Reviewerly/exHarmony huggingface dataset:
Research on Project Cooperation Prediction based on Metapath2vec

Scientific research cooperation is an important way to carry out scientific projects and solve research problems, cooperative relationship prediction is one of the research hotspots at home and abroad in recent years. This paper focus on the academia of science, using Neo4j to build the network diagram of researchers and projects. Optimize on the basis of metapath2vec algorithm to predict cooperative relationship between researchers in different fields. Firstly, this paper studies and compares the cooperative prediction algorithms. Secondly, combined with the data set of project module of dataset, this paper optimizes and experiments the metapath2vec algorithm. Meanwhile, we design the weight coefficient under the influence of time factor, member contribution degree and project level, using heterogeneous knowledge graph to improve the computational efficiency and enhance the interpretability of prediction at the same time. Finally, the experiment and verification are carried out by using the project data of the National Fund Committee. The experimental result shows that the optimized cooperative prediction algorithm can take into account many factors such as time, member contribution, project level and so on, which helps predicting the potential cooperative relationship quickly, and the accuracy is improved by about 7% compared with the baseline.
Computational Treatment for The Solution of A Set of Coupled Differential Equations for Any Physical System Using Scilab and Generating Scilab Codes

Abstract A set of coupled differential equations with initial conditions and its computational treatment as well as Scilab codes have been done. For constant E the variation of x &amp; y with t shows the higher amplitude for y that’s a dependent variable; when E is cos(ωt) then oscillatory motion has been observed at different frequencies, ω= π/6, π/3, π/2 and π. The direction of the E in downward and upward motion has been observed at different frequencies and many times E touches the horizontal axis for downward motion. In the study of this paper it has been found that the oscillations are much higher in case of y as compared to x. All Scilab codes have been developed successfully.
A Systematic Literature Review on Risk Assessment and Mitigation Approaches in Requirement Engineering

Risk assessment and management practice is an organized way to identify, analyze, and assess the impacts of risks and mitigate them when they arise. Risk can occur in any phase of software development and is a significant step for better supervision of threats. The purpose of this study is to identify and analyze existing risk assessment and management techniques from a historical perspective that address and study risk management and perception of risk. The chapter presents extensive summary of existing literature on various techniques and approaches related to requirements defects, defect taxonomy, its classification, and its potential impact on software development as the main contributions of this research work. The primary objective of this study was to present a systematic literature review of techniques/methods/tools for risk assessment and management. This research successfully identifies and discovers existing risk assessment and management techniques, their limitations, taxonomies, processes, and identifies possible improvements for better defect identification and prevention.
Virtual Assistant Based on Facial Emotions Using Convolution Neural Networks

The theme of this paper is to build a virtual assistant whose actions are customizable based on basic emotions theory which consists of seven prototype basic emotions. Since facial emotions play an important role in communication, we need a digital assistant which can be able to detect the human facial emotions and adapt its behavior itself accordingly. The idea is to build a neural network which is able to understand the facial emotions to determine the psychological tendencies in humans and integrate this model with the digital avatars. The facial emotions have a strong interconnection with our behavior. There are seven basic emotions which includes happy, sad, anger, terror, surprise, hatred, as well as neutral. This paper is all about to build a machine using state-of-the-art tools which is capable of detecting facial emotions. Nowadays, human–computer interaction (HCI) makes machines to predict emotions which is a difficult task. Current facial emotions recognition (FER) systems are lacking reliable dataset to train and not good in accuracy as well. Finally, incorporate these psychological AI-driven model with a virtual assistant to make our lives more sparkling.
Improved complexities for stochastic conditional gradient methods under interpolation-like conditions

We analyze stochastic conditional gradient methods for constrained optimization problems arising in over-parametrized machine learning. We show that one could leverage the interpolation-like conditions satisfied by such models to obtain improved oracle complexities. Specifically, when the objective function is convex, we show that the conditional gradient method requires O(ϵ−2) calls to the stochastic gradient oracle to find an ϵ-optimal solution. Furthermore, by including a gradient sliding step, we show that the number of calls reduces to O(ϵ−1.5).
Machine Learning for Green Smart Video Surveillance

