This document discusses deploying a private Docker registry using Docker registry. It provides instructions on installing Docker registry using both Python and Go versions, configuring it as a service, setting up authentication and SSL, connecting Docker hosts to the registry, and running the Docker registry frontend tool.
This document describes how to deploy a Kubernetes cluster on CoreOS virtual machines including setting up the Kubernetes master and nodes. It details installing software packages, configuring Kubernetes components like etcd and flannel, and creating replication controllers and services to deploy applications. The cluster consists of a master and two nodes with nginx pods load balanced across nodes using a QingCloud load balancer.
This document discusses deploying a private Docker registry using Docker registry. It provides instructions on installing Docker registry using both Python and Go versions, configuring it as a service, setting up authentication and SSL, connecting Docker hosts to the registry, and running the Docker registry frontend tool.
This document describes how to deploy a Kubernetes cluster on CoreOS virtual machines including setting up the Kubernetes master and nodes. It details installing software packages, configuring Kubernetes components like etcd and flannel, and creating replication controllers and services to deploy applications. The cluster consists of a master and two nodes with nginx pods load balanced across nodes using a QingCloud load balancer.
The document discusses the concept of a service robot and how research groups can best approach developing one. It advocates taking a top-down approach where the robot's functions at the task level are specified first, and the underlying technologies and algorithms are developed to support those high-level behaviors. It provides examples of task structures and behaviors from projects like RoboCup to illustrate how to define a robot's purpose and capabilities in terms of the services it provides to users.
The document summarizes Elisavet Palogiannidi's thesis presentation on affective analysis and modeling of spoken dialogue transcripts. The presentation includes an introduction to affective models, experiments conducted, and results. It discusses contributions such as creating the first Greek Affective Lexicon and extending the Semantic Affective Model to multiple languages. The Semantic Affective Model is described as mapping semantic similarity to affective similarity using a small annotated lexicon. Compositional and sentence-level affective models are also presented.
Building A Conversational Bot Using Bot Framework and MicrosoftPranav Ainavolu
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The document discusses the Microsoft Bot Framework, a service and SDK for building bots. It highlights associated tools like LUIS and Azure, detailing how to set up a simple stock bot template and connect it to users. The document also touches on integrating conversation logic and publishing to Azure.
The document discusses the development of an intelligent assistant capable of understanding and facilitating multi-domain tasks. It highlights challenges faced by existing systems, emphasizes the need for active intention understanding, and presents a proposed framework called 'helpr' for learning assistance at the task level. The findings suggest that personalized models outperform generic ones, utilizing data-driven methods to enhance user interaction and task management.
End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Lan...Yun-Nung (Vivian) Chen
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The document describes an end-to-end memory network model for multi-turn spoken language understanding. The model encodes context from previous utterances using an attention mechanism over the memory of past utterances. It then performs slot tagging on the current utterance incorporating the contextual knowledge. Experiments on a Cortana dataset show the model outperforms alternatives, achieving 67.1% accuracy by encoding both history and current utterances with the memory network.
The document discusses statistical learning from dialogues for intelligent assistants. It describes how spoken dialogue systems process user requests through steps like speech recognition, language understanding, dialogue management and response generation. It highlights current challenges like requiring hand-crafted domain knowledge and labeled data. The author's contributions include methods for automated knowledge acquisition from unlabeled dialogues and semantic decoding and intent prediction for dialogue understanding without supervision.
Cascon 2016 Keynote: Disrupting Developer Productivity One Bot at a TimeMargaret-Anne Storey
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The document discusses the role of bots in disrupting developer productivity, detailing how they automate repetitive tasks and integrate various tools within software engineering. It highlights the potential benefits of bots, such as improved efficiency and team cognition, while also addressing risks and ethical considerations surrounding their use. The document emphasizes the importance of proper bot integration in fostering collaboration and enhancing overall productivity among developers.
Harm van Seijen, Research Scientist, Maluuba at MLconf SF 2016MLconf
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1. The document discusses using deep reinforcement learning for dialogue systems. Deep reinforcement learning combines reinforcement learning with deep learning and can be applied to large, complex problems like dialogue systems.
2. A key challenge in training dialogue managers is the huge number of samples needed; this is addressed through using a user simulator trained on offline data. Deep reinforcement learning can learn directly from the belief state space used by dialogue systems.
3. Pre-training the deep reinforcement learning model on offline data makes the training more sample efficient for learning good dialogue policies.
This document provides an introduction to building bots using the Microsoft Bot Framework. It discusses what bots are and gives an overview of the Bot Framework and its components. It then describes how to build bots using the Bot Builder SDK for .NET or Node.js, test bots locally using the emulator, and publish/register bots. It also covers connecting bots to channels and services like LUIS for natural language understanding.
End-to-End Joint Learning of Natural Language Understanding and Dialogue ManagerYun-Nung (Vivian) Chen
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This document summarizes a research paper on end-to-end joint learning of natural language understanding and dialogue management. The paper proposes an end-to-end deep hierarchical model that leverages multi-task learning using three supervised tasks: user intent classification, slot tagging, and system action prediction. The model outperforms previous pipelined models by accessing contextual dialogue history and allowing the dialogue management signals to refine the natural language understanding through backpropagation. Evaluation on a dialogue state tracking dataset shows the joint model achieves better dialogue management performance compared to baselines and also improves natural language understanding.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
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Sales teams in 2017 need to update their strategies by incorporating messaging into their outreach, fixing outdated BDR processes to engage buyers in real-time, and enhancing account-based marketing to provide personalized experiences. The document emphasizes integrating messaging tools like live chat into existing setups to better connect with prospects. Additionally, it highlights the importance of responding to leads in a timely manner and treating them as individuals rather than forcing traditional form-filling mechanisms.
OwnCloud is an open source file synchronization and sharing software that provides both community and enterprise versions. It can be installed on Linux systems via packages or a one file installer. Initial configuration is required to set up the administrator account, database, and data directory. Files and folders can be managed, shared, and synced across devices. Plugins allow external storage and apps to be added. The software includes APIs for building mobile and desktop clients.
Storage Class Memory: Technology Overview & System ImpactsZhichao Liang
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The document discusses phase change memory (PCM) as a potential storage class memory technology. PCM uses the different resistances of amorphous and crystalline phases of chalcogenide glass to store data. PCM has the potential to bridge the gap between memory and storage by offering non-volatile, solid-state storage that is faster than NAND flash but slower than DRAM. The document analyzes how PCM could impact database systems by replacing DRAM, serving as extended memory paired with DRAM, or replacing hard drives and SSDs. Asymmetric read/write speeds and wear-leveling are challenges to address when using PCM.
Redis is an open source, advanced key-value store that can be used as a data structure server since it supports strings, hashes, lists, sets and sorted sets. It is written in C, works on most POSIX systems, and can be accessed from many programming languages. Redis provides options for data persistence like snapshots and write-ahead logging, and can be replicated for scalability and high availability. It supports master-slave replication, sentinel-based master detection, and sharding via Redis clusters. Redis has been widely adopted by many companies and is used in applications like microblogging services.
Some key value stores using log-structureZhichao Liang
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The document discusses various log-structured key-value stores including Riak, RethinkDB, and LevelDB, emphasizing the advantages of log structure for high write throughput and flash memory performance. It outlines key features, operational methodologies, and installation instructions for each database system. The conclusion highlights the benefits of log-structured storage systems while acknowledging potential challenges with data locality and disk access.
A novel method to extend flash memory lifetime in flash based dbmsZhichao Liang
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This document summarizes a novel method for extending flash memory lifetime in flash-based database management systems (DBMS). The method uses an append-only approach and write buffer to reduce small and random writes. An experiment evaluation compares the proposed method to a traditional LRU buffer approach using four trace files with different read/write ratios, finding reductions in page reads/writes and total latency.
Sub join a query optimization algorithm for flash-based databaseZhichao Liang
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The document presents a novel query optimization algorithm called 'sub-join' specifically designed for flash-based databases, addressing the shortcomings of traditional join algorithms in relational database management systems (RDBMS). It emphasizes the advantages of flash memory, particularly its fast random read capabilities, and details the method and performance evaluation of the sub-join algorithm compared to conventional indexed nested-loop joins. Experimental results indicate that sub-join significantly enhances performance in flash memory environments.
Hush…tell you something novel about flash memoryZhichao Liang
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The document discusses flash memory, highlighting the conservative guidelines from manufacturers regarding performance and reliability. It presents test results showing variances in read, erase, and program latencies, as well as error rates for SLC and MLC chips. Additionally, it introduces applications and extensions, such as variation-aware FTL and a system architecture called Gordon for data-centric applications.
The document provides an extensive overview of distributed storage systems, including the evolution of storage virtualization, object storage, and distributed file systems. It emphasizes the limitations of traditional storage architectures and explores modern solutions like network-based and controller-based virtualization, along with specific systems like Ceph and Lustre. The document highlights the importance of scalability, performance, and security in managing massive amounts of data generated by digital devices.
11. 保存用户状态数据
? Bot Framework提供了三种存储来保存用户的状态数据
- User Data Store (与会话无关的用户数据)
- Conversation Store (与用户无关的会话数据)
- Private Conversation Store (会话中的用户数据)
? 每种存储可用的最大空间均为32KB,使用ETag实现对数据修改的并发控制
User Data Store
https://api.botframework.com/v3/botstate/{channelId}/users/{userId}
Conversation Store
https://api.botframework.com/v3/botstate/{channelId}/conversations/{conversationId}
Private Conversation Store
https://api.botframework.com/v3/botstate/{channelId}/conversations/{conversationId}/users/{userId}