Xiao Ai APK Global: The Ultimate Voice Assistant for Xiaomi Users
Xiao Ai is a voice assistant created by Chinese tech company Alibaba. The assistant can be used to perform tasks such as setting alarms, playing music, and sending messages. The global version of Xiao Ai is available in English and Mandarin.
xiao ai apk global
The next tab shows you the commands in Chinese, almost the exact same as other smart home devices. You verbally activate the speaker by saying four words 小爱同学 (pronounced xiao ai tong xue) which roughly translates to little student.
Last week, Xiaomi officially announced the new Xiao Ai Translation translation feature. One of the most important features is to support global subtitle translation with a superimposable mini window. The feature is able to recognize subtitles in real time when playing in English and other languages.
For now, it seems older OnePlus devices still receiving software updates will also get the nod. But what remains unclear is if and when this OnePlus Voice Assistant will hit the global (OxygenOS) scene. Until then, though, you can grab the APK version below and try it on your OxygenOS device.
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In this paper, a malware classification model has been proposed for detecting malware samples in the Android environment. The proposed model is based on converting some files from the source of the Android applications into grayscale images. Some image-based local features and global features, including four different types of local features and three different types of global features, have been extracted from the constructed grayscale image datasets and used for training the proposed model. To the best of our knowledge, this type of features is used for the first time in the Android malware detection domain. Moreover, the bag of visual words algorithm has been used to construct one feature vector from the descriptors of the local feature extracted from each image. The extracted local and global features have been used for training multiple machine learning classifiers including Random forest, k-nearest neighbors, Decision Tree, Bagging, AdaBoost and Gradient Boost. The proposed method obtained a very high classification accuracy reached 98.75% with a typical computational time does not exceed 0.018 s for each sample. The results of the proposed model outperformed the results of all compared state-of-art models in term of both classification accuracy and computational time.
Four different image-based local features, including SIFT, SURF, KAZE and ORB, and three different image-based global features, including Colour Histogram, Haralick Texture and Hu Moments, have been extracted and used to train multiple machines learning classifiers. To the best of our knowledge, this type of features is used for the first time in android malware detection domain.
The extracted local and global features have been used to train six different machine learning classifiers including Random forest, K-nearest neighbors, Decision Tree, Bagging, AdaBoost and Gradient Boost.
The image processing-based malware detection techniques have been used limitedly to some extent in the android malware detection domain. And since image local and global features have been proven their efficiency in the image classification domain, in this work, it has been suggested testing this type of features in the android malware detection domain. Thus, this paper aims to test the effectiveness of image processing techniques in classifying android applications.
Hu moments are invariant to translation, scale, reflection and rotation. The third global feature that has been used in this paper is Haralick Texture. Texture descriptor provides measures of image properties such as smoothness, coarseness, and regularity [36]. Haralick textures are one of the most famous texture features used in image classification. Haralick texture features are calculated based on the co-occurrence matrix and composed of 13 features.
In this work, a new image-based android malware classification model has been suggested. To this end, some files from the source of the android application have been converted into grayscale images. Then, the image processing techniques and machine learning algorithms have been used to classify the android apps as benign or malicious as illustrated in Fig. 3. The novelty of the proposed model is based on that the used image features have not been used before in the malware detection domain. Particularly, in the first step of the proposed model, each sample in the benign and malware apps dataset has been extracted to use its source for constructing the grayscale image datasets. The proposed model composed of two sub-models, each of which has been trained using a different group of features. In the first sub-model, the APK samples have been extracted and their sources (i.e. Manifest, Dex or Manifest-Dex-Resouces.arsc) have been converted to grayscale images. The global features that have been mentioned before i.e. Colour Histogram, Hu Moments and Haralick Texture have been extracted from each image in the constructed image datasets. The extracted three global features have been stacked in one feature vector. The obtained feature vector has been normalized and used as input to train multiple machine learning classifiers. Six well-known machine learning classifiers i.e. Random forest, K-nearest neighbors, Decision Tree, Bagging, AdaBoost, Gradient Boost have been adopted and trained using the constructed global features-based vectors. In the second sub-model, four different local feature algorithms (i.e. SIFT, SURF, ORB and KAZE) have been used to extract local features descriptors from the constructed image datasets. The extracted local features have been used one by one to train the mentioned machine learning classifiers. Since the local features algorithms use multiple descriptors to represent each image, the output of these algorithms is multiple vectors. Since almost all machine learning algorithms accept n-samples n-feature vectors as an input, the output of the local features cannot be used directly as input for the machine learning classifiers. So, some techniques such as Bag of Visual Words (which has been used in this work) are used to construct one feature vector from multiple local feature descriptors [42]. The Bag of Visual Words is based on using any clustering algorithm for splitting the extracted descriptors vectors to multiple clusters. Then, the clustering algorithm is used to predict the cluster of each vector in the descriptors dataset.
