Reviewerly/exHarmony huggingface dataset:
RBPNET: An Asymptotic Residual Back-Projection Network for Super Resolution of Very Low Resolution Face Image

The super resolution of a very low resolution face image is a challenge task in computer vision, because it is difficult to learn a non-linear mapping of input-to-target space by deep neural network in one step upsampling. In this paper, we propose an asymptotic Residual Back-Projection Network (RBPNet) to gradually learn residual between the reconstructed face image and the ground truth by self-supervision mechanism. We map the reconstructed high-resolution feature map back to the original low-resolution feature space, use the original low-resolution feature map as a reference to self-supervising the learning of the various layers. The real high-resolution feature maps are approached gradually by iterative residual learning. Meanwhile, we explicitly reconstruct the edge map of face image and embed it into the reconstruction of high-resolution face image to reduce distortion of super-resolution results. Extensive experiments demonstrate the effectiveness and advantages of our proposed RBPNet qualitatively and quantitatively.
Early Change Detection in Dynamic Machine Running Status Based on a New Stability Measure

Early change detection in dynamic running status has become an important problem in the online monitoring of industrial machineries. Existing methods, such as using mean and root mean square (rms), often produce significant false alarms in the detected results. To address this problem, this article proposes a new stability indicator based on the exponential smoothing model (ESM) and the structural graph similarity measure, in order to characterize the dynamic machine running status. The proposed stability indicator benefits from two stages, i.e., ESM and graph, and thus is advantageous in terms of computational efficiency and robustness to noise. By means of the extracted stability indicator, a common null hypothesis testing is employed to make the change decision, which does not require a prior collection of anomaly data samples. The proposed method is validated on both simulated and real scenarios. Experimental results demonstrate the outperforming performance of the method over the state-of-the-art competitors, suggesting its great potential in real applications.
Pool Water Acidity Gauge Using Fuzzy Mamdani Method

Abstract Swimming pool is one of the means of sports and play that is fun. Swimming pools must also be considered clean. In order to be replaced with new water. The goal is so that pool users are not infected with infectious diseases due to the dirty water inside the pool. The Mamdani method is the most common method when discussing fuzzy methodology. It was Ebrahim Mamdani who first proposed this method in 1975 when building a steam engine and boiler control system. Mamdani uses a collection of IF-THEN rules obtained from experienced operators / experts. Mamdani’s work is actually based on the article “The Father of Fuzzy, Lotfi A. Zadeh: fuzzy algorithms for complex systems and decision processes.
The Application of Robust Extended H<sub>∞</sub> Filter and Consensus Control to The Multi-robot Motion Systems

The development of robots is increasingly advanced now, especially for multi-robots (a group of single robots) that are capable of carrying out cooperative tasks given. With a lot of research on robots, we consider an approach that can estimate the next motion, namely the Robust Extended H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter method, which is one of the development methods of data assimilation. Data assimilation is an estimation method that requires a system model and measurement model of the system. Besides, to approach the motion of robots, the implementation of consensus control was carried out in this study. Consensus can be interpreted as an agreement, where multi-robots were observed to be controlled to meet at a point of an agreement. The results of this study are very small errors generated by estimation and consensus control methods that work well on the system.
Privacy Preserving Visual Log Service with Temporal Interval Query using Interval Tree-based Searchable Symmetric Encryption

Visual logs become widely available via personal cameras, visual sensors in smart environments, or surveillance systems. Storing such data in public services is a common convenient solution, but it is essential to devise a mechanism to encrypt such data while enabling the capability to query visual content even in encrypted format at the services. This motivates our proposal to develop a smart secure service for visual logs with a temporal interval query. In our system, visual log data are analyzed to generate high-level contents, including entities, scenes, and activities happening in visual data. Then our system supports data owners to query these high-level contents from their visual logs at the server-side in a temporal interval while the data are still encrypted. Our searchable symmetric encryption scheme TIQSSE utilizes interval tree structure and we prove that our scheme achieves efficient search and update time while also maintaining all important security properties such as forward privacy, backward privacy, and it does not leak information outside the desired temporal range.
Object Detection Model Based on Deep Dilated Convolutional Networks by Fusing Transfer Learning

