Reviewerly/exHarmony huggingface dataset:
Density peak clustering based on sparrow search algorithm and improved shared nearest neighbor

Density Peak Clustering (DPC) has become the hottest unsupervised clustering algorithm today since it does not need any iteration and can find cluster centers at one time. In order to further improve the robustness and usability of DPC while maintaining its accuracy, this paper proposes a novel model named Density Peak Clustering Based on Sparrow Search Algorithm and Improved Shared Nearest Neighbor, SSA-SNN-DCP in short. It can deeply mine the hidden spatial angle features of data points and use the sparrow search algorithm to find the right clustering threshold to avoid the influence of artificial prior knowledge. First, an improved SNN similarity measure based on geometric mean and cosine similarity is proposed, amplifying the shared neighborhood compensation mechanism. Then, a cluster center selection strategy based on SSA is designed to realize automatic cluster center election. Finally, the performance of the algorithm is verified using 3 synthetic datasets and 3 UCI real datasets. The experimental results show that SSA-SNN-DPC has a better performance whether it is on spherical clusters, multi-density overlapping clusters or helical clusters.
Research on text classification model based on ERNIE2.0-DICNN

In various text classification tasks, models based on bidirectional encoder representations from transformers (BERT) have been widely used, but they may lack the semantic knowledge of phrases and the grammar knowledge of the text. In this paper, we propose a joint model (ERNIE2.0-DICNN) based on enhanced representation through knowledge integration (ERNIE2.0) and deep isometric convolutional neural networks (DICNN). Experiments on THUCNews, Weibo, and IMDB datasets demonstrate the higher accuracy of the proposed method.
Dynamic Range Adjustment of HDR Images Using Adaptive Method

The ratio of a variable measurement's maximum and minimum values is known as its dynamic range. This range, as it relates to photography and cinematography, is the proportion between an image's whitest (brightest) and darkest (darkest) values. The main goal here is to enhance the detailed visibility of scenes. In this research, we present a three-adaptive step approach for image dynamic range modification that is both efficient and effective in terms of visibility and ease in use. First, two Gamma functions are adaptively selected on the basis of the histogram of the brightness map independently. Second, to balance the amplification of details in different places, an adaptive fusion technique is presented to integrate the two modified luminance maps. Finally, we propose a method to restore the color to the fused image.
Meme Detection For Sentiment Analysis and Human Robot Interactions Using Multiple Modes

Memes are a way of communicating concepts across social media. However, while most memes are intended to be funny, some can turn into offensive as well when text and images are combined together. Recently many successful studies related to sentiment analysis of both image and text have been performed. Such technology, when developed successfully, can be useful for effective Human-Robot-Interactions, specially with humanoid and collaborative robots. In this research, we intend to first develop such technology with available data set using given classes only, since getting labelled data in the robotics domain, specially in robot grasping domain is difficult. In subsequent research, we may extend the same technology for intelligent robot grasping. However, the majority of the research uses either text or images for the sentiment analysis. Since the content and image in memes are sometimes unrelated, detecting hateful memes is a more challenging problem, so the present work considers both as features and uses a multimodal approach for sentiment analysis which could also be useful for Human-Robot-Interactions. Being constrained however with the available data sets, in the present investigation, our focus is on developing multimodal and sequential approaches for classifying these memes into different required classes, more specifically, here two classes: offensive and non-offensive. The fusion approach has been used within multiple modes to take features of both image and text through different models and then it has been used for the classification. While in the sequential approach, the image captioning model which is trained on the MS COCO dataset, with Optical Character Recognition (OCR), is used and classified with the help of the FastText classifier. Both approaches are used on two datasets, one is the MultiOFF dataset, and the other is the Facebook Hateful Meme dataset. Results on both datasets are found to be promising for both approaches.
P-BERT: Polished Up Bidirectional Encoder Representations from Transformers for Predicting Malicious URL to Preserve Privacy

The usage of internet among user made online social network (OSN) like twitter, facebook, weibo to become popular. Users share their thoughts and perspective on aspects on OSN. In OSN the biggest security threat is the malicious Uniform Resource Locator (URLs) to prevent from privacy. Researchers have found few methods to detect the malicious URL by hard coded eminent features, block listing the URLs. These methods have limitations such as not all malicious URLs are blacklisted and many important features are not considered in hard coding method. Evolution of deep learning techniques have made to extract and analyses the features by own and solutions can be derived easily. In this paper, a novel feature engineering approach and polished up Bidirectional Encoder Representations from Transformers (BERT) is proposed to comprehensively detect the malicious Uniform Resource Locator (URLs). The results show that proposed model gives 98.79% of overall accuracy is achieved which out performs from the state of art models.
Abandoned Luggage Detection: An Ontology-based approach for unusual activity recognition

