Overview of the Croatian Women's First League Matches
The Croatian Women's First League is set to host an exciting series of matches tomorrow, promising thrilling encounters and potential upsets. As fans eagerly anticipate the games, let's delve into the details of each match, providing expert betting predictions and insights. This comprehensive guide will cover key teams, standout players, and strategic analyses to help you navigate the day's events with confidence.
Match 1: Dinamo Zagreb vs. Osijek
Dinamo Zagreb, the reigning champions, are expected to face a tough challenge against Osijek in what promises to be a captivating encounter. Known for their robust defense and strategic play, Dinamo Zagreb has been a dominant force in the league. However, Osijek's recent form suggests they are not to be underestimated.
- Dinamo Zagreb: With a strong lineup led by captain Ana Jelavić, Dinamo is favored to win. Their tactical discipline and experience make them a formidable opponent.
- Osijek: Osijek has shown resilience and improvement, particularly in their midfield play. Key player Ivana Kovačić has been instrumental in their recent victories.
Betting Predictions
Given Dinamo Zagreb's track record and home advantage, they are the favorites to win. However, Osijek's determination could lead to a narrow victory or a draw. A safe bet might be on Dinamo winning with a one-goal margin.
Match 2: Split vs. Rijeka
The clash between Split and Rijeka is anticipated to be a tactical battle. Both teams have been consistent performers this season, making this match highly unpredictable.
- Split: Known for their aggressive attacking style, Split boasts an impressive strike force led by Martina Vuković.
- Rijeka: Rijeka's defensive solidity has been their hallmark this season. Goalkeeper Ana Petrović has been exceptional in keeping clean sheets.
Betting Predictions
This match could go either way, but considering Rijeka's defensive prowess, a low-scoring game seems likely. Betting on under 2.5 goals might be a prudent choice.
Match 3: Hajduk Split vs. Šibenik
Hajduk Split aims to continue their winning streak against Šibenik. With both teams eager to climb the league table, this match is expected to be intense.
- Hajduk Split: Their midfield dominance has been crucial in controlling games. Player of the moment, Jelena Marković, is expected to shine.
- Šibenik: Šibenik's counter-attacking strategy could pose a threat to Hajduk if they manage to exploit any gaps in defense.
Betting Predictions
Hajduk Split is likely to secure a victory given their current form. A bet on Hajduk winning by two goals or more could be rewarding.
Match 4: Zadar vs. Hrvatski Dragovoljac
Zadar faces Hrvatski Dragovoljac in what could be a pivotal match for both teams' league aspirations. Zadar's home advantage might play a significant role in the outcome.
- Zadar: With a focus on maintaining possession, Zadar's playmaker Iva Horvat is expected to orchestrate attacks from midfield.
- Hrvatski Dragovoljac: Their recent resurgence has been fueled by solid teamwork and strategic coaching.
Betting Predictions
Zadar's home ground advantage and recent performances suggest they will likely win. A bet on Zadar winning with both teams scoring could be interesting.
Key Players to Watch
The upcoming matches feature several standout players who could make significant impacts:
- Ana Jelavić (Dinamo Zagreb): Her leadership and experience are invaluable to Dinamo's success.
- Ivana Kovačić (Osijek): A rising star with exceptional midfield skills.
- Martina Vuković (Split): Known for her striking prowess and ability to score crucial goals.
- Ana Petrović (Rijeka): A reliable goalkeeper with impressive reflexes and shot-stopping abilities.
Tactical Analysis
The tactical approaches of the teams will play a crucial role in determining the outcomes of tomorrow's matches:
- Dinamo Zagreb: Likely to employ a possession-based strategy, focusing on maintaining control and creating scoring opportunities through patient build-up play.
- Osijek: Expected to adopt a counter-attacking approach, capitalizing on Dinamo's occasional lapses in concentration.
- Split: Will probably utilize their attacking flair to break down Rijeka's defense with quick transitions and dynamic forward runs.
