Reviewerly/exHarmony huggingface dataset:
Mining Pool Game Model and Nash Equilibrium Analysis for PoW-Based Blockchain Networks

Blockchain technology, has the characteristics of decentralization, openness and transparency, so that everyone can participate in database recording. Therefore, blockchain technology has a good application prospect in various industries. As the most successful application of blockchain technology, the Bitcoin system applies the Proof of Work (PoW) consensus mechanism. Under the PoW consensus mechanism, each miner competes through his own power to solve a SHA256 mathematical problem together, so as to gain profits. Due to the difficulty of the cryptography puzzle, miners tend to join the mining pool to obtain stable income. And the block withholding attacks will be carried out between the mining pools, so as to maximize his own income by controlling the infiltration rate dispatched to other mining pools. In this paper, we build a game model between mining pools based on the PoW consensus algorithm, and analyze its Nash equilibrium from two perspectives. The influence of the mining pools' power, the ratio of the power to be infiltrated, and the betrayed rate of dispatched miners on the mining pool's infiltration rate selection and income were explored, and the results were obtained through numerical simulations.
ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing

Abstract The rapid development of social media, and special websites with critical reviews of products have created a huge collection of resources for customers all over the world. These data may contain a lot of information including product reviews, predicting market changes, and the polarity of opinions. Machine learning and deep learning algorithms provide the necessary tools for intelligence analysis in these challenges. In current competitive markets, it is essential to understand opinions, and sentiments of reviewers by extracting and analyzing their features. Besides, processing and analyzing this volume of data in the cloud can increase the cost of the system, strongly. Fewer dependencies on expensive hardware, storage space, and related software can be provided through cloud computing and Natural Language Processing (NLP). In our work, we propose an integrated architecture of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to identify the polarity of words on the Google cloud and performing computations on Google Colaboratory. Our proposed model based on deep learning algorithms with word embedding technique learns features through a CNN layer, and these features are fed directly into a bidirectional LSTM layer to capture long-term feature dependencies. Then, they can be reused from a CNN layer to provide abstract features before final dense layers. The main goal for this work is to provide an appropriate solution for analyzing sentiments and classification of the opinions into positive and negative classes. Our implementations show that found on the proposed model, the accuracy of more than 89.02 % is achievable.
Real-Time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices

Recently, deep learning has represented an important research trend in human activity recognition (HAR). In particular, deep convolutional neural networks (CNNs) have achieved state-of-the-art performance on various HAR datasets. For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations. A high number of operations in deep leaning increases computational cost and is not suitable for real-time HAR using mobile and wearable sensors. Though shallow learning techniques often are lightweight, they could not achieve good performance. Therefore, deep learning methods that can balance the trade-off between accuracy and computation cost is highly needed, which to our knowledge has seldom been researched. In this paper, we for the first time propose a computation efficient CNN using conditionally parametrized convolution for real-time HAR on mobile and wearable devices. We evaluate the proposed method on four public benchmark HAR datasets consisting of WISDM dataset, PAMAP2 dataset, UNIMIB-SHAR dataset, and OPPORTUNITY dataset, achieving state-of-the-art accuracy without compromising computation cost. Various ablation experiments are performed to show how such a network with large capacity is clearly preferable to baseline while requiring a similar amount of operations. The method can be used as a drop-in replacement for the existing deep HAR architectures and easily deployed onto mobile and wearable devices for real-time HAR applications.
Learning Latent Forests for Medical Relation Extraction

The goal of medical relation extraction is to detect relations among entities, such as genes, mutations and drugs in medical texts. Dependency tree structures have been proven useful for this task. Existing approaches to such relation extraction leverage off-the-shelf dependency parsers to obtain a syntactic tree or forest for the text. However, for the medical domain, low parsing accuracy may lead to error propagation downstream the relation extraction pipeline. In this work, we propose a novel model which treats the dependency structure as a latent variable and induces it from the unstructured text in an end-to-end fashion. Our model can be understood as composing task-specific dependency forests that capture non-local interactions for better relation extraction. Extensive results on four datasets show that our model is able to significantly outperform state-of-the-art systems without relying on any direct tree supervision or pre-training.
Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy

Knowledge-driven conversation approaches have achieved remarkable research attention recently. However, generating an informative response with multiple relevant knowledge without losing fluency and coherence is still one of the main challenges. To address this issue, this paper proposes a method that uses recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge. Furthermore, we introduce a knowledge copy mechanism using a knowledge-aware pointer network to copy words from external knowledge according to knowledge attention distribution. Our joint neural conversation model which integrates recurrent Knowledge-Interaction and knowledge Copy (KIC) performs well on generating informative responses. Experiments demonstrate that our model with fewer parameters yields significant improvements over competitive baselines on two datasets Wizard-of-Wikipedia(average Bleu +87%; abs.: 0.034) and DuConv(average Bleu +20%; abs.: 0.047)) with different knowledge formats (textual & structured) and different languages (English & Chinese).
Understanding Attention for Text Classification

