Reviewerly/exHarmony huggingface dataset:
Digital methods III: The digital mundane

This report considers a burgeoning strand of scholarship that foregrounds the mundane in engagements with the digital. Research concerned with the digital mundane attends to the ordinary and often taken-for-granted digital objects, practices, productions, and sites that significantly both mediate and are mediated by everyday lives and spatialities. Methodological innovations are advancing new techniques for researching mundane digital objects that participate in the internet of things, everyday spaces of the smart home, banal landscapes of data and digital infrastructures, and quotidian quantifications/datafications of the self. The proliferation of these methods also informs feminist scholarly praxes of digital iteration.
Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks

The thyroid ultrasound standard plane (TUSP) classification is quite essential for the ultrasound diagnosis of thyroid disease. The traditional method relies entirely on the ultrasonography doctor to do it manually, which is not only time-consuming and labor-intensive but also subjectively influenced by the doctor's experience and knowledge reserve. Therefore, a TUSP automatic classification method is desirable in the clinical diagnosis of thyroid ultrasound. In this paper, we proposed that using deep learning convolutional neural network (CNN) method to achieve the automatic classification of TUSP images, and the classification effect of CNN models with different structures is also compared. In our experiment, 4,509 TUSP images collected from the hospital's real data are randomly divided into 3,386 sheets as the training set and 1,123 sheets as the test set. The test set experimental results show that the 18-layer CNN model ResNet has a good performance for automatic classification of TUSP images, and the accuracy of TUSP images classification reaches 83.88%. This indicates that the deep learning method can classify TUSP images effectively, which lays a foundation for the automatic diagnosis of thyroid diseases.
A Decade of Blockchain: Review of the Current Status, Challenges, and Future Directions

In this paper, we present the progress of blockchain technology from the advent of the original publication titled "Bitcoin: A Peer-to-Peer Electronic Cash System," written by the mysterious Satoshi Nakamoto, until the current days.Historical background and a comprehensive overview of the blockchain technology are given.We provide an up-to-date comparison of the most popular blockchain platforms with particular emphasis given to consensus protocols.Additionally, we introduce a BlockLib, an extensively growing online library on blockchain platforms collected from the various sources and designed to enable contributions from the blockchain community.Main directions of the current blockchain research, facing challenges as well as the main fields of applications, are summarized.We also layout the possible future lines in the blockchain technology development.
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing the regions with high sample density in the feature space, which could lead to locally sufficient samples for robust learning. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. This inspires us to propose the Max-Mahalanobis center (MMC) loss to explicitly induce dense feature regions in order to benefit robustness. Namely, the MMC loss encourages the model to concentrate on learning ordered and compact representations, which gather around the preset optimal centers for different classes. We empirically demonstrate that applying the MMC loss can significantly improve robustness even under strong adaptive attacks, while keeping state-of-the-art accuracy on clean inputs with little extra computation compared to the SCE loss.
Reasoning on Knowledge Graphs with Debate Dynamics

We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments – paths in the knowledge graph – with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.
Classification of Kidney Cancer Data Using Cost-Sensitive Hybrid Deep Learning Approach

Recently, large-scale bioinformatics and genomic data have been generated using advanced biotechnology methods, thus increasing the importance of analyzing such data. Numerous data mining methods have been developed to process genomic data in the field of bioinformatics. We extracted significant genes for the prognosis prediction of 1157 patients using gene expression data from patients with kidney cancer. We then proposed an end-to-end, cost-sensitive hybrid deep learning (COST-HDL) approach with a cost-sensitive loss function for classification tasks on imbalanced kidney cancer data. Here, we combined the deep symmetric auto encoder; the decoder is symmetric to the encoder in terms of layer structure, with reconstruction loss for non-linear feature extraction and neural network with balanced classification loss for prognosis prediction to address data imbalance problems. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy by sample type, primary diagnosis, tumor stage, and vital status as risk factors representing the state of patients. Experimental results showed that the COST-HDL approach was more efficient with gene expression data for kidney cancer prognosis than other conventional machine learning and data mining techniques. These results could be applied to extract features from gene biomarkers for prognosis prediction of kidney cancer and prevention and early diagnosis.
Video Surveillance on Mobile Edge Networks—A Reinforcement-Learning-Based Approach

Video surveillance systems or Internet of Multimedia Things are playing a more and more important role in our daily life. To obtain useful surveillance information timely and accurately, not only image recognition algorithms but also computing and communication resources can be bottlenecks of the whole system. In this article, taking face recognition application as an example, we study how to build video surveillance systems by utilizing mobile edge computing (MEC), one of the 5G's key technologies. Specifically, to achieve high recognition accuracy and low recognition time, we design image recognition algorithms for both the camera sensor and MEC server, and utilize the action-value methods to train actions of the system by jointly optimizing offloading decision and image compression parameters. The experimental results show the advantages of the proposed system for enabling communication environment-adaptive, efficient, and intelligent video surveillance.
Evolutionary LSTM-FCN networks for pattern classification in industrial processes

The Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolutional Network (LSTM-FCN) networks are gaining a lot of attention over the last decade due to their capability of successfully modeling nonlinear feature interactions. However, they have not been yet fully applied for pattern classification tasks in time series data within the digital industry. In this paper, a novel approach based on an evolutionary algorithm for optimizing the networks hyperparameters and on the resulting deep learning model for pattern classification is proposed. In order to demonstrate the applicability of this method, a test scenario that involves a process related to blind fastener installation in the aeronautical industry is provided. The results achieved with the proposed approach are compared with shallow models and it is demonstrated that the proposed method obtains better results with an accuracy value of 95%.
A novel hybrid particle swarm optimization and gravitational search algorithm for multi-objective optimization of text mining

Big-data is one of the milestones on the web especially on social media (SM). Due to the widespread popularity of SM on the web, it is a painful task to capture the essence of SM. In this study, mining big social media data is re-formulated into a multi-objective optimization (MOO) task for an extractive summary. A Gravitational Search Algorithm (GSA) is utilized for optimizing several expressive objectives for generating a concise summary of SM. Moreover, particle swarm optimization (PSO) is mixed with GSA in a new shape to strengthen a local search ability and slow convergence speed in standard GSA. Whereas some users may demand the brief at any moment, several groups are constituted for incremental updating process during real-time based on naïve Bayes algorithm. From experimental results, the proposed approach outperformed other notable and state-of-art comparative methods.
Adversarial Classification Under Differential Privacy

The last decade has seen a growing interest in adversarial classification, where an attacker tries to mislead a classifier meant to detect anomalies.We study this problem in a setting where anomaly detection is being used in conjunction with differential privacy to protect personal information.We show that a strategic attacker can leverage the additional noise (introduced to ensure differential privacy) to mislead the classifier beyond what the attacker could do otherwise; we also propose countermeasures against such attacks.We then evaluate the impact of our attacks and defenses in road traffic congestion and smart metering examples.
Comparative analysis of median filter and its variants for removal of impulse noise from gray scale images

Image denoising is a vital pre-processing phase, used to refine the image quality and make it more informative. Many image-denoising algorithms have been proposed with their own pros and cons. This paper presents a comprehensive study of the median filter and its different variants to reduce or remove the impulse noise from gray scale images. These filters are compared with respect to their functionality, time complexity and relative performance. For performance evaluation of the existing algorithms, extensive MATLAB based simulations have been carried out on a set of images. For benchmarking the relative performance, we have used Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Universal Image Quality Index (UQI), Structural Similarity Index (SSIM) and Edge-strength Similarity (ESSIM) as quality assessment metrics. The Extended median filter (EMF) and Modified BDND are best in terms of relative statistical ratios and pleasant visual results where IAMF is having the best time complexity among existing algorithms.
Attention-based LSTM, GRU and CNN for short text classification

Text classification is a fundamental task in Nature Language Processing(NLP). However, with the challenge of complex semantic information, how to extract useful features becomes a critical issue. Different from other traditional methods, we propose a new model based on two parallel RNNs architectur e, which captures context information through LSTM and GRU respectively and simultaneously. Motivated by the siamese network, our proposed architecture generates attention matrix through calculating similarity between the parallel captured context information, which ensures the effectiveness of extracted features and further improves classification results. We evaluate our proposed model on six text classification tasks. The result of experiments shows that the ABLGCNN model proposed in this paper has the faster convergence speed and the higher precision than other models.
OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning

Online Signature Verification (OSV) techniques have been deployed in production systems for decades, yet training the model for efficient classification of the test signature from fewer training signature samples is still an open challenge. The advancements in Convolutional Neural Networks (CNNs) enormously boosted the effectiveness of OSV systems. However, learning subtle and discriminating representations from few training samples to classify the genuineness of test signature has not been explored fully. In this paper, a Convolution Autoencoder (CAE) is used to obtain high-level feature representations from the input signature and these high level features are fused with handcrafted features to constitute a hybrid feature set. The hybrid set of features is presented as an input to an Online Signature Verification framework made up of Depth-wise Separable Convolutional Neural Network (DWSCNN). DWSCNN effectively learn deep feature representations with fewer training samples and parameters than traditional CNNs resulting in a light weight OSV framework. Thorough experimental analysis on three benchmark datasets MCYT-100 (DB1), SVC-2004-Task2 and SUSIG-Visual corpus confirm that the proposed hybrid fusion of feature set and DWSCNN based OSV framework achieve higher classification accuracies and outperforms many contemporary and state-of-the art OSV models.
CTC-Segmentation of Large Corpora for German End-to-End Speech Recognition

Recent end-to-end Automatic Speech Recognition (ASR) systems demonstrated the ability to outperform conventional hybrid DNN/HMM ASR. Aside from architectural improvements in those systems, those models grew in terms of depth, parameters and model capacity. However, these models also require more training data to achieve comparable performance.In this work, we combine freely available corpora for German speech recognition, including yet unlabeled speech data, to a big dataset of over 1700 h of speech data. For data preparation, we propose a two-stage approach that uses an ASR model pre-trained with Connectionist Temporal Classification (CTC) to boot-strap more training data from unsegmented or unlabeled training data. Utterances are then extracted from label probabilities obtained from the network trained with CTC to determine segment alignments. With this training data, we trained a hybrid CTC/attention Transformer model that achieves 12.8% WER on the Tuda-DE test set, surpassing the previous baseline of 14.4% of conventional hybrid DNN/HMM ASR.
Patch-Based Dual-Tree Complex Wavelet Transform for Kinship Recognition

Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities. This paper first presents kinship recognition for similar full facial images using proposed Global-based dual-tree complex wavelet transform (G-DTCWT). We then present novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT): Local Patch-based DT-CWT (LP-DTCWT) and Selective Patch-Based DT-CWT (SP-DTCWT). LP-DTCWT extracts coefficients for smaller facial patches for kinship recognition. SP-DTCWT is an extension to LP-DTCWT and extracts coefficients only for representative patches with similarity scores above a normalized cumulative threshold. This threshold is computed by a novel patch selection process. These representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy. Proposed methods are extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results showcase efficacy of proposed methods on all kinship datasets. SP-DTCWT achieves competitive accuracy to state-of-the-art methods. Mean kinship accuracy of SP-DTCWT is 95.85% on baseline KinFaceW-I and 95.30% on KinFaceW-II datasets. Further, SP-DTCWT achieves the state-of-the-art accuracy of 80.49% on the largest kinship dataset, Families In the Wild (FIW).
Benchmarking photon number resolving detectors

Photon number resolving detectors are the ultimate measurement of quantum optics, which is the reason why developing the technology is getting significant attention in recent years. With this arises the question of how to evaluate the performance of the detectors. We suggest that performance of a photon number detector can be evaluated by comparing it to a multiplex of on-off detectors in a practical scenario: conditional preparation of a photon number state. Here, both the quality of the prepared state and the probability of the preparation are limited by the number of on-off detectors in the multiplex, which allows us to set benchmarks that can be achieved or surpassed by the photon number resolving detectors.
Supervised learning model for identifying illegal activities in Bitcoin

Since its inception in 2009, Bitcoin is mired in controversies for providing a haven for illegal activities. Several types of illicit users hide behind the blanket of anonymity. Uncovering these entities is key for forensic investigations. Current methods utilize machine learning for identifying these illicit entities. However, the existing approaches only focus on a limited category of illicit users. The current paper proposes to address the issue by implementing an ensemble of decision trees for supervised learning. More parameters allow the ensemble model to learn discriminating features that can categorize multiple groups of illicit users from licit users. To evaluate the model, a dataset of 1216 real-life entities on Bitcoin was extracted from the Blockchain. Nine Features were engineered to train the model for segregating 16 different licit-illicit categories of users. The proposed model provided a reliable tool for forensic study. Empirical evaluation of the proposed model vis-a-vis three existing benchmark models was performed to highlight its efficacy. Experiments showed that the specificity and sensitivity of the proposed model were comparable to other models. Due to higher parameters of the ensemble tree model, the classification accuracy was 0.91, with 95% CI - 0.8727, 0.9477. This was better than SVM and Logistic Regression, the two popular models in the literature and comparable to the Random Forest and XGBOOST model. CPU and RAM utilization were also monitored to demonstrate the usefulness of the proposed work for real-world deployment. RAM utilization for the proposed model was higher by 30-45% compared to the other three models. Hence, the proposed model is resource-intensive as it has higher parameters than the other three models. Higher parameters also result in higher accuracy of predictions.
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation

Context-dependent text-to-SQL task has drawn much attention in recent years.Previous models on context-dependent text-to-SQL task only concentrate on utilizing historical user inputs.In this work, in addition to using encoders to capture historical information of user inputs, we propose a database schema interaction graph encoder to utilize historicalal information of database schema items.In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens.We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets.Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets.The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.
A multi-scale descriptor for real time RGB-D hand gesture recognition

The development of depth cameras, e.g., the Kinect sensor, provides new opportunities for human computer interaction (HCI). Although the Kinect sensor has been extensively applied for human tracking, human action recognition and hand gesture recognition, real time hand gesture recognition is still a challenging problem. In this paper, a new real time hand gesture recognition method is proposed. Since fingers are the most important clue for hand gesture classification, a finger-emphasized multi-scale descriptor is proposed. The proposed descriptor incorporates three types of parameters of multiple scales to make a discriminative representation of the hand shape. Furthermore, the features of fingers are emphasized for hand gesture analysis. Three solutions to hand gesture recognition are then investigated with DTW, SVM, and neural network. Extensive experiments are conducted and the results show that the proposed method is robust to noise, articulations and rigid transformations. The comparison with state-of-the-art methods verifies the accuracy and efficiency of our method.
Learning Multi-human Optical Flow

Abstract The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the domain of human motion. Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset. We use a 3D model of the human body and motion capture data to synthesize realistic flow fields in both single- and multi-person images. We then train optical flow networks to estimate human flow fields from pairs of images. We demonstrate that our trained networks are more accurate than a wide range of top methods on held-out test data and that they can generalize well to real image sequences. The code, trained models and the dataset are available for research.