Reviewerly/exHarmony huggingface dataset:
Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.
A blood cell dataset for lymphoma classification using faster R-CNN

Lymphoma has become the seventh most common cancer expected to occur and the ninth most common cause of cancer death in both males and females. However, pathological diagnosis as the main diagnostic method is time-consuming, expensive and error-prone. Nowadays, with the outstanding performance of Convolutional Neural Network (CNN) in image analysis, its application in medical image classification and segmentation is becoming more and more widespread. Since Faster R-CNN achieves state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets, in this work, we use it to identify lymphoma cells from a dataset of blood cells. In order to achieve this goal, first, we focus on a new blood cell dataset that mainly consists of lymphoma cells, blasts and lymphocytes. This dataset will be used to fine-tune a pre-trained network. Then, we use two training methods and three networks to train the same dataset respectively. Finally, we choose the best trained model to diagnose lymphoma cells in a new dataset which also contains lymphoma cells, blasts and lymphocytes. The detection rate of lymphoma cells was higher than 96%, and the false detection rate was less than 13%, which is an improvement compared with the previously proposed results. The results show a potential of the proposed method in lymphoma diagnosis.
Biohacking: An exploratory study to understand the factors influencing the adoption of embedded technologies within the human body

<h2>Abstract</h2> Wearable tech is leading way to embedded tech, i.e., implants inside the body designed to track and enhance human health and productivity among other things. Researchers have used Technology Acceptance Model (TAM) extensively to explain the factors influencing adoption of almost all technological innovations to date. Embedded tech, often referred to as biohacking, presents a unique set of factors that call for yet another revision of the model. Using diffusion of innovations, self-efficacy, and social exchange theory, a revision to the technology acceptance model is proposed with additional factors such as age and gender, embedded technology self-efficacy, perceived risk and privacy concerns to explain the adoption of embedded technologies within the human body. Data was collected through an online survey (N = 1063) using a Qualtrics panel and results suggest that age, gender, perceived usefulness, perceived ease of use, embedded technology self-efficacy, risk and privacy concerns all impact the adoption of embedded tech. Implications for the implant industry, policy makers, and researchers interested in such tech are drawn.
Web Runner 2049: Evaluating Third-Party Anti-bot Services

Given the ever-increasing number of malicious bots scouring the web, many websites are turning to specialized services that advertise their ability to detect bots and block them. In this paper, we investigate the design and implementation details of commercial anti-bot services in an effort to understand how they operate and whether they can effectively identify and block malicious bots in practice. We analyze the JavaScript code which their clients need to include in their websites and perform a set of gray box and black box analyses of their proprietary back-end logic, by simulating bots utilizing well-known automation tools and popular browsers. On the positive side, our results show that by relying on browser fingerprinting, more than 75% of protected websites in our dataset, successfully defend against attacks by basic bots built with Python scripts or PhantomJS. At the same time, by using less popular browsers in terms of automation (e.g., Safari on Mac and Chrome on Android) attackers can successfully bypass the protection of up to 82% of protected websites. Our findings show that the majority of protected websites are prone to bot attacks and the existing anti-bot solutions cannot substantially limit the ability of determined attackers. We have responsibly disclosed our findings with the anti-bot service providers.
A New Hybrid Level Set Approach

Hybrid active contour models with the combination of region and edge information have attracted great interests in image segmentation. To the best of our knowledge, however, the theoretical foundation of these hybrid models with level set evolution is insufficient and limited. More specifically, the weighting factors of their energy terms are difficult to select and are often empirically determined without definite theoretical basis. This problem is particularly prominent in the case of multi-object segmentation when more level set functions must be computed simultaneously. To cope with these challenges, this paper proposes a new level set approach for constructing hybrid active contour models with reliable energy weights, where the weights of region and edge terms can be constrained by the optimization condition deduced from the proposed method. It can be regarded as a general approach since many existing region-based models can be easily used to construct new hybrid models using their equivalent two-phase formulations. Some representative as well as state-of-the-art models are taken as examples to demonstrate the generality of our method. The respective comparative studies validate that under the guidance of the optimization condition, segmentation accuracy, robustness, and computational efficiency can be improved compared with the original models which are used to construct the new hybrid ones.
Contrast Enhancement of Low-Contrast Medical Images Using Modified Contrast Limited Adaptive Histogram Equalization