Attention has been proven successful in many natural language processing (NLP) tasks.Recently, many researchers started to investigate the interpretability of attention on NLP tasks.Many existing approaches focused on examining whether the local attention weights could reflect the importance of input representations.In this work, we present a study on understanding the internal mechanism of attention by looking into the gradient update process, checking its behavior when approaching a local minimum during training.We propose to analyze for each word token the following two quantities: its polarity score and its attention score, where the latter is a global assessment on the token's significance.We discuss conditions under which the attention mechanism may become more (or less) interpretable, and show how the interplay between the two quantities may impact the model performance. 1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction

Recent advances cast the entity-relation extraction to a multi-turn question answering (QA) task and provide an effective solution based on the machine reading comprehension (MRC) models. However, they use a single question to characterize the meaning of entities and relations, which is intuitively not enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which is inefficient and easily leads to confusing questions. In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning. Experiment results show that the proposed method significantly outperforms baseline models, which improves the relation F1 to 62.1% (+1.9%) on ACE05 and 71.9% (+3.0%) on CoNLL04. Our implementation is available at https://github.com/TanyaZhao/MRC4ERE.
Intelligent Data Management and Security in Cloud Computing

This paper will present the authors' own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors' solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.
A Path Toward Explainable AI and Autonomous Adaptive Intelligence: Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action

Biological neural network models whereby brains make minds help to understand autonomous adaptive intelligence. This article summarizes why the dynamics and emergent properties of such models for perception, cognition, emotion, and action are explainable, and thus amenable to being confidently implemented in large-scale applications. Key to their explainability is how these models combine fast activations, or short-term memory (STM) traces, and learned weights, or long-term memory (LTM) traces. Visual and auditory perceptual models have explainable conscious STM representations of visual surfaces and auditory streams in surface-shroud resonances and stream-shroud resonances, respectively. Deep Learning is often used to classify data. However, Deep Learning can experience catastrophic forgetting: At any stage of learning, an unpredictable part of its memory can collapse. Even if it makes some accurate classifications, they are not explainable and thus cannot be used with confidence. Deep Learning shares these problems with the back propagation algorithm, whose computational problems due to nonlocal weight transport during mismatch learning were described in the 1980s. Deep Learning became popular after very fast computers and huge online databases became available that enabled new applications despite these problems. Adaptive Resonance Theory, or ART, algorithms overcome the computational problems of back propagation and Deep Learning. ART is a self-organizing production system that incrementally learns, using arbitrary combinations of unsupervised and supervised learning and only locally computable quantities, to rapidly classify large nonstationary databases without experiencing catastrophic forgetting. ART classifications and predictions are explainable using the attended critical feature patterns in STM on which they build. The LTM adaptive weights of the fuzzy ARTMAP algorithm induce fuzzy IF-THEN rules that explain what feature combinations predict successful outcomes. ART has been successfully used in multiple large-scale real world applications, including remote sensing, medical database prediction, and social media data clustering. Also explainable are the MOTIVATOR model of reinforcement learning and cognitive-emotional interactions, and the VITE, DIRECT, DIVA, and SOVEREIGN models for reaching, speech production, spatial navigation, and autonomous adaptive intelligence. These biological models exemplify complementary computing, and use local laws for match learning and mismatch learning that avoid the problems of Deep Learning.
Graph convolutional networks for graphs containing missing features

Graph Convolutional Network (GCN) has experienced great success in graph analysis tasks. It works by smoothing the node features across the graph. The current GCN models overwhelmingly assume that the node feature information is complete. However, real-world graph data are often incomplete and containing missing features. Traditionally, people have to estimate and fill in the unknown features based on imputation techniques and then apply GCN. However, the process of feature filling and graph learning are separated, resulting in degraded and unstable performance. This problem becomes more serious when a large number of features are missing. We propose an approach that adapts GCN to graphs containing missing features. In contrast to traditional strategy, our approach integrates the processing of missing features and graph learning within the same neural network architecture. Our idea is to represent the missing data by Gaussian Mixture Model (GMM) and calculate the expected activation of neurons in the first hidden layer of GCN, while keeping the other layers of the network unchanged. This enables us to learn the GMM parameters and network weight parameters in an end-to-end manner. Notably, our approach does not increase the computational complexity of GCN and it is consistent with GCN when the features are complete. We demonstrate through extensive experiments that our approach significantly outperforms the imputation based methods in node classification and link prediction tasks. We show that the performance of our approach for the case with a low level of missing features is even superior to GCN for the case with complete features.
Linear-time maximum likelihood decoding of surface codes over the quantum erasure channel