The forthcoming smart environments will have advanced technology for capturing, processing, and analysing high-resolution and high-quality visual information. In future green smart cities, advanced video surveillance with dense deployment of video sensors is definitely necessary to build intelligent and secure urban environments. Since large-scale compression of high-quality video with low bit rate is computationally expensive, a significant impact on energy consumption is inevitable, and green computing approaches become increasingly relevant. The best candidate for video compression is the recently approved Versatile Video Coding (VVC) standard, but its huge computational power requirements have been under investigation to achieve low-energy consumption by means of reducing its algorithmic complexity. This chapter addresses the main sources of computational complexity in the VVC and presents a thorough review of machine learning approaches recently investigated for reducing this burden. Some recent research results, specifically targeted for omnidirectional video surveillance systems, are also presented, including future research directions towards green computing for video coding.
Out-of-Category Document Identification Using Target-Category Names as Weak Supervision

Identifying outlier documents, whose content is different from the majority of the documents in a corpus, has played an important role to manage a large text collection. However, due to the absence of explicit information about the inlier (or target) distribution, existing unsupervised outlier detectors are likely to make unreliable results depending on the density or diversity of the outliers in the corpus. To address this challenge, we introduce a new task referred to as out-of-category detection, which aims to distinguish the documents according to their semantic relevance to the inlier (or target) categories by using the category names as weak supervision. In practice, this task can be widely applicable in that it can flexibly designate the scope of target categories according to users’ interests while requiring only the target-category names as minimum guidance. In this paper, we present an out-of-category detection framework, which effectively measures how confidently each document belongs to one of the target categories. Our framework adopts a two-step approach, to take advantage of both (i) a discriminative text embedding and (ii) a neural text classifier. The experiments on real-world datasets demonstrate that our framework achieves the best detection performance among all baseline methods in various scenarios specifying different target categories.
A Topical Approach to Capturing Customer Insight Dynamics in Social Media

With the emergence of the internet, customers have become far more than mere consumers: they are now opinion makers. As such, they share their experience of goods, services, brands, and retailers. People interested in a certain product often reach for these opinions on all kinds of channels with different structures, from forums to microblogging platforms. On these platforms, topics about almost everything proliferate, and can become viral for a certain time before they begin stagnating, or extinguishing. The amount of data is massive, and the data acquisition processes frequently involve web scraping. Even if basic parsing, cleaning, and standardization exist, the variability of noise create the need for ad-hoc tools. All these elements make it difficult to extract customer insights from the internet. To address these issues, I propose to devise time-dynamic, nonparametric neural-based topic models that take topic, document and word linking into account. I also want to extract opinions accordingly with multilingual contexts, all the while making my tools relevant for pretreatment improvement. Last but not least, I want to devise a proper way of evaluating models so as to assess all their aspects.
Differentially Private Outlier Detection in Correlated Data

The detection of outliers has become increasingly important for the control and monitoring of large-scale networked systems such as transportation and smart grids. Data from these systems, such as location traces or power consumption, are collected from individual agents, and are often privacy-sensitive. Furthermore, the networked nature of these systems results in the data of different individuals being correlated with each other. In this paper, we use the concept of differential privacy to design a privacy-preserving algorithm for outlier detection in correlated data. We determine analytic formulas to evaluate the performance of the proposed differentially private algorithm, and we analyze the trade-off between privacy level and detection accuracy. We illustrate our methodology using an example based on outlier detection in household electricity usage data.
Informative Priors for the Consensus Ranking in the Bayesian Mallows Model

The aim of this work is to study the problem of prior elicitation for the consensus ranking in the Mallows model with Spearman’s distance, a popular distance-based model for rankings or permutation data. Previous Bayesian inference for such a model has been limited to the use of the uniform prior over the space of permutations. We present a novel strategy to elicit informative prior beliefs on the location parameter of the model, discussing the interpretation of hyper-parameters and the implication of prior choices for the posterior analysis.
A New Data Augmentation Method for Intent Classification Enhancement and its Application on Spoken Conversation Datasets

Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous directions for user intents. Before operation begins, these classifiers are generally lacking in real-world training data. Active learning is a common approach used to help label large amounts of collected user input. However, this approach requires many hours of manual labeling work. We present the Nearest Neighbors Scores Improvement (NNSI) algorithm for automatic data selection and labeling. The NNSI reduces the need for manual labeling by automatically selecting highly-ambiguous samples and labeling them with high accuracy. This is done by integrating the classifier’s output from a semantically similar group of text samples. The labeled samples can then be added to the training set to improve the accuracy of the classifier. We demonstrated the use of NNSI on two large-scale, real-life voice conversation systems. Evaluation of our results showed that our method was able to select and label useful samples with high accuracy. Adding these new samples to the training data significantly improved the classifiers and reduced error rates by up to 10%.
An Embedded Method: Improve the Relevance of Text and Face Image with Enhanced Face Attributes

