ETIKA KOMUNIKASI AKADEMIK MAHASISWA MELALUI VIDEO ONLINE

Abstrak dan kata kunci Kemajuan teknologi saat ini telah membawa perubahan dalam cara manusia berinteraksi. Komunikasi tatap muka mulai berkurang dengan adanya penerapan teknologi dalam berkomunikasi. Walaupun mengalami perubahan, tetapi dalam prosesnya, penerapan komunikasi berbasis teknologi juga menerapkan sejumlah prinsip yang ada didalam dunia nyata termasuk etika berkomunikasi. Hal ini makin terasa di masa pandemic Covid – 19 yang terjadi saat ini. Atas dasar situasi ini maka tim penulis tertarik untuk melakukan kegiatan PKM bertema etika berkomunikasi melalui video online khususnya bagi mahasiswa. Mahasiswa dipilih sebagai mitra kegiatan ini karena mereka dianggap sebagai pihak yang cukup terkena dampak perubahan mendadak ini, dimana mereka perlu diperkuat secara kapasitas untuk bisa tetap mengikuti proses perkuliahan online dengan tetap memenuhi standar akademik yang ada. Adapun mahasiswa yang menjadi target sekaligus mitra kegiatan PKM ini adalah mahasiswa baru dari prodi ilmu komunikasi dan prodi ilmu politik, Fisip, Undana. Kata kunci: Etika, Komunikasi, Akademik, Online   Abstract Current technological advances have brought changes in the way humans interact. Face-to-face communication began to decrease with the application of technology in communication. Even though it has changed, however in the process, the application of technology-based communication also applies a number of principles that exist in the real world including communication ethics. This is even more pronounced during the Covid - 19 pandemic. Based on this situation, the writers were interested in carrying out PKM activities under the theme of communication ethics through online videos, especially for students. Students were chosen as partners in this activity because they were considered as parties who were quite affected by this sudden change, where their capacity was needed to be strengthened in order to be able on following the online lecture process while still meeting the academic standards. The students who were being the partners of this PKM were new students from the communication science and political science study programs, FISIP, Undana.  Keywords: Ethics, Communication, Academic, Onlin

Machine vibration monitoring for diagnostics through hypothesis testing

Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher’s Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes

Kepanikan Sosial dan Komunikasi Krisis

Penelitian ini bermaksud untuk melihat secara lebih dalam bagaimana strategi komunikasi krisis yang dibangun oleh Gugus Tugas Percepatan penanganan Covid -19 NTT dalam menciptakan kesadaran publik dan mencegah kepanikan sosial akibat perkembangan wacana baik yang beredar diluar maupun yang diproduksi oleh Gugus Tugas Percepatan penanganan Covid -19 NTT. Tujuan penelitian ini adalah mengetahui strategi komunikasi krisis yang dilakukan oleh gugus percepatan penanganan Covid-19 Provinsi NTT lewat pers release yang dikeluarkan (secara online). Penelitian ini menggunakan metode Analisis Wacana Kritis (Critical Discourse Analysis). Setelah menganalisis berita Covid-19 oleh Gugus Tugas Penanganan Covid-19 dalam laman facebook Pusdalops PB NTT dengan model Roger Fowler, dapat disimpulkan sebagai berikut : wacana berita Covid-19 oleh Gugus Tugas Penanganan Covid-19 dalam laman facebook Pusdalops PB NTT pada umumnya lebih memihak pada pemerintah. Hal ini terlihat dari kosakata yang digunakan dan kalimat-kalimat berita yang disajikan. Berita lebih mengarah pada kondisi korban dan pembelaan diri pemerintah, sedangkan korban dimarjinalkan karena seolah-olah korban sebagai orang yang patut diwaspadai karena dapat menularkan virus kepada orang lain. Sementara kondisi korban akibat Covid 19 tidak dijelaskan. Penulis dapat mengemukakan saran hasil penelitian ini dijadikan sebagai bahan untuk melakukan analisis wacana kritis secara mendalam khususnya dengan menggunakan model Roger Fowler

k-d Tree-Segmented Block Truncation Coding for Image Compression

Block truncation coding (BTC) is a class of image compression algorithms whose main technique is the partitioning of an image into pixel blocks that are then each encoded using a representative set of pixel values. It is commonly used because of its simplicity and low computational complexity. The Quadtree-segmented BTC (QTS-BTC), which utilizes a dynamic hierarchical segmentation technique, is among the most efficient in the BTC class. In this study, we propose a new BTC variant that introduces two ideas: (1) the use of a k-d tree for segmentation and (2) the use of a Mean Squared Error (MSE) threshold for dynamically determining the granularity of the blocks. We refer to this new BTC variant as the k-d Tree Segmented BTC (KTS-BTC), and we test this against some of the existing BTC variants by running the algorithms on a standard image compression dataset. The results show that the proposed variant yields low bit rates of the compressed images, even outperforming the state-of-the-art QTS-BTC, without a significant reduction in image quality as measured using the Peak Signal-to-Noise Ratio (PSNR). The utilization of k-d tree for image segmentation is further shown to have more impact than that of employing the MSE thresholding scheme as a block activity classifier

Analysis and Design of Computational News Angles

A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space

Wind turbine drive-train condition monitoring through tower vibrations measurement and processing

A new method for wind turbine drive-train condition monitoring is proposed: the innovative idea is that vibrations are measured at the tower. The critical point is extracting knowledge about the drive-train from tower measurements: this is achieved by measuring simultaneously at the highest possible number of nearby wind turbines. One wind turbine is selected as target and the others are used as reference. The data are analyzed in the time domain basing on statistical features (root mean square, peak, crest factor, skewness, kurtosis). The data set in the feature space reduces to a matrix, from which the observations at the target wind turbine should be distinguishable. The application of this algorithm is supported by univariate statistical tests and by Principal Component Analysis. A novelty index based on the Mahalanobis distance is finally used to detect the statistical novelty of the damaged wind turbine. This work is based on field measurement campaigns, performed by the authors in 2018 and 2019 at wind farms owned by the Renvico company

Monitoring Active Volcanos Using Aerial Images and the Orthoview Tool

In volcanic areas, where it can be difficult to perform direct surveys, digital photogrammetry techniques are rarely adopted for routine volcano monitoring. Nevertheless, they have remarkable potentialities for observing active volcanic features (e.g., fissures, lava flows) and the connected deformation processes. The ability to obtain accurate quantitative data of definite accuracy in short time spans makes digital photogrammetry a suitable method for controlling the evolution of rapidly changing large-area volcanic phenomena. The systematic acquisition of airborne photogrammetric datasets can be adopted for implementing a more effective procedure aimed at long-term volcano monitoring and hazard assessment. In addition, during the volcanic crisis, the frequent acquisition of oblique digital images from helicopter allows for quasi-real-time monitoring to support mitigation actions by civil protection. These images are commonly used to update existing maps through a photo-interpretation approach that provide data of unknown accuracy. This work presents a scientific tool (Orthoview) that implements a straightforward photogrammetric approach to generate digital orthophotos from single-view oblique images provided that at least four Ground Control Points (GCP) and current Digital Elevation Models (DEM) are available. The influence of the view geometry, of sparse and not-signalized GCP and DEM inaccuracies is analyzed for evaluating the performance of the developed tool in comparison with other remote sensing techniques. Results obtained with datasets from Etna and Stromboli volcanoes demonstrate that 2D features measured on the produced orthophotos can reach sub-meter-level accuracy

Efficient Sparse Matrix-Vector Multiplication on GPUs Using the CSR Storage Format.

Abstract-The performance of sparse matrix vector multiplication (SpMV) is important to computational scientists. Compressed sparse row (CSR) is the most frequently used format to store sparse matrices. However, CSR-based SpMV on graphics processing units (GPUs) has poor performance due to irregular memory access patterns, load imbalance, and reduced parallelism. This has led researchers to propose new storage formats. Unfortunately, dynamically transforming CSR into these formats has significant runtime and storage overheads. We propose a novel algorithm, CSR-Adaptive, which keeps the CSR format intact and maps well to GPUs. Our implementation addresses the aforementioned challenges by (i) efficiently accessing DRAM by streaming data into the local scratchpad memory and (ii) dynamically assigning different numbers of rows to each parallel GPU compute unit. CSR-Adaptive achieves an average speedup of 14.7× over existing CSR-based algorithms and 2.3× over clSpMV cocktail, which uses an assortment of matrix formats