Reviewerly/exHarmony huggingface dataset:
Temporal Sentiment Analysis of Socially Important Locations of Social Media Users

Socially important locations are the places which are frequently visited by social media users. Temporal sentiment analysis of socially important locations is the process of interpretation and classification of emotions within their sharings in their socially important locations over time. Observing the temporal sentiment changes in these locations helps both to examine the emotion change in the locations and to understand the thoughts of the social media users in these locations. In this paper, Twitter is selected as social media data source and temporal sentiment analysis of socially important locations of social media users are analyzed in different time frames. For the analysis, a method, called Temporal Sentiment Analysis of Socially Important Locations (TS-SIL), is proposed in this study. In this method, first of all, socially important locations are discovered from the collected Twitter dataset. Then, sentiment analysis is performed using a dictionary based approach and several machine learning algorithms. Finally, the sharings in the locations are listed and the sentiments at these locations are analyzed by daily, weekly, and monthly basis. As a result, socially important locations of the city of Istanbul are discovered and temporal sentiment analysis of these locations are performed. Results shows that all of the socially important locations of İstanbul, except Beşiktaş Fish Market, showed emotional fluctuations over the time.
Psychological and Pedagogic Characteristic of Older Preschool Children

This paper presents the psychological and pedagogical characteristics of the development of older preschool children. The main periods and factors influencing the formation of the child's personality are described. These include the main activities of a preschooler – play, work, creative work, educational activities. Such concepts as speech, folklore, literature, music, visual activity, theatrical events that influence the development and formation of stable psychological characteristics of a child's personality are substantiated.
Hierarchical Consistency and Refinement for Semi-supervised Medical Segmentation

Semi-supervised learning exploits unlabeled data to improve generalization ability with insufficient annotations. In recent years, Mean Teacher method (MT) obtained impressive performance using prediction consistency as regularization. However, severe ambiguity in medical images makes the targets in the teacher model highly unreliable in obscure regions, thereby limits the model capability. To address this problem, we propose a novel multi-task learning semi-supervised framework to gain hierarchical consistency through training process. Specifically, we introduce region and shape predictions as subtasks to obtain the coarse-grained location and fine-grained boundary information. Then we predict pixel-level segmentation by fusing the hierarchical feature. Since calculating consistency loss in more loose regions typically alleviates the degradation caused by learning from unreliable targets, our teacher model generate guidance from each of the subtasks. Moreover, we focus on the geometrical correlations in different tasks and proposed the constraint method to refine the segmentation for accurate guidance. Experiments on the left atrium segmentation dataset show our algorithm achieves state-of-the-art performance comparing with other semi-supervised methods.
Modified Deep Belief Network Model and Its Application

Abstract Aiming at the problem that the traditional deep belief network (DBN) model has long training time and it is difficult to find the global optimum because of the fixed step. A modified DBN model was proposed. In the DBN pre-training phase, the adaptive step (AS) was introduced into the Contrastive Divergence (CD) algorithm to solve the problem of finding the global optimum difficulty because of the fixed step, improving the accuracy and oscillation resistance. In the fine-tuning phase, the BFGS quasi-newton method (QNM) was used to speed up the convergence and reduced the training time. The modified DBN model was applied to the fuel consumption prediction of the descending section of the aircraft. The experimental outcomes prove that compared with the traditional DBN and its variant model, the modified DBN model improves the convergence speed and the prediction accuracy.
Recommender Systems in Digital Libraries Using Artificial Intelligence and Machine Learning

Searching for contents in present digital libraries is still very primitive; most websites provide a search field where users can enter information such as book title, author name, or terms they expect to be found in the book. Some platforms provide advanced search options, which allow the users to narrow the search results by specific parameters such as year, author name, publisher, and similar. Currently, when users find a book which might be of interest to them, this search process ends; only a full-text search or references at the end of the book may provide some additional pointers. In this chapter, the author is going to give an example of how a user could permanently get recommendations for additional contents even while reading the article, using present machine learning and artificial intelligence techniques.
A Multi-stage Network for Improving the Sample Quality in Aerial Image Object Detection

