Download JAR files tagged by optimized with all dependencies
executor from group com.jano7 (version 2.0.2)
This small library provides an optimized solution to a problem where tasks for a particular key need to
be processed sequentially as they arrive. This kind of problem can be solved by a SingleThreadExecutor, however
it is not efficient. The issue is that the tasks for unrelated keys are not being processed in parallel, instead
they are put into a queue common to all keys and wait for the single thread to execute them. This library allows
them to be executed concurrently. Moreover this library works well in a situation where all the possible keys
and their number is not known upfront.
Artifact executor
Group com.jano7
Version 2.0.2
Last update 20. September 2020
Organization not specified
URL https://github.com/jano7/executor
License MIT License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group com.jano7
Version 2.0.2
Last update 20. September 2020
Organization not specified
URL https://github.com/jano7/executor
License MIT License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
bw-core from group com.github.burningwave (version 1.7.6)
Burningwave core is an advanced, free and open source Java library that contains THE MOST POWERFUL CLASSPATH SCANNER for criteria based classes search.
It’s possible to search classes by every criteria that your immagination can made by using lambda expressions.
Burningwave core scan engine is highly optimized using direct allocated ByteBuffers to avoid heap saturation;
searches are executed in multithreading context and are not affected by “the issue of the same class loaded by different classloaders” (normally if you try to execute "isAssignableFrom" method on a same class loaded from different classloader it return false).
This library is useful also for creating classes during runtime, facilitate the use of reflection and much more...
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Artifact bw-core
Group com.github.burningwave
Version 1.7.6
Last update 30. December 2019
Organization not specified
URL https://github.com/burningwave/bw-core
License not specified
Dependencies amount 2
Dependencies slf4j-api, asm,
There are maybe transitive dependencies!
Group com.github.burningwave
Version 1.7.6
Last update 30. December 2019
Organization not specified
URL https://github.com/burningwave/bw-core
License not specified
Dependencies amount 2
Dependencies slf4j-api, asm,
There are maybe transitive dependencies!
libswresample from group com.tagtraum (version 4.0.0)
The libswresample library performs highly optimized audio resampling, rematrixing and sample format
conversion operations.
Specifically, this library performs the following conversions:
Resampling: is the process of changing the audio rate, for example from an high sample rate of 44100Hz to
8000Hz. Audio conversion from high to low sample rate is a lossy process. Several resampling options and
algorithms are available.
Format conversion: is the process of converting the type of samples, for example from 16-bit signed
samples to unsigned 8-bit or float samples. It also handles packing conversion, when passing from packed
layout (all samples belonging to distinct channels interleaved in the same buffer), to planar layout
(all samples belonging to the same channel stored in a dedicated buffer or "plane").
Rematrixing: is the process of changing the channel layout, for example from stereo to mono. When the
input channels cannot be mapped to the output streams, the process is lossy, since it involves different
gain factors and mixing.
Various other audio conversions (e.g. stretching and padding) are enabled through dedicated options.
Group: com.tagtraum Artifact: libswresample
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Artifact libswresample
Group com.tagtraum
Version 4.0.0
Last update 25. April 2018
Organization FFmpeg.org
URL http://ffmpeg.org/
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group com.tagtraum
Version 4.0.0
Last update 25. April 2018
Organization FFmpeg.org
URL http://ffmpeg.org/
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
commons-crypto from group org.apache.commons (version 1.2.0)
Apache Commons Crypto is a cryptographic library optimized with AES-NI (Advanced Encryption
Standard New Instructions). It provides Java API for both cipher level and Java stream level.
Developers can use it to implement high performance AES encryption/decryption with the minimum
code and effort. Please note that Crypto doesn't implement the cryptographic algorithm such as
AES directly. It wraps to OpenSSL or JCE which implement the algorithms.
Features
--------
1. Cipher API for low level cryptographic operations.
2. Java stream API (CryptoInputStream/CryptoOutputStream) for high level stream encryption/decryption.
3. Both optimized with high performance AES encryption/decryption. (1400 MB/s - 1700 MB/s throughput in modern Xeon processors).
4. JNI-based implementation to achieve comparable performance to the native C/C++ version based on OpenSsl.
5. Portable across various operating systems (currently only Linux/MacOSX/Windows);
Apache Commons Crypto loads the library according to your machine environment (it checks system properties, `os.name` and `os.arch`).
6. Simple usage. Add the commons-crypto-(version).jar file to your classpath.
Export restrictions
-------------------
This distribution includes cryptographic software.
The country in which you currently reside may have restrictions
on the import, possession, use, and/or re-export to another country,
of encryption software. BEFORE using any encryption software,
please check your country's laws, regulations and policies
concerning the import, possession, or use, and re-export of
encryption software, to see if this is permitted.
See <http://www.wassenaar.org/> for more information.
The U.S. Government Department of Commerce, Bureau of Industry and Security (BIS),
has classified this software as Export Commodity Control Number (ECCN) 5D002.C.1,
which includes information security software using or performing
cryptographic functions with asymmetric algorithms.
The form and manner of this Apache Software Foundation distribution makes
it eligible for export under the License Exception
ENC Technology Software Unrestricted (TSU) exception
(see the BIS Export Administration Regulations, Section 740.13)
for both object code and source code.
The following provides more details on the included cryptographic software:
* Commons Crypto use [Java Cryptography Extension](http://docs.oracle.com/javase/8/docs/technotes/guides/security/crypto/CryptoSpec.html) provided by Java
* Commons Crypto link to and use [OpenSSL](https://www.openssl.org/) ciphers
74 downloads
Artifact commons-crypto
Group org.apache.commons
Version 1.2.0
Last update 14. January 2023
Organization not specified
URL https://commons.apache.org/proper/commons-crypto/
License Apache License, Version 2.0
Dependencies amount 1
Dependencies jna,
There are maybe transitive dependencies!
Group org.apache.commons
Version 1.2.0
Last update 14. January 2023
Organization not specified
URL https://commons.apache.org/proper/commons-crypto/
License Apache License, Version 2.0
Dependencies amount 1
Dependencies jna,
There are maybe transitive dependencies!
mahout from group org.apache.mahout (version 14.1)
Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to
reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative
filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict
contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop
cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for
non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially
friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive,
diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come
to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining
takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents
and groups them into groups of topically related documents. Classification learns from existing categorized
documents what documents of a specific category look like and is able to assign unlabelled documents to the
(hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session,
shopping cart content) and identifies, which individual items usually appear together.
Group: org.apache.mahout Artifact: mahout
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Artifact mahout
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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mahout-eclipse-support from group org.apache.mahout (version 0.5)
Artifact mahout-eclipse-support
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
mahout-parent from group org.apache.mahout (version 0.3)
Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.
Group: org.apache.mahout Artifact: mahout-parent
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Artifact mahout-parent
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
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