Reviewerly/exHarmony huggingface dataset:
Pronominal Anaphora Resolution on Spanish Text

Recent technological advances have allowed the development of numerous natural language processing applications with which users frequently interact. When interacting with this type of application, users often search for the economy of words, which promotes the use of pronouns, thereby highlighting the well-known anaphora problem. This chapter describes a proposal to approach the pronominal anaphora for the Spanish language. A set of rules (based on the Eagle standard) was designed to identify the referents of personal pronouns through the structure of the grammatical tags of the words. The proposed algorithm uses the online Freeling service to perform tokenization and tagging tasks. The performance of the algorithm was compared with an online version of Freeling, and the proposed algorithm shows better performance.
Detection and Depth Estimation for Objects from Single Monocular Image

This paper addresses the problem of detecting and estimating depth for objects given a single monocular RGB image. We propose a integrative network to implement multiple tasks of detection and depth estimation at the same time and realize the rate of 6 fps. We use convolutional neural network to extract features and fully connection network to generate depth straightway and evaluate the performance of our model on KITTY. To adapt the model to multiple range scales of objects, we rectify the loss function and further improve the performance of our model.
Research and Development Trend of Intelligent Monitoring Technology

Abstract This paper summarizes the research status of intelligent monitoring and early warning technology in China from the aspects of IOT, big data, cloud computing and artificial intelligence. According to the practical application of engineering intelligent monitoring technology, this paper points out that there are some problems in intelligent monitoring and early warning, such as poor reliability of front-end information collection of monitoring equipment, insufficient integration of cloud platform integration application, and immature application of artificial intelligence technology in monitoring. Finally, the development trend of intelligent monitoring and early warning technology is prospected.
Scalable State Space Search on the GPU with Multi-Level Parallelism

Modern GPUs offer an accessible and scalable supercomputing platform. However, their use for state-space search has so far been auxiliary or in niche methods such as iterative deepening A* or random walks. We describe a scalable deterministic A* state space search implementation running entirely on the GPU. This is made possible by designing core algorithms for lock-step parallelism as well as using CUDA cooperative grids to implement hash-distributed A* load balancing scheme. Comparative benchmarking showed significant improvements in performance as well as resource efficiency.
From Darknets to Light

A prominent way in which the sale of illegal goods such as drugs, weapons and counterfeit occurs online is through anonymous markets on the Dark Web, known as Darknet markets, where buyers and vendors transact. The Dark Web offers a high degree of anonymity and security to its users through its encryption technology and the use of cryptocurrency. Law enforcement agencies have responded to these markets by conducting secret bust operations where they shut down their operation. In this research, we investigate if policing through bust operations deters the activity of buyers and vendors in other Darknet markets that are not subject to the bust. We leverage a joint bust operation conducted by the FBI and Interpol in November 2014 where Silk Road 2.0, a large Darknet market was shut down. We empirically identify the effect of the bust on two other large Darknet markets that were not subject to the bust. Our results indicate that as a result of the bust, prices dropped and the number of transactions per month per vendor increased in these markets. Consequently, the bust did not deter criminal activity in these markets and it was in fact cheaper to buy illegal products. We explore the mechanism for the price drop and nd that vendors decreased prices to attract buyers wary of shopping in Darknet markets due to increased risk of getting caught after the bust. We offer recommendations for law enforcement agencies on the sequencing of conducting busts as well as the characteristics of markets that could be bust so that the unintended consequences in other markets could be minimal.
Characteristics of flood causes in Majalengka Regency 2014 – 2018

Abstract Flood is one of the most common hydrometeorological disasters that occur in West Java, especially Majalengka Regency that has high potency of flood hazards. Furthermore, the floods did not only cause the loss in material, but also non-material things such as the loss of life. The aim of this research is to identify the characteristics of rainfall in flood events in Majalengka District from 2014 to 2018. The variables used in this study included daily rainfall period 2014 - 2018. Rainfall was calculated by the mononobe method to produce daily intensity and was calculated by API5 to calculate soil moisture. The results showed that the incidence of flood in Majalengka Regency was caused by very moist soil moisture, while variations in rainfall and rainfall intensity during the incident did not affect flood.
Fine-Grained Entity Typing for Relation-Sparsity Entities

