Reviewerly/exHarmony huggingface dataset:
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

Persona can function as the prior knowledge for maintaining the consistency of dialogue systems. Most of previous studies adopted the self persona in dialogue whose response was about to be selected from a set of candidates or directly generated, but few have noticed the role of partner in dialogue. This paper makes an attempt to thoroughly explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection in retrieval-based chatbots. Four persona fusion strategies are designed, which assume personas interact with contexts or responses in different ways. These strategies are implemented into three representative models for response selection, which are based on the Hierarchical Recurrent Encoder (HRE), Interactive Matching Network (IMN) and Bidirectional Encoder Representations from Transformers (BERT) respectively. Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models. Besides, our BERT-based model implemented with the context-response-aware persona fusion strategy outperforms previous methods by margins larger than 2.7% on original personas and 4.6% on revised personas in terms of [email protected] (top-1 accuracy), achieving a new state-of-the-art performance on the Persona-Chat dataset.
A machine learning approach for opinion mining online customer reviews

This study was conducted to apply supervised machine learning methods in opinion mining online customer reviews. First, the study automatically collected 39,976 traveler reviews on hotels in Vietnam on Agoda.com website, then conducted the training with machine learning models to find out which model is most compatible with the training dataset and apply this model to forecast opinions for the collected dataset. The results showed that Logistic Regression (LR), Support Vector Machines (SVM) and Neural Network (NN) methods have the best performance in opinion mining in Vietnamese language. This study is valuable as a reference for applications of opinion mining in the field of business.
An Efficient Squirrel Search Algorithm based Vector Quantization for Image Compression in Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) typically fly at low altitudes for capturing high-resolution images covering smaller areas. Since short flights also and high-resolution cameras lead to the generation of massive gigabytes (GBs) of data regions, image compression is essential to compress the data to a compact form resulted in shorter file size without any loss of quality. The vector quantization (VQ) is an effective type of image compression and the conventionally employed technique namely Linde-Buzo-Gray (LBG) algorithm continually created local optimal codebook. The codebook design process can be considered as a high dimensional optimization problem and can be resolved by the use of swarm intelligence algorithms. This paper designs a novel squirrel search algorithm (SSA) with LBG based image compression technique, called SSA-LBG for UAVs. The SSA is applied for the construction of codebooks for VQ and it makes use of LBG model as the initialization of the SSA for VQ. The application of SSA-LBG results in effective compression with low computation time (CT) and high peak signal to noise ratio (PSNR). An extensive set of simulations were performed on benchmark test images and the results are examined with respect to CT and PSNR undervarying bit rates and codebook sizes.
Text-Independent Voice Biometric for User Recognition

This paper implements the biometric system’s category of text-independent voice recognition based upon randomly implied text for user recognition and authentication in order to provide security to a user who is living alone at home. Feature extraction is one of the basic processes to start with any recognition technique. This work uses a combination of MFCC and RASTA-PLP (known as MFRASTA) to extract voice features and the Matlab tool is used for the implementation of this system along with 3G Dongle for sending the text messages on cell phones. An improvement of 98% is achieved in testing time over individual technique MFCC in terms of recognition rate.
Exploring Visual Engagement Signals for Representation Learning

Visual engagement in social media platforms comprises interactions with photo posts including comments, shares, and likes. In this paper, we leverage such Visual Engagement clues as supervisory signals for representation learning. However, learning from engagement signals is non-trivial as it is not clear how to bridge the gap between low-level visual information and high-level social interactions. We present VisE, a weakly supervised learning approach, which maps social images to pseudo labels derived by clustered engagement signals. We then study how models trained in this way benefit subjective downstream computer vision tasks such as emotion recognition or political bias detection. Through extensive studies, we empirically demonstrate the effectiveness of VisE across a diverse set of classification tasks beyond the scope of conventional recognition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Performance of SMOTE in a random forest and naive Bayes classifier for imbalanced Hepatitis-B vaccination status