Object detection is an important research direction in the field of computer vision. In recent years, object detection has made great advances in public datasets, and the algorithm performance is also consistently similar to human capabilities. Therefore, all improvements in this paper are based on two-stage object detection. The algorithm model in the paper has three main innovations: first, considering that the resolution of the feature map and the size of the receptive field are not very good. Therefore, in this paper, a deep dilated convolution network is added to the backbone network to replace the conventional residual module, forming our own backbone network structure-Deep_Dilated Convolution Network (D_dNet). The formation of the D_dNet greatly reduces the number of parameters of the model during the training phase, and improves the quality of feature extraction. In addition, to improve accuracy, the network structure of the second part becomes “thick”, which increases the number of computations and reduces the detection speed. Therefore, by compressing the pretrained feature map and adding an 81-class fully connected layer to replace the original two layers, a light-weight network is formed. The light-weight network has changed the tradition of an overly thick fully connected layer, which not only makes the network lighter, but also significantly increases the corresponding detection speed. Finally, given the good hierarchy and transfer of the deep convolution neural network, this paper adds transfer learning to the training model to further optimize the model. In this paper, transfer learning is implemented to realize the transfer of weights. The training data samples are increased in disguise, there by increasing the stability of the training model. The evaluation of the entire model relies on the MSCOCO and PASCAL VOC datasets. In addition, the accuracy of the model is improved by 1.3%-2.2%.
Artificial intelligence

Artificial Intelligence (AI) is a rapidly growing field; one that is mysterious to the general public. The mention of the word AI fills the imaginations of many with thoughts of talking robots, jobs being replaced, and possibly even the destruction of mankind. Perhaps imaginations are running wild due to, perhaps driven by the loose definition of AI as systems able to perform tasks that normally require human intelligence that allows Hollywood to take some creative license. The experts in the field tend to work directly with AI and often for large companies, allowing for the imagination and news headlines to be where the public gets their information. Many wonder if this new technology is going to be an overall benefit to society or if it will bring unmitigated disaster. When the imagination runs wild, instead of understanding, news stories can perpetuate concerns and anxieties rather than hope and optimism.
Face Representations via Tensorfaces of Various Complexities

Neurons selective for faces exist in humans and monkeys. However, characteristics of face cell receptive fields are poorly understood. In this theoretical study, we explore the effects of complexity, defined as algorithmic information (Kolmogorov complexity) and logical depth, on possible ways that face cells may be organized. We use tensor decompositions to decompose faces into a set of components, called tensorfaces, and their associated weights, which can be interpreted as model face cells and their firing rates. These tensorfaces form a high-dimensional representation space in which each tensorface forms an axis of the space. A distinctive feature of the decomposition algorithm is the ability to specify tensorface complexity. We found that low-complexity tensorfaces have blob-like appearances crudely approximating faces, while high-complexity tensorfaces appear clearly face-like. Low-complexity tensorfaces require a larger population to reach a criterion face reconstruction error than medium- or high-complexity tensorfaces, and thus are inefficient by that criterion. Low-complexity tensorfaces, however, generalize better when representing statistically novel faces, which are faces falling beyond the distribution of face description parameters found in the tensorface training set. The degree to which face representations are parts based or global forms a continuum as a function of tensorface complexity, with low and medium tensorfaces being more parts based. Given the computational load imposed in creating high-complexity face cells (in the form of algorithmic information and logical depth) and in the absence of a compelling advantage to using high-complexity cells, we suggest face representations consist of a mixture of low- and medium-complexity face cells.
Multi-index Cost-benefit Model to Assess Land Development Costs

Abstract In land development and utilization projects, managers rarely consider the loss of ecosystem service value, often resulting in waste of resources. Based on the land development and utilization project, this paper proposes a multi-indicator cost-benefit comprehensive analysis model (MCCAM) . MCCAM divides the four aspects of the indicator system by dichotomy, including determining resource loss costs through MESV, calculating environmental degradation costs using delay factors, considering project planning factors and market fluctuations, and correcting economic benefits and costs. On the other hand, MCCAM quantifies the value of the ecosystem service value model and combines the non-equalized cobweb model with time-delay effects to modify the model parameters. According to the sensitivity analysis of the model, this model provides effective suggestions for the planning and management personnel of the land development and utilization project.
An approach to generate text-based IDEs for syntax completion based on syntax specification

