Reviewerly/exHarmony huggingface dataset:
Detection Fusion of Weak Signal under Chaotic Noise Based on Distributed System

In this paper, the problem of signal detection under chaotic noise was considered in the distributed detection fusion system. The problem which is urgent and difficult has important research value. A new detection and fusion mechanism for weak signal under chaotic noise based on a distributed system is proposed. Due to the short-term predictability of chaotic signals and their sensitivity to small disturbances, observation of each local sensor is reconstructed in phase space according to the Takens delay embedding theorem. The locally weighted regression (LOWESS) model is used to fit the observation of each local sensor in the phase space. Thus, the chaotic noise is stripped out from the observation, and the fitting error without chaotic noise is regarded as the new observation of each local sensor. Based on the new observation without chaotic noise, an optimization model aiming at minimizing the Bayesian risk of the fusion center is established. Under the condition that the observations of local sensors are conditionally independent, the fusion rule and the sensor decision rules are derived. An algorithm is proposed to obtain the fusion rule and local decision rules. The simulation results show that the proposed signal detection and fusion algorithm can effectively detect weak signals under chaotic noise background. Specifically, the fusion performance is obviously better than that of local sensors with low SNR.
Decision Support for Knowledge Intensive Processes Using RL Based Recommendations

Supporting knowledge workers involved in the execution of unstructured Knowledge-Intensive Processes by providing context-specific recommendations remains an interesting challenge. Case data that represents expert decisions recorded in the past can be exploited for building a decision support tool for knowledge workers that can recommend which tasks to execute next. Reinforcement learning (RL) provides a framework for learning from interaction with the environment in order to achieve a certain process goal. RL has widely been used to model sequential decision problems and has shown great promise in solving large scale complex problems with long time horizons, partial observability, and high dimensionality of observation and action spaces [5]. In this paper, we propose a novel framework based on RL aimed at supporting knowledge workers by recommending the optimal course of action to the knowledge worker.
Fake Face Detection using Local Binary Pattern and Ensemble Modeling

Fake faces generated with Generative Adversarial Networks (GANs) are becoming more and more realistic and getting harder to be identified directly by human beings. However, CNN (Convolutional Neural network) based deep learning architecture can achieve almost perfect detection accuracy on such fake faces. In this paper we present a study of fake face detection with the exploration of the global texture features based on the empirical knowledge that the textures of fake faces are quite different from those of real faces. A new architecture, LBP (Local Binary Pattern)-Net, is designed to utilize binary representation image texture for the effective identification of fake images. Experimental results show that the proposed method is more robust than existing algorithms for detecting fake images edited by different image augmentation methods, such as blurring, cutout, brightness and color changing, equalization, etc. Ensemble models are also experimented to combine advantages of individual models. The most significant effect of ensemble models is the robustness for detecting edited fake images compared to single models. Experimental results show that our ensemble models outperform single models for detecting fake images.
Data-Driven-Based Disruption Prediction in GOLEM Tokamak with Missing Values

The sensor readings from the vessel of the Tokamak are inconsistent where some sensors always produce output; others provide output occasionally resulting in missing values on entire data. Typically, imputation methods are applied to both train and test data while training the model offline. But in the real-time application where the decision must be taken based on the instream sensor data, imputation techniques are not practical. Hence, in this paper, a data-driven approach on algorithms that inherently handle missing values and algorithms that have the provision to deal with missing values through a replacement or technique is employed. Individual, bagging and boosting algorithms are utilized to classify normal and disruption charges on the GOLEM Tokamak dataset, which consists of 117 normal and 70 disruptive shots. Boosting algorithms having an inbuilt feature to handle missing values provided better results amongst other algorithms and Categorical Boosting (CatBoost) with its ordered boosting feature gave the best metrics. Optimal thresholds for receiver operating characteristics (ROC) and precision–recall (PR) curves on the models are determined. The optimal PR values are utilized to get improved results. A comparison with the widely employed stand-alone machine learning algorithms and ensemble algorithms is illustrated. The results show the excellent performance of the CatBoost model with an F1 score of 0.943 with optimal PR values. The developed predictive model would be capable of warning the human operator with feedback about the feature(s) causing the disruption.
Low-Complexity and High-Coding-Efficiency Image Deletion for Compressed Image Sets in Cloud Servers