This paper works on fine-grained entity typing without using external knowledge for Knowledge Graphs (KGs). Aiming at identifying the semantic type of an entity, this task has been studied predominantly in KGs. Provided with dense enough relations among entities, the existing mainstream KG embedding based approaches could achieve great performance on the task. However, many entities are sparse in their relations with other entities in KGs, which fails the existing KG embedding models in fine-grained entity typing. In this paper, we propose a novel KG embedding model for relation-sparsity entities in KGs. In our model, we map all attributes and types into the same vector sapce, where attributes could be granted with different weights according to an employed attention mechanism, while attribute values could be trained as bias vectors from attribute vectors pointing to type vectors. Based on this KG embedding model, we perform entity typing from coarse-grained level to more fine-grained level hierarchically. Besides, we also propose ways to utilize zero-shot attribute values that never appear in the training set. Our experiments performed on real-world KGs show that our approach is superior to the most advanced models in most cases.
Research Design of Intelligent Toothbrush for Preschool Children Based on KANO Model

Abstract In order to effectively obtain the user demands of children’s intelligent toothbrush and improve the use experience of children’s products. This paper explores user demands from the perspective of both parents and children. According to the Kano model, the user demands of both parents and children are analyzed, the nature of different demands is clarified, and the importance of various demands is calculated, so that the design can be targeted from the demands. Through this demand analysis method, we summarized the most important demands of children’s intelligent toothbrush, starting from what we can achieve the most satisfactory effect. In this paper, Kano model is used to analyze the demands of parents and children. Taking children’s toothbrush as a design case, the feasibility and validity of Kano model for children’s intelligent toothbrush are verified, and the user demands can be grasped more accurately.
Interpretable deep learning-based risk evaluation approach

There are two goals of modeling, including interpretation that is to extract information about how the response variables are associated to the input variables, and prediction that is to predict what the responses are going to be. The dilemma is that interpretable algorithms such as linear regression or logistic regression are often not accurate for prediction, while complex algorithms for better prediction are much more accurate but not easy to interpret1. Risk could be in the forms of cyber security risk, credit risk, investment risk, operational risk, etc. In this paper, we propose an interpretable method in evaluating risk using Deep Learning.
Real-time image registration method based on improved ORB algorithm for microscope

In recent years, there has been growing request in widening the field of vision of the microscope and stitching multiple images together into a panorama where the entire scene is visible in high-resolution. Considering the characteristics of microscope image, such as high complexity and variety of microstructure, previous researches always could not balance speed and accuracy of registration method. In this study, we creatively present a real-time microscope image registration algorithm by combining the phase correlation algorithm and the feature point matching algorithm. Firstly, the Gamma correction method was employed to preprocess the images to enhance their contrast. Then, the rough overlapping zones of the images were calculated by phase correlation. Thirdly, the improved ORB algorithm was used to extract the image features in the rough overlapping areas. Finally, the features were corresponded by matching algorithm and the distance between the images is determined, then the images were blended seamlessly. Experimental results show that the proposed algorithm has higher efficiency and precision compared with the traditional method.
A Wrinkle in the Promise of Scrum

Jeff Sutherland worked on the first Scrum project in 1993, but the framework as we know it now was formally coined by Jeff and Ken Schwaber in 1995. Since then IT divisions have seen the growing adoption of Scrum within their organization. However, this growth has been peppered with poor examples of adoption leading to lackluster results. This chapter explores why there is such a prevalence of “Bad” Scrum and the impact it has on the culture of the organization. This chapter explores the impact of poorly led transformations and will also provide some ways to reconsider how transformations should pivot.
Robust scale-variation object tracking by twofold weighted phase correlation with the kernel

Tracking objects with the scale variations quickly is a challenging problem in visual tracking. Most existing methods estimate the scale of the object with an exhaustive search strategy, which needs large calculations but with less improvement. We propose to use twofold weighted phase correlation with the kernel (WPCK) to simultaneously estimate the translation and scale for the object during tracking. Besides, from the linear phase correlation, a weighted kernel phase correlator is first introduced to improve tracking performance. It extends the filter into a nonlinear space, which is more robust to signal noises and distortions. To efficiently locate the object in a large three-dimensional space for real-time running, we formulate tracking problem into two independent subproblems: the translation offset and scale offset. The mechanism is: through the log-polar transform of the amplitude spectrum, the scale estimation can be performed with phase correlation, which is also utilized to solve the translation offset. Thus, the accurate object status can be achieved effectively by twofold WPCK. Extensive experimental results on OTB2013, OTB100, and UAV123 datasets reveal that the proposed method has superior performance gains over several state-of-the-art trackers in terms of accuracy, robustness, and speed.
Eye Tracking and Speech Driven Human-Avatar Emotion-Based Communication

Feelings, emotions, and empathy play an important role in many daily activities including verbal and non-verbal communication. Their automatic recognition and interpretation is important in a variety of applications requiring communication skills that are difficult to reproduce in computer-simulated environments, including those involving human-avatar interactions. Our recent work has begun investigating the development of intelligent avatars capable of detecting user (human) emotions to allow for realistic human-avatar interactions within medical-based virtual simulations and serious games. In this paper, we present a system that couples eye tracking and dialogue interpretation to allow for intelligent and realistic human-avatar communication. Although formal testing is required, preliminary results are promising, showing the potential of the system.
Link Prediction Based on Smooth Evolution of Network Embedding

The problem of link prediction in dynamic heterogeneous information networks has been widely studied in recent years. The technique of network embedding has been proved extremely useful for link prediction. However, the existing methods lack the close combination between deep-level features and temporal features of networks, which affects the accuracy of prediction and makes it difficult to adapt to the dynamic networks. In this paper, a Smooth Evolution model for Network Embedding (called SENE) is proposed, which considers both deep-level features and temporal features to obtain the embedded representations of the network structure, and uses the transformer mechanism to effectively obtain the smooth evolution of network embedding. Also an SENE-based link prediction algorithm is proposed, which can effectively guarantee the accuracy of link prediction. The feasibility and effectiveness of the proposed key technologies are verified by experiments.
Identity Management and Access Control for the GNSS Community within a European Research Infrastructure

Identity and access management systems aim to simplify authentication and authorization, however they do not solve all the problems that an application have related to issues such as the aggregation of user identities and token management and usage for user applications. This article describes the authentication and authorization problems related to service orientated architectures that are built on web services and in particular how systems may aggregate identities, often for statistical usage purposes, and how they manage tokens for web services built for open system using proxy servers and how systems should handle user applications that need to manage identity tokens. We discuss the difficulties and implementation solution adopted for the Global Navigation Satellite System Community within the European Plate Observing System research infrastructure project. In particular the paper discusses the thematic core service users using multiple identities and accessing services using both web portals and command line interfaces that are built on accessing RESTful web services.
TTED-PU:A Transferable Tax Evasion Detection Method Based on Positive and Unlabeled Learning

Tax evasion usually refers to taxpayers making false declarations in order to reduce their tax obligations. One of the most common types of tax evasion is to lower the declared taxable amount. This kind of behavior will lead to the loss of tax revenues and damage the fairness of taxation. One of the main roles of the tax authorities is to conduct tax evasion testing through efficient auditing methods. At present, by using machine learning technology along with large amounts of labeled data, tax evasion detection models have achieved good results in specific areas. However, it is a long and costly process for tax experts to label large amounts of data. Since, the data distribution characteristics vary from region to region, models cannot be used across regions. In this paper, we propose a new method called a transferable tax evasion detection method based on positive and unlabeled learning (TTED-PU), which uses only semi-supervised techniques to detect tax evasion in the source domain. In addition, we use the idea of transfer to adapt to the domain to predict tax evasion behavior on the target domain where labeled tax data are unavailable. We evaluate our method on real-world tax data set. The experimental results show that our model can detect tax evasion in both the source and target domains.
Security-Enhanced Timed-Release Encryption in the Random Oracle Model

Timed-release encryption (TRE) is a cryptographic primitive that the sender specifies the future decryption time of the receiver. At present, most TRE schemes implement the control of decryption time based on non-interactive time server to publish time trapdoors periodically. However, the generation of a large number of time trapdoors depend on the generation of the fixed private keys of the time server, so a large number of public parameters about the private keys of the time server can then be used for cryptanalysis, which poses a great threat to the security of the private keys of the time server. To solve this problem, a concrete scheme of TRE in the random oracle model are proposed. In our scheme, time trapdoors published by the time server are generated by the private key of the time server and the random number generated in advance. Compared with the most efficient scheme in the random oracle model, our concrete scheme reduces the time consumption by about 10.8%, at the same time it has achieved the one-time-pad of the time trapdoor, which greatly enhances the security of the private key of the time server, and thus enhances the security and effectiveness of the TRE.
Developing an online book finding tool for a university library

24/7 opening and widening participation have increased the need for tools to help library users find books and subjects independently of library staff. The paper describes a mobile-friendly web-based book finding tool produced using layered three-dimensional vector images of the library, a MySQL database and a simple responsive PHP script. Tapping a button in a library catalogue record or submitting a simple web form enquiry compiles a composite bitmapped image with a custom URL showing the highlighted shelving section(s) where the desired book or subject should be shelved. Responsive web design means that the images display clearly on smartphones, allowing users to carry a visual guide to finding the book or subject with them as they navigate the library. A critical appraisal of the development, success and limitations of the tool is included and ideas for further development discussed.
Improved Teaching-Learning-based Optimization Algorithm with Advanced Learning Strategy

As an important category of computational intelligence, meta-heuristic algorithms have always been a popular research interests over recent two decades. Teaching-learning-based optimization (TLBO) is one of nature-inspired metaheuristic algorithm which is proven to have effectiveness and efficiency in solving complex optimization problems. Although TLBO has remarkable capacity to solve different optimization problems, but the issue of trapping into local optimal is a common drawback of meta-heuristic algorithm, and TLBO is no exception. Thus we use a novel search strategy to improve the performance of TLBO by means of a new selection operation. We select twenty-nine benchmark functions of IEEE CEC2017 to testify the performance of proposed algorithm in terms of effectiveness and robustness. Experimental results exhibit that the proposed algorithm outperforms other state-of-the-art algorithms.
Inside of deep web anonymous technology: a short review

The term is a truly interesting point to be the discussion, specifically for researchers or observers of technology in the field of website applications. This is because the world of underground websites allows a wider range of website pages than conventional website pages such as Facebook, YouTube or Twitter that can be quickly searched or found using various search engines available today. From the 3 levels or layers especially the web surface, deep web and dark web (dark net), this article tries to review the pages of the website in the second layer, especially deep web, where all the web pages that are in the deep web layer cannot accessed using a traditional or conventional method that simply uses a standard browser or searches using an existing search engine. Furthermore, we also try to explain how the mechanism or mechanism that exists in the pages of the website that goes into the deep web area and learn how the internet users can access the pages of the website.
Trusted Connect Technology of Bioinformatics Authentication Cloud Platform Based on Point Set Topology Transformation Theory

The bioinformatics features are collected by pattern recognition technology, and the digital coding and format conversion of the feature data are realized by using the theory of topological group transformation. Authentication and Signature based on Zero Knowledge Proof Technology can be used as the trusted credentials of cloud platform and cannot be forged, thus realizing trusted and secure access.
A Method for Resisting Adversarial Attack on Time Series Classification Model in IoT System

IoT device is often associated with corresponding datasets, algorithms, and infrastructure. However, many potential threats exist in IoT basic infrastructure when deep learning algorithms are applied in these devices. Typically, a deep learning method is widely applied as the basic decision algorithm to classify the time series data, which is an important task in IoT data application. Nevertheless, they are vulnerable to adversarial examples, which bring potential risks to some fields such medical and security in which, a minor disturbing in the time series data could lead to wrong decision. In this paper, we show some white-box attack and random noise attack against time series data. Moreover, we show an adversarial example generated method which only changes one value of the original time series. To resist the adversarial attack, we train an adversarial examples detector to differentiate the adversarial examples from normal examples based on deep features. The adversarial examples detector could filter the adversarial examples before future impair happening. Experiments on UCR data sets show 97% of adversarial examples could be successfully detected generated by two common attack methods: FGSM and BIM.
Automatic Adjustment Method of Digital Electronic Controller Parameters Based on Big Data Analysis

From the perspective of the information industry, big data was a strong driving force for the new generation of information technology industry. To this end, based on the method of automatic adjustment of digital electronic controller parameters under big data analysis, a general iterative control method was proposed to adjust the parameters of digital electronic controller. This control technology dealt with a highly uncertain dynamic system in a very simple way and with less prior knowledge. It was suitable for general nonlinear control systems; it had a small online computational load and was suitable for fast motion control. Experimental results show that the iterative control method is effective for digital electronic controller. Compared with the internal model control method, the control effect and convergence were greatly improved, and the system output could quickly and accurately converge to a given value.