Focusing on the problems of insufficient high-quality training samples to conduct an ideal detector for high-resolution remote sensing (HRRS) image object, we applied a multi-stage based detector to apply a resampling progressively strategy, which guarantees the amount of the positive training set and minimizing overfitting. The method has a sequence of regression heads training on the samples chosen by different Intersection over Union (IoU) thresholds. The first head with a low IoU threshold trained by a large number of positive samples and can prepare more high-quality samples for the remaining branches. The subsequent heads with the increasing IoU thresholds would train on more abundant positive samples and to conduct an accurate detector and avoid the problem of overfitting. The proposed method reached the best mAP value and outperformed the comparison methods by about 10%. The experimental results show that our method can significantly improve detection performance and solve the problem of lacking high-quality samples.
Equidistribution Modulo 1

The generalisation of questions of the classic arithmetic has long been of interest. One line of questioning, introduced by Car in 1995, inspired by the equidistribution of the sequence <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mi>n</mi> </mrow> <mrow> <mi>α</mi> </mrow> </msup> </mrow> </mfenced> </mrow> <mrow> <mi>n</mi> <mo>∈</mo> <mi>N</mi> </mrow> </msub> </math> where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>0</mn> <mo>&lt;</mo> <mi>α</mi> <mo>&lt;</mo> <mn>1</mn> </math> , is the study of the sequence <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mi>K</mi> </mrow> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mn>1</mn> <mo>/</mo> <mi>l</mi> </mrow> </mfenced> </mrow> </msup> </mrow> </mfenced> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>K</mi> </math> is a polynomial having an l-th root in the field of formal power series. In this paper, we consider the sequence <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mi>L</mi> </mrow> <mrow> <mo>′</mo> <mfenced open="(" close=")" separators="|"> <mrow> <mn>1</mn> <mo>/</mo> <mi>l</mi> </mrow> </mfenced> </mrow> </msup> </mrow> </mfenced> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>L</mi> <mo>′</mo> </math> is a polynomial having an l-th root in the field of formal power series and satisfying <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>L</mi> <mo>′</mo> <mo>≡</mo> <mi>B</mi> <mtext> </mtext> <mi mathvariant="normal">mod</mi> <mtext> </mtext> <mi>C</mi> </math> . Our main result is to prove the uniform distribution in the Laurent series case. Particularly, we prove the case for irreducible polynomials.
RAIN: A Relation-based Arithmetic model with Implicit Numbers

Arithmetic question is one type of the frequently asked questions in Machine Reading Comprehension. The key to answering an arithmetic question is constructing an accurate arithmetic expression by selecting the proper numbers from a candidate set and the operations for selected numbers. Previous work pays little attention on building candidate set, and usually constructs the arithmetic expression depending on each individual number, which ignores the relations between numbers. This paper therefore introduces a Relation-based Arithmetic model with Implicit Numbers (RAIN) for representing and utilizing the numbers (including both numbers explicitly occurring in passages and numbers implicitly inferred from passages) and their relations. Specifically, a Number Relation-based Attention (NRA) mechanism is proposed to mine the relations between numbers. Experimental results on a publicly available dataset DROP show that the RAIN achieves 63.97/67.32 EM/F1 and outperforms several strong baselines. Specifically, it yields overwhelming 73.84/73.86 EM/F1 on arithmetic question. The code for our model is available at https://github.com/MyPaperCode/RAIN.
A Contextual Approach for Coastal Tourism and Cultural Heritage Enhancing

In the panorama of Italian coastal tourism, there are many unique and unexplored places. These places, which suffer from the lack of government investment, present the need to be promoted through low consumption systems and widely used: distributed applications.The present work aims to develop innovative solutions to support citizens and tourists to offer advanced services, highly customizable, able to allow, through the use of new technologies, a more engaging, stimulating, and attractive use of information than the current forms. The developed system is based on graph-based formalisms such as Context Dimension Tree and Bayesian Networks, representing the context through its main components and react to it anticipating users’ needs. Through the development of a mobile app, it was analyzed a case study applied in the area of Amalfi Coast (in Italy). Finally, an experimental campaign was conducted with promising results.
Foreign Aid, Instability and Governance in Africa

