670 research outputs found
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
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
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
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