Reviewerly/exHarmony huggingface dataset:
MyoTac: Real-Time Recognition of Tactical Sign Language Based on Lightweight Deep Neural Network

Real-time tactical sign language recognition enables communication in a silent environment and outside the visual range, and human-computer interaction (HCI) can also be realized. Although the existing methods have high accuracy, they cannot be conveniently implemented in a portable system due to the complexity of their models. In this paper, we present MyoTac, a user-independent real-time tactical sign language classification system that makes the network lightweight through knowledge distillation, so as to balance between high accuracy and execution efficiency. We design tactical convolutional neural networks (TCNN) and bidirectional long short-term memory (B-LSTM) to capture the spatial and temporal features of the signals, respectively, and extract the soft target with knowledge distillation to compress the scale of the neural network by nearly four times without affecting the accuracy. We evaluate MyoTac on 30 tactical sign language (TSL) words based on data from 38 volunteers, including 25 volunteers collecting offline data and 13 volunteers conducting online tests. When dealing with new users, MyoTac achieves an average accuracy of 92.67% and the average recognition time is 2.81 ms. The obtained results show that our approach outperforms other algorithms proposed in the literature, reducing the real-time recognition time by 84.4% with higher accuracy.
An Extensive Survey on Blockchain-Based Electronic Health Record Systems

Healthcare systems around the world are beset by problems due to the lack of effective communication. Significant problems relating to patient medical records access, transition, and storage have persisted due to the lack of resources to adequately interact and track records between all main participants. To overcome this challenge, a nationwide Electronic Health Record (EHR) solution may be utilized. To further enhance EHR efficiency, Blockchain technology can be used to improve security, performance, and cost. In this survey, various literature proposing Blockchain-based EHR systems are discussed, along with their benefits and potential research gaps. Also Authors proposed a comprehensive architecture that could bridge all the gaps.
Fine-Grained Image Quality Caption With Hierarchical Semantics Degradation

Blind image quality assessment (BIQA), which is capable of precisely and automatically estimating human perceived image quality with no pristine image for comparison, attracts extensive attention and is of wide applications. Recently, many existing BIQA methods commonly represent image quality with a quantitative value, which is inconsistent with human cognition. Generally, human beings are good at perceiving image quality in terms of semantic description rather than quantitative value. Moreover, cognition is a needs-oriented task where humans are able to extract image contents with local to global semantics as they need. The mediocre quality value represents coarse or holistic image quality and fails to reflect degradation on hierarchical semantics. In this paper, to comply with human cognition, a novel quality caption model is inventively proposed to measure fine-grained image quality with hierarchical semantics degradation. Research on human visual system indicates there are hierarchy and reverse hierarchy correlations between hierarchical semantics. Meanwhile, empirical evidence shows that there are also bi-directional degradation dependencies between them. Thus, a novel bi-directional relationship-based network (BDRNet) is proposed for semantics degradation description, through adaptively exploring those correlations and degradation dependencies in a bi-directional manner. Extensive experiments demonstrate that our method outperforms the state-of-the-arts in terms of both evaluation performance and generalization ability.
Three‐dimensional tissue volume generation in conventional brightfield microscopy

The purpose of the study is to develop and automate a series of steps for enabling digital 3D tissue volume generation in conventional Brightfield microscopy for histopathology applications. Tissue samples were retrieved from the General Hospital of Athens "Hippocration", Greece. Samples were placed on a microtome that produced consecutive 2 μm sections. Each section was stained using Hematoxylin and Eosin and placed on microscope slides. A histopathologist specified the region of interest (ROI) on each slide. A 2D image was created from each ROI using a LEICA DM2500 microscope with a LEICA DFC 420C camera. Τhe 3D volume was created by stacking consecutive 2D images using a deep learning image interpolation method. The reconstructed 3D tissue volumes were evaluated by an expert histopathologist. Results showed that the 3D volumes might reveal information that is not clearly visible or even undetectable in the conventional 2D Brightfield images. In contrast to other 3D tissue imaging technologies, the proposed method (a) does not depend on the distance of the sample from the objectives producing 3D tissue volumes at any desired magnification, (b) does not require a special instrument, it may be implemented with any conventional Brightfield microscope, and (c) can be used for any given routine application, not only for some specialized clinical studies. The proposed study provides the basis for a feasible, cost-less and time-less upgrade of any standard 2D microscope into a 3D imaging instrument that may enhance the quality of diagnostic assessments in histopathology. HIGHLIGHTS: A method for 3D tissue volume generation. 3D volumes reveal information not clearly visible or even undetectable in 2D images. A method for feasible, cost-less and time-less upgrade of any Brightfield 2D microscope into a 3D imaging instrument.
Multi-Stage Model Compression using Teacher Assistant and Distillation with Hint-Based Training

Large neural networks have shown high performance in various applications, however, they are not suitable for small devices such as smartphones. Therefore, there are needs to realize a small network that is easy to deploy in small devices and has high performance. One of the methods to solve this problem is distillation, which can be used to obtain a small neural network with high performance by transferring knowledge from a large, high-performance teacher model. However, if there is a large difference in the number of parameters between the teacher model and the student model, distillation may not work well. In this paper, we use the Teacher Assistant (TA) model, which is intermediate in the number of layers between the teacher model and the student model, to perform multi-step compression both the hidden and output layers which is a technique known as Hint-Based Training. First, we optimize the TA model by using the teacher model and performing distillation focusing on the hidden and output layers. Then, using the TA model as a teacher, we perform the same distillation of the hidden and output layers on the student model. In this way, we improve the performance of the student model by reducing the size of the model while increasing the depth of the layers step by step. Experiments show that the proposed method can compress the simple CNN model to a size with parameters of about 1/7 compared to the original neural network while maintaining the same classification accuracy for the test dataset. In the student model using ResNet with the bottleneck architecture, the proposed method outperformed the teacher model, which was about 8 times larger in parameter numbers. In addition, the proposed method achieved the best performance for the student model when compared with the existing studies.
Piano Automatic Composition and Quantitative Perception under the Data-Driven Architecture

This paper combines automatic piano composition with quantitative perception, extracts note features from the demonstration audio, and builds a neural network model to complete automatic composition. First of all, in view of the diversity and complexity of the data collected in the quantitative perception of piano automatic composition, the energy efficiency-related state data of the piano automatic composition operation is collected, carried out, and dealt with. Secondly, a perceptual data-driven energy efficient evaluation and decision-making method is proposed. This method is based on time series index data. After determining the time subjective weight through time entropy, the time dimension factor is introduced, and then the subjective time weight is adjusted by the minimum variance method. Then, we consider the impact of the perception period on the perception efficiency and accuracy, calculate and dynamically adjust the perception period based on the running data, consider the needs of the perception object in different scenarios, and update the perception object in real time during the operation. Finally, combined with the level weights determined by the data-driven architecture, the dynamic manufacturing capability index and energy efficiency index of the equipment are finally obtained. The energy efficiency evaluation of the manufacturing system of the data-driven architecture proves the feasibility and scientificity of the evaluation method and achieves the goal of it. The simulation experiment results show that it can reduce the perception overhead while ensuring the perception efficiency and accuracy.
Research on the Design of Assistant Basketball Teaching System Based on Big Data

Due to the limitations of the current teaching resources searched by the system, it is difficult to achieve good results in basketball teaching. For this reason, an auxiliary basketball teaching system based on big data is designed. On the basis of clarifying the basic principles of teaching system design, JSP (Java Server Pages) dynamic web page back-end technology and Tomcat 7.0 server are used as the system support structure. Take the big data environment as the source of teaching resources and match them to different databases according to the characteristics of the resources. And combined with real-time evaluation to achieve the assessment of teaching effects and the management of teaching resources, the test results show that the average score of the basketball technical action test under the guidance of this system is higher than 7.5 points when the experimental subjects have the same level of basketball skills. In the paired sample t-test and the independent sample t-test of the performance level test scores P=0.001 < 0.05, there is a significant difference. It shows that the system can effectively improve the teaching effect of basketball and improve students' basketball test scores.
The application of elementary number theory in cryptography

The cryptography has a history of thousands of years, and its development can be divided into three stages, classic cryptography, neoteric cryptography and modern cryptography. Nowadays, it has been further invigorated by 20th-century technology. Previous studies lack a systematic review, so the public has a relatively shallow understanding of the issue. Thus, to increase public awareness, the encryption and decryption are introduced in a easy to understand way. Through extensive reading, generalizing and combing, this paper introduced the number theory applied in some examples of classic, neoteric, and modern cryptography. It is found out that the knowledge of congruences, arithmetic, factorization in number theory are widely utilized in cryptographic algorithms, among them, congruences are the most secure and common type. It is also found out that the number theory can help create the algorithm to encrypt and decrypt messages.
A comparative study of name entity recognition techniques in software engineering texts