This study contributes to the attendant literature by bundling governance dynamics and focusing on foreign aid instability instead of foreign aid. We assess the role of foreign aid instability on governance dynamics in fifty three African countries for the period 1996-2010. An autoregressive endogeneity-robust Generalized Method of Moments is employed. Instabilities are measured in terms of variance of the errors and standard deviations. Three main aid indicators are used, namely: total aid, aid from multilateral donors and bilateral aid. Principal Component Analysis is used to bundle governance indicators, namely: political governance (voice & accountability and political stability/no violence), economic governance (regulation quality and government effectiveness), institutional governance (rule of law and corruption-control) and general governance (political, economic and institutional governance). Our findings show that foreign aid instability increases governance standards, especially political and general governance. Policy implications are discussed.
An Inter-blockchain Escrow Approach for Fast Bitcoin Payment

In recent years, the Bitcoin (BTC) payment is increasingly popular in retailers and service providers. A BTC transaction (tx) needs six confirmations (one hour) to be validated, making it not suitable for fast-pay scenarios. Theoretically, a shorter waiting time period increases the success possibility of a double-spending attack. To address this problem, we propose BTCFast scheme to support fast BTC tx. BTCFast is a novel, decentralized, escrow-based scheme on top of the programmable smart contract (PSC)-enabled blockchains (e.g. Ethereum, EOS). We develop a smart contract (PayJudger) to work as a trusted payment judger, which guarantees the tx fairness. In addition, we devise a proof-of-work (PoW)-based payment judgment mechanism for PayJudger to resolve a BTC payment dispute. Our theoretical and experimental results show that BTCFast can reduce the waiting time to be less than 1 second with comparable security as the current approach (i.e., waiting for six confirmations) with no extra operation fee.
Blockchain-based New Energy Grid Electric Energy Measurement Transaction System

With the maturity of hlockchain technology, it has heen employed in many fields. This article aims to apply hlockchain technology to the real-time electric power transaction of alternative energy sources. A new energy grid electricity measurement transaction system based on hlockchain is designed. First, the architecture design of the system is introduced. Second, the smart contracts are deployed in the private hlockchain to realize the basic power measurement and trading. The real-time active and reactive powers are measured by smart meters which connect to the smart contracts with the MQTT protocol. Data is uploaded to the hlockchain and the trading can be completed automatically. Finally, the comparison with the traditional power trading system shows the advantages of the hlockchain based electricity transaction system.
A Specific Algorithm Based on Motion Direction Prediction

In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.
Exploration And Analysis of The Integration of Civil Engineering Technology and Computer Under New Technical Conditions

Abstract Due to the objective existence of natural disasters such as typhoon, earthquake, fire and flood, the performance of engineering structure needs to face such small probability and large load. It is a very difficult and unrealistic method to conduct experiments to verify. First, it is impossible to fully simulate the parameter variation bar; second, the cost of physical test is too high; third, it is dangerous to destroy the experiment and the equipment is difficult to keep up with it. The computer simulation technology can simulate the whole process from deformation to collapse of civil engineering structures of prototype size under the action of disaster load on the computer, so as to reveal the links and factors of unsafe structure, thus greatly improving the reliability of civil engineering.
Rational Delegation Learning Outsourcing Scheme Based on Game Theory

At present, many enterprises or individuals with a lot of data are limited by their own computing power, so can't mine useful information from the data. To address this problem, this paper proposes a rational delegation machine learning pattern. In the proposed pattern, we firstly construct a reliable game model, and then derive a formal model of rational delegation machine learning according to delegation computation thought. Finally, we design an outsourcing scheme for decision tree model. We analyze the security and performance of the proposed scheme, the analysis results show that the proposed scheme can not disclose any useful information. Last but not least, the experimental result demonstrates that the proposed scheme can obtain a decision tree model with high accuracy.
Grounding Implicit Goal Description for Robot Indoor Navigation via Recursive Belief Update

Natural language-based robotic navigation remains a challenging problem due to the human knowledge of navigation constraints, and destination is not directly compatible with the robot knowledge base. In this paper, we aim to translate natural destination commands into high-level robot navigation plans given a map of interest. We identify grammatically associated segments of destination description and recursively apply each of them to update a belief distribution of an area over the given map. We train a destination grounding model using a dataset of single-step belief update for precise, proximity, and directional modifier types. We demonstrate our method on real-world navigation task in an office consisting of 80 areas. Offline experimental results show that our method can directly extract goal destination from unheard, long, and composite text commands asked by humans. This enables users to specify their destination goals for the robot in general and natural form. Hardware experiment results also show that the designed model brings much convenience for specifying a navigation goal to a service robot.
Analysis and Prediction of Subway Settlement Deformation Based on Grey Model

