue Factor

The purpose of this study is to determine the fatigue component in trunk musculature following a simulated 9 hole round of golf by analyzing the EMG output of the erector spinae, gluteus maximus and abdominal obliques during the golf swing. Four males, ages 22-26 performed 5 EMG monitored golf swings with a driver prior to and following a simulated 9 hole round of golf. The data was subjected to analysis by the Fast Fourier Transformation to determine median frequency. The results show that a significant shift in median frequency occurred, signifying muscle fatigue, in 2 of the 4 subjects when all muscles were analyzed collectively. When individual muscles were analyzed each muscle experienced a significant shift in median frequency except the left abdominal oblique. The swing times for each subject were also analyzed and compared. The 2 subjects who fatigued demonstrated faster swing times suggesting a possible relationship between speed of the golf swing with resulting increased muscle force output, and increased muscle fatigue. This study provides initial support to the theory of muscle fatigue as a possible contributor to faulty swing mechanics associated with golfing and low back pain. The results will attempt to provide information on establishing training and conditioning programs targeting the muscles shown to fatigue. These programs can be developed to increase muscle endurance and decrease the likelihood of faulty swing mechanics and injury