Named Entity Recognition (NER) is an essential sub-task for many important tasks in software engineering (SE), such as automated requirement analysis, opinion mining, question answering, knowledge base construction, and information retrieval. As existing domain-independent NER approaches perform low when applied in the SE domain, we observe the development of different tools and techniques for NER in this domain recently. Despite those developments, we lack our understanding of the effectiveness of the deep learning based state-of-the-art models and existing popular machine learning based tools for NER in SE texts.
Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

Maritime video surveillance of visual perception system has become an essential method to guarantee unmanned surface vessels (USV) traffic safety and security in maritime applications. However, when visual data are collected in a foggy marine environment, the essential optical information is often hidden in the fog, potentially resulting in decreased accuracy of ship detection. Therefore, a dual-channel and two-stage dehazing network (DTDNet) is proposed to improve the clarity and quality of the image to guarantee reliable ship detection under foggy conditions. Specifically, an upper and lower sampling structure is introduced to expand the original two-stage dehazing network into a two-channel network, to further capture the image features from different scale. Meanwhile, the attention mechanism is combined to provide different weights for different feature maps to maintain more image information. Furthermore, the perceptual function is constructed with the MSE-based loss function, so that it can better reduce the gap between the dehazing image and the unhazy image. Extensive experiments show that DTDNet has a better dehazing performance on both visual effects and quantitative index than other state-of-the-art dehazing networks. Moreover, the dehazing network is combined with the problem of ship detection under a sea-fog environment, and experiment results demonstrate that our network can be effectively applied to improve the visual perception performance of USV.
Manipulation detection in cryptocurrency markets

As a financial asset, cryptocurrencies innovated the financial industry in different ways. However, the lack of regulations and transparency in cryptocurrency markets is hindering the industry from reaching its full potential. There is a need for extensive technical analysis of the cryptocurrency market data to detect possible market manipulation attempts. Anomaly detection techniques can reveal information about abnormal activities in the market and provide insights on manipulation attempts. In this study, a robust unsupervised anomaly detection tool (ADT) is developed for this purpose. Experiments show that ADT outperforms a set of methods in detecting the anomalies in features extracted from the cryptocurrency exchanges data and on a set of benchmark data sets.
Application of Artificial Intelligence in Digital Games Based on Mathematical Statistics

Digital games are various games designed and developed with digital technology and implemented on digital equipment. With the active development of the modern game industry, related technologies such as real-time graphics rendering, realistic interaction, and artificial intelligence of games are also constantly improving. Among them, the artificial intelligence technology of games is limited by the development of theoretical artificial intelligence and the calculation time of real-time systems, and the development lags behind graphics and interactive technology. In order to solve these problems, this paper proposes the application of artificial intelligence in digital games based on mathematical statistics methods, aimed at studying and improving the intelligence level of digital games. The method of this paper is to study the mathematical statistics method and the basic principle of Sarsa learning algorithm and then propose the technology of artificial intelligence applied to digital games. The role of these methods is to study different types of mathematical statistics, to study the behavioral tree of artificial intelligence in digital games and the development prospects of the game, and to update the value of artificial intelligence in each iteration according to the Sarsa algorithm formula. This paper proposes a decision-making system to improve the intelligence level of digital games by analyzing the research status of digital games and the mathematical statistics of basketball digital games. In the basketball game experiment, the difference between the operation data of the decision-making system proposed in this paper and the operation data of human players in five game items reaches 0.51, 0.83, 0.58, 0.49, and 0.78, respectively.
Secure and Efficient Item Traceability for Cloud-Aided IIoT

Cloud computing is an essential technique to provide item traceability for industrial internet of things (IIoT) systems by providing item data sharing services. However, a malicious cloud server may prevent industrial participants from acquiring accurate traceability of items by providing inconsistent item data. To fix this issue, we propose Acics, an item data consistency auditing scheme in untrusted cloud services for cloud-aided IIoT systems. Acics presents two variants named S-Acics and L-Acics. S-Acics enables industrial participants to audit item data consistency for each item and circularly play the auditing role. L-Acics further enables industrial participants to audit item data consistency for a sampled subset of items while resisting data selection attack via a new separated storage mechanism. Finally, Acics integrates a fair payment mechanism built on smart contract to incentivize the cloud server to provide consistent item data access service for industrial participants. The experiment results show that our solution can audit item data consistency with reasonable cost.
Integrating Scene Image and Conversational Text to Develop Human–Machine Dialogue

