Reviewerly/exHarmony huggingface dataset:
Parsing Strategies

Abstract In order to successfully comprehend sentences involving ellipsis, the online sentence-processing mechanism must be able to identify the ellipsis site, find its antecedent, and recover the content of the ellipsis site by referring to the antecedent. The psycholinguistic study of ellipsis aims to reveal aspects of the mechanism of online sentence processing by studying how ellipsis constructions are processed online. In this chapter, the following questions are asked. What structure does the parser build in the ellipsis site? How does the parser build the structure of ellipsis? When does the parser recognize ellipsis? Where does the parser find the antecedent of ellipsis? By answering these questions, this chapter tries to reveal what parsing strategies are employed in the online processing of ellipsis constructions.
Mobile Networks and Indian Agricultural Sector

Information dissemination in agricultural sector for its growth using information and communication technology (ICT) as a tool is need of the hour. This can be achieved using information systems. ICT benefits are helpful in exchange and dissemination of information among farming stakeholders. By using the latest tool of mobile technology, farmers can get the current information related to their farming jobs around the clock and at any location, as the mobile network have touched every part/location of the India. Using the features of the mobile-phones like GPS etc. one can get the localized information. Only need is to structure the abundant information available across the various organizations. So, a mobile based agricultural information system framework ‘mAgIDS' has been proposed employing the hybrid mobile application architecture approach. Client-server architecture using the location Application Programming Interface (API) has been proposed. Inference mechanism of the system has implemented on the basis of improved fuzzy rule promotion technique.
Drug Trafficking

In an increasingly intrusive era, the deep web is considered to be a bastion of privacy, while for others it is one of the most evil places to exist. The deep web is a part of the world wide web that cannot be readily accessed by conventional search engines, and a small part of it forms the dark web. The enshrouded nature and complex methodology required for access have made dark web a platform for carrying out numerous illicit activities, one of them being drug trafficking. This article explores how deep web and dark web operate, the trade of illegal drugs online in so-called cryptomarkets or dark net markets, the foundation and shut-down of Silk Road, the new cryptomarkets that popped up to take its place, the use of ‘the onion router' or TOR browser in this anonymous sale and purchase of illegal drugs, the role of encryption and cryptocurrencies, the existing and suggested tactics of the law enforcement to prevent internet-facilitated drug trafficking, and the future research areas.
A Large Margin Learning Method for Matching Images of Natural Objects With Different Dimensions

Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by investigating structured support vector machines to construct and learn a graph-based model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using a graph matching algorithm on extracted weighted graph models. The effectiveness of this method is demonstrated through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions on images of natural objects.
IOT Security Threats and Block Chain based Solutions

The Internet of Things (IoT) is one of the important technologies that has taken the attention of researchers. It is the interconnection of things connected with each other and to also with humans, to achieve some goals. In future IoT is expected to be effortlessly integrated into our environment and human will be solely dependent on this technology for comfort and easy life style. Any security concern of the system will directly affect human life. So security and privacy of this technology is primarily important issue to resolve. In this paper, we discuss the threats and vulnerabilities to the security of IoT devices in different domains, different layers, its deployment architecture and provides possible Block chain solution to overcome these issues. The paper also analyzes how the Block chain technology can be used to provide security and privacy in IoT. : The Internet of Things (IoT) is one of the important technologies that has taken the attention of researchers. It is the interconnection of things connected with each other and to also with humans, to achieve some goals. In future IoT is expected to be effortlessly integrated into our environment and human will be solely dependent on this technology for comfort and easy life style. Any security concern of the system will directly affect human life. So security and privacy of this technology is primarily important issue to resolve. In this paper, we discuss the threats and vulnerabilities to the security of IoT devices in different domains, different layers, its deployment architecture and provides possible Block chain solution to overcome these issues. The paper also analyzes how the Block chain technology can be used to provide security and privacy in IoT.
Analyzing Global Feature for Duplicate Video Retrieval using CNN and PCA

At present, Duplicate video retrieval has attained the interest of researchers because of the vast amount of online videos. This video retrieval has extensive applications like online video monitoring, copyright protection, and automatic video tagging. Some local features constitute primary building blocks in this video retrieval algorithms; with this, most researchers use local information for feature representation. Moreover, this local knowledge-based representation eliminates more prominent information regarding global distribution. However, the discriminative power of local descriptors is diminished by feature quantifiers. The ultimate goal is to use universal features to categorize similar keyframes into the same class that is essential to enhance video retrieval performance. Here, CNN features acquire global geometric distribution of video, from which discrete features are considered for computation. Discretization is performed with principal component analysis. These kinds of features maintain geometry transformation with reduced noise. Next, an integration strategy known as k-NN is used to merge these features with global VR features for enhancing recognition accuracy. Experimentation has been carried out with available datasets to show that the anticipated model outperforms existing approaches in VR applications
Johnson County Library Makerspace

The Johnson County Library Makerspace occupies 1700 ft2 (158 m2) of space in the Central Resource Library in Johnson County, Kansas. Originally intended as a modernization of the small business services resource area within the library, the vision for the space quickly pivoted towards a community-based educational experience centered on making. In its original form, the space had one 3D printer and an idea to create a series of summer, mobile maker experiences dispersed amongst the 13 branch libraries. The widely popular mobile makerspace program paved the way to a permanent space for library patrons young and old. The space adheres to the central mission of the library to provide educational experiences for the public. Accordingly, the library has democratized creativity and innovation by providing free access to the equipment and information to the public. The space has reached out to incarcerated youth to inspire them to take up making and use it as a way to build job skills. In addition, the space provides patrons with a pathway to connect and form learning communities around making skills and interest. This chapter explores the Johnson County Library Makerspace.
Summarized Logical Forms Based on Abstract Meaning Representation and Discourse Trees

We propose a way to build summarized logical forms (SLFs) automatically relying on semantic and discourse parsers, as well as syntactic and semantic generalization. Summarized logical forms represent the main topic and the essential part of answer content and are designed to be matched with a logical representation of a question. We explore the possibilities of building SLFs from Abstract Meaning Representation (AMR) of sentences supposed to be the most important, by selecting the AMR subgraphs. In parallel, we leverage discourse analysis of answer paragraphs to highlight more important elementary discourse units (EDUs) to convert into SLFs (less important EDUs are not converted into SLFs). The third source of SLFs is a pair-wise generalization of answers with each other. Proposed methodology is designed to improve question answering (Q/A) precision to avoid matching questions with less important, uninformative parts of a given answer (to avoid delivering foreign answers). A stand-alone evaluation of each of the three sources of SLFs is conducted as well as an assessment of a hybrid SLF generation system. We conclude that indexing the most important text fragments of an answer such as SLF instead of the whole answer improves the Q/A precision by almost 10% as measured by F1 and 8% as measured by NDCG.
Standard Deviation Based Otsu for Breast Cancer Classification

Segmentation is one of the essential components in Computer Aided Diagnosis (CADx).This paper proposed a modified Otsu method for segmentation phase in classifying breast cancer.In Otsu's method, the process of computation within-class variance for two classes produce lower accuracy for the classification, hence the standard deviation will be utilized to overcome this limitation.After that, the features of the segmented images will be extracted by using Speed Up Robust Features (SURF).Finally, the corresponding features will be classified using several classifiers.It is found that the Support Vector Machine (SVM) classifier shows the highest accuracy rate among other classifiers.Moreover, experimental results show that the proposed method is superior to the original Otsu method.
Security on SM2 and GOST Signatures against Related Key Attacks

The US Standard (EC)DSA is currently almost the most popular digital signature scheme. Chinese and Russian governments also proposed their counterparts: SM2 and GOST R 34.10 (GOST). Nowadays, there are already many industrial applications supporting SM2 and GOST digital signatures. Unfortunately, the existing analyses for SM2 and GOST are rather limited when compared to ECDSA. This paper focuses on the security of SM2 and GOST from the viewpoints of RKA security (related-key attack) and sKRKA security (strong known related key attack). RKA captures the real attacks of tampering and fault injection in hardware-stored secret keys. sKRKA, a recently proposed security model modified from RKA, captures the real attacks in the BIP-32 HD wallet and the stealth address used in Monero. It was proved that ECDSA is insecure in the RKA model (ICISC 2015) and but secure in the sKRKA model (NSS 2019). In this work, we proved that GOST is insecure in both RKA and skRKA models, but SM2 is secure in both RKA and sKRKA models. This result well differentiates the security of ECDSA, SM2 and GOST, and demonstrates that Chinese SM2 is capable to construct secure cryptocurrency systems using BIP-32 HD wallet or stealth address, as secure as ECDSA, but outperforms ECDSA in resisting tampering or fault injection attacks.
Handwritten String Recognition Based on YOLOv4 and CRNN on Different Deep Learning Frameworks

Character recognition is one of the most important contents of pattern recognition research. Among them, due to the instability and continuity of individual differences in handwritten strings, it is difficult to extract features in recognition. At present, the recognition of handwritten strings is based on single character segmentation. However, due to the difficulty of character segmentation of handwritten string, the accuracy of recognition based on segmentation method is relatively low. In this paper, we use the non segmentation method based on RNN for character recognition, combined with the time sequence characteristics to improve the recognition accuracy of continuous strings. Firstly, handwritten string detection methods are studied respectively, based on YOLOv4, YOLOv4-Tiny model and OpenCV, the detection methods are tested; then the handwritten string recognition methods are studied, the ‘CNN+RNN+CTC’ pattern is used to calculate loss function model, thus can identify undivided strings, and then through CTC decoding, string results with higher accuracy can be obtained; finally, after testing was carried out, the experimental result shows that the method proposed in this paper can solve the problem of stylus and variable length of handwritten strings, therefore improve the efficiency of converting handwritten manuscripts into electronic manuscripts.
MAQD: Cooperative Multi-Agent Reinforcement Learning Q-value Decomposition in Actor-Critic Framework

Multi-agent reinforcement learning (MARL) methods witness vigorous progress recently. Decentralized agents coordinate their behaviors while acting in many non-communication and partially observable real-world, and centralized training is a way to obtain global state information. However, centralized training cannot maximize individual and collective rational actions and coordinate the contradiction between them. For this problem, we investigate the causes that hinder the performance of multi-agent Actor-Critic algorithms and make basic assumptions. Meanwhile, this paper presents a cooperative MARL 4-value decomposition algorithm (MA4D). This method introduces a 4-value decomposition network to convert the joint 4-value output by the centralized critic network into a local 4-value for a single agent through accumulation and approximation, and then guide the gradient update of the actor network respectively. The MA4D ensures the consistency of the global optimal action and the local optimal action of MARL, individual and collective rationality is fully utilized to explore joint policy under Nash equilibrium. In addition, we design cooperative agents experimental scenarios to verify this algorithm on the multi-agent particle environment, where agents take actions in continuous spaces. Empirical evaluations demonstrate that our method significantly outperforms state-of-the-art multi-agent Actor-Critic algorithms in terms of performance and stability.
Understanding the Algebra of the Restaurant Patron: A Cartography Using Cognitive Economics and Mind Genomics

Most of these tools are used within the context of understanding the business as a social entity (anthropology, sociology), or as a moneymaking enterprise that can be analyzed and fine-tuned to increase the revenues and the profitability, as well as increase both employee satisfaction and customer satisfaction.The world of the restaurant is of continuing interest to researchers.The restaurant is a microcosm, of interest to businesspeople, organization psychologists, those in the world of food service, and so forth.There are no lack of papers and journals devoted to the world of restaurants in general, and to the world of food service in particular.Most of the papers look at the restaurant from the 'outside, ' observing either the behavior, or asking the customer to evaluate the experience.There have been some papers looking at the mind of the restaurant consumer in some depth, moving beyond the standard surface questions [5,6].Most of these deeper-focused papers deal with the topic from the point of view of the profession of hospitality, and not from the point of view of psychology. The Contribution of Mind Genomics to Understanding the Perception of the Restaurant ExperienceMind Genomics is a newly developing science, dealing with the nature of how we make decisions in our daily lives.Rather than focusing on unusual and artificial situations to propose or disprove a hypothesis, Mind Genomics can be better considered to be a cartography, a study of the landscape, with the goal to uncover patterns in everyday life, specifically patterns involved in the way people take in information, and make decisions.Mind Genomics differs from social psychology
A K-nearest neighbor classifier based on homomorphic encryption scheme

Homomorphic encryption technology can analyze the data stored in the cloud without decryption, because the results of ciphertext calculation after decryption are the same as the corresponding plaintext calculation results. Based on homomorphic encryption and machine learning technology, this paper proposes a K-nearest neighbor classifier based on homomorphic encryption scheme, Homomorphic encryption technology can not only ensure the security of the data, but also analyze the data in the ciphertext state since the characteristics of homomorphism, avoiding the data insecurity problem caused by analyzing the data after decryption in the clound. In this scheme, we first improve the ciphertext comparison algorithm and improve the judgment of sample label in ciphertext state. Then, using k-nearest neighbor classifier, a ring based selection algorithm is designed to reduce the time of ciphertext operation. The results show that our scheme can realizes the ciphertext classification On the condition of ensuring the accuracy of classification. Compared with the original k-nearest neighbor classification method, the classification accuracy of the our algorithm is improved about 1%, but the time cost is larger than the original k-nearest neighbor classification method.
Word Replaceability Through Word Vectors

