Reviewerly/exHarmony huggingface dataset:
3D Facial Biometric Verification Using a DNA Sample for Law Enforcement Applications

A large majority of violent crimes such as homicides, sexual assaults, and missing person cases are not solved within a reasonable timeframe and become cold cases. The ability to predict a person’s facial appearance from a DNA sample may generate important investigative leads and provide an unprecedented advancement in criminal investigations. To achieve the above goal, it is first essential to substantiate, model and measure the intrinsic relationship between the genomic markers and phenotypic features. In the first step, we have standardized the 3D face scans using a widely used 3D data format - CoMA. The standardization was followed by its projection into a low-dimensional latent embedding space. The second step was to reduce the dimensionality of the genetic space. The dimensionality reduction was achieved by performing Principal Component Analysis on the genomic markers to generate compact genomic properties. A simple multi-layer perceptron was trained to classify an ensemble of facial embeddings and genomic properties into genuine and imposter pairings. The classification model could match the DNA with the given 3D face with an average Area Under the Curve score of 0.73. The introduction of hand-picked genomic markers was an important contribution toward improving the final AUC score. Furthermore, results indicated that incorporating additional phenotypical properties such as sex and age leads to better verification. Thus, this study represents an important milestone toward building a functional machine learning pipeline capable of predicting facial appearance and other visible traits from a DNA sample.
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents

Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typical over-fitting on the training environment. RL agents are often capable of solving environments very close to the trained environment, but when environments become substantially different, their performance quickly drops. When agents are retrained on new environments, a second issue arises: there is a risk of catastrophic forgetting, where the performance on previously seen environments is seriously hampered. This paper proposes a novel approach that exploits an eco-system of agents to address both concerns. Hereby, the (limited) adaptive power of individual agents is harvested to build a highly adaptive eco-system.
Implementation of load balancing algorithm based on flink cluster

In real-time computing scenarios, Flink is different from the micro-batch processing idea of Storm and Spark, and truly realizes the stream-batch integration, which is more efficient and fast. However, the default resource scheduling algorithm for heterogeneous clusters leads to local load imbalance and affects task execution time and system throughput. To solve this problem, an improved TTNS algorithm is proposed to improve utilization of cluster resources by classifying task types and dynamically monitoring node resources. Experiments showed that the TTNS scheduling algorithm can reduce the processing time by about 10% on average, increase the system throughput by more than 16% compared with the existing default scheduling algorithm, and make more efficient use of computing resources in clusters.
Recognition of Disyllabic Intransitive Verbs and Study on Disyllabic Intransitive Verbs Taking Objects Based on Structure Retrieval

Verb-object constructions have always been the focus of Chinese grammar research. In Chinese, it has been agreed that transitive verbs take objects, while intransitive verbs taking objects are often confusing. There are very few studies addressing intransitive verbs taking objects. In particular, no investigations have been carried out on the quantitative analysis of the ability of disyllabic intransitive verbs taking objects of different structures and object semantics. Therefore, this study focused on the phenomenon of disyllabic intransitive verbs taking objects and made use of large-scale corpus to investigate the disyllabic intransitive verbs and their objects in order to enrich the understanding of unconventional verb-object constructions in the language. It first proposed a semantic role classification system on the basis of previous studies, and defined the word structure types, as the basis for judging the verb structure types and object semantic types in intransitive verb with object structure. Based on the BCC corpus, this study analyzed the top 4237 high-frequency disyllabic verbs in the fifth edition of Modern Chinese Dictionary. It used structure retrieval to complete the recognition of intransitive verb and its object acquisition, and artificially identified the verb structure types and object semantic roles. It was found that verb-object intransitive verbs have the strongest ability to take objects and the role of “quantity” is the semantic role that most often serves as the object of disyllabic intransitive verbs.
Leveraging ResNet and label distribution in advanced intelligent systems for facial expression recognition