To solve the problem of low quality and lack of specific attributes in the text-to-face synthesis task, this paper proposes EFA, a general embedding method for strengthened face attributes in the text-to-image synthesis models. First, we re-encode the irregular word-level descriptions scattered in sentences to form word coding. Then, we design the embedded local feature extraction layer for discriminators of different models to learn more specific information related to face attributes. Next, we associate the word coding with the extracted face image feature regions to obtain the real image and the generated face attribute domain classification loss. Finally, In the training process, we adopt the loss function to constrain the generator and discriminator and improve their performance. This method can improve the quality of text-to-face synthesis and enhance the semantic correlation between the generated image and text description. A large number of experimental results on the newly released Multi-Modal-CelebA-HQ dataset verify the validity of our method, and the experimental results are competitive compared with state of the art. Especially, our approach boosts the FID by 47.75 % over AttnGAN, by 33.68 % over ControlGAN, by 10.05 % over DM-GAN, and by 12.52 % over DF-GAN. Code is available at https://github.com/cookie-ke/EFA .
Research on Blockchain Security Risk Analysis and Coping Strategies

With the development of blockchain, and its security issues have attracted the attention of more and more researchers. Currently, the related research is still in its infancy. This paper first reviews the basic technology and development of blockchain, summarized and introduced the blockchain structure. In terms of blockchain security, we analyze and summarizes the domestic and foreign literatures in recent years, and divide blockchain security issues into three aspects: protocol security, privacy security and system security, and the problems of blockchain security in these three aspects are analyzed. At present, protocol security research mainly focuses on the encryption mechanism of blockchain technology and smart contract vulnerabilities; Privacy security research covering mainly the potential security issues facing blockchain applications; The system security research mainly includes the classification of attack methods on blockchain. Then, we summarized the defense strategies to deal with the above security issues and analyzed the shortcomings and problems. Finally, based on the current research status of blockchain, we point out two future research directions.
Application of Color Block Code in Image Scaling

Aiming at the high cost of embedding annotation watermark in a narrow small area and the information distortion caused by the change of annotation watermark image resolution, this paper proposes a color block code technology, which uses location information and color code to form recognizable graphics, which can not only simplify the annotation graphics, but also ensure the recognition efficiency. First, the constituent elements of color block code are designed, and then the coding and decoding method of color block code is proposed. Experiments show that color block code has high anti-scaling and anti-interference, and can be widely used in the labeling of small object surface and low resolution image.
Support for the safety of EVM bytecode via function-call interceptor

Security problems in smart contracts are considered critical, because they are usually implemented in the field of financial services, such as cryptocurrency management. Although numerous solutions have been proposed recently, most of these are dedicated to the vulnerabilities of the source code of smart contracts before they are deployed and cannot cover runtime security issues. For instance, the deployment of vulnerable smart contracts on Ethereum block chains could cause serious damage at runtime, because contracts deployed on block chains are indestructible and are freely used later in the public domain. In addition, smart contracts are deployed in bytecode, a form that is naturally not human-readable, and thus, users fail to perceive their vulnerabilities. In this paper, ReByte, a runtime monitoring method for security enhancement of smart contracts deployed on Ethereum, is proposed. This method is based on a function-call interceptor for smart contracts that operates at runtime, and it checks the security-related properties of smart contracts and takes the corresponding measures described in a JSON policy file. At the beginning of its runtime, a smart contract bytecode is dynamically rewritten to embed within it the policy enforcement logic. The results of experiments on real-world Ethereum smart contracts on EtherScan [18] show that the ReByte prototype efficiently enhances the safety of smart contracts.
Preliminary Analysis and Design of a Customized Tourism Recommender System

This work presents preliminary analysis and design of the tourism recommender system. The work considers Pokhara city of Nepal as the domain of study as it is the second-largest city and tourism capital of Nepal. The work is built on data from travel websites, published and unpublished reports, literature, and a pilot study conducted in the same city. Tourist decision factors and motivational factors along with seven demographic data are taken into consideration to build an open-end questionnaire for the study. A total sample of 250 respondents is taken as part of the pilot study. The study uses kNN as the classification algorithm to form clusters of data and calculate silhouette to confirm the consistency and validity of clusters. Scatter plots with different combinations of data, UML-based diagrams, and decision tree are used to come up with an initial recommender system design. The work is vital for the design and development of recommendation systems based on a model, mixed, and customized approach. As the requirements directly come from the user end, the system is more specific in terms of recommendations especially for tourists for a specific destination that can be further elaborated to build a generalized model. This work produces an initial system, a knowledge base, different design diagrams, and a fully functional algorithm for the recommender system that can be used by individuals, business entities, and tourism organizations as a tool and knowledge base.
Unseen Domain Generalization for Prostate MRI Segmentation via Disentangled Representations

