Reviewerly/exHarmony huggingface dataset:
Design and development of anti-theft tracking APP based on geofence

With the development of economy, people's living standards are improving day by day. Nowadays, people begin to own more and more relatively valuable items, including but not limited to bicycles, private cars, smart furniture, smart appliances, etc. Some of them have corresponding anti-theft measures, such as the built-in anti-theft system of the car, the positioning system of some smart devices, etc., but most of the items have no anti-theft and recovery mechanism after being stolen. Therefore, people often find out after the item is stolen, and it is often accompanied by regret of not knowing where to find the item. For the above problems, this article provides a general solution based on geo-fence technology for anti-theft and tracking and retrieval of stolen items. And based on this design and development of a cross-iOS and Android application. This article describes in detail the specific design and implementation process of the new APP.
DirectDebug: A software package for the automated testing and debugging of feature models

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images

The clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering framework, which learns a deep neural network in an end-to-end fashion, providing direct cluster assignments of images without additional processing. Multi-Modal Deep Clustering (MMDC), trains a deep network to align its image embeddings with target points sampled from a Gaussian Mixture Model distribution. The cluster assignments are then determined by mixture component association of image embeddings. Simultaneously, the same deep network is trained to solve an additional self-supervised task of predicting image rotations. This pushes the network to learn more meaningful image representations that facilitate a better clustering. Experimental results show that MMDC achieves or exceeds state-of-the-art performance on six challenging benchmarks. On natural image datasets we improve on previous results with significant margins of up to 20% absolute accuracy points, yielding an accuracy of 82% on CIFAR-10, 45% on CIFAR-100 and 69% on STL-10.
Neural Architecture Search for LF-MMI Trained Time Delay Neural Networks

Deep neural networks (DNNs) based automatic speech recognition (ASR) systems are often designed using expert knowledge and empirical evaluation. In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper-parameters of state-of-the-art factored time delay neural networks (TDNNs): i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer. These include the DARTS method integrating architecture selection with lattice-free MMI (LF-MMI) TDNN training; Gumbel-Softmax and pipelined DARTS reducing the confusion over candidate architectures and improving the generalization of architecture selection; and Penalized DARTS incorporating resource constraints to adjust the trade-off between performance and system complexity. Parameter sharing among candidate architectures allows efficient search over up to 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">28</sup> different TDNN systems. Experiments conducted on the 300-hour Switchboard corpus suggest the auto-configured systems consistently outperform the baseline LF-MMI TDNN systems using manual network design or random architecture search after LHUC speaker adaptation and RNNLM rescoring. Absolute word error rate (WER) reductions up to 1.0% and relative model size reduction of 28% were obtained. Consistent performance improvements were also obtained on a UASpeech disordered speech recognition task using the proposed NAS approaches.
Progressive Unsupervised Domain Adaptation for Image-based Person Re-Identification

Unsupervised domain adaptation (UDA) has emerged as an effective paradigm for reducing the huge manual annotation cost for Person Re-Identification (Re-ID). Many of the recent UDA methods for Re-ID are clustering-based and select all the pseudo-label samples in each iteration for the model training. However, there are many wrong labeled samples that will mislead the model optimization under this circumstance. To solve this problem, we propose a Progressive Unsupervised Domain Adaptation (PUDA) framework for image-based Person Re-ID to reduce the negative effect of wrong pseudo-label samples on the model training process. Specifically, we first pretrain a CNN model on a labeled source dataset, then finetune the model on unlabeled target dataset with the following three steps iteratively: 1) estimating pseudo-labels for all the images in the target dataset with the model trained in the last iteration; 2) extending the training set by adding pseudo-label samples with higher label confidence; 3) updating the CNN model with the expanded training set in a supervised manner. During the iteration process, the number of pseudo-label samples added increased progressively. In particular, a Moderate Initial Selections (MIS) strategy for pseudo-label sampling is also proposed to reduce the negative impacts of random noise features in the early iterations and mislabeled samples in the late iterations on the model. The proposed framework with MIS strategy is validated on the Duke-to-Market, Market-to-Duke unsupervised domain adaptation tasks and achieves improvements of 4.2 points (absolute, i.e., 80.0% vs. 75.8%) and 1.7 points (absolute, i.e., 70.7% vs. 69.0%) in mAP correspondingly.
Enhancing Multimodal Interaction Between Performers and Audience Members During Live Music Performances

