Reviewerly/exHarmony huggingface dataset:
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces

After the tremendous development of neural networks trained by backpropagation, it is a good time to develop other algorithms for training neural networks to gain more insights into networks. In this paper, we propose a new algorithm for training feedforward neural networks which is fairly faster than backpropagation. This method is based on projection and reconstruction where, at every layer, the projected data and reconstructed labels are forced to be similar and the weights are tuned accordingly layer by layer. The proposed algorithm can be used for both input and feature spaces, named as backprojection and kernel backprojection, respectively. This algorithm gives an insight to networks with a projection-based perspective. The experiments on synthetic datasets show the effectiveness of the proposed method.
Prior-Knowledge-Driven Local Causal Structure Learning and Its Application on Causal Discovery Between Type 2 Diabetes and Bone Mineral Density

Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affects the glucose metabolism of the human body, which decreases the quantity of life and brings a heavy burden on social medical care.Patients with T2DM are more likely to suffer bone fragility fracture as diabetes affects bone mineral density (BMD).However, the discovery of the determinant factors of BMD in a medical way is expensive and time-consuming.In this paper, we propose a novel algorithm, Prior-Knowledge-driven local Causal structure Learning (PKCL), to discover the underlying causal mechanism between BMD and its factors from the clinical data.Since there exist limited data but redundant prior knowledge for medicine, PKCL adequately utilize the prior knowledge to mine the local causal structure for the target relationship.Combining the medical prior knowledge with the discovered causal relationships, PKCL can achieve more reliable results without long-standing medical statistical experiments.Extensive experiments are conducted on a newly provided clinical data set.The experimental study of PKCL on the data is proved to highly corresponding with existing medical knowledge, which demonstrates the superiority and effectiveness of PKCL.To illustrate the importance of prior knowledge, the result of the algorithm without prior knowledge is also investigated.
Upper Half Face Recognition Using Hidden Markov Model and Singular Value Decomposition Coefficients

Face recognition is the process of recognizing a special face from a set of different faces. A human face is a very complex object/structure with varying features from childhood to old age. The first main objective of this paper is to recognize a person even if the person is not in a neutral state or with any facial expression. The second main objective of this paper is to recognize those people whose upper half face is available. In this paper, a sequence of blocks has been created for an image of each face and each block is featured by singular value decomposition parameters. Seven-state Hidden Markov Model has been used to cover whole face details. The system has been evaluated on 400 face images of Olivetti Research Laboratory face database, in MATLAB with different illuminations, lighting conditions and Cohn-Kanade database with different expressions. For both databases, recognition rate of approximately 100% has been achieved with the increase of training images. The major achievement of this algorithm is that it recognizes the person correctly even if an upper half face of the person is provided as test image and it recognizes the person with correct expression in CK+ database. This result is achieved by using a small number of feature describing blocks and resizing the images into a smaller size.
The Finance-Investment-Growth Causal Connection: Evidence from the Republic of Congo

This paper examines the causal relationship between financial development and investment in the Republic of Congo for the period 1960 to 2017. The causal relationship between financial development, investment and economic growth is examined using four multivariate Granger-causality models. The study found that the finance-investment-growth causal relationship is highly dependent on the choice of the financial development indicator used. In addition, all financial development indicators, except domestic credit provided to the private sector, were found to Granger cause investment in the short run. Furthermore, investment has been found to Granger cause economic growth in the short run when domestic credit provided by the financial sector and the composite financial development index are included as measures of financial development in subsequent models. Therefore, dependent on the policy focus, economic policy through financial development should take into account the financial indicator so as to predict and/or achieve the resultant policy objectives.
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing

Recently, deep learning has been used in Medical Subject Headings (MeSH) indexing to reduce the labor costs associated with manual annotation, including DeepMeSH, TextCNN, etc.However, these models fail to capture the complex correlations between MeSH terms.To this end, we use a Graph Convolution Network (GCN) to learn the relationship between these terms and present a novel Hybrid Graph Convolution Net for MeSH index (HGCN4MeSH).We utilize two bidirectional GRUs to learn the embedding representation of the abstract and the title of the MeSH index text respectively.We construct the adjacency matrix of MeSH terms, based on the co-occurence relationships in corpus, and use the matrix to learn representations using the GCN.On the basis of learning the joint representation, the prediction problem of the MeSH index keywords is an extreme multi-label classification problem after the attention layer operation.Experimental results on two datasets show that HGCN4MeSH is competitive with the state-of-the-art methods.
F-Cardinal Conflicts in Conflict-Based Search

Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF) which features strong performance. In CBS, one conflict in a high-level node is resolved to generate two child nodes, until a node with no conflicts is found. Choosing the right conflict to resolve can greatly speed up the search. It is currently recommended to resolve cardinal conflicts first, resolving them yields two child nodes with a higher cost than the cost of their parent. However, since the recent addition of high-level heuristics to CBS, when resolving cardinal conflicts, the h-value of high-level child nodes often decreases by the same amount as their cost increases. This diminishes the effectiveness of the cardinal conflicts distinction. We propose an expanded categorization of conflicts into f-cardinal, g-cardinal, and non-cardinal. F-cardinal conflicts should be resolved first. Resolving f-cardinal conflicts generates child nodes with an increased f-value relative to their parent. We propose two methods for identifying f-cardinal conflicts. Finally, we demonstrate on standard benchmarks that choosing conflicts according to this expanded categorization increases the effectiveness of modern CBS.
News Aggregator and Efficient Summarization System

News Aggregator is simply an online software which collects new stories and events around the world from various sources all in one place.News aggregator plays a very important role in reducing time consumption, as all of the news that would be explored through more than one website will be placed only in a single location.Also, summarizing this aggregated content absolutely will save reader's time.A proposed technique used called the TextRank algorithm that showed promising results for summarization.This paper presents the main goal of this project which is developing a news aggregator able to aggregate relevant articles of a certain input keyword or key-phrase.Summarizing the relevant articles after enhancing the text to give the reader understandable and efficient summary.
UC Updatable Databases and Applications

We define an ideal functionality $$\mathcal {F} _{\mathrm {UD}}$$ and a construction $$\mathrm {\Pi _{\mathrm {UD}}}$$ for an updatable database ( $$\mathrm {UD}$$ ). $$\mathrm {UD}$$ is a two-party protocol between an updater and a reader. The updater sets the database and updates it at any time throughout the protocol execution. The reader computes zero-knowledge (ZK) proofs of knowledge of database entries. These proofs prove that a value is stored at a certain position in the database, without revealing the position or the value. (Non-)updatable databases are implicitly used as building block in priced oblivious transfer, privacy-preserving billing and other privacy-preserving protocols. Typically, in those protocols the updater signs each database entry, and the reader proves knowledge of a signature on a database entry. Updating the database requires a revocation mechanism to revoke signatures on outdated database entries. Our construction $$\mathrm {\Pi _{\mathrm {UD}}}$$ uses a non-hiding vector commitment (NHVC) scheme. The updater maps the database to a vector and commits to the database. This commitment can be updated efficiently at any time without needing a revocation mechanism. ZK proofs for reading a database entry have communication and amortized computation cost independent of the database size. Therefore, $$\mathrm {\Pi _{\mathrm {UD}}}$$ is suitable for large databases. We implement $$\mathrm {\Pi _{\mathrm {UD}}}$$ and our timings show that it is practical. In existing privacy-preserving protocols, a ZK proof of a database entry is intertwined with other tasks, e.g., proving further statements about the value read from the database or the position where it is stored. $$\mathcal {F} _{\mathrm {UD}}$$ allows us to improve modularity in protocol design by separating those tasks. We show how to use $$\mathcal {F} _{\mathrm {UD}}$$ as building block of a hybrid protocol along with other functionalities.
Comparative Analysis of Three Language Spheres: Are Linguistic and Cultural Differences Reflected in Password Selection Habits?

This work aims to determine the propensity of password creation through the lens of language spheres. To this end, we consider four different countries, each with a different culture/language: China/Chinese, United Kingdom (UK) and India/English, and Japan/Japanese. We first employ a user study to verify whether language and culture are reflected in password creation. We found that users in India, Japan, and the UK prefer to create their passwords from base words, and the kinds of words they are incorporated into passwords vary between countries. We then test whether the findings obtained through the user study are reflected in a corpus of leaked passwords. We found that users in China and Japan prefer dates, while users in India, Japan, and the UK prefer names. We also found that cultural words (e.g., “sakura” in Japan and “football” in the UK) are frequently used to create passwords. Finally, we demonstrate that the knowledge on the linguistic background of targeted users can be exploited to increase the speed of the password guessing process.
Interactive Pedagogical Agents for Learning Sequence Diagrams

Students struggle to learn sequence diagrams (SDs), as the designs must meet the requirements without violating the constraints imposed by other UML diagrams. Providing manual timely feedback, though effective, cannot scale for large classes. Our pedagogical agent combining data dependencies and quality metrics with rule-based techniques capturing consistency constraints allowed generation of immediate and holistic feedback. The scaffolding approach helped to lower the cognitive overload. The pre- and post-tests and survey results revealed substantially improved learning outcomes and student satisfaction.
Scanpath Analysis of Student Attention During Problem Solving with Worked Examples

We report on the analysis of scanpath data captured by an eye tracker as students solved problems with access to worked examples. Our work makes two contributions: (1) it reports on scanpath analysis using the MultiMatch tool, (2) it investigates how type of problem-example similarity and assistance influenced attention patterns captured by scanpaths. We show that both problem-example similarity and type of assistance impact scanpaths.
Empowering Information Retrieval in Semantic Web

Until the inception of Web 1.0, the Information Retrieval was the center of the stage for library and it was defined as search and passive.Later on, the emergence of Web 2.0 was encouraged into the community, social interaction and user-generated content.Web 3.0 is a modern phenomenon and also known to "3D Web or the Semantic Web", and it often used for specifically to formats and the technologies.The advanced Web 4.0 is the Ultra-Intelligent Agent Interactions between humans and machines.Semantic web technology finds meanings from various sources to enabling the machines and people to understand and share knowledge.The semantic web technology helps to add, change and implement the new relationships or interconnecting programs in a different way which can be as simple as changing the external model that these programs are shared.To give an information need, the semantic technologies can directly search, capture, aggregate, and make a deduction to satisfy the user needs.The paper presents a framework for knowledge representation assembling semantic technology based on ontology, semantic web, and an intelligent agent algorithm as a connectivity framework to share the appropriate knowledge representation which includes the web ontology language that discovers related information's from various sources to serve the information needs.The research addresses the intelligent agent algorithm is the key contribution that reveals appropriate information and empowers Web 3.0 and embraces Web 4.0 into the coming semantic web technology.
Medical image registration based on PCA and M_PSNR

Medical image registration is not only the premise of medical image fusion, but also the focus of medical image processing. It plays an important role in clinical diagnosis and treatment planning. Aiming at the problem of mutual information value in image registration, a PCA neural network and M_PSNR based method for rigid registration of reference and floating images was proposed. In this method, the centroid of the image needs to be figured out through the image matrix, and the rotation Angle and translation of the image registration can be calculated by PCA method to obtain the optimal rotation transformation and the initial registration value. Finally, M_PSNR method improved by PSNR was used for similarity test to increase the speed and efficiency of registration and simplify the calculation process.
Impact of Methodological Choices on the Evaluation of Student Models

The evaluation of student models involves many methodological decisions, e.g., the choice of performance metric, data filtering, and cross-validation setting. Such issues may seem like technical details, and they do not get much attention in published research. Nevertheless, their impact on experiments can be significant. We report experiments with six models for predicting problem-solving times in four introductory programming exercises. Our focus is not on these models per se but rather on the methodological choices necessary for performing these experiments. The results show, particularly, the importance of the choice of performance metric, including details of its computation and presentation.
An Efficient Segmentation Technique for Different Medical Image Modalities

In this paper a study on the segmentation of the medical image is carried out. Image segmentation is the process of splitting an image into a number of non-overlapped segments (sets of pixels, also known as image objects). The success of image analysis process depends on accuracy of segmentation process, but a successful segmentation of an image is generally a difficult problem. During an image preprocessing operation, the input given is an image and its output is an enhanced high-quality image as per the techniques used. This paper provides a solid introduction to image enhancement along with image segmentation technique fundamentals. Firstly, the local spatial information of the image is combined with fuzzy c-mean by introducing morphological reconstruction operation to ensure noise-immunity and image detail-protection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the modification of membership partition depends only on the spatial neighbors of membership partition instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed algorithm is very simple to implement and significantly fast, since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy image because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image multimodalities illustrate that the proposed algorithm can achieve better results, as well as it requires short time for the image segmentation process.
Research on Treatment of Retaining Wall Foundation with Geosynthetics Based on BP Neural Network

Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The maximum error between the predicted value and the actual value is 0.91%, which is a lot better than the error of the least square 3.4%. This method Found a new way for creep prediction.
Preventing active re-identification attacks on social graphs via sybil subgraph obfuscation

Abstract Active re-identification attacks constitute a serious threat to privacy-preserving social graph publication, because of the ability of active adversaries to leverage fake accounts, a.k.a. sybil nodes , to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. Several formal privacy properties have been enunciated with the purpose of characterising the resistance of a graph against active attacks. However, anonymisation methods devised on the basis of these properties have so far been able to address only restricted special cases, where the adversaries are assumed to leverage a very small number of sybil nodes. In this paper, we present a new probabilistic interpretation of active re-identification attacks on social graphs. Unlike the aforementioned privacy properties, which model the protection from active adversaries as the task of making victim nodes indistinguishable in terms of their fingerprints with respect to all potential attackers, our new formulation introduces a more complete view, where the attack is countered by jointly preventing the attacker from retrieving the set of sybil nodes, and from using these sybil nodes for re-identifying the victims. Under the new formulation, we show that k -symmetry, a privacy property introduced in the context of passive attacks, provides a sufficient condition for the protection against active re-identification attacks leveraging an arbitrary number of sybil nodes. Moreover, we show that the algorithm K-Match , originally devised for efficiently enforcing the related notion of k -automorphism, also guarantees k -symmetry. Empirical results on real-life and synthetic graphs demonstrate that our formulation allows, for the first time, to publish anonymised social graphs (with formal privacy guarantees) that effectively resist the strongest active re-identification attack reported in the literature, even when it leverages a large number of sybil nodes.
Evaluating Pilot’s Perceived Workload on Interacting with Augmented Reality Device in Flight Operations

Augmented Reality (AR) is a tool which can be used to improve human-computer interaction in flight operations. The application of AR can facilitate pilots integrating the information from interfaces in the flight deck to analyze various sources of messages simultaneously. There are seventeen subjects aged from 23 to 53 (M = 29.82, SD = 8.93) who have participated in this experiment. Their flight experience ranged from zero flight hours to 3000 flight hours (M = 605.00, SD = 1051.04). Two types of HCI AR design (gesture or voice control checklist) have been compared with traditional paper checklist. The results show that AR gesture control induced the highest perceived workload compared with AR voice checklist and traditional paper checklist. There are lots of complicated cognitive processes and physical movements involved in the AR gesture checklist that induced the highest level of effort and frustration based on NASA-TLX. The AR checklist application has relied on the use of the default HoloLens interactions including cursor movement linked with head movements, Air Tap gesture and Microsoft voice recognition system. The current technological features embedded in the HoloLens device are not certified to be used in the cockpit yet. The improvement in the types of interaction and displays with AR devices could lead to changes in pilot’s perceived workload while interacting with an innovative device. This research demonstrated that AR integrated with voice command has potential of significant benefits to be applied in the flight deck for future flight operation.
Recommendation method of smart TV programs reflecting content consumption concentration calculation

The proliferation of smart TVs and the surge of broadcasting contents due to the effects of Internet broadcasting and the creation of comprehensive programming channels have provided an environment in which various programs can be selected and consumed by TV viewers today. However, it is very time-consuming for users to select the programmes that people want by identifying the contents of a large number of programmes. Therefore, it is necessary to study the recommendation of TV programmes with high user satisfaction based on the user's viewing history. However, the research to date does not consider how focused the programme was when generating a list of programmes viewed by the user. As a result, we recommend TV programmes with relatively low satisfaction by recommending TV programme without distinction between the TV programme and the TV programme. In this study, we propose a method to calculate the concentration of content consumption based on user's TV programme consumption pattern and to recommend smart TV programme based on it. In order to verify the proposed method, we compared the satisfaction with the existing method based on the TV viewing records of the top 100 who had many TV watching records.
Multicultural education: issues and perspectives, 10<sup>th</sup> edition

Gradually but inevitably it appears that books in print, hard or soft copy are disappearing. This includes education textbooks. Many people, among them teachers and instructors, are distressed by s...