- Rijeka: Anticipated to rely on disciplined defensive organization and quick counter-attacks when possession is regained.
Potential Upsets
While favorites like Dinamo Zagreb and Hajduk Split are expected to perform well, potential upsets could occur if underdogs like Osijek or Šibenik capitalize on any weaknesses in their opponents' strategies. Close attention should be paid to these matches for unexpected results.
Betting Tips for Tomorrow's Matches
To maximize your betting experience, consider the following tips based on expert analysis:
- Dinamo Zagreb vs. Osijek: Bet on Dinamo winning with a one-goal margin or opt for both teams scoring if you believe Osijek can find the net.
- Split vs. Rijeka: Consider betting on under 2.5 goals due to Rijeka's defensive strength and Split's potential inability to break through consistently.
- Hajduk Split vs. Šibenik: A bet on Hajduk winning by two goals or more seems promising given their current form and Šibenik's defensive vulnerabilities.
- Zadar vs. Hrvatski Dragovoljac: Opt for Zadar winning with both teams scoring if you believe Hrvatski Dragovoljac can challenge defensively but struggle offensively at Zadar's ground.
In-Depth Player Performances
Analyzing individual performances can provide deeper insights into how the matches might unfold:
- Ana Jelavić (Dinamo Zagreb): Her ability to read the game and make decisive plays will be crucial for Dinamo's success against Osijek.
- Ivana Kovačić (Osijek): Watch out for her midfield orchestrations that could disrupt Dinamo's rhythm and create opportunities for Osijek.
- Martina Vuković (Split): Her knack for finding space and converting chances will be vital for Split against Rijeka's tight defense.
- Ana Petrović (Rijeka): Her shot-stopping skills will be key in keeping Rijeka competitive against Split's attacking threats.
Possible Game-Changing Moments
Sports fans often look forward to moments that can alter the course of a match unexpectedly:
- Dinamo Zagreb vs. Osijek: A red card or injury could significantly impact the game dynamics, especially if it involves key players like Ana Jelavić or Ivana Kovačić.
Spli<|repo_name|>mtzdev/AndroidAudioSample<|file_sep|>/app/src/main/java/com/example/androidaudiosample/MainActivity.kt
package com.example.androidaudiosample
import android.content.Context
import android.media.AudioAttributes
import android.media.AudioFocusRequest
import android.media.AudioManager
import android.media.AudioManager.OnAudioFocusChangeListener
import android.media.AudioRecord
import android.media.AudioTrack
import android.media.MediaRecorder
import android.os.Build
import androidx.appcompat.app.AppCompatActivity
import android.os.Bundle
import android.util.Log
import kotlinx.android.synthetic.main.activity_main.*
class MainActivity : AppCompatActivity() {
companion object {
const val SAMPLE_RATE = AudioTrack.getNativeOutputSampleRate(AudioManager.STREAM_MUSIC)
const val CHANNEL_CONFIG = AudioFormat.CHANNEL_OUT_STEREO
const val AUDIO_ENCODING = AudioFormat.ENCODING_PCM_16BIT
const val SAMPLE_PERIOD = SAMPLE_RATE / AudioTrack.getMinFrameCount(CHANNEL_CONFIG,AUDIO_ENCODING,SAMPLE_RATE)
}
private lateinit var audioRecord: AudioRecord
private lateinit var audioTrack: AudioTrack
private lateinit var focusRequest: AudioFocusRequest
private lateinit var focusChangeListener: OnAudioFocusChangeListener
override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
setContentView(R.layout.activity_main)
//request audio focus
val audioManager = getSystemService(Context.AUDIO_SERVICE) as AudioManager
// Create focus change listener.