The low contrast medical images seriously affect the clinical diagnosis process. To improve the image quality, we propose an effective medical images contrast enhancement technique in this paper. Shear wavelet transformation is used for decomposition of image components into low-frequency and high-frequency. The low-frequency part contrast is adjusted by applying modified contrast limited adaptive histogram equalization (CLAHE). The resultant image is further processed through technique of fuzzy contrast enhancement to maintain the spectral information of an image. Results of the experimentation show that our proposed technique enhance the image contrast up to a good degree while preserving the image details.
Provable Self-Play Algorithms for Competitive Reinforcement Learning

Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. However, the majority of exisiting theory in reinforcement learning only applies to the setting where the agent plays against a fixed environment; it remains largely open whether self-play algorithms can be provably effective, especially when it is necessary to manage the exploration/exploitation tradeoff. We study self-play in competitive reinforcement learning under the setting of Markov games, a generalization of Markov decision processes to the two-player case. We introduce a self-play algorithm---Value Iteration with Upper/Lower Confidence Bound (VI-ULCB)---and show that it achieves regret $\tilde{\mathcal{O}}(\sqrt{T})$ after playing $T$ steps of the game, where the regret is measured by the agent's performance against a \emph{fully adversarial} opponent who can exploit the agent's strategy at \emph{any} step. We also introduce an explore-then-exploit style algorithm, which achieves a slightly worse regret of $\tilde{\mathcal{O}}(T^{2/3})$, but is guaranteed to run in polynomial time even in the worst case. To the best of our knowledge, our work presents the first line of provably sample-efficient self-play algorithms for competitive reinforcement learning.
Skeleton-Based Action Recognition Using Multi-Scale and Multi-Stream Improved Graph Convolutional Network

Graph convolutional networks (GCNs) have achieved outstanding performances on skeleton-based action recognition. However, several problems remain in existing GCN-based methods, and the spatial-temporal features are not discriminative enough. Temporal convolution with one fixed kernel cannot obtain more discriminative temporal features for different actions. Besides, only a single-scale feature is used for classification, which ignores the multilevel information. In this article, we propose a novel multi-scale and multi-stream improved graph convolutional network (MM-IGCN). In each spatial-temporal block of MM-IGCN, we employ an improved temporal convolution with multiple parallel kernels to enhance the temporal features. An improved GCN and an enhanced attention module are adopted in the block to strengthen spatial-temporal features. A multi-scale structure is first introduced in action recognition to obtain the multilevel information. The improved spatial-temporal blocks and multi-scale structure compose our single-stream model. Moreover, we adopt the bone cosine distance as a novel input feature. Five streams (joint, bone, their motions, and bone cosine distance) of features are fed into our single-stream model respectively, which compose our MM-IGCN. Experiments on two large datasets, NTU-RGB+D and NTU-RGB+D-120, illustrate that our single-stream model achieves state-of-the-art, and our MM-IGCN is far superior to other models.
Improved PV Forecasts for Capacity Firming

Some balancing authorities give owners of medium to large photovoltaic (PV) generation plants a choice between firming the production of their plants using battery energy storage or paying the balancing authority for the cost that these imbalances impose on the system. If the owner of a PV plant decides to do capacity firming, the net production of the PV plant and the battery must match a forecast value. A more accurate forecast of the PV production reduces the energy throughput of the battery and hence its degradation. This article compares capacity firming using persistence forecasts with predictions based on long short-term memory recurrent neural networks (LSTM-RNN), encoder-decoder LSTM-RNN and multi-layer perceptrons. This article also proposes to use the type-of-day, such as sunny, cloudy etc, which can be generated by clustering historical PV generation data according to the total daily PV generation, as a feature of the PV forecasting model. Results based on the Snohomish County Public Utility District's Arlington Microgrid show that the machine learning techniques perform significantly better than the persistence method in forecasting PV generation. In particular, encoder-decoder LSTM-RNN would reduce the yearly battery energy throughput by 29% and the number of battery cycles with a greater than 10% depth-of-discharge (DoD) by 51%. Including the day-type as a feature in PV forecasting reduces the battery energy throughput by 5.3% and the number of cycles with a DoD larger than 10% by 5.9%.
Deep Hybrid Neural Network and Improved Differential Neuroevolution for Chaotic Time Series Prediction