The integrated development environments provide several types of functionalities. Herein, we intend to generate a syntax completion functionality from the grammar of the target language as long as the sentences of the language can be analyzed via LR parsing. We specify the syntax candidates to be completed based on the sentential forms and reductions in LR parsing. Furthermore, we implement a prototype system for computing the syntax candidates to be completed at the cursor position in the source code written in a small subset of Standard ML; the system only uses the program text up to the cursor position to ensure simplicity.
Efficiency Analysis on Robots Exclusion Protocol Based on Game Theory

As we know, there is a dilemma between Web servers and crawlers. The server may allow certain Web crawlers to visit it to harvest some useful feedbacks, while the interactions might draw the server into a vulnerable situation like being overloaded and consequently even harm the network security with possible DDOS (distributed denials of service attacks). Robots exclusion protocol (REP) which is implemented by robots.txt, serves as a reminder to inform crawlers whether or not a specific page they can crawl. However, several acute cases show that many crawlers do not obey the rules defined in the robots.txt. Hence, it is necessary to explore the validity of REP so that the key factors that lead to the disobey situation can be found. In this paper, we try to answer the question why many crawlers do not obey the REP from a point of view of game theory. We represent the interactions between servers and crawlers in the framework of game theory. The Nash equilibrium of the game model is attained, and the result shows that the best strategy for both parties of Web servers and crawlers in the context of present robots.txt will be the (Disobey, Disobey). Furthermore, several key factors, such as the reputation loss of the crawler, the cost of servers to take the crawler to court, and the benefit the server can get from crawlers, are shown to have influences in the effective implementation of REP.
Man - Machine Knowledge Mediation: Overview of Deep Learning Methods for Natural Language Processing

In this paper we suggest that efficient processing of natural language should be considered as a complex procedure, which comprises such stages as knowledge encoding by means of natural language units, its digitalization, and machine processing, which make up man – machine knowledge mediation. For this reason, methods chosen for natural language processing should be efficient for both knowledge encoding and knowledge retrieval, as well as for working with textual data of different length, since knowledge can be compressed either to words or to larger syntactic units. The paper provides an overview of modern trends in Natural Language Processing, namely methods for deep learning. We have studied them as potential add-ons to the TSBuilder program based on the Rosenblatt’s Perceptron initiated for automated term system building. Such methods as word embedding, convolutional, recurrent, and recursive neural networks are viewed through the prism of their application for the refinement of term extraction and categorization, as well as subsequent neural network training. Statistics on these methods’ representation in scientific databases is also provided.
Adjusting automatically marked voiced English allophone borders

In many areas related to speech analysis and processing, including automatic transcription of speech to the phonetic alphabet, it is important to identify the individual allophones included in each word. Consequently, it is also important to mark the boundaries of these allophones. Marking these boundaries in a manual way by phonetician helps to correct boundaries, but it takes much time, and it requires specialist knowledge. Borders can be determined automatically to save time, but they not always are defined perfectly. The paper describes the algorithm for the correction of the borders of English voiced allophones obtained automatically. It shows a method for modifying them in order to obtain the boundaries determined more precisely. It was achieved by comparing the initial periods of the laryngeal tone in the voiced allophones for the beginning of the allophone, and the periods at the end of the allophone for the case of a modification introduced to the end border of allophone. It relies on the features obtained employing the algorithm for automatic marking of allophone boundaries based on which the correction is performed. The results of the correction and statistical comparison of the allophone boundaries before and after the correction are presented concerning the manually obtained allophone boundaries.
A Practical Dynamic Enhanced BFT Protocol

Emerging as a distributed system maintaining a public ledger via consensus protocol, blockchain technology is showing its great potential in various scenarios such as supply chain, financial industry, internet of things (IoT), etc. Among kinds of consensus protocols, Byzantine Fault Tolerance (BFT) protocols are playing an important part in the design of the blockchain system. Most BFT protocols, however, are static with no support for a dynamic property (i.e. nodes can join/leave a working system) and lack mechanisms to punish faulty nodes, which highly limit their wider adoption in the practical settings. This paper presents a dynamic enhanced BFT (DEBFT) protocol that is designed to support dynamic property and faulty nodes punishment. Based on HoneyBadger BFT, DEBFT employs Dynamic Threshold Identity-based Encryption and Distributed Key Generation to enable changes of the consensus group without reconfiguring the whole system, besides, evaluation metrics are also introduced to evaluate consensus nodes and clear faulty ones out of the system.
A User Behavior Aware Immersive Video Caching Algorithm