Image deletion refers to removing images from a compressed image set in cloud servers, which has always received much attention. However, in some cases images are not successfully deleted, and coding performance still remains to rise. In this paper, we propose a low-complexity and high-coding-efficiency image deletion algorithm. First, all the images are classified into to-be-deleted images, images unneeded to be processed, and images needed to be processed further divided into images needed to be only decoded and images needed to be re-encoded. Then, we also propose a depth- and subtree-constrained minimum spanning tree (DSCMST) heuristics to produce the DSCMST of images needed to be processed. Third, every image unneeded to be processed is added to the just obtained DSCMST as the child of the vertex that is still its parent in the compressed image set. Finally, after the encoding of images needed to be re-encoded, a new compressed image set is constructed, implying the completion of image deletion. Experimental results show that under various circumstances our proposed algorithm can effectively remove any images, including root vertex, internal vertices, and leaf vertices. Moreover, compared with state-of-the-art methods, the proposed algorithm achieves higher coding efficiency while having the minimum complexity.
Tracking and Interception of a Ballistic Target on Reentry Using Adaptive Gaussian Sum Quadrature Filters

In this work, new nonlinear filtering algorithms based on Gaussian sum framework is formulated and then applied to track a ballistic target on reentry. Other than representing the predicted and updated probability density function of states given measurements as a sum of weighted Gaussian densities, a weight update scheme in time update step is also considered. This weight update scheme is based on a quadratic optimization problem that minimizes the integral square error between the true and the approximated prior density. The Gaussian densities in the weighted sum is realized using various quadrature filters. These filters are applied to track a ballistic target on reentry using measurements from an inbuilt seeker of an interceptor missile, where a 6 degrees of freedom state-dependent coefficient model (SDC) of target–interceptor dynamics is considered. Further, the estimated states are provided to a guidance law for generating interceptor missile accelerations. For performance analysis, root mean square error (RMSE) of relative states and also the range between target and interceptor when they cross each other (miss-distance) are considered. It was found that the miss-distances obtained from the Gaussian filters are smaller than that of the conventional EKF. Further, the Gaussian sum and its weight adaptation scheme with quadrature filter proposals provided far better results, with the weight adaptation filters giving more accurate miss-distances.
JoyFlick: Japanese Text Entry Using Dual Joysticks for Flick Input Users

We show JoyFlick: A Japanese text entry method on game consoles with dual joysticks for flick-input users. To reduce the learning cost, the procedure of JoyFlick is designed based on a flick-input, which is a Japanese text entry method that approximately 80% of young Japanese people use daily on smartphones. The user using JoyFlick is able to enter a Japanese basic phonetic character with two joystick operations. In addition, JoyFlick’s widgets spend a little amount of real estate on a screen, making it easy for developers to allocate space for other content. We conducted user studies that compared the performance of JoyFlick with a Kana syllabary keyboard, the de-facto standard for the Japanese text entry method on game consoles. The results showed the followings: (1) after training employing 28 phrases (289 characters, mean entry time \(= 373 \,\mathrm {s}\)), the flick-input users using JoyFlick can enter texts faster than using the Kana syllabary keyboard; (2) the flick-input users can enter text using JoyFlick 1.53-fold faster (60.1 CPM) than the Kana syllabary keyboard (39.2 CPM) after one-week training in which the users enter texts less than 140 (\(\mathrm {M} = 135 \)) characters per day. These results show that JoyFlick provides a fast and easy Japanese text entry method to the flick-input users using the game controllers and yet spends a little amount of real estate on a screen.
A Neural Model for Aspect-Level Sentiment Classification of Product Reviews Assissted by Question-Answering

Aspect-level sentiment analysis targets at judging the associated polarity of the opinion holder&#x2019;s view towards given aspect category, and it usually contains two sub-tasks, namely aspect term extraction and aspect-related sentiment classification. However, the aspect term extraction, an auxiliary sub-task, receives too much emphasis, and it is labor-consuming. With the development of deep learning theory and language models, quite a few problems in natural language processing (NLP) domain have been solved. To address the problems mentioned above, we propose to remove the aspect term extraction component and regard aspect categories as guidelines to apply the question answering theory. Simultaneously, we utilize BERT as the base support unit to sufficiently encode the context information. For the purpose of evaluating our method, we also construct our Chinese review corpus for aspect-level sentiment analysis assignment. Experimental results show that our proposed method achieves an appreciable performance on our dataset.
3D Modeling and Printing with Vulnerable Adults

3D Printing (3DP) has trending applications in many areas. 3DP has grown in popularity in recent years and it is also attracting significant research attention in Human-Computer Interaction. However, engaging in 3DP is not straightforward and not everyone participates or has the same opportunities to take part in it. Working towards making 3DP more democratic, and empowering vulnerable adults, this paper presents a participant observational study of the 3D modeling and printing experiences of (N=12) immigrants and low-literate older people. The study was conducted in two courses in an adult educational center in a working-class neighborhood in <blind review> over a 3-month period. The results show that the 3D modeling and printing experiences of a number of vulnerable people at risk of social exclusion can be both examined and facilitated, challenging stereotyped (mostly negative) views of these populations and digital technologies. The results have identified two key aspects to examine further to make 3DP more democratic and empower vulnerable adults: interaction issues and spatial visualization. Future and ongoing research perspectives are outlined.
Research on Elementary School Students’ Books Recommendation Algorithm Based on Words and Character Library

