Reviewerly/exHarmony huggingface dataset:
To improve the performance of genetic algorithms by using a novel selection operator

The Genetic Algorithm (GA) was developed as a search engine for difficult non-deterministic polynomial optimization problems. However, it suffers from internal weaknesses, such as premature convergence and low computation efficiency. One critical aspect of the GA is the selection process, which determines new paths and ultimately guides the algorithm towards a solution. This paper details a novel selection procedure that is a perfect blend of the two extremes, namely exploitation and exploration. The proposed technique eliminates the fitness scaling problem by changing the selection pressure continuously during the selection stage. Utilizing traveling salesman problem library (TSPLIB) instances, a performance comparison of the proposed method with a few traditional selection methods was conducted, and the proposed strategy yielded much better outcomes in the form of standard deviations and mean values. A two-sided t-test was also developed, and the results revealed that the proposed strategy enhanced the performance of a GA substantially.
Unmasking the potential: evaluating image inpainting techniques for masked face reconstruction

The performance of most Face Recognizers tends to degrade when dealing with masked faces, making face recognition challenging. Image inpainting, a technique traditionally used for restoring old or damaged images, removing objects, or retouching photos, could potentially aid in reconstructing masked faces. In this paper, we compared three state-of-the-art image inpainting models-PatchMatch, a traditional algorithm, and two deep learning GAN-based models, Edge Connect and Free form image inpainting-to assess their performance in regenerating masked faces. The evaluation was conducted using own created synthetic datasets MaskedFace-CelebA and MaskedFace-CelebA-HQ, along with a synthetic masked dataset created for paired comparisons of masked images with ground truth for face verification. The computed results for Image Quality Assessment (IQA) between ground truth and reconstructed facial images indicated that the Gated Convolution model performed better than the other two models. To further validate the results, the reconstructed and ground truth images were also subject to VGG16 classifier, a widely used benchmark model for image recognition. The classifier outcomes supported the quantitative and qualitative assessment based on IQA.
Model-Efficient TTS

Text-to-speech synthesis systems are usually deployed in different environments such as cloud-edge-end devices, which require fast synthesis speed, small memory storage, and low computation cost. However, early neural TTS models usually leverage deep neural networks that are usually with large computation/memory/time costs. In this chapter, we introduce the technologies for model-efficient TTS. We categorize these technologies according to the methods they use as follows: (1) Parallel generation, which can increase the parallelism of the computation and improve the inference (or training) speed. (2) Lightweight modeling, which aims to develop lightweight and efficient models with small model sizes, low computation, and fast inference speed. (3) Efficient modeling with domain knowledge, which designs efficient models by leveraging the domain knowledge of speech.
Basics of Deep Learning

This chapter introduces some basics of deep learning, including some learning paradigms and key components of machine learning, and some representative model structures and frameworks in deep learning. Since TTS is a typical data generation task, we also introduce some representative deep generative models.
Acoustic Models

In this chapter, we introduce acoustic models, which generate acoustic features from linguistic features or directly from phonemes or characters. With the development of TTS, different kinds of acoustic models have been adopted, including the early hidden Markov models and deep neural networks in statistical parametric speech synthesis, and then the sequence-to-sequence models based on an encoder-attention-decoder framework (including RNN, CNN, and Transformer), and the latest feed-forward models (CNN or Transformer) and advanced generative models (GAN, Flow, VAE, and Diffusion).
Data-Efficient TTS

Deep learning-based TTS models require a large amount of training data to learn the mapping patterns between text and speech as well as the complicated distributions in speech. However, in many scenarios, the training data is usually limited, which hinders the effective learning of neural TTS models. In this chapter, we introduce data-efficient methods to solve the low data resource issue in TTS. Generally speaking, there are different scenarios with low data resources: (1) language level, where there lack of training data when we want to build TTS models for a language, and (2) speaker level, where there lack of training data when we want to build TTS models for a speaker. Thus, we mainly introduce data-efficient TTS methods from the two scenarios in this chapter.
Vocoders

In this chapter, we introduce vocoders, which generate waveforms from acoustic features or directly from linguistic features. With the development of TTS, different kinds of vocoders have been adopted, including the vocoders in statistical parametric speech synthesis (SPSS), and neural network-based vocoders. We first view vocoders from a historic perspective, covering vocoders in SPSS and neural TTS, and then introduce the vocoders in neural TTS, mainly from the perspective of different deep generative models used.
Expressive and Controllable TTS