focusChangeListener = object : OnAudioFocusChangeListener {
override fun onAudioFocusChange(focusChange: Int) {
when(focusChange){
AudioManager.AUDIOFOCUS_GAIN -> Log.d("TAG","AUDIOFOCUS_GAIN")
AudioManager.AUDIOFOCUS_LOSS -> Log.d("TAG","AUDIOFOCUS_LOSS")
AudioManager.AUDIOFOCUS_LOSS_TRANSIENT -> Log.d("TAG","AUDIOFOCUS_LOSS_TRANSIENT")
AudioManager.AUDIOFOCUS_LOSS_TRANSIENT_CAN_DUCK -> Log.d("TAG","AUDIOFOCUS_LOSS_TRANSIENT_CAN_DUCK")
}
}
}
if(Build.VERSION.SDK_INT >= Build.VERSION_CODES.O){
focusRequest = AudioFocusRequest.Builder(AudioManager.AUDIOFOCUS_GAIN)
.setAudioAttributes(
AudioAttributes.Builder()
.setUsage(AudioAttributes.USAGE_MEDIA)
.setContentType(AudioAttributes.CONTENT_TYPE_MUSIC)
.build()
)
.setOnAudioFocusChangeListener(focusChangeListener)
.build()
audioManager.requestAudioFocus(focusRequest)
}else{
audioManager.requestAudioFocus(focusChangeListener,
AudioManager.STREAM_MUSIC,
AudioManager.AUDIOFOCUS_GAIN)
}
//create audio record
val buffer_size = AudioRecord.getMinBufferSize(
SAMPLE_RATE,
CHANNEL_CONFIG,
AUDIO_ENCODING)
Log.d("TAG",buffer_size.toString())
audioRecord = AudioRecord(
MediaRecorder.AudioSource.MIC,
SAMPLE_RATE,
CHANNEL_CONFIG,
AUDIO_ENCODING,
buffer_size *8)
//create audio track
audioTrack = AudioTrack.Builder()
.setAudioAttributes(
AudioAttributes.Builder()
.setUsage(AudioAttributes.USAGE_MEDIA)
.setContentType(AudioAttributes.CONTENT_TYPE_MUSIC)
.build())
.setAudioFormat(
AudioFormat.Builder()
.setSampleRate(SAMPLE_RATE)
.setChannelMask(CHANNEL_CONFIG)
.setEncoding(AUDIO_ENCODING)
.build())
.setTransferMode(AudioTrack.MODE_STREAM)
.build()
button.setOnClickListener {
startRecording()
}
}
private fun startRecording(){
audioRecord.startRecording()
Thread {
val buffer_size = audioRecord.readSizeInBytes()
val buffer = ByteArray(buffer_size)
while(true){
val read_size = audioRecord.read(buffer,0,buffer_size)
if(read_size >0){
runOnUiThread{
textView.text= read_size.toString()
}
//play buffer with audiotrak
audioTrack.play()
//enqueue data into audiotrak buffer.
audioTrack.write(buffer.bufferOffset(),read_size)
}
}
}.start()
}
override fun onDestroy() {
super.onDestroy()
if(Build.VERSION.SDK_INT >= Build.VERSION_CODES.O){
val audioManager = getSystemService(Context.AUDIO_SERVICE) as AudioManager
audioManager.abandonAudioFocusRequest(focusRequest)
}else{
val audioManager = getSystemService(Context.AUDIO_SERVICE) as AudioManager
audioManager.abandonAudioFocus(focusChangeListener)
}
}
}
<|repo_name|>mtzdev/AndroidAudioSample<|file_sep|>/settings.gradle
rootProject.name='AndroidAudioSample'
include ':app'
<|file_sep|># Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
Utility functions.
"""
from __future__ import absolute_import
from typing import List
from ..data.dataset import Dataset
def load_and_split_dataset(dataset_name: str) -> List[Dataset]:
"""
Load dataset from datasets folder based on dataset name.
Args:
dataset_name (str): Name of dataset file without extension.
Returns:
List[Dataset]: list of datasets.
"""
return Dataset.load(dataset_name).split_train_test(0.8)
def load_and_split_dataset_with_label(dataset_name: str) -> List[Dataset]:
"""
Load dataset from datasets folder based on dataset name.
Args:
dataset_name (str): Name of dataset file without extension.
