Web of Science (Previously known as Thomson Reuters/ISI)
In the search field, write the full journal name to search against “Web of Science Core Collection”.
In coverage, if you see Science Citation Index Expanded (SCIE), this means that the journal is indexed.
A lower level of indexing is Emerging Sources Citation Index (ESCI).
https://www.thailibrary.in.th/2022/11/01/6-core-collection-web-of-science/
By OpenAI.
It's a chatbot with extremely large knowledge base. Anyway, its capabilities are comparable to remember and understanding and application (e.g. it can do coding) abilities of human according to Bloom's model. ChatGPT cannot yet synthesize new knowledge and draw pictures as it supports only text recognition.
By normalizing for different fields of research and their widely varying rates of publication and citation, the Journal Citation Indicator provides a single journal-level metric that can be easily interpreted and compared across disciplines.
The Journal Citation Indicator will be calculated for all journals in the Web of Science Core Collection™ – including those that do not have a Journal Impact Factor (JIF)™ – and published in the 2021 JCR in June.
cf.https://clarivate.com/blog/introducing-the-journal-citation-indicator-a-new-field-normalized-measurement-of-journal-citation-impact/
https://systemzone.net/how-to-get-vmware-esxi-free-license/?amp=1
An Internet of things (IoT) company which provides a platform for building mobile (IOS and Android) applications that can connect electronic devices to the Internet and remotely monitor and control these devices.
There are three major components in the platform:
Blynk App - allows to you create amazing interfaces for your projects using various widgets we provide.
Blynk Server - responsible for all the communications between the smartphone and hardware. You can use our Blynk Cloud or run your private Blynk server locally. It’s open-source, could easily handle thousands of devices and can even be launched on a Raspberry Pi.
Blynk Libraries - for all the popular hardware platforms - enable communication with the server and process all the incoming and outcoming commands.
https://m.thaiware.com/tips/2138.html
AWS' TPU/NPU service is AWS Trainium: https://aws.amazon.com/machine-learning/trainium/
https://mindsdb.com/
https://youtu.be/HMn1jTIbOTI
It integrates machine learning model into RDB to enable query of predicted value.
Cloud native technologies focus on minimizing users' operational burden. Frequently, cloud-native applications are built as a set of microservices that run in Docker containers, and may be orchestrated in Kubernetes and managed and deployed using DevOps and Git CI workflows (although there is a large amount of competing open source that supports cloud-native development). The advantage of using Docker containers is the ability to package all software needed to execute into one executable package.
[๑๔๘] ดูกรภิกษุทั้งหลาย สัปปุริสทาน ๕ ประการนี้ ๕ ประการเป็นไฉน คือ
สัตบุรุษย่อมให้ทานด้วยศรัทธา ๑พระไตรปิฎก เล่มที่ ๒๒ พระสุตตันตปิฎก เล่มที่ ๑๔ อังคุตตรนิกาย ปัญจก-ฉักกนิบาต
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Cloud-Edge-End architecture :
With the increasing production of Big Data and due to latency, privacy, and other concerns, a Cloud-Edge-End computing paradigm has emerged in order to facilitate Big Data processing in proper places. This infrastructure seamlessly integrates hardware and software resources across multiple computing tiers, from the End to Edge, and to the Cloud. The flexibility of hierarchical cloud-edge-end computing architecture makes possible to decide whether data processing and analysis—including data cleaning, machine learning training, and decision making—applied to data coming from end sensors is carried out directly at the end device in-place, the computing edge or uploaded to the cloud infrastructures with higher computation capabilities. Therefore, advanced cloud–edge–end computing architectures needs to provide the kind of support that IoT applications need to deploy data processing and analytic methods in a flexible, efficient and scalable way. (cf. https://www.journals.elsevier.com/journal-of-systems-architecture/call-for-papers/special-issue-on-cloud-edge-end-architecture-for-internet-of-things-applications-vsi-cloud-edge-end-iot)
So the term End refers to end devices and the term Edge refers to network edge.
However, the term Cloud-Fog-Edge is found more on Google.
A cloud service that emulates mobile OS. It's like a VM with mobile OS installed. Then VM can run on any computer platforms just like any cloud services. Examples include https://www.cloudemulator.net/
An approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.
ในส่วนของ unlabeled data จะถูกสร้าง label ให้โดยเทียบเคียงจาก labeled data โดยวิธี เช่น cluster assumption คืออยู่ใน cluster เดียวกันก็น่าจะมี label เดียวกัน
A licensed enterprise information management software ที่มีความสามารถทำ document classification ได้ แต่ผู้ใช้ต้องเลือก segment บนรูปเอกสารก่อนเพื่อใช้ทำ classification
สำหรับใช้โดยคนในห้องประชุมแบบ onsite ที่เข้าร่วมประชุม google meet แบบ hybrid คือมีบางคนประชุมแบบ remote ซึ่งต้องใช้ google meet ใน mode ปกติ
ทำให้ remote participants ได้ยินเสียงคนในห้องประชุมปกติ แต่คนในห้องประชุมจะไม่ได้ยินเสียงต้นที่ผ่านเข้าไปใน google meet companion mode ออกมากจากอุปกรณ์ของผู้เข้าร่วมประชุม onsite คนอื่นๆ ทำให้ไม่ echo (no voice feedback)
There are many algorithms for reinforcement learning, please see https://en.wikipedia.org/wiki/Reinforcement_learning
Well-known algorithm is Q-learning.
Reinforcement learning involves an agent, a set of states , and a set of actions per state. By performing an action , the agent transitions from state to state. Executing an action in a specific state provides the agent with a reward (a numerical score).
Algorithm
After steps into the future the agent will decide some next step. The weight for this step is calculated as , where (the discount factor) is a number between 0 and 1 () and has the effect of valuing rewards received earlier higher than those received later (reflecting the value of a "good start"). may also be interpreted as the probability to succeed (or survive) at every step .
The algorithm, therefore, has a function that calculates the quality of a state–action combination:
Before learning begins, is initialized to a possibly arbitrary fixed value (chosen by the programmer). Then, at each time the agent selects an action , observes a reward , enters a new state (that may depend on both the previous state and the selected action), and is updated. The core of the algorithm is a Bellman equation as a simple value iteration update, using the weighted average of the current value and the new information.
Cf. https://en.wikipedia.org/wiki/Q-learning#Deep_Q-learning
https://www.datasciencecentral.com/temporal-convolutional-nets-tcns-take-over-from-rnns-for-nlp-pred/
TCN vs RNN vs LSTM vs GRU: https://arxiv.org/pdf/1803.01271.pdf