With the development of AI (Artificial Intelligence), facial expression recognition (FER) is a hot topic in computer vision tasks. Many existing works employ a single label for FER. Therefore, the label distribution problem has not been considered for FER. In addition, some discriminative features can not be captured well. To overcome these problems, we propose a novel framework, ResFace, for FER. It has the following modules: 1) a local feature extraction module in which ResNet-18 and ResNet-50 are used to extract the local features for the following feature aggregation; 2) a channel feature aggregation module, in which a channel-spatial feature aggregation method is adopted to learn the high-level features for FER; 3) a compact feature aggregation module, in which several convolutional operations are used to learn the label distributions to interact with the softmax layer. Extensive experiments conducted on the FER+ and Real-world Affective Faces databases demonstrate that the proposed approach obtains comparable performances: 89.87% and 88.38%, respectively.
Review of Metaheuristic Techniques for Feature Selection

Due to the rise of high-dimensional datasets in various sectors, feature selection is one of the most important issues. The primary goal of the feature selection problem is to lower the dimension of the feature set while retaining the performance accuracy. Several techniques of feature selection have been developed to obtain the optimal subset of features. To obtain optimal features, metaheuristics are modern optimization techniques that are used by the research community. In this paper, four groups of metaheuristic techniques have been identified based on their behavior. The classifier name, datasets, and assessment metrics for the metaheuristic methods used to solve the feature selection challenges are provided. After reviewing the papers, difficulties and problems are also observed while trying to use various metaheuristic methods to find the best subset of features. For those researchers who desire to continue their work on creating or refining metaheuristic techniques for feature selection, several research gaps are also mentioned.
Analysis of Bao-Zhou-Chen-Liu’s Hybrid Chaotic System

One of the key encryption techniques commonly used nowadays in a variety of multimedia security applications is the hybrid chaos-based approach. The effectiveness of a hybrid chaotic system proposed by Bao-Zhou-Chen-Liu for multimedia encryption is examined in the paper. The effectiveness is evaluated by analysing the profile, trajectory, sensitivity, Lyapunov exponent, bifurcation, key space, entropy, zero–one test, and performing the randomness tests. Analysis reported in the paper reveals that Bao-Zhou-Chen-Liu’s hybrid chaotic system (BZCLS) has a number of flaws including low sensitivity, small chaotic range, weaker ergodicity, smaller key space, failure of zero–one test, and several NIST randomness tests. Because of such reported drawbacks and shortcomings, BZCLS is not sufficiently secure and is unsafe to employ for information security applications.
Dignet: A Deep Learning-Based Efficient Digit Recognition System

Character recognition through handwritten documents’ images has got more interest in the pattern recognition community, due to its associated real-time applications. For handwritten digit recognition, depending on a classification technique, character recognition and extraction of features are performed. For the recognition of handwritten digits, previous techniques lacked high accuracy and processing speed. The proposed model aims to make the route to digitization more obvious by delivering high accuracy and speed. The system of digit recognition is implemented using a convolutional neural network (CNN) with a rectified linear unit (ReLU) activation function. The proposed CNN based architecture is well developed for MNIST digit classification accuracy with appropriate parameters. The system is also evaluated for different layers of CNN. Handwritten digits are recognized using computational methods. For handwritten digit recognition, the current study used a neural network using convolutions as a classifier, MNIST as a set of data with appropriate training and assessment criteria, and an ensemble model in combination with data augmentation technique. The approach achieves a level of accuracy of 99.70%, which is greater than previously presented techniques.
A Study of Nominal Verbs in Modern Chinese Based on Shannon-Wiener Index——Case Studies on “Bianhua” Words

Part of speech (POS) has always been a significant problem in Chinese grammar research, and the research on nominal verbs is especially hot and complicated. It not only relates to the establishment of dictionary sense, but also involves the specification of POS tagging in language knowledge engineering, which is of great significance. Based on the concept of “entropy” in information theory, this paper applies Shannon-Weiner index, a measurement tool of information diversity, to linguistic research, which is called “word pluripotency index”. At the same time, based on the BCC corpus of Beijing Language and Culture University, this paper studies the lexical pluripotency of five words under the “变化(Bianhua, change)” sense category. The experimental results demonstrate that the word pluripotency indexes of these words are all bigger than 0.6, and reveal the uniformity of word distribution under different word classes and the degree of verb-to-noun shifting. This paper also explores the significance of the word pluripotency index to the establishment of dictionary sense and the tagging of word class.
MSDD: A Multimodal Language Dateset for Stance Detection

