Tinjauan Yuridis Pemberian Pembebasan Bersyarat Terhadap Narapidana Di Lembaga Pemasyarakatan Klas II a Palu

Dalam Hukum Pidana dikenal adanya sanksi pidana berupa kurungan, penjara, pidana mati, pencabutan hak dan juga merampas harta benda milik pelaku tindak pidana. Harus diketahui, narapidana sewaktu menjalani pidana di Lembaga Pemasyarakatan dalam beberapa hal kurang mendapat perhatian, khususnya perlindungan hak-hak asasinya sebagai Manusia. Dalam Peraturan Menteri Hukum dan Hak Asasi Manusia Republik Indonesia Nomor 21 Tahun 2013 tentang syarat dan tata cara pemberian Remisi, Asimilasi, Cuti mengunjungi Keluarga, Pembebasan Bersyarat, Cuti Menjelang Bebas, dan Cuti Bersyarat menimbang bahwa Remisi, Asimilasi, Cuti mengunjungi Keluarga, Pembebasan Bersyarat, Cuti Menjelang Bebas, dan Cuti Bersyarat dilakukan untuk memberikan motivasi dan kesempatan kepada Narapidana dan Anak didik Pemasyarakatan untuk mendapatkan kesejahtraan, social, pendidikan, keterampilan guna mempersiapkan diri di tengah masyarakat serta mendorong peran serta masyarakat untuk secara aktif ikut serta mendukung penyelanggaraan Sistem Pemasyarakatan. Berdasarkan hasil yang diperoleh ada juga warga binaan yang tidak bisa diberikan Pembebasan Bersyarat ialah warga binaan yang melakukan pelanggaran tata tertib di Lapas

In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations

Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data

Pengaruh Pendekatan Student Center Learning terhadap Hasil Belajar Pendidikan Jasmani

Student center learning is a learning approach that focuses on students during the teaching and learning process to achieve maximum learning outcomes. In this context, learning outcomes are considered as a measure of the success of an educational program and can help teachers determine effective teaching strategies. This research aims to examine the effect of the student center learning model on physical education learning outcomes. The sample used in this study consisted of 16 students from the Elementary School Teacher Education Program at Triatma Mulya University who participated in the program during the odd semester of the 2022/2023 academic year. This study used an experimental method with data collection techniques through tests. The results of the study show that the student center learning model has a significant effect on physical education learning outcomes with a significance value of 0.000 < 0.05. Based on these findings, it is recommended to develop effective teaching strategies based on the research results to improve physical education learning outcomes through the implementation of the student center learning model

Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data

We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image data with either 2D or 3D annotations, as well as stand-alone 3D animations without corresponding image data. It features a 3D hand joint detection module and an inverse kinematics module which regresses not only 3D joint positions but also maps them to joint rotations in a single feed-forward pass. This output makes the method more directly usable for applications in computer vision and graphics compared to only regressing 3D joint positions. We demonstrate that our architectural design leads to a significant quantitative and qualitative improvement over the state of the art on several challenging benchmarks. Our model is publicly available for future research

Terrain: Fetal Growth Telehealth System Based on 2d Fetal Head Image Using Randomized Hough Transform

Intrauterine growth restriction (IUGR) is one of many fetal abnormalities, which has high contribution on maternal mortality rate and perinatal mortality rate in Indonesia. Apparently, IUGR impact can be reduced if only the symptoms are detected earlier and the correct treatment is applied. However, fetal growth detection and monitoring process in Indonesia is obstructed because the number of physicians is very limited and ultrasonography (USG) devices are expensive. Moreover, both the physicians and USG devices are only available in big cities. To answer those problems, this research proposed an intelligent system that can provide fetal growth telemonitoring in rural areas. This system consists of three components: portable USG device, mobile application which is developed using Android operating system, and server application which is developed using Django. The main feature of this system is automatic fetal head parameter detection and its ability to operate in the limited internet access environment. In this system, automatic fetal head parameter detection uses RHT method to approximate fetal head’s ellipse shape. Experiment result shows that RHT detection ability with ∆ellipse average of 79.564 and running time average of 0.373 second

Learning Speech-driven {3D} Conversational Gestures from Video

We propose the first approach to automatically and jointly synthesize both the synchronous 3D conversational body and hand gestures, as well as 3D face and head animations, of a virtual character from speech input. Our algorithm uses a CNN architecture that leverages the inherent correlation between facial expression and hand gestures. Synthesis of conversational body gestures is a multi-modal problem since many similar gestures can plausibly accompany the same input speech. To synthesize plausible body gestures in this setting, we train a Generative Adversarial Network (GAN) based model that measures the plausibility of the generated sequences of 3D body motion when paired with the input audio features. We also contribute a new way to create a large corpus of more than 33 hours of annotated body, hand, and face data from in-the-wild videos of talking people. To this end, we apply state-of-the-art monocular approaches for 3D body and hand pose estimation as well as dense 3D face performance capture to the video corpus. In this way, we can train on orders of magnitude more data than previous algorithms that resort to complex in-studio motion capture solutions, and thereby train more expressive synthesis algorithms. Our experiments and user study show the state-of-the-art quality of our speech-synthesized full 3D character animations