Live performance provides a good example of enthusiastic interaction between people gathered together in a large group and one or more performers. In this research, we focused on elucidating the mechanism of such enthusiastic group interaction (collective effervescence) and how technology can contribute to its enhancement. We propose a support system for co-experience and physical co-actions among participants to enhance enthusiastic interaction between performers and audiences during live performances. This system focuses on a physical synchronization between the performer and the audience as the key that generates enthusiastic interaction during a live performance. Also, it supports enhanced bidirectional communication of the performer's actions and the audience's cheering behaviors. An experiment in an actual live performance environment in which collective effervescence was already occurring demonstrated that the bidirectional communication and visualization of physical movements in the proposed system contributed to the further unification of the group.
A Systematic Survey of Cognitive Chatbots in Personalized Learning Framework

The usage of technology in education sector has taken a leap with the development of Artificial Intelligence. The aim of this study is to review the works done related to the usage of chatbots in pedagogical context in the period between 2017-2021. The findings include (a) the various contexts in which chatbots are being used, (b) advantages and disadvantages of using chatbots in e-learning, (c) ways to improve and address the disadvantages and (d) how personalization helps in the improvement of students.
Multi-Task Transformer with Input Feature Reconstruction for Dysarthric Speech Recognition

Dysarthria is a motor speech disorder caused by damage to the part of the nervous system that controls the physical production of speech. It poses great challenges in building robust dysarthric speech recognition (DSR) due to the high inter- and intra-speaker variability. To this end, we propose a multi-task Transformer with input feature reconstruction as an auxiliary task, where the main task of DSR and the auxiliary reconstruction task share the same encoder network. The auxiliary task aims to reconstruct clear speech features from corrupted speech of healthy speakers (intra-domain) or dysarthric speakers (cross-domain). Further, to alleviate the imbalanced distribution of dysarthria data sets, we devise an adaptive rebalance sampling scheme to improve the utterance sampling frequency of dysarthric speech. Experimental results show that the proposed model considerably outperforms other baselines across speakers with varying severity of dysarthria.
An empirical study on the contribution of formal and semantic features to the grammatical gender of nouns

Abstract This study conducts an experimental evaluation of two hypotheses about the contributions of formal and semantic features to the grammatical gender assignment of nouns. One of the hypotheses (Corbett and Fraser 2000) claims that semantic features dominate formal ones. The other hypothesis, formulated within the optimal gender assignment theory (Rice 2006), states that form and semantics contribute equally. Both hypotheses claim that the combination of formal and semantic features yields the most accurate gender identification. In this paper, we operationalize and test these hypotheses by trying to predict grammatical gender using only character-based embeddings (that capture only formal features), only context-based embeddings (that capture only semantic features) and the combination of both. We performed the experiment using data from three languages with different gender systems (French, German and Russian). Formal features are a significantly better predictor of gender than semantic ones, and the difference in prediction accuracy is very large. Overall, formal features are also significantly better than the combination of form and semantics, but the difference is very small and the results for this comparison are not entirely consistent across languages.
A Job Scheduling Algorithm for Edge Computing Based on Modified Monte Carlo Tree Search

Job scheduling is a crucial and promising task for maximizing computing resources utilization, which significantly improves the efficiency of edge cloud. Nevertheless, the existing job scheduling algorithms mainly ignore the scarce computing resources, i.e., heterogeneous resources, in edge cloud. A novel job scheduling algorithm based on Modified Monte Carlo Tree Search is proposed to take full advantage of the computing resources. The experiment results manifest that the proposed algorithm is valid in job scheduling for edge computing.
Different Applications of PSO

Particle swarm optimization (PSO) is an evolutionary optimization algorithm. PSO is a robust and well researched optimization technique. There are a large number of applications of PSO. “Applications of PSO” chapter tries to present a classified literature review for the applications of PSO in different fields. The applications are classified into different sections based on the area of implementation.The chapter also presents a table with references to multiple other applications over and above those covered in the chapter. References of some largely cited review papers dealing with the applications of PSO are also mentioned at the end of the chapter.
Is There Any Further Scope for Improving the Efficiency of Modern Websites?