In the first experiment, the global features have been extracted from the Manifest image dataset and used for training the first sub-model. The obtained results showed that the classification accuracies of Random forest, K-nearest neighbors, Decision tree, Bagging, AdaBoost and Gradient Boost classifiers were 98.19, 95.78, 95.78, 95.78, 98.59, 97.69 and 98.09% respectively.
In the second experiment, the global features have been extracted from the DEX files-based image dataset and used for training the first sub-model in the proposed model. The classification accuracies of Random forest, K-nearest neighbors, Decision tree, Bagging, AdaBoost and Gradient Boost classifiers were 98.45, 98.25, 97.22, 97.53, 98.55 and 98.25% respectively.
In the third experiment, the global features have been extracted from the image dataset constructed using the Manifest, DEX and ARSC files. When the extracted global features have been used for training the machine learning classifiers, the classification accuracies of Random forest, K-nearest neighbors, Decision tree, Bagging, AdaBoost and Gradient Boost were 98.75, 98.3, 98.1, 98.2, 98.7 and 98% respectively.
The classification accuracies obtained using global features were ranging from 95.78 to 98.75% based on the nature of the image dataset and the trained classifier. It has been noted that the results obtained using the Manifest image-based global features were worse than that obtained using the DEX image dataset and the Manifest-DEX-ARSC image dataset. Also, it has been noted that the best results have been obtained using the Random forest, AdaBoost and Gradient Boost classifiers. Furthermore, it is noted that the results obtained using the Decision tree and Bagging classifiers were worse than that obtained using the other classifiers to some extent.
The proposed model has been tested using AMD dataset (Android Malware Dataset) to prove its efficiency in detecting any android malware dataset. The AMD android dataset is one of the largest android malware datasets contains more than 24000 samples related to 71 families. 4850 malware samples have been selected randomly from the AMD dataset, and three malware image datasets have been constructed. After that, the proposed model has been tested using the constructed AMD based image datasets. Since the results obtained using the global features were better than that obtained using the local features, only the classification accuracy obtained using the global features extracted from this dataset has been tested. The obtained results (illustrated in Table 3) showed that the classification accuracy reached more than 98% when the proposed model has been trained using the global features extracted from the AMD-based Manifest-ARSC-DEX image dataset.
In this work, a malware visualisation method has been proposed for detecting Android malware based on grayscale image representation and machine learning techniques. Two types of image-based features have been extracted from the constructed malware image datasets and used to train six machine learning classifiers in multiple scenarios. It has been observed that the global features can give better classification accuracy than that obtained using the local features almost in all experiments. Particularly, the classification accuracy reached more than 98% when the global features extracted from each of Manifest, DEX and Manifest-DEX-ARSC image dataset have been used to train the AdaBoost classifier. Also, the classification accuracy reached more than 98% when the local features extracted from the Manifest-DEX-ARSC image dataset have been used to train the AdaBoost classifier. With other words, the best classification accuracies in this work have been obtained using the AdaBoost classifier. In general, the local features extracted from the Manifest image dataset gave classification accuracies less than that obtained using the local features extracted from the DEX or Manifest-DEX-ARSC image datasets. Also, it has been noted that the ORB local features gave the worst classification accuracies in all experiments where its classification accuracy was ranging from 65.16% to 93.56% based on the used image dataset and the trained classifier. According to the results of the conducted time complexity analysis study, the ORB features need the smallest total computational time for extracting the features, training the model and detecting the malware samples. Particularly, the average of the total time needed for training the model using the ORB features was 74.31 s; which means 0.008 s for each malware sample on average. But in contrast, the ORB features gave the worst classification accuracy results in all conducted experiments. Furthermore, it has been concluded that the SIFT features need the highest cost time for extracting the features and training the model (the worst case), where its average computational time was 866.43 s, which means 0.091 s on average for each sample.