Analysis of video data generated by surveillance systems requires an efficient way to represent, store, and retrieve for performing reasoning to identify unusual events. The recognition of unusual events is often difficult with existing machine-learning/Deep Learning approaches as they suffer due to lack of training examples. Abandoned luggage identification is one of the critical problem which poses security threat in public places. It may occur in several forms with various scenarios. However training for each possible case is extremely challenging due to limited amount of training examples. In this work, an ontology-based reasoning and analysis for identifying the complex event of left luggage in public places. A novel ontology is presented that represents the public place surveillance video data to represent various scenarios. Moreover, a reasoning is performed using Semantic Web Rule language (SWRL) for inferring relations. The proposed ontology-based approach extracts and represents salient information present in video data as a knowledge graph. The unusual events (Abandoned Luggage) is identified form the knowledge using SPARQL queries. Furthermore, the SPARQL queries can also be formulated to retrieve salient information and for question answering. The proposed framework is validated by identifying the complex events left luggage in PETS 2006, PETS 2007, AVSS 2007 and ABODA Dataset.
Estimating Depth Map of an RGB image using Encoders and Decoders

Depth estimation from a single RGB picture has emerged as one of the most significant study areas in recent years because of the wide variety of applications, from robotics to medical sciences. Monocular depth estimation has often had low resolution and blurry depth maps, which are not usable for further training of models with specific applications. The main drawback of generic depth estimation models is that they take an object and its environment into consideration. Because of this, traditional deep learning-based systems often experience severe setbacks in forecasting depths. This paper proposes an encoder-decoder network that, using transfer learning, can forecast high-quality depth pictures from a single RGB image. After initialising the encoder using augmentation algorithms and significant feature extraction from pre-trained networks, the decoder predicts the high-end depth maps. We have also used several boundary detection techniques to remove the object from its environment without losing the object's pixel information. Our network performs comparable to the state-of-the-art on two datasets and also generates qualitatively better results that more accurately represent object boundaries which can be further used in 6D pose estimation to perform robotic grasping.
Electrooculography Signals Classification for FPGA-based Human-Computer Interaction

Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.
A feature extraction with pose-auxiliary method for person re-identification

Person re-identification is widely considered as a sub problem of image retrieval. It is a technology that uses computer vision technology to determine whether there is a specific pedestrian in an image or video, given a monitored pedestrian image, the pedestrian image under the cross device is retrieved. Many researches have been made to study how to match one people from two picture, but how to locating proper body parts has become a problem which could lead to misalign and wrongly divided sample. To solve this problem, we introduce a pose-auxiliary network to extract the position feature of pedestrian. We use a post estimation network to estimate the person’s body location, then we decide whether it’s a body part or a static obstacle by using a separated trained classifier. Then we compare these features from two picture and calculate their loss. Meanwhile, we use a graph-matching strategy to compare two pedestrians pose structure, which is also used into jugging the match problem. By using a multi-verification strategy, our method has some improvement than some previous study using Market-1501 and DukeMTMC-Reid datasets, but still remains room for improvement.
An improved seagull optimization algorithm based on Cauchy variation and nonlinear convergence factor

Aiming at the shortcomings of the seagull optimization algorithm in the iterative process, such as slow convergence speed and easy to fall into local optimum, an improved seagull optimization (CNSOA) algorithm based on nonlinear convergence factor and mutation using the Cauchy operator is proposed. The tent chaotic mapping strategy is used to initialize the population that make the seagull population more uniformly distributed in the search space. In the process of seagull migration, a nonlinear convergence factor is used to guide the seagull to seek optimization, so that the algorithm has better search ability. The Cauchy mutation perturbation strategy is adopted to make the algorithm better jump out of the local optimum. Finally, 9 benchmark test functions are used to test the CNSOA, and the results are compared with the SOA and 5 famous algorithms. The experimental results show that the CNSOA performs better in convergence speed and jumping out of the local optimum.
Automatic Brain White Matter Hyperintensities Segmentation with Swin U-Net

