Reviewerly/exHarmony huggingface dataset:
Image Segmentation Based Privacy-Preserving Human Action Recognition for Anomaly Detection

Human Action Recognition and Anomaly Detection significantly improved automatic video analysis, assisted living, and video-based surveillance. The focus of this work is on those applications where privacy protection is required, such as surveillance and assisted living. RGB video data is the most common source for human action recognition. However, RGB data also contains privacy-related data, such as the identity of the target. In this paper, we prove that human action recognition accuracy mostly depends on contextual data, rather than on privacy-related data. Therefore, human target data can be occluded by using an image segmentation mask. The proposed method achieves almost similar accuracy in comparison with the privacy case and provides the platform for privacy-preserving anomaly detection. Simulations are performed on the two popular datasets for human action recognition, i.e. UCF101 and HMDB51.
Detection of cancerous tissue in histopathological images using Dual-Channel Residual Convolutional Neural Networks (DCRCNN)

Computer-aided detection techniques to improve precision diagnostic capability and efficiency in the diagnosis process has been regarded as one of the most important topics in the field of computer vision. The medical imaging data with respect to a patient is primarily considered as one of the most important sources to derive the information regarding the biomarkers of a particular disease. But the successful detection of biomarkers requires the radiologist and the pathologist to have long term experience in this field. Therefore, the development of computer-aided detection is one of the primary concerns that need to be discussed. Moreover with the advent of Deep Learning and Artificial Intelligence, now the detection of anomalies and aneurysms in the medical imagery can become much more precise and efficient. Therefore this particular paper presents a dual-channel residual convolution neural network (CNN) model for the automated classification and detection of cancerous tissues in histopathological images. The proposed CNN model has been trained with 220,025 histopathological images and has achieved an overall accuracy of 96.475%, average recall of 95.72% and an average precision of 95.92% respectively.
Using FGSM Targeted Attack to Improve the Transferability of Adversarial Example

At present, many people pay attention to the safety problems of artificial intelligence, and the emergence of adversarial examples is one of these problems. The adversarial examples can be used to attack a neural network classification model to make its classification wrong. It is an important method to improve the attack effect of adversarial examples by improving the transferability of adversarial examples and enabling them to attack multiple different neural network classification models at the same time. When we use FGSM algorithm to attack a model, first, we set ϵ a medium magnitude value, and then use targeted attack, which can improve the transferability of the adversarial examples generated by this algorithm. We can use FGSM algorithm to carry out white-box attack on the neural network model. The first step is to set a fixed value. Then, because FGSM algorithm is non-targeted attack, when adding a termination condition of iterative attack, we can use FGSM algorithm to carry out targeted attack, so that the generated adversarial examples can be identified as specific tags by the neural network model. We found that the transferability of the adversarial examples generated in this way is improved and these adversarial examples can attack many different neural network models.
Knowledge attention sandwich neural network for text classification

Text Classification is a fundamental and crucial issue in many Natural Language Processing (NLP) tasks. An effective and efficient representation model is the key to text classification. However, most existing representation models either learn insufficient structural information or just rely on pre-defined structures, resulting in degradation of performance and generalization capability. We propose a novel Sandwich Neural Network (SNN), which is able to learn local semantic and global structural representations automatically without parsers. To combine semantic and structural representations sensibly, we propose four fusion methods incorporated with SNN: Static Fusion, Adaptive Learning, Self-Attention, and Knowledge Attention methods. Static Fusion weights semantic and structural representations equally, Adaptive Learning learns the weights at corpus level, and Self-Attention learns the weights at instance level. More importantly, within Knowledge Attention fusion method, external semantic and structural knowledge are incorporated into SNN to improve the attention procedure and further boost the performance of SNN. Evaluated with four mainstream datasets: Text REtrieval Conference (TREC), SUBJectivity (SUBJ), Movie Reviews (MR) and Stanford Sentiment Treebank with Five Labels (SST-5), the proposed Knowledge Attention Sandwich Neural Network(KA-SNN) model achieves very competitive performance. Specifically, the accuracies achieve 96.2% (TREC), 94.1% (SUBJ), 82.3% (MR) and 51.5% (SST-5). Moreover, the proposed Knowledge Attention reduces the structural complexity of attention module by 77.66-81.43% with a computing time reduction of 21.47-34.05%, compared with Self-Attention fusion method.
Open Set Video Camera Model Verification

We introduce a new open set video forensics problem called video camera model verification. The video camera model verification task is to determine if two query videos were captured by the same camera model. Importantly, verification must be reliable on videos from camera models unknown to the investigator, referred to the as the open set scenario. While researchers have considered other open set problems for digital images, video forensics introduces unique challenges. In this work we propose a new, video-specific system for open set verification of camera models. To do this, we design a deep-learning based system that 1) uses a CNN to extract expressive deep features from video patches, 2) compares pairs of these features using a similarity network, and 3) fuses multiple comparisons to produce a video-level verification decision. We experimentally show that this technique accurately verifies the source camera model of videos in open set scenarios.
A Neural Network Based on First Principles

In this paper, a Neural network is derived from first principles, assuming only that each layer begins with a linear dimension-reducing transformation. The approach appeals to the principle of Maximum Entropy (MaxEnt) to find the posterior distribution of the input data of each layer, conditioned on the layer output variables. This posterior has a well-defined mean, the conditional mean estimator, that is calculated using a type of neural network with theoretically-derived activation functions similar to sigmoid, softplus, and relu. This implicitly provides a theoretical justification for their use. A theorem that finds the conditional distribution and conditional mean estimator under the MaxEnt prior is proposed, unifying results for special cases. Combining layers results in an auto-encoder with conventional feed-forward analysis network and a type of linear Bayesian belief network in the reconstruction path.
FCEM: A Novel Fast Correlation Extract Model For Real Time Steganalysis Of VoIP Stream Via Multi-Head Attention

Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to their highly parallelizable computation and flexibility in modeling correlation in sequence, to tackle steganalysis problem of Quantization Index Modulation (QIM) based steganography in compressed VoIP stream. We design a light-weight neural network named Fast Correlation Extract Model (FCEM) only based on a variant of attention called multi-head attention to extract correlation features from VoIP frames. Despite its simple form, FCEM outperforms complicated Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) models on both prediction accuracy and time efficiency. It significantly improves the best result in detecting both low embedded rates and short samples recently. Besides, the proposed model accelerates the detection speed as twice as before when the sample length is as short as 0.1s, making it a excellent method for online services.
Improved End-To-End Spoken Utterance Classification with a Self-Attention Acoustic Classifier

While human language provides a natural interface for humanmachine communication, there are several challenges concerning extracting the intents of a speaker when interacting with a virtual agent, especially when the speaker is in a noisy acoustic environment, that still remains to be solved. In this paper, we propose a new architecture for end-to-end spoken utterance classification (SUC) and also explore the impact of leveraging lexical information in conjunction with acoustic information obtained from the end-to-end model for SUC. We demonstrate that strong performance can be obtained by the model with acoustic features alone compared to a text classifier on ASR outputs. Furthermore, when acoustic and lexical embeddings from these classifiers are combined, accuracy that is on par with human agents can be achieved.
Future public policy and its knowledge base: shaping worldviews through counterfactual world-making

Research in diverse areas such as climate change, happiness and wellbeing emphasizes the need for transformative change, stressing the importance of rethinking established values, goals and paradigms prevailing among civil servants, policy- and decision makers. In this paper, we discuss a role that design can play in this, especially how processes of counterfactual world-making can help facilitate reflection on worldviews and the shape of future forms of governance. By exploring different presents, rather than conditions in the future, this approach allows civil servants to consider, create and resist playful alternatives to business-as-usual. In this way, we demonstrate how design can stimulate imagination both as to futures and people’s role in shaping these futures.
An LSTM-based Spell Checker for Indonesian Text

A spell checker is a tool for detecting and correcting various spelling errors. While it might be trivial for humans, spell detecting and correcting can be very useful for machines, because machines could not detect spelling errors and correct them automatically. In Natural Language Processing (NLP), detecting and correcting spelling errors is a task that has been widely performed to normalize data, since most raw texts are noisy and have many spelling errors. In recent years, Long Short-term Memory (LSTM) has shown to give an extraordinary result in solving sequential problems, including spelling correction. In this paper, we propose an LSTM model that encodes input word at character level, that also uses word and POS tag contexts as features. We performed the experiment on an artificial dataset based on Indonesian Wikipedia articles that we made by simulating some artificial spelling errors at character level and tested it on real dataset, mostly are Indonesian online news articles. The evaluation on test dataset gives 83.76% accuracy.
Classification and Diagnosis of Invasive Ductal Carcinoma Using Deep Learning