There are many book recommendation systems, but there are few recommendation systems for Chinese elementary school students. Children's cognitive models are very different from adults, the personalized and accurate recommendation algorithm for them needs to be recommended based on the knowledge structure of children at different ages. In the paper, a new framework model of classified books for Chinese elementary school students was proposed. First of all, a words library and a character library are extracted from six grades textbooks, which named as B_W and B_C respectively; secondly, four machine learning methods are used for prediction, through the indicator of ACC, Recall and F1-Scores, iRF has the best performance in four methods, the max ACC of iRF is 95.3%, which is 10 percentage points better than NB method.
On primitive elements of finite fields avoiding affine hyperplanes

Let n≥2 be an integer and let Fq be the finite field with q elements, where q is a prime power. Given Fq-affine hyperplanes A1,…,An of Fqn in general position, we study the existence and distribution of primitive elements of Fqn, avoiding each Ai. We obtain both asymptotic and concrete results, relating to past works on digits over finite fields.
A Hybrid Recommendation System for E-commerce

There is a huge dependency on e-commerce for goods, electronics, clothes, etc., nowadays. People are searching e-commerce websites frequently for their daily needs. There are various techniques to filter relevant data and make it available to the user, but it remains difficult to provide data that fits user preferences because of which recommendation system came into play. The recommendation system filters data based on users preferences and refers this filtered data to the user. These systems use machine learning, data mining, and various other algorithms to accomplish this task. Collaborative and content-based filtering are among the most popular recommendation system techniques. Both these techniques have numerous limitations; hence, this paper aims to provide a hybrid algorithm that combines collaborative filtering as well as content-based filtering to levy the benefits of both. A comparative analysis of the proposed hybrid algorithm against the content and collaborative filtering methods has also been carried out.
A Permissioned Blockchain System for Secure Multiparty Computation

Abstract Permissioned blockchain is the blockchain network that requires access to be part of the network. Participant’s actions are governed by the control layer that runs on top of the blockchain. This type of blockchains is preferred by individuals who need role description, identity, and security within the blockchain. Secure multi-party computation (MPC) is a part of cryptography that involves the modeling of procedures for two or more participants who want to work together. These participants involve sharing of input and required computational data for a particular function without sharing their confidential data actively to each other and achieving a common goal which is beneficial to both as the outcome achieved is only revealed to the participants and is highly required for their functional purpose. In this work, SPDZ (Speedz) implementation is explored leveraging additive secret sharing on the private blockchain (Hyperledger fabric). SPDZ protocol is chosen over any other computational protocol as it is highly secured from any active deceptive n-1 participant among the n participants. In this work, a backend is developed that uses a fabric SDK node.js library that interacts with the Hyperledger Fabric network. The proposed solution is shown through a demonstration. This paper concludes that for business-to-business scenarios, using SPDZ protocol on permissioned blockchain provides more security against adversaries as permissioned blockchain provides transparency over the participants of the network. As a result, permissioned blockchain is a more secure choice for enterprises to compute confidential data rather than permissionless blockchain.
Fast Batch Reading Densely Deployed QR Codes

This paper presents BatchQR, a mobile APP that can batch read densely arranged QR codes attached to caps of the tubes and vials in clinical and biological labs. BatchQR could work in two modes: photo mode and preview mode, which are respectively suitable for scenarios that favor one-time batch processing capacity and real-time tracking performance. For the photo mode, we first propose an IFFT based lightweight code detection mechanism, which can adaptively adjust operating parameters to identify densely arranged QR codes in practice. We propose an image refocus mechanism to deal with blurs/distorts that may appear in the photo, and a lightweight learning based classifier to filter out falsely detected QR codes. We further optimize BatchQR by enabling batch reading in the preview mode of the camera, which is more in line with common usage habits. To this end, we develop a QR code tracking algorithm based on Kalman filtering, which keeps track of each code image dynamically. We also design a parallel acceleration mechanism based on multi-core CPU and GPU, which significantly mitigates the end-to-end delay. Comprehensive experimental results show that BatchQR can read up to 160 Version 1-H QR codes in one shot, with 95 percent accuracy and 100-400ms delay, which is only 0.1 percent of the time consumed by the regular QR code reader in the same situation.
Comparative Analysis of Classifiers Based on Spam Data in Twitter Sentiments