This work proposes an automatic segmentation approach to detect White Matter Hyperintensities (WMH) using Fluid Attenuated Inversion Recovery (FLAIR) and the corresponding T1 weighted MRI images. In this work, we used the Swin U-Net architecture based on Transformers and compared its performance with two currently reported CNN U-Nets architectures. Sixty pairs of images and their corresponding ground truth labels were used from the MICCAI challenge. The following metrics were obtained: Dice similarity score of 0.80; lesion F1 score of 0.63; Hausdorff distance of 3.16 mm; and, 16.93 average volume difference, locating the Swin U-Net second in the ranking of the tested algorithms. However, the computational resources needed to process the imaging data were the lowest compared to the previous U-Nets. Therefore, the Swin U-Net architecture shows potential to become a promising and fast tool for segmenting medical images.
Voistutor 2.0: A Speech Corpus with Phonetic Transcription for Pronunciation Evaluation of Indian L2 English Learners

In computer assisted pronunciation training (CAPT), robust automatic models are critical for pronunciation assessment and mispronunciation detection and diagnosis (MDD). In the modelling, besides the audio data of second language (L2) learners, CAPT requires manually annotated ratings of overall pronunciation quality, and the MDD uses manually annotated phonetic transcriptions. Though the pronunciation quality and the mispronunciation are interdependent, to the best of our knowledge, none of the existing corpora contains both ratings and phonetic transcriptions. This could be due to the cost involved in obtaining phonetic transcriptions. However, a corpus with both kinds of information could benefit the researchers to obtain robust models by exploring the interdependencies. For addressing this, we develop voisTUTOR 2.0 corpus considering the existing voisTUTOR corpus referred to as voisTUTOR 1.0. We obtain phonetic transcriptions manually from a linguist for the entire Indian L2 learners’ English audio data (26529 utterances approximately 14 hours) in voisTUTOR 1.0 for which overall quality ratings and binary scores of factors influencing the pronunciation quality are available. A preliminary analysis of voisTUTOR 2.0 suggests that the phonetic errors correlated with the ratings and the binary scores indicating mispronunciations and phoneme quality.
Designing a Speaking Assessment Task Using Elicited Imitation to Build a Korean Learner Corpus

In this study, we propose an L2 Korean-speaking assessment task that uses elicited imitation (EI) as a tool to build an L2 Korean learner corpus. The corpus in this study differs from other Korean learner corpora as it is meant for use as a language teaching tool. Consequently, several speaking assessment tasks linked to accredited L2 Korean proficiency tests were performed to collect a large amount of speech data from learners of various nationalities. EI is used to measure language proficiency and has the advantage of being able to explore the spoken interlanguage of learners. For this study, we designed and proposed a Korean EI task and showed how it can be used, including scoring it. We also explained how it can be validated in the future and used to build an L2 Korean learner corpus.
MNASR: A Free Speech Corpus For Mongolian Speech Recognition And Accompanied Baselines

Thanks to the development of deep learning and the emergence of open source data sets, automatic speech recognition (ASR) has made great strides in mainstream languages such as Chinese and English. However, the research of ASR in Mongolian and other minority languages lags far behind the mainstream, due to low attention and limited open source data sets. To promote the development of new models and new methods for Mongolian ASR, this paper releases the MnASR database which contains 345 hours of Mongolian speech signal and the corresponding transcription. MnASR is the largest publicly available and free Mongolian speech database so far. Speech recognition baselines are made public at the same time. Both the database and the accompanied baselines are free for research purpose.
Development of a High Quality Text to Speech System for LAO

Text-to-speech, or speech synthesis, transforms text input to audio signal. It has a variety of applications and is a popular research area in the field of natural language and speech processing. However, many languages in the world have not been carefully researched. This paper presents the first study about high-quality Lao language speech synthesis; despite not receiving much attention, the language has a lot of future potential with applications in areas such as customer care, reading news, GPS navigation, etc. The database has been designed, processed, and used to build an end-to-end Lao text-to-speech model. Many tests are conducted and evaluated by native speakers, then chosen as the best model to demonstrate the potential of the proposed scheme to produce a high-quality Lao language synthesis.
NASAM 2.0: Cleft-Palate Speech Assessment Application

This paper describes the incremental development of Naso-ariticulometer (NASAM) equipment for assessing children with cleft lip and palate pronunciation. The NASAM collects patients’ words and sentences, predicts phoneme sequences, and delivers pronunciation and nasality scores. This program faces a number of issues, including identifying children’s speech, applying automatic phoneme recognition to exact speech evaluation, and developing the application’s UI/UX for both children and language and speech assessors. This study focuses on enhancing acoustic modeling for Thai children aged 6 to 13 years old in the phoneme recognition module. We address the issue of determining an acceptable acoustic characteristic for children’s speech and compare acoustic model training methods. The phone error rate (PER) using collected speech from 75 cleft children and 104 normal children is used to test the acoustic models. The experimental results show that the vocal tract length normalization method provides the best PER in normal children, resulting in a 19.17 percent relative error reduction from the baseline, while Mel-filter bank and pitch performs well in cleft children, resulting in a 4.61 percent relative error reduction from the baseline. Additionally, we conducted a satisfaction poll during the function test and discovered that the most pleasing response was 42 percent.
Neural Network Models For User Attribute Extraction From Dialogues

In this paper, we present an approach to extracting user attributes represented in the form of Subject-Predicate-Object (SPO) triplets from utterances in dialogues. We treated user attribute extraction as a two-tasks process: 1) Predicate Classification and 2) Entity Generation. The predicate classification task is to determine predicates triggered in a sentence, and the entity generation task will generate related subjects and objects for each triggered predicate. Since multiple predicates may be triggered in one sentence, we formalized the predicate classification task as a multi-label classification problem. We fine-tuned a pre-trained BERT model to solve the problem. We formalized the entity generation task as a sequence-to-sequence problem and fine-tuned the T5 model on the task-specific data to create an entity generation model. Experimental results showed that our proposed method out-performs a state-of-the-art baseline model by a large margin.
Towards The Development Of Accent Conversion Model For (L1)Bengali Speaker Using Cycle Consistent Adversarial Network (Cyclegan)

The goal of foreign accent conversion (FAC) is to create a new voice with the voice identity of a given second-language (L2) speaker but with a native (L1) accent. The main motivation for this work is to solve the problem of Indians understanding foreigners’ accents and foreigners understanding Indian accents. We propose a non-parallel accent conversion (AC) method that can learn mapping from source to target without the use of parallel speech data. The proposed method is particularly advantageous in that it does not require any additional data or an alignment procedure. A cycle-consistent adversarial network is used in our proposed method. This enables the identification of an optimal pseudo pair. from nonparallel dataset. We applied our method on a speech data contained only American English and English speech recorded by Bengali speaker. As per our knowledge this is the first attempt to convert the accent native (L1 American English speaker) and nonnative (L1 Bengali speaker). We trained our model under disadvantageous condition (non-parallel data). Result shows that this approach will be useful in accent conversion field.
A formal approach for secured risk analysis in information security management

Information security management is a managerial activity that tries to keep the level of the information security risk of an enterprise under some acceptable level. The process requires the iteration of information security risk assessment and the resources needed for the effort. At the same time, business executives compete for finite resources to invest in general business activities. The executives can intentionally reduce the resource assignment for risk assessment to broaden the variance of estimated risks and then choose the lower limit to underestimate the true risks. This study proposes a model-based risk assessment approach; it guarantees that an additional, thus expected to be costly, fine-grained risk assessment always gives a refined subset of the possible incidents compared to that of the relatively coarse-grained, thus expected to be cheaper, risk assessment. In other words, estimated incidents roughly decrease with the growth of assessment cost. We expect this property to secure the risk management process from conflicts between the business executives and the security practitioners. We refer to several actual incidents to show that the conflict is the reality and then elaborate on our approach to show how it works. Our approach introduces the concept named aggregation and segregation of facts appearing in Datalog programs. Datalog is a logic programming language used for attack graph analysis, a prominent approach in model-based network security risk analysis. This study contributes to expanding the attack graph analysis to enable secured business risk assessment.
Toward Automatic Generation of Transcript From Spoken Lectures: The “Dream of the Red Chamber” Series

We present a corpus of Mandarin literature lectures. The recording device, environment, and the topics covered in the lectures are described briefly; then, we developed speech recognition and text simplification approaches for automatic transcription of the lectures. Given that the size of this corpus is relatively small, we applied transfer learning on AISHELL-1 by varying the number of transferred layers and fine-tuning the learning rates to improve the accuracy of speech recognition. Experimental results showed that a character error rate (CER) of 15.83% could be obtained. Additionally, by reducing the perplexity of the language model and the number of out-of-vocabulary words, the CER improved by another 0.29%. Further, to improve the fluency of transcription, we chose to deal with punctuation and pleonasm. Accuracy of 81% and 91.5% were reported by professional judges on adding punctuation and deleting pleonasm, respectively.