In recent years, it has become a trend to build social dialogue systems to achieve human–robot interaction. While current conversational systems mostly focus on the dialogue utterances, visual context also plays an important role in determining the relevance of the system responses to the dialogue. In this study, we develop an integrated approach to investigate how to achieve such a visual-language task. Our approach takes both scene images and dialogue utterances into account and trains a neural model to generate machine responses of natural language. We have conducted a series of experiments to evaluate the presented approach, and the results confirm its usefulness and effectiveness.
A New Body Weight Lifelog Outliers Generation Method: Reflecting Characteristics of Body Weight Data

Lifelogs are generated in our daily lives and contain useful information for health monitoring. Nowadays, one can easily obtain various lifelogs from a wearable device such as a smartwatch. These lifelogs could include noise and outliers. In general, the amount of noise and outliers is significantly smaller than that of normal data, resulting in class imbalance. To achieve good analytic accuracy, the noise and outliers should be filtered. Lifelogs have specific characteristics: low volatility and periodicity. It is very important to continuously analyze and manage them within a specific time. To solve the class imbalance problem of outliers in weight lifelog data, we propose a new outlier generation method that reflects the characteristics of body weight. This study compared the proposed method with the SMOTE-based data augmentation and the GAN-based data augmentation methods. Our results confirm that our proposed method for outlier detection was better than the SVM, XGBOOST, and CATBOOST algorithms. Through them, we can reduce the data imbalance level, improve data quality, and improve analytics accuracy.
Theoretical analysis of garden balsam optimization algorithm

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A New Joint Approach with Temporal and Profile Information for Social Bot Detection

With the increasing popularity of online social networks (OSNs), a huge number of social bots have emerged. Social bots are involved in various cybercrimes like cyberbullying and rumor dissemination, which have seriously affected the normal order of OSNs. Nowadays, existing studies in this field almost focus on English OSNs like Twitter and Facebook. However, it is difficult to directly apply these detection technologies to Sina Weibo, which is one of the largest Chinese microblogging services in the world. In addition, social bots are evolving rapidly and time-consuming feature engineering may not perform well in detecting newly emerging social bots. In this paper, we propose a new joint approach with Temporal and Profile information for social bot detection (TPBot). The approach includes data collection module, feature extraction module, and detection module. To begin with, data collection module uses a web crawler to obtain user data from Sina Weibo. Next, the feature extraction module regards the user posts as temporal data to extract temporal-semantic and temporal-metadata features. Furthermore, this module extracts features based on users’ profile. Finally, a detection model based on BiGRU and attention mechanism is designed in the detection module. The results show that TPBot performs better than baselines with the F1-score of 0.9837 on the Sina Weibo dataset. Moreover, we have also conducted an experiment on the two datasets collected from Twitter to evaluate the generalization ability of TPBot. It is found that TPBot outperforms baselines on the new datasets and has good generalization ability.
Cancer detection from stained biopsies using high-speed spectral imaging

The escalating demand for diagnosing pathological biopsies requires the procedures to be expedited and automated. The existing imaging systems for measuring biopsies only measure color, and even though a lot of effort is invested in deep learning analysis, there are still serious challenges regarding the performance and validity of the data for the intended medical setting. We developed a system that rapidly acquires spectral images from biopsies, followed by spectral classification algorithms. The spectral information is remarkably more informative than the color information, and leads to very high accuracy in identifying cancer cells, as tested on tens of cancer cases. This was improved even more by using artificial intelligence algorithms that required a rather small training set, indicating the high level of information that exists in the spectral images. The most important spectral differences are observed in the nucleus and they are related to aneuploidy in tumor cells. Rapid spectral imaging measurement therefore can bridge the gap in the machine-aided diagnostics of whole biopsies, thus improving patient care, and expediting the treatment procedure.
Higher-Order Logic and Disquotational Truth

Abstract Truth predicates are widely believed to be capable of serving a certain logical or quasi-logical function. There is little consensus, however, on the exact nature of this function. We offer a series of formal results in support of the thesis that disquotational truth is a device to simulate higher-order resources in a first-order setting. More specifically, we show that any theory formulated in a higher-order language can be naturally and conservatively interpreted in a first-order theory with a disquotational truth or truth-of predicate. In the first part of the paper we focus on the relation between truth and full impredicative sentential quantification. The second part is devoted to the relation between truth-of and full impredicative predicate quantification.