Returns:
List[Dataset]: list of datasets.
"""
return Dataset.load(dataset_name).split_train_test_with_label(0.8)
def get_example_dataset():
"""
Return an example Dataset object.
Returns:
Dataset: example Dataset object.
"""
return Dataset.load('example')
<|repo_name|>msr-inria/multimodal-explainability-toolkit<|file_sep|>/README.md
# Multimodal Explainability Toolkit
## Introduction
The Multimodal Explainability Toolkit is developed as part of our work at MSR Inria team at Microsoft Research Lab India.
This toolkit aims at providing researchers with tools needed for studying explainability aspects of multimodal models.
The toolkit comes with data loading utilities which helps users access multimodal datasets like [MSVD](http://msvd.is.tue.mpg.de/) , [MSRVTT](http://msrvtt.is.tue.mpg.de/), [MSR-VTT](https://www.microsoft.com/en-us/research/project/msr-vtt-a-large-multimodal-video-description-benchmark/) etc.
The toolkit also provides users with utilities required for training models using these datasets like image captioning , video captioning etc.
## Getting Started
### Prerequisites
To use our toolkit you need python version >=3.x installed in your system along with some python libraries which are listed below:
torch==1.7.*
torchvision==0.8.*
Pillow==7.*
numpy==1.*
tqdm==4.*
transformers==4.*
scikit-image==0.*
### Installation
You can install all the prerequisites mentioned above using `pip` command as follows:
pip install torch==1.7.*
pip install torchvision==0.8.*
pip install Pillow==7.*
pip install numpy==1.*
pip install tqdm==4.*
pip install transformers==4.*
pip install scikit-image==0.*
## Usage
To access multimodal datasets provided by us just run following command:
python -m multimodal_explainability_toolkit.data.dataset --help
usage: dataset.py [-h] [--dataset-name DATASET_NAME]
[--split-ratio SPLIT_RATIO]
optional arguments:
-h show this help message and exit
--dataset-name DATASET_NAME
Name of dataset file without extension.
--split-ratio SPLIT_RATIO
Ratio used for splitting train test data.
### Example Usage
To load `example` dataset just run following command:
python -m multimodal_explainability_toolkit.data.dataset --dataset-name example --split-ratio 0.8
This command will give output as follows:
Loading dataset from ./datasets/example.csv...
Loading images from ./datasets/example_images...
Loaded example images...
Loaded example dataset...
Dataset type : Video Captioning Dataset
Dataset size : Train set : {'vid_00001.jpg': ['This is an example sentence', 'Another example sentence']}
Test set : {'vid_00002.jpg': ['This is another example sentence']}
Split ratio : {'train': {'vid_00001.jpg': ['This is an example sentence', 'Another example sentence']}, 'test': {'vid_00002.jpg': ['This is another example sentence']}}
## Contributing
We welcome contributions from researchers working in this area who would like to improve this toolkit further.
For contributing please follow steps mentioned below:
* Fork this repo.
* Clone your forked repo locally using following command:
git clone https://github.com//multimodal-explainability-toolkit.git
* Create new branch with desired name using following command:
git checkout -b new_branch_name_here
* Make necessary changes according to your requirements.
* Add changed files using following command:
git add .
* Commit your changes using following command:
git commit -m "Commit message here"
* Push changes using following command:
git push origin new_branch_name_here
* Create pull request from new branch.<|file_sep|># Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
Utilities related with training models.
"""
from __future__ import absolute_import
from typing import Dict
class TrainingArguments(object):
"""
Class containing all arguments required for training model.
Args:
batch_size (int): Batch size used during training.
epochs (int): Number of epochs used during training.
learning_rate (float): Learning rate used during training.
weight_decay (float): Weight decay used during training.
model_type (str): Type of model used during training .
save_dir_path (str): Path where model needs ot be saved after training .
"""
def __init__(self,
batch_size: int,
epochs: int,
learning_rate: float,
weight_decay: float,
model_type: str,
save_dir_path