Abstract In this paper, the monitoring data of settlement and deformation along the western section of a metro in H city are collated and analyzed, the actual situation of settlement and deformation is grasped, and the long-term settlement and deformation trend are predicted by using grey prediction model. In the process of forecasting, a non-equidistant grey forecasting model is established by using the data of the first several groups of monitoring points, and the predicted values are compared with the last group of monitoring data to verify the reliability of the model and further optimize the model. Then, the optimized model is used to predict the long-term settlement deformation of the subway, which provides a basis for the deformation control and management of the subway.
Blockchain Smart Contract for Cellular Automata-Based Energy Sharing

This paper deals with energy management in a microgrid through peer-to-peer (P2P) energy exchange method. The P2P process is executed on the basis of cellular automaton (CA) approach and implemented by smart contracts blockchain over a time horizon, enabling consensus to be recorded between consumers in a secure and fully automated transaction. The CA proposed model identifies the end-user state in a set of five possible states and supports the convergence of supply and demand decisions, thus ensuring the decentralization of energy distribution.
An Embedded AI Computing Platform for Aircrafts Based on VPX Bus

This paper presents a VPX-based computing platform for aircrafts with an artificial intelligence module built in. VPX bus connects together a serial computing module, a parallel computing module and an AI computing module, providing all the computing resources for the platform. These modules can communicate with varying topological types through SRIO connection, with a maximum data transfer rate of 2.5Gbps. The AI module, which consists of an ARM-based CPU and a machine learning unit focusing on accelerating neural networks, has been tested to show the ability to handle ImageNet classification tasks up to 2214 frames per second when using the TensorFlow framework.
P‐4.1: A Hardware Optimized Dehazing Algorithm for Video

The dehazing technology has made great progress on a single image. But for the problem that the time complexity is high and cannot meet the real‐time requirement of HD video dehazing, a dark channel prior dehazing algorithm based on hardware architecture optimization is proposed in this paper. The hardwareoriented two‐scale joint filtering method is used to simplify the computational complexity of transmittance and atmospheric light values, while using interframe dependence constraints to suppress flicker noise in video dehazing. The experimental results show that the proposed algorithm is equivalent to the current mainstream dehazing algorithm under software and hardware platforms, even brighter and coordinated in the detail color, and the atmospheric light value of the algorithm is basically stable, indicating the proposed interframe dependency constraints can effectively adjust atmospheric light values.
Image Authentication using Radon Transform and Local Features

Here we have presented a new image hash technique based on radon transform and saliency detection technique using Markov absorption probability. Radon transform is widely used in medical image processing. It is robust against content preserving activities like blurring, compression, sharpening, and invariant characteristics regarding scaling and rotation. Markov absorption probability is used in salient area detection. We have consolidated the above methods to create a new image hash method that gives better results. This method was tested on large image pairs and is found to be robust for content preserving methods. Our proposed algorithm is also able to identify the forged area.
ChainPKI - Towards Ethash-based Decentralized PKI with Privacy Enhancement

Public key infrastructure (PKI) is the most widely adopted framework aiming to protect the communications between servers and clients, by authenticating users and devices in the digital world. In order to mitigate the single point of failure (SPOF) posed by a centralized PKI architecture, decentralized PKI has received much more attention, which can decentralize the responsibility of certificate authorities. Many blockchain-based PKI systems are developed such as BlockPGP, Blockstack and NoPKI. However, it is found that some existing systems are susceptible to privacy leakage. In this work, we first compare the functionality between blockchain-based PKI systems and analyze the potential privacy issues. Then we focus on Luo's PKI system that adopts the concept of neighborhood and propose a privacy-aware blockchain-based PKI called ChainPKI, by enhancing the privacy. Our experimental results indicate the effectiveness of our enhanced system auainst notential nrivacv leakaoe,