Abstract The alternative approach to predict the classification of HB vaccination status is using a machine learning approach such as random forest and naive Bayes classifier. However, for imbalanced classification, the algorithms are biased towards the majority class. To increase the accurate prediction of the classifier, we consider the Synthetic Minority Oversampling Technique (SMOTE) to have more balanced data. The purpose of this study was to compare the performance of SMOTE in the random forest and naive Bayes classifier for imbalanced Hepatitis-B vaccination data. The study used the National Socio-Economic Survey data in 2017 for Aceh province with 2264 cases and 14 variables. The results show that the application of SMOTE in the random forest and naive Bayes classifier improves the accuracy of identification of Hepatitis-B non-vaccination status by 30.08% and 26.09%, respectively, compared to non-SMOTE. Random forest with SMOTE is the best model for classification HB vaccination status. The most important factors that influence the Hepatitis-B vaccination status of Aceh province are the mother’s last education, mother’s occupation, father’s occupation, father’s previous education, and the number of health facilities.
A tripartite game model of trust cooperation in cloud service

Cloud computing is a very popular information technology, to obtain mutual trust and win-win cooperation between the users and the cloud service provider (CSP), we propose a game model-based trust prediction. Different participants have different motives and interests in the service game process. First, we use the Bayesian formula to predict the trust level of behavior attribute, further propose trust decision function and weight algorithm of trust attribute to ensure the accuracy by maximum dispersion principle. Second, we discuss the existence possibility of the game equilibrium and establishes a dynamic game model from the short-term benefit of the user. Third, from the long-term benefit of the CSP, we establish a tripartite game model that has multiple possible equilibria that can be converted into a mixed strategy under specific common conditions. Finally, we design simulation experiments to verify the regulation and several related factors have an important influence to improve trust and honesty and control the malicious behavior of participants; further, we compare and discuss the research results with other papers in the real data set scenario, and show the advantages and highlight value.
News Text Summarization Method based on BART-TextRank Model

With the rapid development of computer technology and network technology, the Internet has become the main place for people to release and obtain information. The amount of data on the network is growing explosively. The urgent problem is how to accurately obtain the real valuable information from a large amount of data. News report has always been the most important way for us to understand society and current affairs. Summarizing the main content of each news report in short language can help readers understand the content faster and save time. At present, automatic text summarization technology is used to generate news content summarization, which mainly includes extractive summarization and abstractive summarization. In this paper, we propose an improved strategy to solve the problem of topic deviation in abstractive summarization method. We combine TextRank with BART model. First, we use TextRank and BART to extract and generate summarization from news text. Then we splice the results of two methods to get the new text which improves the weight of the key sentences in the articles and make it more thematic. Finally, we input the above new texts enter the BART model again to get the final summarization. Experimental results show that compared with single BART model, the average recall scores of Rouge-1, Rouge-2 and Rouge-L are improved by 1.5%, 0.5% and 1.3% respectively.
A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms

Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.
Debugging incoherent ontology by extracting a clash module and identifying root unsatisfiable concepts

The calculation of minimal incoherence-preserving sub-TBox (MIPS) is an important task in incoherent ontology debugging. Existing approaches to this task are inefficient when debugging large incoherent ontology with a lot of unsatisfiable concepts. To resolve the problem, we first extract a clash module from the ontology and then identify the root unsatisfiable concepts from the clash module. Afterward, the minimal unsatisfiability-preserving sub-TBox (MUPS) of each root unsatisfiable concept can be calculated on the basis of the clash module. We have proved that the MIPS of an incoherent ontology is equal to the union of the MUPSes of the root unsatisfiable concepts. Our approach can successfully avoid calculating a considerable number of MUPSes irrelevant to the MIPS. Experiments with a variety of test ontologies show that the proposed approach of calculating MIPS is efficient and practical.
Sequential routing framework: Fully capsule network-based speech recognition