Chaos is widespread in non-linear systems such as finance, energy, and weather. In the chaos system, a variable changing with time generates a chaotic time series, which contains a wealth of information about the non-linear system, and it is helpful for us to analyze and understand chaos systems. Traditional hybrid models for chaotic time series prediction based on neural networks have problems such as low prediction accuracy and difficulty in determining the network topologies. In recent years, the chaotic time series prediction has attached the attention of researchers in the area of deep learning. In this paper, we use a deep hybrid neural network (DHNN) based on convolutional neural network (CNN), gated recurrent unit (GRU) network, and attention mechanism to predict chaotic time series. Besides, we use the idea of neuroevolution to optimize the topologies of the DHNN. In the DHNN, we use CNN to capture spatial features from phase space reconstruction of chaotic time series. Then, we combine spatial features with the original chaotic time series. GRU extracts the spatio-temporal features from the combined sequence, and an attention mechanism with a non-linear activation function is designed to capture critical spatio-temporal features. Besides, we propose an improved differential evolution (IDE) algorithm to optimize the topologies of the DHNN, including the filter sizes of CNN and the number of hidden neurons of GRU. We develop the IDE with an adaptive mutation operator and dynamic chaos crossover operator, which can improve convergence speed and reduce optimization time. In this paper, we use the theoretical Lorenz datasets, monthly mean total sunspot datasets, and the actual coal-mine gas concentration datasets to verify the prediction accuracy of the proposed prediction model. Experimental results have shown that the proposed prediction model performs well in chaotic time series forecasting.
Blockchain and IoT Convergence—A Systematic Survey on Technologies, Protocols and Security

The Internet of Things (IoT) as a concept is fascinating and exciting, with an exponential growth just beginning. The IoT global market is expected to grow from 170 billion USD in 2017 to 560 billion USD by 2022. Though many experts have pegged IoT as the next industrial revolution, two of the major challenging aspects of IoT since the early days are having a secure privacy-safe ecosystem encompassing all building blocks of IoT architecture and solve the scalability problem as the number of devices increases. In recent years, Distributed Ledgers have often been referred to as the solution for both privacy and security problems. One form of distributed ledger is the Blockchain system. The aim of this paper consists of reviewing the most recent Blockchain architectures, comparing the most interesting and popular consensus algorithms, and evaluating the convergence between Blockchain and IoT by illustrating some of the main interesting projects in this research field. Furthermore, the paper provides a vision of a disruptive research topic that the authors are investigating: the use of AI algorithms to be applied to IoT devices belonging to a Blockchain architecture. This obviously requires that the devices be provided with adequate computational capacity and that can efficiently optimize their energy consumption.
An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences

Sign language translation (SLT) is an important application to bridge the communication gap between deaf and hearing people. In recent years, the research on the SLT based on neural translation frameworks has attracted wide attention. Despite the progress, current SLT research is still in the initial stage. In fact, current systems perform poorly in processing long sign sentences, which often involve long-distance dependencies and require large resource consumption. To tackle this problem, we propose two explainable adaptations to the traditional neural SLT models using optimized tokenization-related modules. First, we introduce a frame stream density compression (FSDC) algorithm for detecting and reducing the redundant similar frames, which effectively shortens the long sign sentences without losing information. Then, we replace the traditional encoder in a neural machine translation (NMT) module with an improved architecture, which incorporates a temporal convolution (T-Conv) unit and a dynamic hierarchical bidirectional GRU (DH-BiGRU) unit sequentially. The improved component takes the temporal tokenization information into consideration to extract deeper information with reasonable resource consumption. Our experiments on the RWTH-PHOENIX-Weather 2014T dataset show that the proposed model outperforms the state-of-the-art baseline up to about 1.5+ BLEU-4 score gains.
Training Binary Neural Networks through Learning with Noisy Supervision