In the present scenario, sentiment analysis has gained much attention in the field of text mining. As social media have a huge impact on one’s life, people use social media as a tool to express their feelings, thoughts, opinions, emotions and ideology. With the help of sentiment analysis, we can provide computational treatment for sentiments, opinions and subjectivity of text. Detecting spams from various social media sites is a challenging task as the messages contain the short informal text. In this paper, we have tested different classifiers on spam data of users’ tweets based on spammer and non-spammers. The classifiers used for the purpose are Naives Bayes, Simple Logistic, J48 pruned tree, Bayes network classifier and Random Forest. Analysis results showed that random forest is proving the highest accuracy among all the algorithms under consideration.
The Relevance of Preprocessing in Text Classification

The tremendous growth of information emerging from diverse sources has resulted in digital information overwork. The vast information availability has initiated the need for automatic text classification for managing, organizing massive data, and knowledge discovery. Most of these data is in an unstructured form. The main hindrance in achieving good classification accuracy is the natural form of data, i.e., unstructured form. Such issues can be handled by applying preprocessing steps so that unstructured data can be transformed into structured data. In this paper, we have applied different preprocessing steps like bag of words, stemming, and lemmatization on raw data to study the impact of preprocessing in the text classification process. We have verified this by providing preprocessed data to various classifiers like multinominal Naive Bayes, logistic regression, stochastic gradient descent classifier, and k-nearest neighbors and found improvement in classification accuracy results. We have carried out experimentation on the 20-newsgroup dataset.
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions

In this work, we introduce a reduced-rank algorithm for Gaussian process regression. Our numerical scheme converts a Gaussian process on a user-specified interval to its Karhunen–Loève expansion, the $$L^2$$ -optimal reduced-rank representation. Numerical evaluation of the Karhunen–Loève expansion is performed once during precomputation and involves computing a numerical eigendecomposition of an integral operator whose kernel is the covariance function of the Gaussian process. The Karhunen–Loève expansion is independent of observed data and depends only on the covariance kernel and the size of the interval on which the Gaussian process is defined. The scheme of this paper does not require translation invariance of the covariance kernel. We also introduce a class of fast algorithms for Bayesian fitting of hyperparameters and demonstrate the performance of our algorithms with numerical experiments in one and two dimensions. Extensions to higher dimensions are mathematically straightforward but suffer from the standard curses of high dimensions.
An Encoder-decoder Architecture with Graph Convolutional Networks for Abstractive Summarization

We propose a single-document abstractive summarization system that integrates token relation into a traditional RNN-based encoder-decoder architecture. We employ pointer-wise mutual information to represent the token relation and adopt Graph Convolutional Networks (GCN) to extract token representation from the relation graph. In our experiment on Gigaword, we consider importing two kinds of structural information: token (node) representation from the relation graph. Also, we implement two kinds of GCNs, a spectral-based one and a spatial-based one, to extract structural information. The result shows that the spatial based GCN-enhanced model with node representation outperforms the classical RNN-based encoder-decoder model.
Lightweight selective disclosure for verifiable documents on blockchain

To achieve lightweight selective disclosure for protecting privacy of document holders, we propose an XML format for documents that can hide arbitrary elements using a cryptographic hash function and salts, which allows to be partially digitally signed and efficiently verified, as well as a JSON format that can be converted to such XML. The documents can be efficiently proven to exist by representing multiple such structures as a Merkle tree and storing its root in blockchain. We show that our proposal has advantages over known methods that represent the document itself as a Merkle tree and partially hide it.
Conversations across the table: shared cognition in top management teams

Purpose This paper aims to examine the shared mental models (SMMs) of a top management team (TMT) using an emergent perspective in conditions of uncertainty. The paper examines how a TMT conversation represents an emergent cognitive process to reach an action for future planning. Design/methodology/approach The design uses an emergent SMM approach based on a TMT discussion in an uncertain context. Cognitive mapping techniques illustrate how concepts emerge and are structured. This approach addresses the need for an alternative to aggregate mapping methods and supports the notion of team cognition as an emergent and dynamic process. Findings Findings showed that the emergence of a SMM could be elicited and represented using cognitive mapping techniques. Domain knowledge and social relationships supported the emergence of shared knowledge relevant for action on team tasks. A SMM based on team contribution and concept connectivity was identified. Research limitations/implications The study is based on data collected from a recorded discussion in a quarterly company meeting, ten days before the UK’s original planned exit date, March 2019. Originality/value This research study contributes to the SMM and team cognition literature streams by examining the TMT’s shared understanding as an emergent process. Empirical studies using cognitive mapping techniques in this context are rare.