Stance Detection is the task of automatically determining whether the author of a text is positive, negative, or neutral towards a given target. Correct detecting stance is conducive to false news detection, claim validation, and argument search. Detecting stance from certain types of conversation, especially multimodal conversation is an interesting problem which has not been carefully explored. In social interaction, people usually express their stance on instance, which is produced in a multimodal manner, through the usage of words (text), gestures (video) and prosodic cues (audio). Stance detection is an established research area in NLP, but in a multimodal context it is an understudied area. In this paper, we present MSDD, a novel multimodal dataset for stance detection, to explore multimodal language for expressing stance in conversation. We conducted a series of experiments on MSDD, and the result shows that multimodal information indeed improves the dialogue stance detection to some extent, but the fusion of the multimodal language needs to be enhanced.
Development of STEM Education in China

The purpose of the article is to explore the development of STEM education in China in a comprehensive manner.China has been focused on developing the STEM education system that defines the upgradation of learning methods to prioritize the science, technology, engineering, and mathematics subjects.This learning method has an impact on the society of the country along with the international community.Skill development, according to the 21st century, establishes a bridge between the gender gap, enhancing the management system along with economic benefits.The STEM education system has impacted the development of the future generation and the benefits in daily life.Besides this, the challenges faced by the education system create obstacles to adopting the educational system.The STEM education system also has to face some barriers, such as infrastructure challenges and awareness of the students.Some beneficial strategies have been discussed over here to resolve the challenges.The modification of the infrastructure, the development of the pedagogical approaches, and the promotion of the STEM education system enhance the student's interest in the STEM education system and can be implicated in developing STEM education learning methods.The study's conclusion has summarized the whole topic of the study with proper justification.
Enhancing the Performance of WSD Task Using Regularized GNNs With Semantic Diffusion

The ambiguity of a polysemous word, regardless of its language, is referred to as the ability to have more than one meaning.Many techniques from Word Sense Disambiguation (WSD) have been used to clarify the ambiguity.With these techniques, the correct sense of a word according to its particular context can be determined.In English, there exist many ambiguous words.For example, the word cell may have different meanings based on its context.For instance, it may mean the basic structural unit of all organisms in biology domains, or it may mean a device that delivers an electric current as the result of a chemical reaction in textual materials about technology or electricity, or it completely means another different thing: a room where a prisoner is kept in textual materials about prison.In this study, to enhance the performance of the WSD task, we develop a methodology with Graph Convolutional Neural Networks (GCN), including normalization, thresholding, and regularization modules.We attempt to improve the traditional Text GCN algorithm with a semantic diffusion process, which increases the classification performance of the WSD task.As far as we know, there is no work for such a comprehensive classifier for the WSD task in English.To show the effect of the suggested model, we performed experiments on the SensEval dataset, which is a very popular dataset and benchmark in the WSD domain.The experiment results show that the regularization effect and diffusion process in GCN and Text GCN architectures are powerful strategies for the WSD task.
Event Detection from Cricket Videos Using Video-Based CNN Classification of Umpire Signals

Cricket is a long duration game. It can last for several hours or even days. Due to cricket’s lengthy nature, it becomes indispensable for viewers to have the option of watching selected interesting events from a cricket match. As cricket videos are unscripted in nature, detecting key events from cricket videos is a big challenge. In this paper, we propose a method to detect 5 key events, namely FOUR, SIX, OUT, NO BALL and WIDE from cricket videos by recognizing the umpire’s signal. Analysis of umpire signals is performed using a pretrained Convolutional Neural Network (CNN) architecture, namely I3D. A new dataset, containing 504 videos and 2000 images from cricket matches, is introduced in this work for the detection of key events in cricket. For the tasks of umpire frame detection and umpire signal recognition, test accuracies of 97.76% and 86.14% are achieved respectively. Employing the proposed framework on official cricket match videos, we achieve precision, recall and F1 scores of 95.23%, 86.95% and 90.90% respectively. Furthermore, by evaluating performances of both proposed video-based approach and existing image-based approach on novel cricket videos, we can affirm that the proposed approach outperforms the image-based one.
Bengali Handwritten Digit Recognition using CNN with Explainable AI

Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily under-stand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.
An Unsupervised Pairwise Comparison Learning Approach With Adaptive Network Structure for Equipment Health Quantitative Assessment