In the past decades, researchers have demonstrated abilities to automate the process of detection and analysis of different kinds of cancers using Whole Slide Images (WSI) datasets. The breast cancer detection in histopathology images (one of the WSI dataset) using deep learning is one of the key research areas among the Computer AiDed (CAD) diagnostic systems. When it is done manually, it is a very tedious and challenging task for a pathologist as it involves thorough scanning of tissues to detect malignancy. This paper presents Convolutional Neural Network (CNN) classifier for breast cancer detection on the Breast Histopathology Images (BHI) dataset. A confusion matrix is computed for the BHI samples to analyze the prediction results of the CNN classifier. The CNN detects carcinoma tissues while labeling 55,505 image test samples as positive or negative; and achieves accuracy of 84.93%, recall of 84.70% and F-measure as 76.07% respectively.
Fine-Grained Action Recognition on a Novel Basketball Dataset

Currently most works on action recognition focus on the coarsely-grained actions, while the fine-grained action recognition is seldom addressed which is of vital importance in many applications such as video retrieval. To tackle this issue, in this paper, we release a challenging dataset by annotating the fine-grained actions in basketball game videos. A benchmark evaluation of the state-of-the-art approaches for action recognition is also provided on our dataset. Furthermore, we propose an approach by integrating the NTS-Net into two-stream network so as to locate the most informative regions and extract more discriminative features for fine-grained action recognition. Our experiments show that the proposed approach significantly outperforms the existing approaches.
Optimizing Deep Learning for Detection Cyberbullying Text in Indonesian Language

Cyberbullying in Indonesia currently become a concern due to the increasing usage of social media. Cyberbullying detection is an important step to make good environments in social media interactions. This research is part of computational linguistics that focuses on the usage of deep learning to detect bullying sentence on Twitter. There are two important processes in this study. First, the process of forming a word representation. Second, the classification process for detecting bullying sentences. Pre-trained process to build the new representation of term/word is performed independently. Word2vec is used as a tool for the pre-trained process. There are two types of data used in the pre-training process. The first type of data only used testing data and training data, while the second type of data is the overall data, total 26,800 unique Twitter sentences including test data and training data. The classification process is formed using three main algorithms that are popular for text classification: LSTM, bi-LSTM, and CNN. 9.854 labeled sentences are extracted from 2.584 Twitter conversations used as the dataset. The dataset consists of 1.680 sentences are labeled as a bully and 6.343 sentences are labeled as neutral. A total of 504 experiments are conducted in this research by exploiting the preprocessing stage for determining machine learning features, dropout layers configuration and the algorithm of deep learning. The experiments show that the accuracy score reaches 90.57% while the recall score for bully class reaches 75.7%.
Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images

Deep learning based image denoising methods have been extensively investigated. In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adopted to first refine the feature maps and to further make a full use of the inter-frame and intra-frame redundancies within the whole image burst. The proposed AME-KPNs output per-pixel spatially-adaptive kernels, residual maps and corresponding weight maps, in which, the predicted kernels roughly restore clean pixels at their corresponding locations via an adaptive convolution operation, and subsequently, residuals are weighted and summed to compensate the limited receptive field of predicted kernels. Simulations and real-world experiments are conducted to illustrate the robustness of the proposed AME-KPNs in burst image denoising.
1.4 The Future of Computing: Bits + Neurons + Qubits

The laptops, cell phones, and Internet applications commonplace in our daily lives are all rooted in the idea of zeros and ones - in bits. This foundational element originated from the combination of mathematics and Claude Shannon's Theory of Information. Coupled with the 50-year legacy of Moore's Law, the bit has propelled the digitization of our world. In recent years, artificial intelligence systems, merging neuron-inspired biology with information, have achieved superhuman accuracy in a range of narrow classification tasks by learning from labelled data. Advancing from Narrow Al to Broad Al will encompass the unification of learning and reasoning through neuro-symbolic systems, resulting in a form of Al which will perform multiple tasks, operate across multiple domains, and learn from small quantities of multi-modal input data. Finally, the union of physics and information led to the emergence of Quantum Information Theory and the development of the quantum bit - the qubit - forming the basis of quantum computers. We have built the first programmable quantum computers, and although the technology is still in its early days, these systems offer the potential to solve problems which even the most powerful classical computers cannot. The future of computing will look fundamentally different than it has in the past. It will not be based on more and cheaper bits alone, but rather, it will be built upon bits + neurons + qubits. This future will enable the next generation of intelligent mission critical systems and accelerate the rate of science-driven discovery.
CurveCluster: Automated Recognition of Hard Landing Patterns Based on QAR Curve Clustering