Expressive and controllable TTS covers broad topics including modeling, disentangling, controlling, and transferring the content, timbre, prosody, style, emotion, etc. In this chapter, we first conduct a comprehensive analysis of this variation information, and then introduce some technologies for expressive and controllable TTS, including modeling, disentangling, controlling, and transferring this variation information.
Fully End-to-End TTS

Fully end-to-end TTS models can generate speech waveforms from character or phoneme sequences directly. However, there are big challenges to training TTS models in an end-to-end way, mainly due to the different modalities between text and speech waveform, as well as the huge length mismatch between character/phoneme sequence and waveform sequence. Thus, the development of end-to-end TTS is progressive. In this chapter, we first review the progressively end-to-end process in TTS from a historical perspective and then introduce some fully end-to-end TTS models.
Text Analyses

Through text analyses, we can transform input text into linguistic features, which contain rich information about pronunciation and prosody that can ease speech synthesis. Text analyses consist of several components: (1) text processing, which processes raw text from documents, normalizes the text from the written form into spoken form, and conducts some linguistic analyses; (2) phonetic analysis, which converts text into phonetic symbols, including polyphone disambiguation and grapheme-to-phoneme conversion; (3) prosodic analysis, which analyzes some prosodic features such as pitch, duration, loudness, stress, and pauses. In this chapter, we first introduce these components in the first three sections and then discuss the development of text analysis in TTS in the last section.
Introduction

In this chapter, we first discuss the motivation of this book and then introduce the history of text-to-speech (TTS) technologies. We further overview neural network-based TTS through different taxonomies, and finally, describe the organization of this book.
Beyond Text-to-Speech Synthesis

In this chapter, we briefly introduce other speech tasks that are related to TTS and discuss their relationships. The closest task to text-to-speech synthesis is singing voice synthesis (SVS). Actually, SVS can be regarded as a subtask of TTS, as SVS shares similar input/output, model pipeline, and methodology with TTS. Voice conversion (VC) is also closely related to TTS since they both aim to synthesize speech. The difference is that TTS synthesizes speech from the text while VC synthesizes speech from another speech. Other related tasks include speech enhancement and separation, which separate different signals from source speech (e.g., separate clean signal from noise in speech enhancement).
Robust TTS

In this chapter, we introduce how to address the robustness issues in TTS. We summarize some popular techniques to improve robustness, including enhancing attention, replacing attention with duration prediction, enhancing autoregressive generation, and replacing autoregressive generation with non-autoregressive generation.
Hajj pilgrimage abnormal crowd movement monitoring using optical flow and FCNN

Abstract This article discusses an effective technique for detecting abnormalities in Hajj crowd videos. In order to guarantee the identification of anomalies in scenes, a trained and supervised FCNN is turned into an FCNN using FCNNs and temporal data. By minimizing computational complexity, incorrect movement detection is utilized to achieve high performance in terms of speed and precision. This FCNN-based architecture is designed to handle two primary tasks: feature representation and the detection of incorrect movement outliers. Additionally, to overcome the aforementioned issues, this research will generate a new crowd anomaly video dataset based on the Hajj pilgrimage scenario. On the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets, the proposed FCNN-based technique obtained ultimate accuracy of 100%, 90%, 95%, and 89%, respectively. Additionally, the ResNet50-based technique achieved ultimate accuracy of 96%, 89%, 94%, and 92%, respectively, for the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets.
Quantum Simulation of Tunable Neuron Activation

Abstract The activation of the neuron is simulated by a quantum circuit. When the circuit is deep enough, the output qubit is deterministically in the state if the probability of the input qubit in the designated state is larger than the threshold value. Conversely, the output qubit is deterministically in the state if the probability is smaller than the threshold value. The threshold value is adjustable. When the depth of the circuit is limited, the nonlinear relations between the input and the output can also be realized.
Improved content recommendation algorithm integrating semantic information

Abstract Content-based recommendation technology is widely used in the field of e-commerce and education because of its intuitive and easy to explain advantages. However, due to the congenital defect of insufficient semantic analysis of TF-IDF vector space model, the traditional content-based recommendation technology has the problem of insufficient semantic analysis in item modeling, fails to consider the role of semantic information in knowledge expression and similarity calculation, and is not accurate enough in calculating item content similarity. The items with semantic relevance in content can not be well mined. The research goal of this paper is to improve the semantic analysis ability of the traditional content-based recommendation algorithm by integrating semantic information with TF-IDF vector space model for item modeling and similarity calculation and proposed an improved content recommendation algorithm integrating semantic information. In order to prove the effectiveness of the proposed method, several groups of experiments are carried out. The experiments results showed that the overall performance of the proposed algorithm in this paper is the best and relatively stable. This verified the validity of our method.
Contextual Analysis of Tamil Proverbs for Automatic Meaning Extraction