Abstract There have been many numerical methods developed recently that try to capture the semantic meaning of words through word vectors. In this study, we present a new way to learn word vectors using only word co-appearances and their average distances. However, instead of claiming semantic or syntactic word representation, we lower our assertions and claim only that we learn word vectors, which express word’s replaceability in sentences based on their Euclidean distances. Synonyms are a subgroup of words which can replace each other, and we will use them to show differences between training on words that appear close to each other in a local window and training that uses distances between words, which we use in this study. Using ConceptNet 5.5.0’s synonyms, we show that word vectors trained on word distances create higher contrast in distributions of word similarities than was done with Glove, where only word appearances close to each other were engaged. We introduce a measure, which looks at intersection of histograms of word distances for synonyms and non-synonyms.
Adaptive Steganography using 3D Color Texture Feature

Human vision framework is commonly an emotional recognition which differs according to people. Intricacy of a picture assumes huge job while verifying information in to it. In this paper another steganography approach is introduced which uses joined 3D Color Texture Feature (CTF) to distinguish complex districts of picture for information stowing away so visual assault to identify shrouded message turns out to be very testing. Recurrence area is utilized to shroud the information in these chose complex areas by means of Discrete Cosine Transform (DCT). These sorts of zones are initially boisterous and separating additional data is difficult. Each picture has diverse multifaceted nature levels and spatial districts, and since information covering up is legitimately reliant on it, so the steganography framework ends up versatile. The outcome demonstrates that proposed versatile strategy gives secure message stowing away while keeping up subtlety quality and high implanting limit. Last spread pictures keeps up PSNR estimation of over 50. Inserting limit is around multiple times higher in contrast with comparative calculation which uses Gray Level Co-event Matrices (GLCM) highlight to recognize complex districts of pictures for information covering up.
Learning Environments

This chapter addresses changes in learning environments from primarily brick and mortar buildings to include online learning platforms, MOOCs, blended learning spaces, and virtual worlds. Learning management systems, virtual reality, and a plethora of technological tools have revolutionized education allowing schools and universities to offer distance courses and degrees entirely online and constructive learning experiences for students using innovative tools in creative spaces. Advantages and disadvantages of these various environments are discussed along with predictions for the future. Metamodern digital citizens may encounter rich learning experiences physically face-to-face or virtually through immersion in 3D simulated environments, requiring metaliteracy to communicate, collaborate, construct knowledge, and make sense of our rapidly changing world.
The Research on PDA Class in Comprehensive English Teaching from Eco-view

With the reform of College English teaching, many scholars from the perspective of ecology, combing with the elaboration of the concept of ecological education and the current situation of College English teaching came up with The College English classroom teaching.Other scholars have analyzed the application of sub-classes in integrated English teaching in order to enhance students' learning initiative and improve classroom teaching effect.Based on the previous studies of scholars, this paper introduces the perspective of ecology and the connotation of Pad class pedagogy in detail, and then combines the existing problems in College English classroom teaching with systematic and comprehensive eco-classroom language method, puts forward the application of sub-classes in English and Chinese teaching from the perspective of ecology.This study provides a new way for the reform of sub-classes in College English teaching from the perspective of ecology.
Multimodal Aesthetic Analysis Assisted by Styles through a Multimodal co-Transformer Model

Many real-world applications could profit from the ability of image aesthetic analysis. A simultaneous understanding of both the visual content of images and the textual content of user comments and style attributes appears to be more vivid and adequate than single-modality and single-dimension information to help people learning to identify beauty or not. In this paper, we propose a multimodal co-transformer model to learn a joint representation of multimodal contents based on the co-attention mechanism, and then we conduct multi-dimension aesthetic analysis assisted by style attributes. Towards this goal, we propose a stacked multimodal co-transformer module encoding the feature under interactive guidance, and then we utilize a multi-task learning strategy for predicting multiple aesthetic dimensions. Experimental results indicate that the proposed model achieves state-of-the-art performance on the AVA datasets benchmark.