Capsule networks (CapsNets) have recently gotten attention as a novel neural architecture. This paper presents the sequential routing framework which we believe is the first method to adapt a CapsNet-only structure to sequence-to-sequence recognition. Input sequences are capsulized then sliced by a window size. Each slice is classified to a label at the corresponding time through iterative routing mechanisms. Afterwards, losses are computed by connectionist temporal classification (CTC). During routing, the required number of parameters can be controlled by the window size regardless of the length of sequences by sharing learnable weights across the slices. We additionally propose a sequential dynamic routing algorithm to replace traditional dynamic routing. The proposed technique can minimize decoding speed degradation caused by the routing iterations since it can operate in a non-iterative manner without dropping accuracy. The method achieves a 1.1% lower word error rate at 16.9% on the Wall Street Journal corpus compared to bidirectional long short-term memory-based CTC networks. On the TIMIT corpus, it attains a 0.7% lower phone error rate at 17.5% compared to convolutional neural network-based CTC networks (Zhang et al., 2016).
A Stochastic Neighbor Embedding Approach for Cancer Prediction

Nowadays, cancer is the most prevalent cause of death worldwide. Breast cancer is the 2nd most common cancer registered globally and the leading death factor among women. So, if we predict cancer in the primitive stage, we can avoid death. Therefore, there is an imperative need of a tool that can quickly detect whether the breast cancer tumor is a malignant or a benign one. In this paper, two-dimensionality reduction methods, namely Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding(t-SNE) are used. t-SNE visualizes high-dimensional data by reducing it in a two or three-dimensional map. It optimizes the cost function and produces much better scatter plots by decreasing the tendency to the crowd points in the center of the map. For classification, two machine learning algorithms, namely Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), are employed. The experimental result reveals that t-SNE performed better as compared to PCA.
Comparison of Deep Reinforcement Learning Algorithms in a Robot Manipulator Control Application

In this study, we apply deep reinforcement learning (DRL) to control a robot manipulator and investigate its effectiveness by comparing the performance of several DRL algorithms, namely, deep deterministic policy gradient (DDPG) and distributed distributional deterministic policy gradient (D4PG) algorithms. We conducted computational training and testing experiments on a control model for a reaching task of the robot manipulator. Experimental results show that the D4PG algorithm achieves a higher learning success rate than the DDPG algorithm and demonstrate the potential application of DRL for controlling robot manipulators.
Factorized WaveNet for voice conversion with limited data

WaveNet is introduced for waveform generation. It produces high quality text-to-speech synthesis, music generation, and voice conversion. However, it generally requires a large amount of training data, that limits its scope of applications, e.g. in voice conversion. In this paper, we propose a factorized WaveNet for limited data tasks. Specifically, we apply singular value decomposition (SVD) on the dilated convolution layers of WaveNet to reduce the number of parameters. By doing so, we reduce the data requirement for WaveNet training, while maintaining similar network performance. We use voice conversion as a case study to validate the proposed idea. Two sets of experiments are conducted, where WaveNet is used as a vocoder and an integrated converter–vocoder respectively. Experiments on CMU-ARCTIC and CSTR-VCTK corpora show that factorized WaveNet consistently outperforms its original WaveNet counterpart when using the same amount of training data. We also apply SVD similarly to real-time neural vocoder Parallel WaveGAN for voice conversion, and observe similar improvement.
Potential use of Augmented Reality in pre-contract design communication in construction projects

The proactive management of a design can prevent design errors. Design changes can be mitigated by using Augmented Reality (AR) prototype digital models. However, the use of these models in construction is still in its infancy. Thus, this study assessed how AR could be used to overcome traditional pre-contract design communication related issues in construction. The study used a qualitative approach. The data required were collected through interviews conducted with experts in the fields of construction and information technology. Manual content analysis was used to analyse the collected empirical data. The study revealed that design communication issues could be related to architectural design, structural design, service design, and design collaboration. AR toolkit models, 360-degree model visualisation, walkthroughs, model registration, clash detection, and digital information overlays would minimise design communication issues, while AR features would enable design knowledge acquisition and negotiation. However, in construction, technical and social challenges are found to hinder AR implementation.
Automorphisms on normal and convex fuzzy truth values revisited