Immersive video creates a unique experience by enabling viewers to freely control the viewing direction and has been regarded as a killer application of 5G. Huge bandwidth requirement and delay-sensitive are two major challenges faced by immersive video transmissions. Edge caching is believed as a promising scheme to solve these challenges. This paper proposes a cache algorithm specialized for immersive video service. First, we explore the relationship between quantity of video versions and users' watching quality by actual users' immersive video watching traces through which we have found it is different for different video frames. Based on this observation, we propose a user behavior aware immersive video caching algorithm to jointly optimize the video version design and cache space allocation based on users' watching behavior history. Numerical experiment driven by actual users' watching traces shows that our scheme is superior to baseline scheme under different cache sizes. Specifically, it can improve the average MO by at least 2.57%, and the improvement percentage can even reach 36.14% under some specific conditions.
Risk Management for Agile Projects in Offshore Vietnam

With Agile Development increasingly becoming "the norm" in software industry, experienced Project Managers from a hierarchical, 14,000-employee Company often find themselves facing difficulties in adapting their Risk Management practices into Agile projects. While Risk Management in an Agile offshore has been explored in various studies, these works do not offer a direct explanation on why certain Company had placed an emphasis on Risk Management, and how this emphasis has led to difficulties in the Agile era. From this explanation, the paper will propose a model to more seamlessly integrate Risk Management into Agile projects. Empirical results from case studies will also be provided to prove the effectiveness of the model. Lastly, a reference Risk Register (Risks and their Responses) will be provided for Project Managers to use as a starting point in their project.
Sentiment Dataset Creation using Multi-Agent System Improved by Deep Learning

This paper proposes an approach to create automatically a sentiment dataset with Multi Agent System (MAS) by collecting data from social media website and classify it using a deep learning model. Sentiment dataset can be used to build a deep learning model to analyze opinions about a field. For testing our approach, we create a dataset of 100000 tweets and classify it using an automatic sentiment classifier. Using this dataset, we build a deep learning-based model for sentiment classifier, which is able to determine positive, negative and neutral sentiments. The tests show that that the proposed model can classify sentiment perfectly better than the generic model.
A Novel Infringement Detection Method for GIS Vector Data

There is undoubtedly a groundswell of support for the concept of geographic data sharing with the rapid development and wide-ranging application of geographic information science. However, copyright protection and infringement detection in the process of geographic data sharing has always been an important issue that needs to be addressed urgently. In this paper, we present a novel infringement detection method for GIS vector data to compensate for the shortcomings of vector data digital watermarking technology in infringement detection. The method determines whether infringement exists by the duplication degree between the original data and the vector data to be detected in three features including feature features, included angle features and vertex features which gets by using the spatial information of vector data to perform the feature matching based on GeoJSON format data. The experimental results indicate that the proposed algorithm can effectively resist common geometric attacks, such as interpolation attack, deletion attack, similarity transformation attack, feature order scrambling attack, and feature simplification attack, on vector data, which proves that the proposed algorithm has excellent robustness and meets the requirements of practical application.
Move-Step Analyze of the Introduction in Chinese and English Accounting Journals: Based on Analysis of Accounting Research and Journal of Accounting and Economics (2016-2018)

At present, domestic universities are paying more and more attention to publishing research papers in foreign journals, so it is very important to be familiar with the structural features of the introduction of academic papers in top English journals for writing English research papers (Hu, 2007).In this paper, John Swale's move-step analysis method was used by author, the introductions of 110 papers in the last three years (2016-2018) from the authoritative Chinese accounting journal Accounting Research and the Journal of Accounting and Economics, one of the three major American accounting journals were selected, as the research objects, and make a comparative analysis of the length of the introduction, the use frequency of each step and the writing differences.The results show that there are significant differences in the number of words and structure between Chinese and English introductions.Compared with English introductions, Chinese introductions have fewer words, and no specific steps as the framework of writing.The research results of this paper are of great significance to the writing of the introduction of accounting papers, the development of contrastive linguistics between English and Chinese, and the enrichment of textual structure research.