Interface is an intermediate medium of interaction between human and computer for effective communication. In recent years, with the advancement of WWW, efficient user interface design has become evident for better user-experiences. Efficiency is used as a key component for determining the key metric of a website-Usability. The purpose of this study is to evaluate the efficiency of different websites, popularly used in our life. In order to carry out this task, ten websites were considered. For each website, a representative task was selected. Ten participants were used to complete this task in the Latin Square method. Experimental results show that most of the interfaces are efficient. However, there is a further scope for improving efficiency of those websites.
[DC] VirSec: Virtual Reality as Cost-Effective Test Bed for Usability and Security Evaluations

In this doctoral thesis, we explore how virtual reality (VR) can better support the development and evaluation of prototype systems, with a focus on usable security (USEC) as a sub-domain of Human-computer Interaction (HCI) research. We argue that VR, as a study platform, can augment existing USEC research paradigms by 1) enabling researchers to study systems and user behaviour in contexts that are otherwise challenging due to, for example, ethical or legal constraints and 2) improving evaluations that are constrained to conditions that can be physically replicated in the lab.
Customer Complaints Clusterization of Government Drinking Water Company on Social Media Twitter using Text Mining

Social media is considered one of the most effective platforms to communicate between companies and customers. Frequently, the customer of a product or service sends complaints via social media. Customers' complaint data serve as a good suggestion for companies and organizations to improve their products and services. With the increasing number of customer complaints that have entered through social media accounts, government-owned drinking water companies need a more efficient way to extract information from complaint data. In this research, text mining is used to extract information about customer complaints against drinking water companies from social media Twitter. Latent Dirichlet Allocation (LDA) and self-organizing maps (SOM) approach is applied to model complaint topics and find out which are most frequently complained. The test results indicate grouping the data into five classes is the most appropriate model. Pipes leakage are the most frequently reported topics, 27.8% of total datasets.
Hybrid Face Reflectance, Illumination, and Shape from a Single Image

We propose HyFRIS-Net to jointly estimate the hybrid reflectance and illumination models, as well as the refined face shape from a single unconstrained face image in a pre-defined texture space. The proposed hybrid reflectance and illumination representation ensure photometric face appearance modeling in both parametric and non-parametric spaces for efficient learning. While forcing the reflectance consistency constraint for the same person and face identity constraint for different persons, our approach recovers an occlusion-free face albedo with disambiguated color from the illumination color. Our network is trained in a self-evolving manner to achieve general applicability on real-world data. We conduct comprehensive qualitative and quantitative evaluations with state-of-the-art methods to demonstrate the advantages of HyFRIS-Net in modeling photo-realistic face albedo, illumination, and shape.
One-Shot Voice Conversion Based on Speaker Aware Module

Voice conversion (VC) is a task to convert the voice of speech while preserving its linguistic content. Although several methods have been proposed to enable VC with non-parallel data, it is still difficult to model the voice without a great number of data or an adaptive process. In this paper, we propose a speaker-aware voice conversion (SAVC) system realizing one-shot voice conversion without an adaptation stage. The SAVC utilizes a speaker aware module (SAM) to disentangle speaker embeddings. The SAM comprises a dynamic reference encoder, a static speaker knowledge block (SKB), and a multi-head attention layer. The reference encoder is used to compress a variable-length utterance to a fixed-length vector, the SKB is made up of pre-extraction x-vectors, and the multi-head attention layer is designed to generate weighted combined speaker embeddings. Subsequently, phonetic pos- teriorgrams (PPGs) as context encoding are concatenated with speaker embeddings and sent to the decoder module for generating acoustic features. Experimental results on the Aishell-1 corpus show that the proposed method can improve speaker similarity and converted utterances' speech quality.
A Lightweight Classifier for Facial Expression Recognition based on Evolutionary SVM Ensembles

Evaluation criteria for solutions to facial expression recognition usually bias to classification accuracy. Hence, the utilization of deep neural networks has become a straightforward and popular option in theoretical studies despite the limitations in real usage from data collection, storage space, and power consumption issues. Our work proposes a practical alternative that is consisted of a minimum model configuration and still matches the state-of-the-art performance of deep learning approaches. We establish a conventional two-stage procedure, where feature extraction of a facial subject depends on a universal filter, histogram of oriented gradients (HOG), and classification is implemented through an ensemble learning approach using basic binary classifiers, support vector machines (SVM). Our two designs considerably improve prediction accuracy. One is that we adopt post-hoc statistics, rather than a priori expectations, to interpret the outputs of weak classifiers. The other is we design a genetic algorithm to search for the optimal ensemble of weak classifiers efficiently. Our method demonstrates supreme performance in several benchmark datasets and even outperforms those based on deep learning from big data. Besides, from a practical viewpoint, our model shows the advantage and flexibility of its storage size and power consumption. Lastly, we further display how the evolutionary SVM ensembles in our model contain information about the dependency and similarity among facial expression categories.
Binary logistic regression for identification of high school student interest in Banda Aceh city in continuing study at Universitas Syiah Kuala

