Reviewerly/exHarmony huggingface dataset:
Speech Recognition and Machine Translation Using Neural Networks

The application of deep recurrent neural networks LSTM (long short-term memory) for English-Russian translation from speech to text is studied. Deep neural network learning and validation was performed based on English audiobooks. The neural network learning dataset was compiled by splitting an English audiobook and Russian text into sentences, and extracting features from audio files. Algorithms for learning and validating a neural network are available as part of the TensorFlow machine learning library. The calculations were performed on a Linux server with two high-performance NVIDIA video cards. Two types of models were learned: models for translating text to text and models for translating speech to text. As a result of the work, it was revealed that models based on deep neural networks are effective for machine translation of a language.
Weakly-supervised temporal attention 3D network for human action recognition

From a series of observations, we have inferred that human actions in videos are defined by a set of significant frames. In this paper, we propose a weakly-supervised temporal attention 3D network for human action recognition, called as TA3DNet, to accelerate 3D convolutional neural networks (3D CNNs) by temporally assigning different importance to each frame. First, we obtain short-term frames with long-term connection by regularly or randomly skipping frames to avoid temporal redundancy, and apply 3D convolutional layers to extract features for action recognition. Then, we apply a temporal attention module to assign different weights to each frame. We train the temporal attention module in a weakly-supervised manner that updates weights based on only class labels without event information and extra labels. Thus, TA3DNet reduces the number of input frames and constructs a lightweight network for action recognition. Experimental results demonstrate that TA3DNet achieves high performance on two challenging datasets (UCF101 and HMDB51) and outperforms state-of-the-art methods for action recognition.
A Self-Adaptive Reinforcement-Exploration Q-Learning Algorithm

Directing at various problems of the traditional Q-Learning algorithm, such as heavy repetition and disequilibrium of explorations, the reinforcement-exploration strategy was used to replace the decayed ε-greedy strategy in the traditional Q-Learning algorithm, and thus a novel self-adaptive reinforcement-exploration Q-Learning (SARE-Q) algorithm was proposed. First, the concept of behavior utility trace was introduced in the proposed algorithm, and the probability for each action to be chosen was adjusted according to the behavior utility trace, so as to improve the efficiency of exploration. Second, the attenuation process of exploration factor ε was designed into two phases, where the first phase centered on the exploration and the second one transited the focus from the exploration into utilization, and the exploration rate was dynamically adjusted according to the success rate. Finally, by establishing a list of state access times, the exploration factor of the current state is adaptively adjusted according to the number of times the state is accessed. The symmetric grid map environment was established via OpenAI Gym platform to carry out the symmetrical simulation experiments on the Q-Learning algorithm, self-adaptive Q-Learning (SA-Q) algorithm and SARE-Q algorithm. The experimental results show that the proposed algorithm has obvious advantages over the first two algorithms in the average number of turning times, average inside success rate, and number of times with the shortest planned route.
Predictive classification of ICU readmission using weight decay random forest

Intensive care unit (ICU) readmissions of critically ill patients result in significant increases in mortality rates and costs, but most readmissions could be avoided. Therefore, the medical management community has devoted considerable effort to developing predictive classifications for ICU readmissions. However, the existing classification methods lack effective feature engineering and are dependent on large quantity of imbalanced and sparse data. In this paper, we use an objective quantitative data set to estimate the probability of ICU readmission for patients who have been transferred from the ICU to the general ward at various risk levels. To implement valuable feature selection for imbalanced time series data, we integrate the missing value analysis and the likelihood ratio test for the distribution characteristics of time series indicators and introduce a weight decay random forest model to achieve ICU readmission classification based on sparse data. Using these approaches, we can rank the most relevant factors that affect the probability of ICU readmission and identify the missing indicators that have the greatest impact on ICU readmission classification. Comprehensive experimental results show that our proposed method can outperform other traditional methods according to seven different performance indicators.
#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic

We investigated the differential impacts of a new Twitter-based Market Uncertainty index (TMU) and variables for Bitcoin before and during the COVID-19 pandemic. Results showed that TMU is a leading indicator of Bitcoin returns only during the pandemic, and the effect of the TMU on Bitcoin’s conditional volatility is significantly greater during the pandemic. Furthermore, during the pandemic, the uncertainty content of people’s tweets is impacted by the highly salient Bitcoin market. Taken together, our results suggest that the information contained in virtual communities such as Twitter have a much larger impact on cryptocurrency markets following COVID-19.
Evaluation of active learning algorithms for formation lithology identification

Lithology identification using well log data plays an important part in formation characterization and reservoir exploration. Recent years, the development of machine learning has provided new technologies for lithology identification research. Most of the existing studies utilized supervised learning algorithms, which needs a large quantity of labeled well-logging data to train the model. Labeling the well logging is usually accomplished by the acquiring and then analyzing the cores and cuttings, so the labels are relatively expensive, which motivates us to find a more effective way to select more informative labels actively. Such an issue could be solved by resorting to active learning which helps us to reduce the labeling cost significantly meanwhile preserving the classification accuracy. Specifically, we evaluate five popular categories of learning methods, namely the Uncertainty, UncertaintyEntropy, Committee, Diversity and CoreSet for lithology identification problem. The data is collected from the Shengli Oil Field and the Hangjinqi Gas Field. We conduct extensive experiments to evaluate the experiment results by adjusting the hyperparameters, e.g., batch size and iterations. The results suggest that Uncertainty and UncertaintyEntropy are better choices for active learning algorithms of well logging-based lithology classification. In the case of only 560 labeled samples, the macro-R and the macro-F1 of Uncertainty can reach 89.1% and 90.2% in Shengli Oil Field dataset and the macro-R and the macro-F1 of UncertaintyEntropy in Hangjinqi Gas Field dataset can reach 77.3% and 72.1%.
Block Access Control in Wireless Blockchain Network: Design, Modeling and Analysis

Wireless blockchain network is proposed to enable a decentralized and safe wireless networks for various blockchain applications. To achieve blockchain consensus in wireless network, one of the important steps is to broadcast new block using wireless channel. Under wireless network protocols, the block transmitting will be affected significantly. In this work, we focus on the consensus process in blockchain-based wireless local area network (B-WLAN) by investigating the impact of the media access control (MAC) protocol, CSMA/CA. With the randomness of the backoff counter in CSMA/CA, it is possible for latter blocks to catch up or outpace the earlier one, which complicates blockchain forking problem. In view of this, we propose mining strategies to pause mining for reducing the forking probability, and a discard strategy to remove the forking blocks that already exist in CSMA/CA backoff procedure. Based on the proposed strategies, we design Block Access Control (BAC) approaches to effectively schedule block mining and transmitting for improving the performance of B-WLAN. Then, Markov chain models are presented to conduct performance analysis in B-WLAN. The results show that BAC approaches can help the network to achieve a high transaction throughput while improving block utilization and saving computational power. Meanwhile, the trade-off between transaction throughput and block utilization is demonstrated, which can act as a guidance for practical deployment of blockchain.
A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.
Quantum autoencoders with enhanced data encoding

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.
Entanglement Robustness via Spatial Deformation of Identical Particle Wave Functions

We address the problem of entanglement protection against surrounding noise by a procedure suitably exploiting spatial indistinguishability of identical subsystems. To this purpose, we take two initially separated and entangled identical qubits interacting with two independent noisy environments. Three typical models of environments are considered: amplitude damping channel, phase damping channel and depolarizing channel. After the interaction, we deform the wave functions of the two qubits to make them spatially overlap before performing spatially localized operations and classical communication (sLOCC) and eventually computing the entanglement of the resulting state. This way, we show that spatial indistinguishability of identical qubits can be utilized within the sLOCC operational framework to partially recover the quantum correlations spoiled by the environment. A general behavior emerges: the higher the spatial indistinguishability achieved via deformation, the larger the amount of recovered entanglement.
PCA-based Hotelling's T2 chart with fast minimum covariance determinant (FMCD) estimator and kernel density estimation (KDE) for network intrusion detection

In this work, the combination between the Principal Component Analysis (PCA) and the Hotelling’s T2 chart is proposed to solve problems caused by the many highly correlated network traffic features and to reduce the computational time without reducing its accuracy detection. However, a new issue arises due to the difficulty of the network traffic observations to follow the multivariate normal distribution as required in Hotelling’s T2 chart. Consequently, many false alarms are found in inspecting network intrusion detection. To solve this issue, the Kernel Density Estimation (KDE) procedure is applied to obtain an optimum control limit. Also, to improve the accuracy detection, the Fast Minimum Covariance Determinant (FMCD) is employed to estimate the robust mean vector and covariance matrix. Experiments using the simulated dataset are conducted to assess the proposed chart’s performance in detecting the presence of outlier for the normal and non-normal of multivariate data. According to the simulation studies, the proposed chart yields higher accuracy and a high detection rate with a low false alarm rate. Further, the proposed Intrusion Detection System (IDS) is utilized in scanning attacks. The reputable KDD99 data is used as the benchmark to make a fair comparison between the proposed IDS and some algorithms. The monitoring outputs show that the proposed approach produces advancements in the speed of computational time with 87.42% of time efficiency. Compared to the other charts in detecting intrusion, the proposed chart produces the lower False Negative Rate (FNR). Also, compared to some classifiers the proposed chart yields a higher accuracy at about 0.9893.
Deplatforming the Far-right: An Analysis of YouTube and BitChute

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DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search and derives the cell topology from the operation weights. However, the operation weights can not indicate the importance of cell topology and result in poor topology rating correctness. To tackle this, we propose to Decouple the Operation and Topology Search (DOTS), which decouples the topology representation from operation weights and makes an explicit topology search. DOTS is achieved by introducing a topology search space that contains combinations of candidate edges. The proposed search space directly reflects the search objective and can be easily extended to support a flexible number of edges in the searched cell. Existing gradient-based NAS methods can be incorporated into DOTS for further improvement by the topology search. Considering that some operations (e.g., Skip-Connection) can affect the topology, we propose a group operation search scheme to preserve topology-related operations for a better topology search. The experiments on CI-FAR10/100 and ImageNet demonstrate that DOTS is an effective solution for differentiable NAS. The code is released at https://github.com/guyuchao/DOTS.
A method Based on an Attention Mechanism to Measure the Similarity of two Sentences

Bidirectional LSTMs and the attention mechanism play an essential role in many areas of natural language processing. Many studies give equal importance to words, which leads to a flawed model. This research offers a method based on Attention-Based Bidirectional Long-Short Term Memory (BLSTM) to solve the problem of plagiarism at the sentence level. For this purpose, word embedding is first made with Glove and Word2Vec methods and is considered as initial embedding. Then the two BLSTM networks are used separately for sentence embedding. Finally, the embedding of sentences and their differences are connected and passed through a classifier. We evaluate our model on two datasets of Persian and English. The evaluation results show the superiority of the proposed model over other compared methods.
Exploration of Polymetallic Nodules and Resource Assessment: A Case Study from the German Contract Area in the Clarion-Clipperton Zone of the Tropical Northeast Pacific

In 2006, the BGR signed a contract with the International Seabed Authority (ISA) for the exploration of polymetallic nodules in the Clarion-Clipperton Zone of the northeast Pacific. During nine expeditions, in particular, “Area E1”, the eastern part of the contract area, with a size of ~60,000 km2, was explored in detail. Here, we outline BGR’s exploration methods and provide resource estimates for Area E1 and three sub-areas. The resource assessment is predominantly based on statistical analyses of data obtained by 12-kHz multibeam bathymetry and backscatter mapping, box core sampling and geochemical analysis of nodules. The main parameter for the assessment is the nodule abundance (kg/m2), as its coefficient of variation (CoV) over the entire eastern contract area is relatively high at 36%. In contrast, the metal contents of nodules show only minor variation, with a CoV of 8% for manganese and 8% for the sum of copper, nickel and cobalt. To estimate mineral resources for the entire Area E1, we used an artificial neural network approach with a multivariate statistical correlation between nodule abundance derived from box cores and hydro-acoustic data. The total estimated resources are 540 ± 189 million tonnes (Mt) of dry nodules, and the total estimated metal contents are 168 Mt of manganese, 7.5 Mt of nickel, 6.3 Mt of copper, 0.9 Mt of cobalt, 0.4 Mt of rare-earth elements and 0.3 Mt of molybdenum. A geostatistical resource estimate of three economically prospective areas with a total size of 4498 km2, intensively sampled by box cores, was carried out using ordinary kriging of nodule abundance and metal grades. Within these three nodule fields, 7.14 Mt of dry nodules are classified as measured mineral resources covering an area of 489 km2. Indicated mineral resources amount to 11.2 Mt, covering an area of 825 km2, and inferred mineral resources of 35.5 Mt of dry nodules were estimated for an area of 3184 km2. In total, the metal contents of the three prospective areas amount to 16.8 Mt of manganese, 0.74 Mt of nickel, 0.63 Mt of copper and 0.09 Mt of cobalt.
A comprehensive investigation of the impact of feature selection techniques on crashing fault residence prediction models

Software crash is a serious form of the software failure, which often occurs during the software development and maintenance process. As the stack trace reported when the software crashes contains a wealth of information about crashes, recent work utilized classification models with the collected features from stack traces and source code to predict whether the fault causing the crash resides in the stack trace. This could speed-up the crash localization task. As the quality of features can affect the performance of the constructed classification models, researchers proposed to use feature selection methods to select a representative feature subset to build models by replacing the original features. However, only limited feature selection methods and classification models were taken into consideration for this issue in previous work. In this work, we look into this topic deeply and find out the best feature selection method for crash fault residence prediction task. We study the performance of 24 feature selection techniques with 21 classification models on a benchmark dataset containing crash instances from 7 real-world software projects. We use 4 indicators to evaluate the performance of these feature selection methods which are applied to the classification models. The experimental results show that, overall, a probability-based feature selection, called Symmetrical Uncertainty, performs well across the studied classification models and projects. Thus, we recommend such a feature selection method to preprocess the crash instances before constructing classification models to predict the crash fault residence. This work conducts a large-scale empirical study to investigate the impact of feature selection methods on the performance of classification models for the crashing fault residence prediction task. The results clearly demonstrate that there exist significant performance differences among these feature selection techniques across different classification models and projects.
Generative Incomplete Multi-View Prognosis Predictor for Breast Cancer: GIMPP

In today's digital world, we are equipped with modern computer-based data collection sources and feature extraction methods. It enhances the availability of the multi-view data and corresponding researches. Multi-view prediction models form a mainstream research direction in the healthcare and bioinformatics domain. While these models are designed with the assumption that there is no missing data for any views, in the real world, certain views of the data are often not having the same number of samples, resulting in the incomplete multi-view dataset. The studies performed over these datasets are termed incomplete multi-view clustering or prediction. Here, we develop a two-stage generative incomplete multi-view prediction model named GIMPP to address the missing view problem of breast cancer prognosis prediction by explicitly generating the missing data. The first stage incorporates the multi-view encoder networks and the bi-modal attention scheme to learn common latent space representations by leveraging complementary knowledge between different views. The second stage generates missing view data using view-specific generative adversarial networks conditioned on the shared representations and encoded features given by other views. Experimental results on TCGA-BRCA and METABRIC datasets proves the usefulness of the developed method over the state-of-the-art methods.
3D Semantic Deep Learning Networks for Leukemia Detection

White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses.Abnormal cell growth destroys the body's immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage.In this research, a deep learning technique is proposed for the detection of leukemia.The proposed methodology consists of three phases.Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs.The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model.The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs.The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of different metrics, such as precision, accuracy, sensitivity, and dice scores.The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness.