Reviewerly/exHarmony huggingface dataset:
Straight Line Reconstruction for Fully Materialized Table Extraction in Degraded Document Images

Tables are one of the best ways to synthesize information such as statistical results, key figures in documents. In this article we focus on the extraction of materialized tables in document images, in the particular case where acquisition noise can disrupt the recovering of the table structures. The sequential printings/scannings of a document and its deterioration can lead to “broken” lines among the materialized segments of the tables. We propose a method based on the search for straight line segments in documents, relying on a new image transform that locally defines primitives well suited for pattern recognition and on a proposed theoretical model of lines in order to confirm their presence among a set of confident potential line parts. The extracted straight line segments are then used to reconstruct the table structures. Our approach has been evaluated both from quality and stability points of view.
Research on joint ranking recommendation model based on Markov chain

Summary In this paper, a supervised learning framework with strong expansibility is first established for search engine joint ranking problem. It can transform existing algorithms into corresponding learning algorithms, and design new algorithms under this framework. Second, with Markov chain model as the core algorithm, this paper combines the ranking results of three main factors, including content relevance, hyperlink prediction, and query click behavior, and transforms the joint problem of ranking results into a positive semi‐definite programming problem, and deduces the detailed process of solving the problem. Finally, this paper analyzes the rationality and efficiency of the joint ranking recommendation model based on Markov chain by setting the weight coefficient through experimental data.
Design of Underground Integrated Corridor Monitoring System in Yude Road, Liupanshui City

At present, there is limited experience in the construction of environmental monitoring system for urban underground integrated corridors. In order to meet the needs of corridor environment risk management, a comprehensive road condition management system of Liupanshui Yude road was designed. The system hardware system which combines the system workstation with the data transmission module is established, and a software system platform based on environmental monitoring, compression coding, encapsulation and wireless module is designed. A real-time monitoring system for environmental risk sources is established, which integrates environmental monitoring data, and provides a reference for the design of engineering environment and equipment monitoring system.
Optimal defense strategy based on the mean field game model for cyber security

With the evolution of research on defense strategies in cyber security, the choice of an optimal strategy has become a key problem in current studies. Focusing on the balance between individual cost and overall network cost, we present an application of mean field game in large-scale defenders in cyber security, where players seek to construct an optimal defense strategy at their minimum cost. The contributions are threefold: first, we propose an individual cost function based on the mean field game in Hilbert space and discuss the overall network cost function, where each player has discrete-time dynamics. Then, the Nash equilibrium of the individual cost function with infinite players is researched. Finally, we establish an optimal condition in which the game equilibrium is the optimal solution to the overall cost function. Numerical examples are provided to illustrate the effectiveness of the presented strategy with an appropriate assumption.
Opera-oriented character relations extraction for role interaction and behaviour Understanding: a deep learning approach

There are a great number of complex relations among different characters in an opera. Retrieving such relations is crucial for performers and audience to accurately understand the features and behaviour of roles. Aiming to automatically extract relations among characters in an opera, in this paper we propose an effective method that can extract character relations from opera scripts. Firstly, we construct a uniform reasoning framework for opera scripts. Based on this model, we propose a deep syntax-parsing method to detect character relations from opera scripts. After that, we propose a new deep learning approach called SL-Bi-LSTM-CRF to extract the objects involved in character relations. The proposed SL-Bi-LSTM-CRF algorithm is a sentence-level relation extraction algorithm based on the Bi-directional LSTM with a CRF layer. With this mechanism, we are able to get a detailed description for character relations. We conduct experiments on a real dataset of opera scripts. The experimental results in terms of precision, recall, and F-score suggest the effectiveness of our proposal.
When Relaxations Go Bad: "Differentially-Private" Machine Learning.

Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $\epsilon$, about how much information is leaked by a mechanism. However, implementations of privacy-preserving machine learning often select large values of $\epsilon$ in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, differential privacy variants that offer tighter analyses are used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is a huge gap between the upper bounds on privacy loss that can be guaranteed, even with advanced mechanisms, and the effective privacy loss that can be measured using current inference attacks. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs with guarantees for complex learning tasks: settings that provide limited accuracy loss provide meaningless privacy guarantees, and settings that provide strong privacy guarantees result in useless models. Code for the experiments can be found here: this https URL
Optimization driven cluster based indexing and matching for the document retrieval

Document retrieval methods concentrate on minimizing the time taken for the navigator to recall the entire document while analyzing the concepts, themes, and contents of the document based on their research goals. The exploitation of the repetitiveness in order to reduce the usage space is a hectic challenge. This paper proposes a document retrieval mechanism using an optimization, Monarch Butterfly optimization-based FireFly (MB-FF), developed with the integration of the Monarch Butterfly Optimization (MBO) and Firefly Algorithm (FA). The keywords from the documents are identified from the pre-processed document, which is pre-processed using stemming and stop word removal. The Term Frequency-Inverse Document Frequency (TF-IDF) is used in the extraction of the keywords and the concept of holoentropy is used in the selection of the significant keywords. The selected keywords assures the retrieval of the relevant documents, which initially is processed through cluster-based indexing using the Monarch Butterfly optimization-based firefly (MB-FF) that is followed with the two-level mod-Bhattacharya distance match. The performance of the MB-FF algorithm in document retrieval mechanism is evaluated using Precision, recall, and F-measure.
An Upper Bound of the Longest Impossible Differentials of Several Block Ciphers

Impossible differential cryptanalysis is an essential cryptanalytic technique and its key point is whether there is an impossible differential path.The main factor of influencing impossible differential cryptanalysis is the length of the rounds of the impossible differential trail because the attack will be more close to the real encryption algorithm with the number becoming longer.We provide the upper bound of the longest impossible differential trails of several important block ciphers.We first analyse the national standard of the Russian Federation in 2015, Kuznyechik, which utilizes the 16-byte LFSR to achieve the linear transformation.We conclude that there is no any 3-round impossible differential trail of the Kuznyechik without the consideration of the specific S-boxes.Then we ascertain the longest impossible differential paths of several other important block ciphers by using the matrix method which can be extended to many other block ciphers.As a result, we show that, unless considering the details of the S-boxes, there is no any more than or equal to 5-round, 7-round and 9-round impossible differential paths for KLEIN, Midori64 and MIBS respectively.
RETRACTED ARTICLE: The impact mechanism of rural land circulation on promoting rural revitalization based on wireless network development

Abstract The objective and accurate evaluation of land value is one of the biggest interest issues that farmers are concerned about in the transfer of rural land, and it is one of the influencing factors that influence the country’s rural rejuvenation strategy recommendation. Considering the nonlinear characteristics of various policy and social issues, such as the nature of land and market chaos in the process of rural land circulation, neural networks are introduced to study rural land evaluation based on the development of wireless networks, and the mechanism of influence of promotion of rural regeneration is explored. After analyzing the structure and flow of the neural network, the related equations of the BP neural network are updated, and the introduction of a genetic algorithm to establish a hybrid model is proposed to promote the effective evaluation of the complexity problem by the BP neural network algorithm and improve the prediction accuracy. After simulation experiments show that this study can provide a fair evaluation of the value of rural land transfer, and it has positive significance for promoting the development strategy of rural rejuvenation.
ACCOUNTING FOR PLANETARY SURVIVAL ACKNOWLEDGE: THE BACKGROUND

The proposal of this paper is to present a summary of ten years of research at the P2P Foundation, including by our own P2P Lab but also by our partners in common research programs, of what we know today about the emerging commons economy. It includes a basic account of why the ‘invention’ of the blockchain has been important, but stresses that the needed distributed ledgers may take other forms in the future. This section may not offer a lot of new elements for those that are already technologically savvy about the topic, but it does offer a critical engagement with the qualities and flaws of the current model, and suggests how it can be tweaked and transformed, to also serve as a basis for a post-capitalist, commons-centric economy.
Kinds are calling conventions

A language supporting polymorphism is a boon to programmers: they can express complex ideas once and reuse functions in a variety of situations. However, polymorphism is pain for compilers tasked with producing efficient code that manipulates concrete values. This paper presents a new intermediate language that allows for efficient static compilation, while still supporting flexible polymorphism. Specifically, it permits polymorphism over not only the types of values, but also the representation of values, the arity of primitive machine functions, and the evaluation order of arguments---all three of which are useful in practice. The key insight is to encode information about a value's calling convention in the kind of its type, rather than in the type itself.
Verification of management-support of professional and educational trajectory of students in the socio-cultural educational environment of the university

The relevance of the study is determined by the processes of modernization of vocational education and the transition to the educational standards of the third generation, which open up new opportunities for students and teachers. Step-by-step management support of professional and educational trajectory of students requires organizational conditions and the development of technology for its practical implementation. Identifying the level of motivation in students when choosing a future profession allows assessing the level of inclusion of students in educational and cognitive activities and determining how far it is from the optimal motivational level, which allows the student achieving their goals, advancing in professional and personal development. At the initial stage of diagnosis, the analysis of professional experience of students, successes and problems in the training practice of the first year of training is carried out, at the next stage, the results of research activities on the results of the second year of training are analyzed. Activity-practical criterion, indicators of which are formed professional skills, primary professional experience, being in a sense the total overall performance indicator of professional and personal development of students, also changes qualitatively in the direction of improvement.
A Deep Neural Network Approach for Classification of Watermarked and Non-watermarked Images

Digital watermarking is the process of embedding an unique mark into digital data to prevent counterfeit. With the exponential increase in the data, the process of segregating a watermarked and non-watermarked images is very time consuming. It is necessary to automate the process of differentiating a watermarked and a non-watermarked images as well as identifying whether the given image is watermarked or not for identifying the authenticity. In this paper, we propose to use Deep Autoencoders, a form of deep neural networks for classification and identification of watermarked and non-watermarked images. The experiments are carried out using NWND dataset originally with 444 images. These images are watermarked using image, shape and text watermarking techniques to make the entire dataset to 1776 images. The experiment results show that, deep neural networks performed better that traditional feed forward neural networks. The classification accuracies with Original - IW for DAEN and ANN are 77.9% and 25.9 % respectively. Whereas for Original - SW and Original - TW, it is 82.1% and 32.7%, 64.2% and 20.06% respectively. The DAEN was able to identify 86 images correctly out of 100 images supplied which is 86% of accuracy with an average training rmse of 0.06423 and testing rmse of 0.0784.
Evaluation of Spatial-Temporal Anomalies in the Analysis of Human Movement

In industrial contexts, the performed tasks consist of sets of predetermined movements that are continuously repeated. The execution of improper movements and the existence of events that might prejudice the productive system are regarded as anomalies. In this work, it is proposed a framework capable of detecting anomalies in generic repetitive time series, adequate to handle human motion from industrial scenarios. The proposed framework consists of (1) a new unsupervised segmentation algorithm; (2) feature extraction, selection and dimensionality reduction; (3) unsupervised classification based on Density-Based Spatial Clustering Algorithm for applications with Noise. The proposed solution was applied in four different datasets. The yielded results demonstrated that anomaly detection in human motion is possible with an accuracy of 73±19%, specificity of 74 ± 21% and sensitivity of 74 ± 35%, and also that the developed framework is generic and may be applied in general repetitive time series with little adaptation effort for different domains.
Designing and Developing Lifelike, Engaging Lifestyle Coaching Agents and Scenarios for Multiparty Coaching Interaction.

The use of Embodied Conversational Agents (ECAs) in the domain of lifestyle coaching has been the subject of many studies in the last decades. With ageing populations in many countries, the pressure on healthcare systems is increasing. ECAs can be a potential tool to help combat this pressure through the promotion of a healthier lifestyle among older adults, as well as through coaching and counselling of older adults on how to manage their health. Designing more lifelike and engaging ECAs is of key importance to further this goal, as this could help encourage self-disclosure by the users towards the ECAs, could make the ECAs more persuasive, and could increase engagement and enjoyment of the interactions by the users, among other things. The Council of Coaches project aims to provide users with virtual coaching done by several lifestyle coaching agents with different domains of expertise as well as varying backstories, personalities, mannerisms, motivations and looks, among other things. To work towards this goal, work has been done on character and personality design for an initial set of lifestyle coaching agents and defined initial interaction scenarios. These were inspired by literature on coaching and ECAs, videos of interactions between healthcare professionals and patient actors, conversations with healthcare professionals, and brainstorming sessions. This paper presents the work done within the project thus far and closes out by presenting our plans to address the question: What are the possible advantages and disadvantages of a multiple agent approach as compared to a single agent approach to coaching interactions with older adults?
ANALYSIS AND DETECTION OF HOAX CONTENTS IN INDONESIAN NEWS BASED ON MACHINE LEARNING

Hoax newsthat contain incorrect (false) information often become public consumption on social media today. This hoax phenomenon raises doubts about information and makes confusion in the community. In this study, experiments conducted aimed at selecting the best algorithm in classifying hoax and non-hoax news with the number of data in 251 articles in Indonesian language (100 hoax articles and 151 non-hoax articles) using text mining method and machine learning based approaches. This research undergoes the text preprocessing phase which consists of tokenizing, case folding, filtering, stopwords removing, stemming and TF-IDF weighting using unigram and bigram combine features before processing it into classification text. The results of this research is the Random Forest algorithm that gets the best accuracy in classifying hoax and non-hoax news compared to the Multilayer Perceptron algorithm, Naive Bayes, Support Vector Machine, and Decision Tree with an accuracy value of 76.47%.
An adaptive model combining a total variation filter and a fractional-order filter for image restoration

In this paper, we propose a model that combines a total variation filter with a fractional-order filter, which can unite the advantages of the two filters, and has a remarkable effect in the protection of image edges and texture details; simultaneously, the proposed model can eliminate the staircase effect. In addition, the model improves the PSNR compared with the total variation filter and the fractional-order filter when removing noise. Zhu and Chan presented the primal-dual hybrid gradient algorithm and proved that it is effective for the total variation filter. On the basis of their work, we employ the primal-dual hybrid gradient algorithm to solve the combined model in this article. The final experimental results show that the new model and algorithm are effective for image restoration.
Accurate Tracking by Overlap Maximization

Visual object tracking is one of the fundamental problems in computer vision, with a wide number of practical applications in e.g. robotics, surveillance etc. Given a video sequence and the target ...
All Your Clicks Belong to Me: Investigating Click Interception on the Web

Click is the prominent way that users interact with web applications. For example, we click hyperlinks to navigate among different pages on the Web, click form submission buttons to send data to websites, and click player controls to tune video playback. Clicks are also critical in online advertising, which fuels the revenue of billions of websites. Because of the critical role of clicks in the Web ecosystem, attackers aim to intercept genuine user clicks to either send malicious commands to another application on behalf of the user or fabricate realistic ad click traffic. However, existing studies mainly consider one type of click interceptions in the cross-origin settings via iframes, i.e., clickjacking. This does not comprehensively represent various types of click interceptions that can be launched by malicious third-party JavaScript code. In this paper, we therefore systematically investigate the click interception practices on the Web. We developed a browser-based analysis framework, Observer, to collect and analyze click related behaviors. Using Observer, we identified three different techniques to intercept user clicks on the Alexa top 250K websites, and detected 437 third-party scripts that intercepted user clicks on 613 websites, which in total receive around 43 million visits on a daily basis. We revealed that some websites collude with third-party scripts to hijack user clicks for monetization. In particular, our analysis demonstrated that more than 36% of the 3,251 unique click interception URLs were related to online advertising, which is the primary monetization approach on the Web. Further, we discovered that users can be exposed to malicious contents such as scamware through click interceptions. Our research demonstrated that click interception has become an emerging threat to web users.
Congestion Control for Multi-Source Content Retrieval in Content Centric Networks

CCN/NDN (Content-Centric Networking/Named-Data Networking) is one of the most promising content-oriented network architectures. In CCN/NDN, forwarding information base (FIB) might have multiple entries for a same content name prefix, which means CCN/NDN potentially supports multi-source download. When a content is obtained from multiple sources, the technical knowledge obtained for congestion control in the current Internet cannot be simply applied. This is because in the current Internet, FIB is restricted to have only one entry for each IP address prefix, which causes quite different path feature from CCN/NDN. This paper proposes a new congestion control for CCN/NDN with multi-source content retrieval. The proposed congestion control is composed of end-to-end window flow control and router assisted Interest forwarding control, and enables transmission rate regulation only on a congested branch.
User Modeling on Twitter with Exploiting Explicit Relationships for Personalized Recommendations

The use of social networks sites has led to a challenging overload of information that helped new social networking sites such as Twitter to become popular. It is believed that Twitter provides a rich environment for shared information that can help with recommender systems research. In this paper, we study Twitter user modeling by utilizing explicit relationships among users. This work aims to build personal profiles through a alternative methods using information gained from Twitter to provide more accurate recommendations. Our method exploits the explicit relationships of a Twitter user to extract information that is important in building the user’s personal profile. The usefulness of this proposed method is validated by implementing a tweet recommendation service and by performing offline evaluation. We compare our proposed user profiles against other profiles such as a baseline using cosine similarity measures to check the effectiveness of the proposed method. The performance is measured on an adequate number of users.