Surface codes are among the best candidates to ensure the fault-tolerance of a quantum computer. In order to avoid the accumulation of errors during a computation, it is crucial to have at our disposal a fast decoding algorithm to quickly identify and correct errors as soon as they occur. We propose a linear-time maximum likelihood decoder for surface codes over the quantum erasure channel. This decoding algorithm for dealing with qubit loss is optimal both in terms of performance and speed.
Towards the Development of an Integrated Incident Response Model for Database Forensic Investigation Field

For every contact that is made in a database, a digital trace will potentially be left and most of the database breaches are mostly aimed at defeating the major security goals (Confidentiality, Integrity, and Authenticity) of data that reside in the database. In order to prove/refute a fact during litigation, it is important to identify suitable investigation techniques that can be used to link a potential incident/suspect to the digital crime. As a result, this paper has proposed suitable steps of constructing and Integrated Incident Response Model (IIRM) that can be relied upon in the database forensic investigation field. While developing the IIRM, design science methodology has been adapted and the outcome of this study has shown significant and promising approaches that could be leveraged by digital forensic experts, legal practitioners and law enforcement agencies. This is owing to the fact, that IIRM construction has followed incident investigation principles that are stipulated in ISO guidelines.
Annotation-free learning of plankton for classification and anomaly detection

The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.
A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.
COVID-19 Impact on Ukrainian Higher Education

The pandemic and the announced quarantine have made adjustments to the bachelors and master degree student education in almost all universities around the world.The transition to the online education using modern Internet technologies has been made, that has led to certain challenges in higher education system.The purpose of the article is the analysis of COVID-19 impact on Ukrainian higher education.The following research methods were used in the article: empirical methods, methods of theoretical analysis, questionnaire, and interview.In the article the approaches to the online education in universities in Ukraine have been analyzed.The results of the article have a few significant implications for science and practice of online education and its perspectives for Ukrainian higher education system.Prospects of online education of students have been offered during COVID-19.The main recommendations are the following: to organize training courses of online education methods for lecturers; to organize in-depth training courses of online education methods for lecturers of non-pedagogical specialties (including training in interactive online teaching methods, formation of an individual learning trajectory, online multidisciplinary courses development); university's management should provide constant monitoring of the satisfaction of students and lecturers of the online education organization for the accumulation of statistical data in the dynamics.
Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

In oral surgery, accurate segmentation of the teeth has critical significance for the orthodontic treatment and research. However, in the cone beam computed tomography (CBCT) images, the boundaries of the teeth are blurred and some tissues around the teeth have similar intensities to the teeth, which makes the tooth segmentation more difficult and challenging. In this paper, an accurate and automatic active contour model is proposed for the tooth segmentation. First, we apply deep convolutional neural networks to automatically detect the approximate position of each dental pulp. Then, we take the barycenter point of the pixels in the marked area as the center of the tooth. Based on this, we design the shape prior information by a series of mathematical methods to describe the shape, size and position of the tooth, which is achieved by further detecting the direction and length of the tooth. To make full use of the shape prior information, we define the prior constraint term to limit the segmentation curve to evolve around the shape prior information, while making the segmentation contour as close as possible to the shape prior information. Last, combining the image data term, the length term, the regularization term and the prior constraint term, we give the level set formulation of the energy functional and minimize it by the steepest descent method. To test the feasibility and effectiveness of the proposed model, we apply the proposed model to segment the tooth images in different slices. Experimental results show that the proposed model can accurately segment the tooth images. Qualitative comparison results demonstrate the proposed model is superior to the CV model, the RSF model, the LGIF model, the LIC model and the U-Net model in terms of the segmentation accuracy. In addition, the sensitivity test verifies that the proposed model is insensitive to the initial contours and deep network outputs.
Complexity/informativeness trade-off in the domain of indefinite pronouns

The vocabulary of human languages has been argued to support efficient communication by optimizing the trade-off between complexity and informativeness (Kemp &amp; Regier 2012). The argument has been based on cross-linguistic analyses of vocabulary in semantic domains of content words such as kinship, color, and number terms. The present work extends this analysis to a category of function words: indefinite pronouns (e.g. someone, anyone, no-one, cf. Haspelmath 2001). We build on previous work to establish the meaning space and featural make-up for indefinite pronouns, and show that indefinite pronoun systems across languages optimize the complexity/informativeness trade-off. This demonstrates that pressures for efficient communication shape both content and function word categories, thus tying in with the conclusions of recent work on quantifiers by Steinert-Threlkeld (2019). Furthermore, we argue that the trade-off may explain some of the universal properties of indefinite pronouns, thus reducing the explanatory load for linguistic theories.