Flight safety is of vital importance to the aviation industry. As one of the most typical security events, hard landing is extremely concerned by airlines and related studies have received extensive attention in recent years. However, existing regression or risk based models either suffers from low prediction accuracy, or cannot provide good interpretability, making themselves impractical in real applications. To solve these problems, in this paper we propose CurveCluster: a curve clustering-based approach which is able to automatically recognize hard landing patterns from quick access recorder (QAR) data. We first provide a two-level hierarchical classification of hard landing events based on different hard landing patterns. Then we extract curve features from several key QAR parameters through interpolation and resampling. Finally, we apply K-means clustering algorithm on the curve features to automatically recognize the hard landing patterns. We test our approach on a dataset of 9,203 A320 flight QAR data samples and the overall recognition accuracy reaches up to 93.1%. Moreover, our results directly reflect the reasons of different types of hard landing events, which show strong interpretability.
Architecting Noisy Intermediate-Scale Quantum Computers: A Real-System Study

Current quantum computers have very different qubit implementations, instruction sets, qubit connectivity, and noise characteristics. Using real-system evaluations on seven quantum systems from three leading vendors, our work explores fundamental design questions concerning hardware choices, architecture, and compilation.
Classification of childhood medulloblastoma into WHO‐defined multiple subtypes based on textural analysis

Summary Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture‐based computer‐aided categorization of childhood medulloblastoma samples. According to the World Health Organization, it has four subtypes (desmoplastic, classic, nodular and large). Classification is done in two levels: (i) normal and abnormal and (ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework for automated classification is based on the architectural property and the distribution of cells. Five texture features were extracted for the feature set, namely: grey‐level co‐occurrence matrix, grey‐level run length matrix, first‐order histogram features, local binary pattern and Tamura features. The performance of each feature set was evaluated, both individually and in combinations, using five different classifiers. Fivefold cross‐validation was used for training and testing the data set. Experiments on both individual feature sets and combinations (best‐2, best‐3, best‐4 and all‐5) of feature sets were evaluated based on the accuracy of performance. It was revealed that the combined best‐4 feature set resulted in the highest accuracy of 91.3%. The precision, recall and specificity were 0.913, 0.913 and 0.97, respectively. Significantly, it implied that the all‐5 feature set is not necessary to have a useful classification. Feature reduction by principal component analysis resulted in increased accuracy of 96.7%. Lay description Childhood medulloblastoma is a case of childhood brain tumour that requires high attention due to a low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture‐based computer‐aided categorization of childhood medulloblastoma samples. According to the World Health Organization (W.H.O), it has four subtypes (desmoplastic, classic, nodular, and large). Classification is done in two levels: i) normal and abnormal ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework is a model for the automatic classification of the samples. The tissue samples obtained post‐operation by doctors are converted into images, and then necessary algorithms are applied so that certain features describing each group of the image are known and studied for classification. Later these images are classified using the image features into the subtypes of abnormal samples.
Stacked Pooling for Boosting Scale Invariance of Crowd Counting

In this work, we take insight into the dense crowd counting problem by exploring the phenomenon of cross-scale visual similarity caused by perspective distortions. It is a quite common phenomenon in crowd scenarios, suggesting the crowd counting model to enable a good performance of scale invariance. Existing deep crowd counting approaches mainly focus on the multi-scale techniques over convolutional layers to capture scale-adaptive features, resulting in high computing costs. In this paper, we propose simple but effective pooling variants, i.e., multi-kernel pooling and stacked pooling, to take place of the vanilla pooling layers in convolutional neural networks (CNNs) for boosting the scale invariance. Our proposed pooling modules do not introduce extra parameters and can be easily implemented in practice. Empirical studies on two benchmark crowd counting datasets show that the proposed pooling modules beat the vanilla pooling layer in most experimental cases.