In clinical practice, medical images obtained from different sites often exhibit appearance variations, resulting in limited generalizability of deep learning models for segmentation in deployment. It is an important but challenging task to train a model which can directly generalize to unseen domains with distribution shifts. In this paper, we propose to disentangle content from style representations for prostate MRI segmentation to improve the model generalization, considering anatomical content information is domain invariant and decides the segmentation masks. Our method roots in a representation disentanglement network, sharing the content encoder with the segmentation module to remove the effect of appearance discrepancy. Besides, we introduce two domain discriminators to further regularize the disentangled representation learning. We extensively validate our model on a multi-site dataset for prostate MRI segmentation. Both quantitative and qualitative experimental results demonstrate the effectiveness of our method, outperforming the baseline method and many state-of-the-art generalization methods.
ICT Beyond the Classroom: New Media and Learning

This chapter is a hands-on chapter on the use of Web 2.0 tools so that students’ own practices of new media can be leveraged to take learning beyond the classrooms. These tools include various software applications, tools, and platforms and how they can be effectively used to supplement classroom activities and make learning ubiquitous. The chapter also discusses the use of e-portfolios that can integrate all the skills discussed in the previous chapters. In the process, we will consider the various levels of learning in accordance with Bloom’s taxonomy and map different software and tools catering to the specific level(s) of learning.
Transformer Component Recognition in Pictures Taken by Submersible Robots

Inspection of transformers is very important to ensure power supply reliability. Compared with manual methods, using submersible robots to automatically take and analyze pictures is much more time-efficient and lower-cost. To solve the problems of varying illumination and view point in transformer component recognition task, a framework called Transformer Network with Image Enhancement (TRIE) is proposed. This method first enhances the picture based on local contrast information, and then recognizes the inside components with the help of Transformer Network which considers extra context information in the unique encoder-decoder structure and attention mechanism. Experiments show that this framework performs much better than other three deep-learning models on field data of transformer inside pictures, largely improving the development of automatic power equipment inspection.
Mask Image to Real Image Generation Based on Semantic Control Context Encoder

In the field of image inpainting, there are some deep learning schemes, but the pixel inpainting of these schemes generally does not consider the semantics of the image. In this paper, the Semantic Control Context Encoder(SCCE) is proposed, which combines the confrontation network of text-generated images with traditional image restoration to form a comprehensive image restoration method. In this method, a context encoder is used as the generator, and a picture generated from the text is compared with the restored pictures. At the same time, the difference between the text itself and the restored picture mapped to the same space is regarded as the loss to judge the restored result, thus introducing the semantic meaning represented by the picture generated by the text on the basis of the original context encoder, and increasing the rationality of the generated picture. Experimental results on the open data set show that the proposed algorithm is superior to the traditional context encoder algorithms and the edge first algorithms.
Kernel-based Hidden Markov Conditional Densities

A natural way to obtain conditional density estimates for time series processes is to adopt a kernel-based (nonparametric) conditional density estimation (KCDE) method. To this end, the data generating process is commonly assumed to be Markovian of finite order. Markov processes, however, have limited memory range so that only the most recent observations are informative for estimating future observations, assuming the underlying model is known. Hidden Markov models (HMMs), on the other hand, can integrate information over arbitrary lengths of time and thus describe a wider variety of data generating processes. The KCDE and HMMs are combined into one method. The resulting KCDE-HMM method is described in detail, and an iterative algorithm is presented for estimating its transition probabilities, weights and bandwidths. Consistency and asymptotic normality of the resulting conditional density estimator are proved. The conditional forecast ability of the proposed conditional density method is examined and compared via a rolling forecasting window with three benchmark methods: HMM, autoregressive HMM, and KCDE-MM. Large-sample performance of the above conditional estimation methods as a function of training data size is explored. Finally, the methods are applied to the U.S. Industrial Production series and the S&P 500 index.The results indicate that KCDE-HMM outperforms the benchmark methods for moderate-to-large sample sizes, irrespective of the number of hidden states considered.