The Tamil proverbs are treasure of Tamil language, as they are crisp and easy to remember the advice, awareness and knowledge embedded in it. The current Tamil generation does not know the cultural values of proverbs as they are oral and passed on from one generation to the other by word of mouth. Many Tamil proverbs are misunderstood by the global society and explanation is not known for vast amount of Tamil proverbs. The Tamil proverbs have many nuggets hidden, which need to be explored for the goodness of the society. There is no state of the art technique to extract the meaning of the Tamil proverbs till now. In the proposed research, the proverb dataset is created and the meanings of the Tamil proverbs are automatically extracted by contextual analysis. This work is evaluated with the precision, recall and F1-score and achieved 92% of precision, 87% of recall and 89% of F1-Score.
Active Contour Segmentation and Deep Learning Based Hand Gesture Recognition System for Deaf and Dumb People

Sign language facilitates hearing and speech impaired individuals to communicate with other members of the society. However, there are only few trained interpreters available for effective communication through sign language. Hence, an assistive system is mandate and hand gesture recognition system is one such technique. In this system, the hand gesture is captured by the camera and is fed to recognition system. The system involves segmentation and recognition based on trained images. To attain this, the image is segmented through skin, k-Means, edge detection and active contour methods and their quality metrics such as mean square error, root mean square error, signal to noise ratio, peak signal to noise ratio and normalized cross correlation are compared. From the results, it is inferred that the active contour based segmentation yield high quality segmentation. Further, active contour based segmented image is given as input to the deep learning algorithm to predict hand gesture. Furthermore, the generated text is converted to speech using in-built system speaker module.
Paraphrase Detection in Indian Languages Using Deep Learning

Multiple sentences that reveal the same meaning are considered to be paraphrases. Paraphrases restate a given text, passage or statement using different words in which the original context and the meaning are kept intact. It can be used to expand, clarify or summarize the content of essays, research papers and journals. Semantic identity of sentences are detected during the process of paraphrase detection. Paraphrase detection can be related to different applications, like plagiarism detection, text summarizing, text mining, question answering, and query ranking, in the domain of Natural Language Processing. Effective paraphrase detection could be implemented if the semantics of the language and their interactions are adequately captured. The process of paraphrase detection is considered to be a difficult and challenging task due to the wide range of complex morphological structures and vocabulary that prevails in most of the Indian languages. The approaches that exist for paraphrase detection include machine learning techniques like Multinomial Logistic Regression model and Recursive Auto Encoders, which lacks in hand-crafted feature engineering. The problem could be solved when deep learning approaches are used for paraphrase detection. In the proposed system, the classification of paraphrase, semi-paraphrase and non-paraphrase sentences are implemented using an ensemble of three deep learning algorithms which includes BERT (Bidirectional Encoder Representations from Transformers), USE (Universal Sentence Encoder) and Seq2Seq (Sequence to Sequence). The DPIL corpus has been used for the evaluation of the proposed system and the highest accuracy obtained considering languages Hindi and Punjabi are 85.22% and 85.80% respectively.
Inference Analysis of Lightweight CNNs for Monocular Depth Estimation

Monocular Depth Estimation (MDE) from a single image is a challenging problem in computer vision, which has been intensively investigated in the last decade using deep learning approaches. It is essential in developing cutting-edge applications like self-driving cars and augmented reality. The ability to perceive depth is essential for various tasks, including navigation and perception. Monocular depth estimation has attracted much attention. Their popularity is driven by ease of use, lower cost, ubiquitous, and denser imaging compared to other methods such as LiDAR scanners. Traditional MDE approaches heavily rely on depth cues for depth estimation and are subject to strict constraints, such as shape-from-focus and defocus algorithms, which require a low depth of field of scenes and images. MDE without some particular environmental assumptions is an ill-posed problem due to the ambiguity of mapping between the depth and intensity of color measurements. Recently, Convolutional Neural Networks (CNN) approaches have demonstrated encouraging outcomes in addressing this challenge. CNN can learn an implicit relationship between color pixels and depth. However, the mechanism and process behind the depth inference of a CNN from a single image are relatively unknown. In many applications, interpretability is very important. To address this problem, this paper tries to visualize a lightweight CNN (Fast-depth) inference in monocular depth estimation. The proposed method is based on [1], with some modifications and more analyses of the results on outdoor scenes. This method detects the smallest number of image pixels (mask) critical to infer the depth from a single image through an optimization problem. This small subset of image pixels can be used to find patterns and features that can help us to better formulate the behavior of the CNN for any future monocular depth estimation tasks.
End-to-End Unified Accented Acoustic Model for Malayalam-A Low Resourced Language