This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize full-precision neurons, we propose to learn a mapping from full-precision neurons to the target binary ones. Each individual weight entry will not be binarized independently. Instead, they are taken as a whole to accomplish the binarization, just as they work together in generating convolution features. To help the training of the binarization mapping, the full-precision neurons after taking sign operations is regarded as some auxiliary supervision signal, which is noisy but still has valuable guidance. An unbiased estimator is therefore introduced to mitigate the influence of the supervision noise. Experimental results on benchmark datasets indicate that the proposed binarization technique attains consistent improvements over baselines.
Field Demonstration of Distributed Quantum Sensing without Post-Selection

Distributed quantum sensing can provide quantum-enhanced sensitivity beyond the shot-noise limit (SNL) for sensing spatially distributed parameters. To date, distributed quantum sensing experiments have been mostly accomplished in laboratory environments without a real space separation for the sensors. In addition, the post-selection is normally assumed to demonstrate the sensitivity advantage over the SNL. Here, we demonstrate distributed quantum sensing in field and show the unconditional violation (without post-selection) of SNL up to 0.916 dB for the field distance of 240 m. The achievement is based on a loophole free Bell test setup with entangled photon pairs at the averaged heralding efficiency of 73.88%. Moreover, to test quantum sensing in real life, we demonstrate the experiment for long distances (with 10-km fiber) together with the sensing of a completely random and unknown parameter. The results represent an important step towards a practical quantum sensing network for widespread applications.
Distance education during the pandemic: new challenges to Russian higher education

COVID-19 was the catalyst for many processes that were actively developing in the previous period, including the further introduction of distance education in Russian higher educational institutions. Russian higher education has faced new challenges due to the total transition to distance learning in the face of the pandemic. In March–May 2020, a sociological study was conducted in a number of universities in Yekaterinburg, Chelyabinsk, Moscow. The research purpose was to identify the main challenges universities faced during the pandemic and the prospects for the further development of distance education in Russian higher education. The study used the qualitative and quantitative strategies: a questionnaire survey of students (N=500) from different fields of study (social, humanitarian, technical, economic ones) who received distance learning experience during the pandemic; in-depth interviews with teachers of Russian universities (N=20), frame analysis of students’ cases on their daily practices in online educational interaction (N=42). The study made it possible to identify the challenges faced by Russian higher education in the total transition to distance education in a pandemic: the need to adapt students and teachers to work online (80% of respondents’ answers); decline in the quality of education (62.7% of respondents’ answers); changing the role of a teacher in the educational process (68% of the respondents’ answers); digital divide in the digital culture of the younger and older generation of teachers (67% of respondents’ answers); etc. A number of contradictions were recorded in the further implementation of distance education in Russian higher education. Considering that the policy towards digitalization in Russia has now been declared a priority, higher education cannot remain aloof from technological progress. Besides, the further introduction of distance learning should take place with overcoming the challenges and contradictions faced by Russian higher education during the pandemic, while maintaining the national achievements of Russian higher education, acquired over its long history.
Self-Supervised Representations Improve End-to-End Speech Translation

End-to-end speech-to-text translation can provide a simpler and smaller system but is facing the challenge of data scarcity. Pre-training methods can leverage unlabeled data and have been shown to be effective on data-scarce settings. In this work, we explore whether self-supervised pre-trained speech representations can benefit the speech translation task in both high- and low-resource settings, whether they can transfer well to other languages, and whether they can be effectively combined with other common methods that help improve low-resource end-to-end speech translation such as using a pre-trained high-resource speech recognition system. We demonstrate that self-supervised pre-trained features can consistently improve the translation performance, and cross-lingual transfer allows to extend to a variety of languages without or with little tuning.
Collaborative Filtering With Network Representation Learning for Citation Recommendation

Citation recommendation plays an important role in the context of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is challenging due to the cold start problem and the lack of paper ratings. To address these challenges, in this article, we propose a collaborative filtering with network representation learning framework for citation recommendation, namely CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and MRR (Mean Reciprocal Rank). Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines.