Binder is an inter-process communication system used in Android to allow processes to interact. It uses a client-server model where a client contacts a service via its binder handle. Data is packaged into a parcel and sent to the kernel binder driver via ioctl calls. The driver then delivers the transaction to the target service's thread to be processed by its stub and dispatched to the actual service code.
binder-for-linux is an experimental project to evaluate the feasibility of porting Android Binder IPC subsystem to Ubuntu Linux.
GitHub: https://github.com/hungys/binder-for-linux
Binder is what differentiates Android from Linux, it is most important internal building block of Android, it is a subject every Android programmer should be familiar with
Android is a Linux-based architecture. In addition to the original Linux driver, Android need other additional device driver, like Android Logger, Binder, Low Memory killer, Power Management for android(wakelock), ASHMEM, etc out of which ashmem ,logger and binder are all character device drivers.
In order to understand HAL layers of Android Framework, having Linux device driver knowledge is important. Hence Day-2 of the workshop focuses on the same.
binder-for-linux is an experimental project to evaluate the feasibility of porting Android Binder IPC subsystem to Ubuntu Linux.
GitHub: https://github.com/hungys/binder-for-linux
Binder is what differentiates Android from Linux, it is most important internal building block of Android, it is a subject every Android programmer should be familiar with
Android is a Linux-based architecture. In addition to the original Linux driver, Android need other additional device driver, like Android Logger, Binder, Low Memory killer, Power Management for android(wakelock), ASHMEM, etc out of which ashmem ,logger and binder are all character device drivers.
In order to understand HAL layers of Android Framework, having Linux device driver knowledge is important. Hence Day-2 of the workshop focuses on the same.
This presentation covers the working model about Process, Thread, system call, Memory operations, Binder IPC, and interactions with Android frameworks.
Embedded Android System Development - Part II talks about Hardware Abstraction Layer (HAL). HAL is an interfacing layer through which Android service can place a request to device. Uses functions provided by Linux system to service the request from android framework. A C/C++ layer with purely vendor specific implementation. Packaged into modules (.so) file & loaded by Android system at appropriate time
There is a surge in number of sensors / devices that are getting connected under the umbrella of Internet-Of-Things (IoT). These devices need to be integrated into the Android system and accessed via applications, which is covered in the course. Our Android system development course curriculum over weekends with practicals ensures you learn all critical components to get started.
For new age touch-based embedded devices, Android is becoming a popular OS going beyond mobile phones. With its roots from Embedded Linux, Android framework offers benefits in terms of rich libraries, open-source and multi-device support. Emertxe’s hands-on Embedded Android Training Course is designed to customize, build and deploy custom Embedded OS on ARM target. Rich set of projects will make your learning complete.
Android Things is the latest attempt from Google to connect the dots between the cloud and devices by introducing an OS that is exclusively built for IoT devices. Initially announced as project Brillo, Android Things helps developers to build devices faster and enable them integrate with cloud services. This presentation traces the architectural aspects of Android Things by connecting it back with Embedded Linux, Embedded Android and Brillo.
This slides is about SMP internals for Dalvik.Monitor is a synchronization construct that allows threads to have both mutux and the ability to wait for a certain condition to become true in Dalvik.
The latest source code of demos could be got from https://github.com/tek-life/Understanding_Monitor_for_Dalvik.
If there is something inappropriate in slides or demos, please let me know.
This presentation covers the working model about Process, Thread, system call, Memory operations, Binder IPC, and interactions with Android frameworks.
Embedded Android System Development - Part II talks about Hardware Abstraction Layer (HAL). HAL is an interfacing layer through which Android service can place a request to device. Uses functions provided by Linux system to service the request from android framework. A C/C++ layer with purely vendor specific implementation. Packaged into modules (.so) file & loaded by Android system at appropriate time
There is a surge in number of sensors / devices that are getting connected under the umbrella of Internet-Of-Things (IoT). These devices need to be integrated into the Android system and accessed via applications, which is covered in the course. Our Android system development course curriculum over weekends with practicals ensures you learn all critical components to get started.
For new age touch-based embedded devices, Android is becoming a popular OS going beyond mobile phones. With its roots from Embedded Linux, Android framework offers benefits in terms of rich libraries, open-source and multi-device support. Emertxe’s hands-on Embedded Android Training Course is designed to customize, build and deploy custom Embedded OS on ARM target. Rich set of projects will make your learning complete.
Android Things is the latest attempt from Google to connect the dots between the cloud and devices by introducing an OS that is exclusively built for IoT devices. Initially announced as project Brillo, Android Things helps developers to build devices faster and enable them integrate with cloud services. This presentation traces the architectural aspects of Android Things by connecting it back with Embedded Linux, Embedded Android and Brillo.
This slides is about SMP internals for Dalvik.Monitor is a synchronization construct that allows threads to have both mutux and the ability to wait for a certain condition to become true in Dalvik.
The latest source code of demos could be got from https://github.com/tek-life/Understanding_Monitor_for_Dalvik.
If there is something inappropriate in slides or demos, please let me know.
Slab allocation is a memory management mechanism intended for the efficient memory allocation of kernel objects which displays the desirable property of eliminating fragmentation caused by allocations and deallocations.
Gene Hynson [InfluxData] | How We Built the MQTT Native Collector | InfluxDay...InfluxData
The Native MQTT collector is an all-new way to natively ingest MQTT data from your MQTT broker into InfluxDB Cloud. This zero-code solution makes it easier than ever to monitor your IoT devices in InfluxDB. This session provides an overview of the Native MQTT collector, describes when and how to use it, and explains how it works behind the scenes. Lastly, Gene demos his recent IoT project that utilizes the Native MQTT collector.
Hyperledger Fabric Application Development 20190618Arnaud Le Hors
Slides presented at the Hyperledger Fabric Workshop in Barcelona on July 10th, 2019.
This covers the development of a Fabric application and smart contract (i.e. chaincode), with some tips on good practices and the IBM Blockchain Platform extension for VS Code.
Apache Kafka is a distributed streaming platform that forms a key part of the infrastructure at many companies including Uber, Netflix and LinkedIn. In this talk, Matt gave a technical overview of Apache Kafka, discussed practical use cases of Kafka for IoT data and demonstrated how to ingest data from an MQTT server using Kafka Connect.
The presentation provides an introduction to enterprise applications capacity planning using queuing models. Oracle’s Consulting uses presented methodology to estimate hardware architecture and capacity of planned for deployment enterprise applications for Oracle customers.
This talk shows how to extract (structured) value from the huge amount of (unstructured) information that logs contain using InfluxData technologies.
Particularly the task is achieved using two pieces of code I wrote: the influxdata/go-syslog library and the Telegraf Syslog Input Plugin.
The slides demonstrate how to parse logs and to store consequent time-series data into InfluxDB.
At this point it is possible to visualize them via the new Chronograf's Log Viewer, eliciting new meaningful metrics to plot (eg., number of process OOM killed) processing them via a Kapacitor UDF.
The stack used to achieve this is:
- Telegraf with the syslog input plugin, which uses this blazing fast go-syslog parser
- Chronograf with its new Log Viewer
- InfluxDB
- Kapacitor
Companion source code and repository is at http://bit.ly/logs-2-metrics-influx-code
Cascon Decentralized IoT update - Blockchain and Smart ContractsMehdi Shajari
The pervasiveness of IoT devices makes the delivery mechanism of security updates a challenge. Current IoT systems rely on centralized or brokered paradigms or clouds with huge computational and storage capacities. The existing centralized IoT setups are therefore expensive, owing to the high costs associated with cloud server infrastructures and maintenance, as well as other factors such as network equipment. Thus, the need for a fully decentralized peer to peer and secure technology to overcome these problems rises into the realm of existence. Blockchain provides a solution that fulfills the requirements of such a platform. Ideally, the update infrastructure should implement the CIA triad properties (Confidentiality, Integrity, and Availability). In this article, we study how a blockchain application can meet these requirements and propose a novel system to decentrally distribute digital content in a peer-to-peer network using the blockchain technology and smart contracts to overcome the concerns mentioned above. Additionally, in order to prevent the issues stemming from the free-riding challenge in P2P networks (peers refrain to generously share their resources to distribute updates), we exploit a Nash equilibrium micropayment mechanism to grant adequate incentive for peers to participate in distributing IoT update files.
Purpose of the session is to have a dive into Apache, Kafka, Data Streaming and Kafka in the cloud
- Dive into Apache Kafka
- Data Streaming
- Kafka in the cloud
Sharding and things we'd like to see improvedIgor Donchovski
Starting with MongoDB version 5.0, we can reshard a collection using the new shard key. But there are still something we think could be improved like:
Cluster metadata lack of details on chunks
TTL indexes
Improvements in the rebalancing process
Performance issues on rebalancing data
Observability
A 16-bit microprocessor I designed during my final semester (2005) of my Bachelor of Technology program. The microprocessor circuitry design was coded in VHDL and then configured in a Xilinx XC9572 PC84 CPLD kit. Most of the design, the architecture and the instruction set were taken from Computer System Architecture (3rd ed.) by M. Morris Mano. See https://github.com/susam/mano-cpu for VHDL source code and other related files.
Create C++ Applications with the Persistent Memory Development KitIntel® Software
Persistent memory retains data after a program crash or power failure. This demonstration shows how to make your application aware of persistent memory using the Persistent Memory Development Kit and includes a C++ code sample walk-through.
Build an Edge-to-Cloud Solution with the MING StackInfluxData
FlowForge enables organizations to reliably deliver Node-RED applications in a continuous, collaborative, and secure manner. Node-RED is the popular, low-code programming solution that makes it easy to connect different services using a visual programming environment. InfluxData is the creator of InfluxDB, the purpose-built time series database run by developers at scale and in any environment in the cloud, on-premises, or at the edge.
Jump-start monitoring your industrial IoT devices and discover how to build an edge-to-cloud solution with the MING stack. The MING stack includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana. This solution can be used to improve fleet management, enable predictive maintenance of industrial machines and power generation equipment (i.e. turbines and generators) and increase safety practices (i.e. buildings, construction sites). Join this webinar to learn best practices from industrial IoT SME's.
In this webinar, Robert Marcer and Jay Clifford dive into:
Best practices for monitoring sensor data collected by everyone — from the edge to the factory
Tips and tricks for using Node-RED and InfluxDB together
Demo — see Node-RED and InfluxDB live
Vawtrak Trojan, also known as Neverquest or Snifula, has been an enduring banking Trojan for a long time and is still one of the most prevalent banking Trojans in the wild today.
This report forms part of Blueliv’s investigation into the Vawtrak group. Reversing the Trojan was a mandatory element of this investigation in order to understand and track the cybercriminal groups and malicious actors behind the Vawtrak malware technical security researchers and reverse engineers can use this report to increase their understanding of how the banking Trojan works.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
2. Outline
•Background
–What is Binder
–Binder Communication Model
–Terminology
–Binder Software Stack
•Client(user space)
•Binder driver (Kernel Space)
•Service(user space)
3. Background
•What is Binder?
–An Inter-process communication system for developing object-oriented OS services.
clientserverData
4. Binder Communication Model
User SpaceKernel SpaceCclientserviceBinder Driverioctl/open/...ioctl/open/... mInmOutioctl/open/... mInmOutmInmOutService Manager/system/bin/servicemanagerCGet ServiceAdd ServiceService 1 – handle 1Service 2 – handle 2...
5. Terminology
•Service Handle
•Remote Binder
•Local Binder
•Binder node
•Binder reference
•Service Manager(Context Manager)
–It’s handle is 0 in all binder client and server.
Binder DriverRemote Binder/HandleLocal BinderBinder NodeBinder ref.
6. IPC Software Stack
App1 service
Proxy(BpBinder) Stub(BnBinder)
IPCThreadState IPCThreadState
Binder Module
User Space
Kernel Space
Client Server
1
2
3 4
5
6
1. BpBinder(n) -> transact(OP, data, &reply)
2. IPCThreadState::transact(handle, OP, data, reply)
3. ioctl(binder_fd, BINDER_OPERATION, &bwr)
4. IPCThreadState::getAndExecuteCommand()
5. Bnxxx::onTransact(OP, data, reply)
7. Client(user space)
• Initialization
– Call system call open(), which is binder_open in kernel. Open /dev/binder file
and get a file description. Create some key data structures.
– mmap 1MB-8KB virtual space for data transaction by binder_mmap in kernel.
• Get handle of service from service manager
• Sent request to Service by BpBinder(handle)
• Data transact to kernel by IPCThreadState.
App1 service
Proxy(BpBinder) Stub(BnBinder)
IPCThreadState IPCThreadState
Binder Module
User Space
Kernel Space
Client Server
1
2
3 4
5
6
8. Data Transaction in Client(1)
•Package in Client
102 virtual void Client::Foo(int32_t push_data) {
103 Parcel data, reply;
104 data.writeInterfaceToken(IDemo::getInterfaceDescriptor());
105 data.writeInt32(push_data);//writeStrongBinder(service)
109
110 remote()->transact(OP, data, &reply);
Parcel...flat_binder_objectmDataflat_binder_objectmObjectsService
9. Data Transaction in Client(2)
•Packaged to binder_transaction_data
IPCThreadState::writeTransactionData(int32_t cmd, …, int32_t handle, uint32_t code, const Parcel& data…)
•cmd will add to mData.(BC_TRANSACTION, BC_REPLY,… Binder Command)
•Target: target handle
•Cookie: will be define according to handle in binder driver
•Code: Operation of client.
•Offsets could help the binder driver to process binder object reference.
Parcel...flat_binder_objectmDataflat_binder_objectmObjectstargetcookiecode... data.ptr.bufferdata.ptr.offsets... binder_transaction_data
11. Data Transaction in Client(4)
•Repackage to Parcel(mOut)
•Each working thread has two parcel: mOut and mIn. mOut is for write buffer, mIn for read buffer.
Parcelcmdbinder_transactin_datamDatawrite_sizeWrite_bufferRead_sizeRead_bufferbinder_write_read
12. Outline
•Background
•Client(user space)
•Binder driver (Kernel Space)
•Service(user space) App1 service
Proxy(BpBinder) Stub(BnBinder)
IPCThreadState IPCThreadState
Binder Module
User Space
Kernel Space
Client Server
1
2
3 4
5
6
13. Binder Driver:Binder Protocol
•The protocol used for ioctl() system call.
–BINDER_WRITE_READ
–BINDER_SET_MAX_THREADS
–BINDER_SET_CONTEXT_MGR
–BINDER_THREAD_EXIT
–BINDER_VERSION
–BINDER_SET_IDLE_TIMEOUT
14. Key Data Structure (1)
Binder Driver
Process 1 Process 2 Process 3
binder_proc binder_proc binder_proc
threads node refs_by_desc refs_by_node
• The binder_proc is mapped to process by 1:1. It is created on binder_open().
• All binder_proc are listed in binder_procs.
• The binder_proc has 4 red-black tree.
• The binder_thread represents a working thread, inserted into threads rbtree.
• The binder_node represents a service, inserted into node rbtree.
• refs_by_desc and refs_by_node represent reference to a proc_node.
15. Key Data Structure (2)
User spaceKernel spaceBpBinderRemote BinderBBinderLocal Binderbinder_procnoderefs_by_nodebinder_procnoderefs_by_nodebinder_refbinder_node
16. Key Data Structure (3)
... todothreadbinder_procbinder_threadtodobinder_workbinder_workbinder_workbinder_work... ... binder_transactionbinder_transaction
17. Input Data Format
ParcelParcelwrite_sizewrite_bufferread_sizeread_bufferbinder_write_readbufferdata_sizeoffsets... binder_transaction_datacodecookiehandleoffset 1offset 2... ... flat_binder_objectflat_binder_objectBC_TRANSACTIONBC_XXXXxxx data structmOut... headIPCThreadStateClient
18. Transaction in Binder – Client (1)
1. Copy binder_write_read from user space
2. Copy xxx data to kernel space. Iterate the items:
① Get Binder command from write_buffer(BC_XXX).
② Get target thread/proc/node by handle.
③ Allocate binder_buffer from target space, and copy effective data.
④ Build a session(build_transaction).
⑤ Mount the session to target_thread’s todo list.
⑥ Mount a BINDER_WORK_TRANSACTION_COMPLETE binder_work to source thread
⑦ Wake up corresponding thread.
Parcel
Parcel
write_size
write_buffer
read_size
read_buffer
binder_write_read
buffer
data_size
offsets
...
binder_transaction_data
code
cookie
handle
offset 1
offset 2
...
...
flat_binder_object
flat_binder_object
BC_TRANSACTION
BC_XXX
Xxxx data struct
mOut
...
head
IPCThreadState Client
1
2
①
②
③
19. Transaction in Binder –Client (2)
Target SpacedataCopyParcelParcelwrite_sizewrite_bufferread_sizeread_bufferbinder_write_readbufferdata_sizeoffsets... binder_transaction_datacodecookiehandleoffset 1offset 2... ... flat_binder_objectflat_binder_objectBC_TRANSACTIONBC_XXXXxxx data structmOut... headIPCThreadStateClientbufferpriorityoffsetssender_euidbinder_transactioncodeto_threadto_procdata... offsets_sizedata_sizetarget_nodebinder_bufferOffsets[] Copy
20. Transaction in Binder -- Server
1.Server thread wake up and get a binder_work(binder_transaction) from todo list
2.Build a binder_transaction_data from binder_transaction
3.Set priority of current thread of client.
4.The buffer and offsets will be virtual address
–Virtual address = kernel address + address offset
5.Copy binder_transaction_data to mIn
6.Return from kernel
7.IPCThreadState iternate the command and data.
–For BR_TRANSACTION, call stub’s onTransact function,
which will call server service function finally.
Target Spacedatabufferpriorityoffsetssender_euidbinder_transactioncodeto_threadto_procdata... offsets_sizedata_sizetarget_nodebinder_bufferOffsets[] bufferdata_sizeoffsets... binder_transaction_datacodecookiehandle Kernel Space write_sizewrite_bufferread_sizeread_bufferbinder_write_readParcelmInBR_NOOPDatabinder_transaction_dataBR_TRANSACTIONCopy to user User Space
21. Transaction stack(1)
•The stack is for recording transaction session.
•Commonly, to_parent and from_parent is null. But, when the transaction rely on other session, what will happen?
–The session in B will lost.
A(list_head)todoBinder_thread BBinder_thread transaction_stackwait queue... (list_head)todotransaction_stackwait queue... to_parentfrom_parent... sender_euidbinder_transaction... to_threadtodo