The present paper extends some previous works studying automorphisms in type-2 fuzzy sets. The framework for the analysis is the set of convex and normal functions from [0,1] to [0,1] (fuzzy truth values). The paper concentrates on those automorphisms that, in this framework, leave the constant function 1 fixed. This function is quite important since it defines the boundary between the functions that represent “TRUE” (increasing functions) and those that represent “FALSE” (decreasing functions), being at the same time the only normal function that is simultaneously increasing and decreasing. While C.L. Walker, E.A. Walker and J. Harding introduced in 2008 a family of functions leaving the constant function 1 fixed, the main goal of this paper is to prove that the functions of that family are in fact automorphisms, and moreover, that they are the only automorphisms (in the mentioned set of convex and normal functions from [0,1] to [0,1]) that preserve the function 1.
Quality Improvement of Richardson Lucy Based De-blurred Images using Krill Herd Optimization

In the present scenario; everyone wants captures the beautiful moments. For this purpose; most of the people use the inbuilt camera in their smart phone, digital camera etc.. However there is a common problem which degrades the quality of those beautiful moments called "blurring" due to which captured images get distorted. This problem comes into existence due to several reasons. The biggest reason for blurring is the displacement of the pixels from its original place to another neighboring place. Many de-blurring methods have been proposed by the researchers to fix this problem. One of the method is Lucy-Richardson algorithm. It de-blurs the blurred images to a certain extent. However; the quality of the restored image can be further fine tuned by applying optimization method. In this paper; a bio inspired optimization technique called Krill Herd has been employed. To know the efficacy of this combined method; some performance evaluation parameters have been calculated; which chant the success story of this technique.
Decomposed Meta Batch Normalization for Fast Domain Adaptation in Face Recognition

Face recognition systems are sometimes deployed to a target domain with limited unlabeled samples available. For instance, a model trained on the large-scale webfaces may be required to adapt to a NIR-VIS scenario via very limited unlabeled faces. This situation poses a great challenge to Unsupervised Domain Adaptation with Limited samples for Face Recognition (UDAL-FR), which is less studied in previous works. In this paper, with deep learning methods, we propose a novel training remedy by decomposing the model into the weight parameters and the BN statistics in the training phase. Based on decomposing, we design a novel framework via meta-learning, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decomposed Meta Batch Normalization</i> (DMBN) for fast domain adaptation in face recognition. DMBN trains the network such that domain-invariant information is prone to store in the weight parameters and domain-specific knowledge tends to be represented by the BN statistics. Specifically, DMBN constructs distribution-shifted tasks via domain-aware sampling, on which several meta-gradients are obtained by optimizing discriminative representations across different BNs. Finally, the weight parameters are updated with these meta-gradients for better consistency across different BNs. With the learned weight parameters, the adaptation is very fast since only the BN updating on limited data is needed. We propose two UDAL-FR benchmarks to evaluate the domain-adaptive ability of a model with limited unlabeled samples. Extensive experiments validate the efficacy of our proposed DMBN.
Few-Shot Knowledge Validation using Rules

Knowledge graphs (KGs) form the basis of modern intelligent search systems – their network structure helps with the semantic reasoning and interpretation of complex tasks. A KG is a highly dynamic structure in which facts are continuously updated, added, and removed. A typical approach to ensure data quality in the presence of continuous changes is to apply logic rules. These rules are automatically mined from the data using frequency-based approaches. As a result, these approaches depend on the data quality of the KG and are susceptible to errors and incompleteness.
On Automorphism Groups of Finite Chain Rings

A finite ring with an identity is a chain ring if its lattice of left ideals forms a unique chain. Let R be a finite chain ring with invaraints p,n,r,k,k′,m. If n=1, the automorphism group Aut(R) of R is known. The main purpose of this article is to study the structure of Aut(R) when n&gt;1. First, we prove that Aut(R) is determined by the automorphism group of a certain commutative chain subring. Then we use this fact to find the automorphism group of R when p∤k. In addition, Aut(R) is investigated under a more general condition; that is, R is very pure and p need not divide k. Based on the j-diagram introduced by Ayoub, we were able to give the automorphism group in terms of a particular group of matrices. The structure of the automorphism group of a finite chain ring depends essentially on its invaraints and the associated j-diagram.