Reviewerly/exHarmony huggingface dataset:
Callback2Vec: Callback-aware hierarchical embedding for mobile application

Although numerous embedding approaches have been proposed for code representation of mobile applications, insufficient attention has been paid to its essential running nature: event-driven. As a result, the contextual semantics of event-driven callbacks re hardly captured. Existing solutions either discard the information of event callbacks such as their sequences, or simply treat event callbacks as ordinary APIs. Both of the solutions deviate from the actual running behavior of the applications and thus suffer from critical information loss of the callback contexts. To address the problem, in this paper, a callback based hierarchical embedding approach Callback2Vec is proposed, in which ordinary APIs and callbacks are distinguished and tackled at different levels in a top-down fashion. As such, the contextual semantics of callbacks can be reasonably represented by the embedding vectors. In particular, a fine-grained callback-sequence-generation algorithm is devised to capture the running behavior of callbacks. To evaluate the representation capability of Callback2Vec, a systematic analysis targeting at the embedding results is conducted, whereby the conventional embedding characteristics are rigorously investigated and new implications are identified. Of significance, the proposed embedding approach has been validated to be capable of providing novel solutions for typical downstream applications, through comprehensive experiments with large scale public datasets.
Investigating the Requirements for Building a Blockchain-Based Achievement Record System

A trusted achievement record is a secure system that aims to record and authenticate certificates as well as key learning activities and achievements. This paper intends to gather important information on the thoughts and outlooks of stakeholders on an achievement record system that uses blockchain and smart contract technology. The system would allow stakeholders (for example employers) to validate learning records. Two main aims are investigated. The first is to evaluate the suitability of the idea of building a trusted achievement record for learners in higher education, and to evaluate potential user knowledge of blockchain technology. This is to ensure that a designed system is usable. The second aim includes an interview conducted with a small group of participants to gather information about the challenges individuals have when creating, and reviewing CVs. Overall, 90% of participants agreed that there was a strong need for a trusted achievement record. In addition, 93.64% of respondents stated that they felt it was invaluable to have a system that is usable by all stakeholders. When tackling the second aim it was found that a primary challenge is lack of knowledge of blockchain and its complexity. From the employers' perspective, there is a lack of trust due to inaccuracies when students describe skills and qualifications in their resumes.
Do Make me Think!

Traditionally, a key concern of HCI has been to design interfaces that should not make the user think. While this is - and will continue to be - desirable for most systems, there are also situations in which a system that prompts and questions the user may be more appropriate. In educational systems for instance, tasks are often intentionally made more challenging to enable "deeper" thinking and more thorough learning. Although conversational interfaces are still relatively limited in their capabilities, they seem very promising for contexts where questioning is needed, such as learning, analytics or sensemaking as well as safety-critical systems. Overly simple interactions - such as when the user can just tap or drag and drop - may not be beneficial in this context or may even be risky. In this position paper, we discuss previous work as well as opportunities where questioning users through conversation can be beneficial, based on insights from our own research.
Toward a Mexican Sign Language System using Human Computer Interface

This article presents a new algorithm that serves as support for people interested in learning sign language base on the Mexican Sign Language Dictionary provided by the CONAPRED (National Council to Prevent Discrimination) in the 2011 edition. The system uses the Leap Motion machine shadow interaction system to capture the position of each finger of both hands, and by geometric modeling up to 27 signs can be statically modeled with an accuracy of 99.80%.
Encrypted rich-data steganography using generative adversarial networks

Steganography has received a great deal of attention within the information security domain due to its potential utility in ensuring network security and privacy. Leveraging advancements in deep neural networks, the state-of-the-art steganography models are capable of encoding a message within a cover image and producing a visually indistinguishable encoded image from which the decoder can recover the original message. While a message of different data types can be converted to a binary message before encoding into a cover image, this work explores the ability of neural network models to encode data types of different modalities. We propose the ERS-GAN (Encrypted Rich-data Steganography Generative Adversarial Network) - an end-to-end generative adversarial network model for efficient data encoding and decoding. Our proposed model is capable of encoding message of multiple types, e.g., text, audio and image, and is able to hide message deeply into a cover image without being detected and decoded by a third-party adversary who is not given permission to access the message. Experiments conducted on the datasets MS-COCO and Speech Commands show that our model out-performs or equally matches the state-of-the-arts in several aspects of steganography performance. Our proposed ERS-GAN can be potentially used to protect the wireless communication against malicious activity such as eavesdropping.
Known-item searches and search tactics in library search systems: Results from four transaction log analysis studies

A better understanding of users' search interactions in library search systems is key to improving the result ranking. By focusing on known-item searches (searches for an item already known) and search tactics, vast improvement can be made. To better understand user behaviour, we conducted four transaction-log studies, comprising more than 4.2 million search sessions from two German library search systems. Results show that most sessions are rather short; users tend to issue short queries and usually do not go beyond the first search engine result page (SERP). The most frequently used search tactic was the extension of a query ('Exhaust'). Looking at the known-item searches, it becomes clear that this query type is of great importance. Between 38%–57% of all queries are known-item queries. Titles or title parts were the most frequent elements of these queries, either alone or in combination with the author's name. Unsuccessful known-item searches were often caused by items not available in the system. Results can be applied by libraries and library system vendors to improve their systems, as well as when designing new systems. Future research, in addition to log data, should also include background information on the usage, for example, through user surveys.
Generate Contextual Insight of Product Review Using Deep LSTM and Word Embedding

Abstract Nowadays, in every day live, majority people face in many internet options. For example, what meal to eat, what news to read, what vehicle to ride, what the best path to travelling, what the best group in social network to joint, what the best video to watch, what the best video in YouTube to watch and etc. The best way to recommend the internet content to customer is by using recommender system. Recommender system calculate product recommendation by detecting user behaviour in the past. The user behaviour in the past was being variable to compute similarity between many customers. One of the majority user behaviour is in the term of document. Most of document interpret model in recommender system use traditional NLP model such as TF-IDF, LDA model. According to NLP point of view, traditional NLP face the weakness in contextual understanding. Aims to handle the problem on above, we proposed novel model to generate contextual understanding by involve two important aspect considered subtle word and word sequence. We implemented word embedding based on GLOVE and detecting word sequential using RNN-LSTM. According to qualitative evaluation report, our model successful to capture contextual insight of the document of movie review by IMDB. This model suitable to integrated with latent factor based on matrix factorization to generate product recommendation in Collaborative filtering model.
Research and application of a real-time operation risk assessment method for power grid based on random forest algorithm

Abstract Real-time operation risk assessment of power grid is not only an important content of power grid dispatching and operation control, but also an important reference factor in power grid accident disposal. It is difficult for the traditional evaluation methods to meet power system online operation control requirements, because of their complex process and long calculation time. To this end, a real-time power grid operation risk assessment method based on random forest intelligent algorithm is proposed in this paper, The basic concepts and implementation points of random forest algorithm is introduced. The implementation framework of real-time operation risk assessment based on random forest is proposed taking the need of power grid operation risk assessment into consideration. Compared with the traditional evaluation method, this method can calculate the power grid operation risk level directly according to some key operation indexes without complex power grid analysis, such as N-1 security check. Finally, the two-years practical application in a certain provincial power grid in China shows that the method has high evaluation accuracy and computational efficiency, which can match the traditional evaluation method and improve the calculation efficiency and accuracy of risk assessment.
Deep neural networks with pre-train model BERT for aspect-level sentiments classification

With the rapid development of the Internet, the amount of text on the network has shown a rapid growth trend, and the demand for text classification technology is increasing day by day, especially for aspect-level sentiments classification. However, the traditional method mainly relies on time-consuming feature engineering, but due to its context-independent nature, it ignores the rich context information in the text, which greatly reduces the performance in Natural Language Processing (NLP) tasks. Bidirectional Encoder Representations from Transformers (BERT), a pre-trained language models, refreshed records on eleven NLP tasks and became a new baseline model for text classification. Although BERT has been widely used for other NLP tasks, it is rarely used on aspect-level sentiments classification. We use the BERT output containing rich contextual information as the input of the optimized DNN network, and use the DNN network for further classification to obtain better performance in aspect-level sentiment classification. We performed comparative experiments on three public datasets. Compared with other latest baseline models, our model has better performance in aspect-level sentiments classification.
The Spherical Grid Filter for Nonlinear Estimation on the Unit Sphere

Filters for the unit sphere have to consider its inherent periodic nature. Since the unit sphere is a domain of finite size, suitable grids covering the manifold can be provided. We explain the considerations for the grid generation and provide efficient ways to implement the prediction and update steps of a novel grid filter for this manifold. The filter supports nonlinear system and measurement models in the form of transition densities and likelihoods. In the evaluation, the proposed filter achieves a higher estimation accuracy than competing approaches.
Perceived Usefulness of Conversational Agents Predicts Search Performance in Aerospace Domain

This paper presents a user-centered approach to the design and evaluation of conversational search user interfaces to support the pilot in cockpits. Our findings of a controlled user experiment suggest that user perception of the usefulness of the system in completing the search task and the system's responses to the relevance of the topic are good predictors of search performance. User satisfaction with the system's responses may not be a good predictor of search performance in the context of safety of life scenarios such as cockpit procedures for pilots.
QUANTIFIED MODAL RELEVANT LOGICS

Abstract Here, I combine the semantics of Mares and Goldblatt [20] and Seki [29, 30] to develop a semantics for quantified modal relevant logics extending ${\bf B}$ . The combination requires demonstrating that the Mares–Goldblatt approach is apt for quantified extensions of ${\bf B}$ and other relevant logics, but no significant bridging principles are needed. The result is a single semantic approach for quantified modal relevant logics. Within this framework, I discuss the requirements a quantified modal relevant logic must satisfy to be “sufficiently classical” in its modal fragment, where frame conditions are given that work for positive fragments of logics. The roles of the Barcan formula and its converse are also investigated.
On the extremal maximum agreement subtree problem

Given two phylogenetic trees with leaf set {1,…,n} the maximum agreement subtree problem asks what is the maximum size of the subset A⊆{1,…,n} such that the two trees are equivalent when restricted to A. The long-standing extremal version of this problem focuses on the smallest number of leaves, mast(n), on which any two (binary and unrooted) phylogenetic trees with n leaves must agree. In this work, we prove that this number grows asymptotically as Θ(logn); thus closing the enduring gap between the lower and upper asymptotic bounds on mast(n).
Reliability Management for Blockchain-Based Decentralized Multi-Cloud

Blockchain-based decentralized multi-cloud has the potential to reduce cloud infrastructure costs and to enable geographically distributed providers of any size to monetize their computational resources. In this context, guarantees that the computational results are delivered within the promised time and budget must be provided despite the limited information available about the location and ownership of resources. Providers might claim to execute the services to get compensated for the computation even though returning incomplete or incorrect results. In this paper, we define a model to predict provider reliability, that is, the probability of failure-free execution of computational tasks and correctness of the computed outputs, by extracting the potential dependencies between providers from historical log traces. This model can then be utilized in the definition of provider reputation or the scheduling of new services. Indeed, we propose a probabilistic scheduler that chooses the providers that meet the reliability constraints among others. Finally, we validate the proposed solutions with real traces from a decentralized cloud provider and hint at the benefits of predicting reliability in this context.
A Target Tracking Method Based on Improved Camshift Algorithm

In order to solve the problem of tracking frame drift and tracking failure in traditional Camshift algorithm under color interference or occlusion, an improved multi feature fusion method for Camshift target tracking is proposed. Firstly, the color feature and the HOG feature of the target are extracted respectively and fused according to a certain weight coefficient to establish the target model; secondly, the target occlusion is judged according to the similarity measurement. When the target is occluded, the target is divided into four parts, and the color feature and the HOG feature of the target are extracted respectively, and weighted fusion is carried out to establish the occluded target model; finally, a linear prediction method is proposed to replace the traditional Kalman filtering algorithm to complete the position prediction, and substitute the predicted position results into the Camshift algorithm for tracking. Experimental results show that the algorithm can improve the robustness and meet the real-time requirements.
Image Denoising in Wavelet Domain Based on Thresholding with Applying Wiener Filter

An image is often corrupted with noise throughout procurement, compression, transmission, storage and retrieval processes. These effects are leading to distortion and loss of image information. Image denoising used to eliminate the noise in order to reserve all the fine details in the image while retaining as much as possible the vital signal features. Wavelet denoising aims to remove the noise in the signal while maintaining the features of the signal, regardless of its frequency content. In this work, a new approach is introduced to denoising image that has been affected by Additive White Gaussian Noise (AWGN). The proposed system realized using Wiener filter before and after the wavelet transform. To remove noise from pixels in the wavelet domain, discrete wavelet transform (2D-DWT) is applied. Threshold techniques and Wiener filter have been used for denoising. Then, the 2DIDWT inverse discrete wavelet transform applied to remove noise and complete the denoising technique. Also, in this work, the image is denoised using the connotation of Wiener filtering and denoising method in the wavelet domain with multiresolution at three levels. The performance of the proposed methods has been measured by using the Peak Signal to Noise Ratio (PSNR). Experimental evaluation shows that the results of the proposed methods give an improvement with about 17.5% through the comparison with the results of the related works and the essence of images is improved in terms of noise-reducing better than using a wavelet transform or Wiener filter solo as well as edge preservation.
Data Representation by Joint Hypergraph Embedding and Sparse Coding

Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning. According to different application scenarios, one can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for the given data, and learns the compact representation corresponding to the basis. We note that almost all previous work on MF in data mining has ignored to find such a basis, which can carry high-order semantics in the data. In this article, we propose a novel MF framework called Joint Hypergraph Embedding and Sparse Coding (JHESC), in which the obtained basis captures high-order semantic information in data. Specifically, we first propose a new hypergraph learning model to obtain a more discriminative basis by hypergraph-based Laplacian Eigenmap, then sparse coding is conducted on the learned basis such that the new representation has stronger identification capability. In addition, we extend the proposed method to the reproducing kernel Hilbert space for dealing with nonlinear data more effectively. Extensive experimental results on data clustering demonstrate that the proposed method consistently outperforms the other state-of-the-art matrix factorization methods.
Bullet Impact Detection in Silhouettes Using Mask R-CNN

The bullet impact detection in the silhouette plays an important role in the institutions which have a shooting range, specially where are regulated the license to carry a gun and where the evaluation manual process could present distortion in the ability's evaluation of the shooter and like a consequence the license's issuance.This paper proposes a method for an automatic detection of the bullet impacts in silhouettes based on deep learning and image processing, which consist of the following steps: pre-processing, impacts detection, edge detection and evaluation results.The experiments about 600 silhouettes with 2401 bullet impacts images of the proposed and implemented method considering the models Resnet 50 and Resnet 101 for Mask R-CNN show that Resnet 50 get better results than Resnet 101, achieving 97.6 %, 99.5 %, and 97.9 %, of accuracy, precision and recall, respectively, above the methods Circular Hough Transform, Circlet Detection, Random Sample Consensus, Randomized Hough Transform, Randomized Circle Detection, Support Vector Machine, Faster R-CNN, MnasNet and YOLO.Also, the results show 100 % of effectiveness in the edge detection and the count of the detected bullet impacts.
Noisy, Intermediate-Scale Quantum Computing and Industrial Revolution 4.0

The fourth Industrial Revolution (4IR) integrates digital, physical and biological systems to create Cyber-Physical Systems (CPS) that blur the line between the digital, physical and biological systems. One of the drivers of the 4IR is quantum computing; which harnesses quantum mechanical concepts such as entanglement, superposition and tunneling to perform computation. A full-scale quantum computer has not yet been realized. However, Noisy Intermediate-Scale Quantum (NISQ) computers are already in use. In this chapter, we explore NISQ as a disruptive technology of the fourth Industrial Revolution. Furthermore, we discuss the NISQ disruptors together with the applications of the NISQ frameworks. Finally, a novel privacy-preserving quantum machine learning scheme is introduced in this chapter. In essence, the various applications discussed in this chapter show various sectors that are disrupted by NISQ computing. Therefore, the overall objective of this chapter is to provide an exposition to NISQ computing, and demonstrate a link between NISQ computing and the fourth Industrial Revolution.
Imputation of Missing Values Affecting the Software Performance of Component-based Robots

Intelligent robots are foreseen as a technology that would be soon present in most public and private environments. In order to increase the trust of humans, robotic systems must be reliable while both response and down times are minimized. In keeping with this idea, present paper proposes the application of machine learning (regression models more precisely) to preprocess data in order to improve the detection of failures. Such failures deeply affect the performance of the software components embedded in human-interacting robots. To address one of the most common problems of real-life datasets (missing values), some traditional (such as linear regression) as well as innovative (decision tree and neural network) models are applied. The aim is to impute missing values with minimum error in order to improve the quality of data and consequently maximize the failure-detection rate. Experiments are run on a public and up-to-date dataset and the obtained results support the viability of the proposed models.
Improving Ranking in Document based Search Systems

In this 21st century, where technology is blooming producing tons of data, efficient retrieval techniques are required to manage these loads of data to endow users with the right information. This paper discusses two neural network techniques applied towards ranking in document-based search systems on two distinct scales: semantic similarity and relevance factor. Semantic similarity focuses on retrieving most contextually similar documents based on a query. Experiments using a semantic approach provides information about how well the system can identify word order. Also, in ideal conditions, the performance was better than traditional benchmark ranking models. The relevance factor focuses on building a neural model based on kernel pooling to work for soft match signals. Experiments are conducted using neural models like K-NRM, Pre-trained embedding models, etc, to prove how their ranking efficiency is better than other traditional models like BM25, RankSVM, DRMM.