Accented Automatic Speech Recognition(AASR) takes into account the accent information that poses a great challenge to the construction of ASR. The authors in this work have constructed multiple unified acoustic models for the Malayalam language that captured the accented and accent unspecific knowledge which also worked fine with unknown accents. Malayalam is a Dravidian language rich in accents that are spoken in the Indian state of Kerala. Accented speech dataset was constructed initially comprising five different accents. The paper discusses various experiments in the feature engineering of accented speech data. The accented features were extracted using Mel Frequency Cepstral Coefficients(MFCC), Short Term Fourier Transform (STFT), Mel Spectrogram, and Tempogram approaches individually and combined approaches to find the better feature set. Different experiments were conducted to construct different Unified accented models using machine learning, deep learning, and LSTM-RNN for all individual feature set extracted. The experiment resulted in finding a novel approach to constructing accented ASR for the Malayalam language with a reduced WER than many other baseline models. The model worked fine with known accents, unknown accents, and accent unspecific standard accents.
Development of Multi-lingual Models for Detecting Hope Speech Texts from Social Media Comments

Comments on social media can be written in any number of languages, and many of them may also be written in languages with few resources. Hope Speech comments are kind expressions that support or critique a viewpoint without offending the individual or the community. On the other hand, non-hope speech is made up of harsh, mocking, or demotivating words. Since the Covid-19 pandemic, the need for positive reinforcement on the internet has made the field of natural language processing pay more attention to hope speech detection. Hope speech detection looks for words and phrases in social media comments that make people feel good. In this paper, an attempt to share content on these platforms that is positive and helpful is made. The models that are based on transformers to figure out whether a social media comment is “hope speech” or “non-hope speech” has been used. The objective of this work is to find the “hope speech” comments in YouTube datasets that were made as part of the “LT-EDI-ACL 2022: Hope Speech Detection for Equality, Diversity, and Inclusion” shared task. The shared task dataset was suggested in five different languages: Malayalam, Tamil, English, Spanish, and Kannada. The model based on a transformer was used as both a fine-tuner and an adapter transformer. In the end, adapters and fine-tuners do the same thing, but adapters add layers to the main model that has already been trained and freeze the weights of those layers. This study shows that models that are based on adapters do better than models that are fine-tuned. The proposed model classifies the Tamil dataset with an accuracy of 51.7% and the English dataset with an accuracy of 92.1%, which is the highest among all the datasets.
Age-Group Estimation of Facial Images Using Multi-task Ranking CNN

Age as one of the facial attributes plays a significant role in surveillance, web content filtering in electronic customer relationship management, face recognition, among others. The process of ageing changes the colour and texture of skin, as well as the facial skeleton lines with other additional attributes. This process is burdened by such features as ethnicity, gender, emotion, illumination, pose, makeup, and other artifacts that make the task of age estimation non-trivial. One of the promising approaches is to consider the problem as multi-task in order to improve accuracy. We propose a new multi-task CNN for identity verification in real-time access systems. It includes a ranking sub-CNN that groups facial images into predefined age ranges and two sub-CNNs that binary gender classification and multi-class facial expression classification. Even a small number of additional attributes shows more accurate estimates of the age grouping. We have tested our multi-task CNN using five public datasets with promising results.
Arabic Text-to-Speech Service with Syrian Dialect

This research aims to develop an Arabic text-to-speech (TTS) service with Syrian dialect, which is a variety of Arabic spoken in Syria and some neighboring countries, with easy access to it for people with disabilities or difficulty reading Arabic, such as people with visual impairments or learning disabilities. To achieve this goal, we employ two state-of-the-art Machine Learning (ML) approaches: Tactron2 and Transformers, which have achieved impressive results in various natural language processing tasks, including TTS. We compared the two approaches and evaluated the resulting TTS service using subjective measures. Our results show that both approaches can produce high-quality speech in the Syrian dialect, but transformers have the advantage of being more efficient and more flexible in handling different languages and accents.
A Systematic Review of Sentiment Analysis in Arabizi

Sentiment analysis (SA), also called opinion mining, is concerned with the automatic extraction of opinions conveyed in a certain text. Many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention. Recently, Arabic Sentiment Analysis (ASA) has received a great deal of interest in the research community. Several studies have been conducted on Arabic and especially in arabizi. This survey presents a systematic review of Arabic sentiment analysis research related to arabizi.