Abstract Universitas Syiah Kuala is the oldest university in Aceh. The campus has the nickname of the jantong hate campus to the Acehnese people, which of course can be interpreted as the campus of pride of the Acehnese people. Universitas Syiah Kuala increased its accreditation status from C to A accreditation awarded by the research, technology and higher education ministry. This showed a very significant quality of improvement. With this condition a study was conducted on the interest of high school students in the Banda Aceh City in choosing Universitas Syiah Kuala as a place to study. This study aims to determine the factors that influence the interest of Banda high school students in continuing their studies to Universitas Syiah Kuala and get a model of interest in the Banda Aceh city students through a binary logistic regression approach. The results show that the factors that influence students’ interest in continuing their studies at Universitas Syiah Kuala are the ability to be affiliated with others, goals, and expectations. With the binary logistic regression model obtained, it can be seen the tendency of students to continue their studies at Universitas Syiah Kuala.
Kernel Non-Negative Matrix Factorization Using Self-Constructed Cosine Kernel

Kernel-based non-negative matrix factorization (KNMF) can non-linearly extract non-negative features for image-data representation and classification. However, different kernel functions would lead to different performance. This means that selecting an appropriate kernel function plays an important role in KNMF algorithms. In this paper, we construct a novel Mercer kernel function, called cosine kernel function, which has the advantages of translation invariance and robustness to noise. Based on the self-constructed cosine kernel, we further propose a cosine kernel-based NMF (CKNMF) approach. The iterative formulas of CKNMF are deduced using the gradient descent method. We empirically validate that our CKNMF algorithm is convergent. Compared with some state of the art kernel-based algorithms, experimental results indicate that the proposed CKNMF algorithm achieves superior performance on face recognition.
Impact of Noise on the Resilience and the Security of Quantum Computing

Qubits, which are building blocks of quantum computers, are fragile and prone to noise and error. Some prominent errors include decoherence/dephasing, gate error, readout error, leakage, and crosstalk. The computation quality degrades in the presence of errors. Therefore, even though quantum computing can theoretically speed-up certain classes of problems, the present-day Noisy Intermediate-Scale Quantum (NISQ) computers can only solve small-scale ones. In this work, we present an overview of various noise sources and their impact on the resilience and the security of quantum circuits. Our analysis indicates that noise sources (e.g., crosstalk) create a new attack surface (e.g., fault injection), especially for future largescale quantum computers that may employ a multi-programming compute model. We also propose countermeasures against the reliability and security issues.
Machine Learning and Deep Learning-Based Computing Pipelines for Bangla Sentiment Analysis

Sentiment analysis (SA) is the process of extracting the sentimental level of someone's observation, evaluation, or opinion on different social aspects such as products, services, or individuals. It has become a popular research area in the field of natural language processing (NLP) for enabling a machine to recognize the right sentiments of texts. Lately, there have been many works on SA focusing English or other languages, but comparatively less work is notable in the field of Bangla. Many previous works on SA in Bangla language have been carried out incorporating machine learning algorithms but deep learning approach is rarely found due to the scarcity of large datasets. In this work, we present the deep learning and machine learning approaches for the binary sentiment analysis, then compare among all algorithms using some statistical procedures to find out the best performance techniques for Bangla binary sentiment from texts. This work also provides a pre-trained fine-tuned model named BnSentiment to be used by the researchers as well as individuals who want to automate the detection of sentiment polarity on their user review system as well. This module makes strong the Bangla NLP community for further research. The model has an accuracy of 84.5% which has been trained on a sufficiently large dataset.
Comparative Analysis of LSTM-based Deep Learning Models for HAR using Smartphone Sensor

Due to rapid advancement of wearable sensor technology, Human Activity Recognition (HAR) using smartphone sensors data are becoming a trendy research topic. There are many mobile applications adopting from outstanding HAR researches such as health monitoring, performance tracking of sport, and etc. In the last decade, machine learning methods have been introduced to solve the HAR problem. However, the conventional approaches were limited their performance in the process of feature extraction. With the limitation, deep learning approaches have been recently presented with outstanding performances. This paper proposed a comparative analysis of deep learning techniques for HAR using sensor data captured from accelerometer and gyroscope. In the proposed study, we employ various types of Convolutional Neural Networks (CNNs) with Long Short-Term Memory Networks (LSTMs), disregarding the needs for hand-crafted feature extraction. The comparative results indicate that the hybrid LSTM outperforms baseline models.