Accurate health assessment of large and complex equipment is essential to ensure their safety, availability, and affordability. Existing machine-learning-based approaches for health assessment face the challenges of difficult access to high-quality labeled data, large variation in operation for different equipment under complex operating conditions, and data-intensive and time-consuming network training. In this article, we propose an error minimized pairwise comparison learning approach (EM-PASCAL) that uses a lightweight network to achieve equipment health assessment under unsupervised conditions. Specifically, a lightweight network model with variable structure inspired by error minimized extreme learning machine (EM-ELM) is designed. Then, we propose EM-PASCAL, which makes full use of the nonincreasing characteristic of equipment health state, to identify the appropriate network structure dynamically and calculate the corresponding network parameters efficiently. The proposed method is expected to achieve promising evaluation results with a low computational effort. Experimental studies using publicly available dataset show that our approach not only enables quantitative equipment health assessment under unsupervised conditions but also offers advantages in terms of evaluation accuracy and computational effort compared with existing benchmarks of machine learning models.
On using the DIET architecture for sentiment analysis and emotion detection

The Dual Intent and Entity Transformer (DIET) architecture has recently been proposed to perform intent classification and entity recognition in conversational agents. In this paper, we show that this architecture is also effective at other common tasks, such as sentiment analysis and emotion classification. The results have been validated in 4 different datasets and they show that DIET exhibits a comparative performance to other state-of-the-art methods, at the same time it provides a low code and fully configurable alternative that can be easily trained and deployed by using the Rasa conversational toolkit.
Anti-Rounding Image Steganography With Separable Fine-Tuned Network

Image steganographic methods based on encoder-decoder model with end-to-end network architecture recently have been proposed. However, in steganographic applications, the feature map (called stego matrix) generated by the encoder needs to be rounded as a real stego image for the receiver. The loss of precision by rounding stego matrix leads to the decline in the accuracy of extracted secret messages. The challenge of using end-to-end network to preserve robustness against rounding operation is that it is non-differentiable. In this paper, we propose an anti-rounding image steganography method with separable fine-tuning network architecture which includes the joint training stage (JT-stage) and the separable fine-tuning stage (SF-stage). Firstly, in JT-stage, an embedded generator and a stego matrix extractor are jointly learned without rounding operation. Utilizing concatenation in embedded generator can realistically fuse cover image and secret messages. And the multi-scale fusion block and residual dense block in stego matrix extractor can make secret messages more correctly decoded. Moreover, the discriminator is constructed by generative adversarial nets (GAN) in JT-stage to effectively improve the authenticity and steganalysis security. Then, in SF-stage, the embedded generator is frozen, and the stego matrix is obtained and rounded as a stego image. A stego image extractor is constructed by fine-tuning the layers of the stego matrix extractor to improve the accuracy of message extraction. As the loss will not backpropagate in the embedded generator, the non-differentiability of rounding operation can be offset. Experiments show that the proposed separation fine-tuning network is robust to rounding operation, and effectively reduces the degradation of the image quality and steganalysis performance.
A Novel Position-based VR Online Shopping Recommendation System based on Optimized Collaborative Filtering Algorithm

This paper proposes a VR supermarket with an intelligent recommendation. It consists of three parts: VR supermarket, recommendation system, and database. The VR supermarket provides a 360-degree virtual environment for users to move and interact in the virtual environment through VR devices. The recommendation system will make recommendations to the target users based on the data in the database. The intelligent recommendation system is developed based on item similarity (ICF), which solves the cold start problem of ICF. This allows VR supermarkets to present real-time recommendations in any situation. The VR supermarket not only makes up for the lack of user perception of item attributes in traditional online shopping system but also improve user shopping efficiency through an intelligent recommendation system. The application can be extended to enterprise-level systems and can add behavioral and voice interaction with NPC, adding new possibilities for users to do VR shopping at home.
Cognitive Ability Classification using On-body Sensors

This work presents an on-body sensor setup to keep track of the learning progress while performing cognitive activities in formal education. The proposed approach uses the sensor collected data for cognitive ability classification, i.e., confidence score, cognitive load, and expertise, while attempting to solve physics-related problems. Data is collected using a combination of instruction material, an eye-tracker, and a sensor pen to capture the cognition progress. The collected data is processed and transformed to feature representation using a novel feature-set presented in this paper for learning analytics. The used features help to highlight the difference in learner’s cognitive abilities and enable teachers to cater to individual needs and requirements, an essential aspect of need-based learning.