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STRATEGI DAN KENYATAAN PENDIDIKAN DAN PEMBANGUNAN DI KENYA DAN TANZANIA
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-11, NOPEMBER (No. 11) |
| Halaman | : | 1001-1028 |
| Abstrak | : | - |
Computer Audition: From Task Specific Machine Learning to Foundation Models
| Pengarang | : | Andreas Triantafyllopoulos; Iosif Tsangko; Alexander Gebhard; Annamaria Mesaros; Tuomas Virtanen; Björn W. Schuller |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 4) |
| Halaman | : | 3174-343 |
| Abstrak | : | Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition—i.e., the use of machines to understand sounds. They feature several advantages over traditional pipelines: among others, the ability to consolidate multiple tasks in a single model, the option to leverage knowledge from other modalities, and the readily available interaction with human users. Naturally, these promises have created substantial excitement in the audio community and have led to a wave of early attempts to build new, generalpurpose FMs for audio. In the present contribution, we give an overview of computational audio analysis as it transitions from traditional pipelines toward auditory FMs. Our work highlights the key operating principles that underpin those models and showcases how they can accommodate multiple tasks that the audio community previously tackled separately. |
BEBERAPA MASALAH KOMUNIKASI INTERPERSONAL DALAM MASYARAKAT PEDESAAN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-11, NOPEMBER (No. 11) |
| Halaman | : | 986-1000 |
| Abstrak | : | - |
POLA-POLA KOMUNIKASI UNTUK MASYARAKAT KOTA DAN MASYARAKAT DESA: SEBUAH PENDEKATAN
| Pengarang | : | Suparlan, Parsudi |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-11, NOPEMBER (No. 11) |
| Halaman | : | 971-985 |
| Abstrak | : | - |
Collaborative Brain-Computer Interface for People with Motor Disabilities
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 56-66 |
| Abstrak | : | This research investigates the effects of collaboration mode, luminance contrast and motor disability on task performance, brain activity and satisfaction of users with motor disabilities who performed a robot control task using a collaborative brain-computer interface (C-BCI) based on steady-state visually evoked potentials (SSVEPs). Users can perform the task by himself/herself (individual mode), working together (simultaneous mode), or taking turns (sequential mode). Fourteen amyotrophic laterals sclerosis (ALS) participants and fourteen able-bodied participants of similar age were recruited from local ALS association and local communities. Results showed that participants in both groups had significantly better task performance and stronger brain activity collaborative modes than individual mode. High luminance contrast produced significantly better task performance and stronger brain activity than low luminance contrast. There was no significant effect of motor disability between ALS participants and able-bodin ied participants. The two groups showed very similar task performance and brain activity. These results could provide precious empirical data and invaluable insights to the real-world applicability of the SSVEP-based BCI applications for people with motor disabilities. |
MASALAH PEDOMAN PENGHAYATAN DAN PENGAMALAN PANCASILA (P4): DITINJAU DARI SEGI MASYARAKAT
| Pengarang | : | Lahur Rufinus |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-11, NOPEMBER (No. 11) |
| Halaman | : | 960-970 |
| Abstrak | : | - |
Statistical Learning Theory and ELM for Big Social Data Analysis
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 45-55 |
| Abstrak | : | The science of opinion analysis based on data from social networks and other forms of mass media has garnered the interest of the scientific community and the business world. Dealing with the increasing amount of information present on the Web is a critical task and requires efficient models developed by the emerging field of sentiment analysis. To this end, current research proposes an efficient approach to support emotion recognition and polarity detection in natural language text. In this paper, we show how to exploit the most recent technological tools and advances in Statistical Learning Theory (SLT) in order to efficiently build an Extreme Learning Machine (ELM) and assess the resultant model's performance when applied to big social data analysis. ELM represents a powerful learning tool, developed to overcome some issues in back-propagation networks. The main problem with ELM is in training them to work in the event of a large number of available samples, where the generalization performance has to be carefully assessed. For this reason, we propose an ELM implementation exploits the Spark distributed in memory technology and show how to take advantage of the most recent advances in SLT in order to address the issue of selecting ELM hyperparameters that give the best that generalization performance. |
PENDIDIKAN KEJURUAN DALAM SISTEM PENDIDIKAN DI INDONESIA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-11, NOPEMBER (No. 11) |
| Halaman | : | 944-959 |
| Abstrak | : | - |
HAKIKAT DAN SASARAN PENDIDIKAN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-11, NOPEMBER (No. 11) |
| Halaman | : | 933-943 |
| Abstrak | : | - |
Learning User and Product Distributed Representations Using a Sequence quence Model for Sentiment Analysis
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 34-44 |
| Abstrak | : | In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-the-art results on these benchmark datasets. |