Reviewerly/exHarmony huggingface dataset:
Utilizing Keywords Evolution in Context for Emerging Trend Detection in Scientific Publications

This paper studies the dynamics between how the representation of terms changes through time and its potential emergence as a trending topic in the future. Previous research focused on contrasting directly two of the most recent representations of detected keywords to form a basis for predicting emerging topics. We, thus, propose the Term Context Evolution approach that extends the range of comparison and analyzes multiple continuous previous representations compared to the current one to form a series that reflects the evolution of context around selected terms to evaluate potential emerging topics. We experimented with our approach to abstracts of computer science publications from 1995 to 2012 by first choosing the most frequent terms from the titles and extracting their representations using a Transformer-based pre-trained language model. Our findings reveal that the proposed method outperformed existing models in terms of recall by being able to detect critical emerging trends and points of emergence.
An integration of Pseudo Anomalies and Memory Augmented Autoencoder for Video Anomaly Detection

Video anomaly detection (VAD) has received a lot of attention from the research community in recent years. The purpose of VAD is to identify the anomalous appearance and behavior of objects in videos. Due to the difficulty in collecting anomalous data, video anomaly detection is often based on a one-class classification (OCC) problem. Among the methods, deep autoencoders have been shown to be effective for anomaly detection in video. Specifically, autoencoders are only trained on normal images during the training time, then it is expected to reconstruct or predicted well for normal frames but poorly for anomalous ones at the test time. Contrary to the expectation, abnormal frames may have a chance of being well constructed by trained autoencoders because of the powerful representation capacity of the deep neural network as well as the diversity of normal patterns. To address the issue, we propose a strategy called and shortened as PA-MAE (Pseudo Anomalies-Memory-augmented Autoencoder) which feeds pseudo anomalies into a memory-augmented autoencoder network during training time. Therefore, the proposed model is able to take advantage of both memory-based autoencoder networks and pseudo-anomaly synthesizers which can store the prototypical features of the normal frames and produce high reconstruction errors on dummy anomaly examples. Experimental results on several benchmark video datasets (i.e. Ped2, Avenue, and ShanghaiTech) demonstrate that our method outperforms some state-of-the-art memory-augmented methods as well as several recent models using pseudo-anomaly synthesizers.
An LSTM-based Approach for Predicting Resource Utilization in Cloud Computing

Predicting future resource consumption has become a significant issue as large-scale cloud computing centers surpass individual servers in popularity. Public cloud service providers can proactively assign or reallocate resources for cloud services by forecasting resource needs. This research aims to forecast the usage of resources such as the central processing unit, random access memory, and hard disk across both short-term and long-term time scales. In this paper, we propose to use Long Short-Term Memory network (LSTM) with our own approach for resources’ usage prediction in cloud workloads. The proposed approach has been evaluated and compared with other traditional approaches on predicting cloud workloads. The experimental results show that such approach provides more accurate predictions with at least two times lower loss values, measured in terms of median absolute error for both long-term and short-term prediction. This work helps the cloud service provider (CSP) to analyze and predict the workload accordingly to acknowledge over and under provisioning of the cloud resources.
Intent Detection and Slot Filling with Low Resource and Domain

This study takes an investigation on intent detection and slot filling, which are two critical tasks for spoken language understanding, of low-resource languages, for instance, the Vietnamese language. Specifically, recent joint models for intent detection and slot filling have achieved state-of-the-art performance, which has proved the strong relationship between the two tasks. In this paper, we focus on the problem of the joint models for two tasks on low resource language, in which the data resource is limited, which leads to the problem of similarity between samples (e.g., in the same domain). Particularly, we present a new method for the aforementioned issues by combining two recent state-of-the-art models in this research field such as JointBERT and JointIDSF for Vietnamese languages. The experiment on PhoATIS, the first public Vietnamese dataset, and our custom similarity for intent detection and slot filling, indicate the promising results of the proposed method.
ml-Codesmell: A code smell prediction dataset for machine learning approaches

In recent years, many studies on detecting code smells in source code have published datasets with limited characteristics, such as the ambiguity of code smell definitions leads to different interpretations for each code smell, the number of samples of the datasets is small, and the features of the datasets are heterogeneous. Therefore, comparing performance between detecting code smell models is challenging, and the datasets are often not reusable in other code smell detection studies. In this work, we propose the ml-Codesmell dataset created by analyzing source code and extracting massive source code metrics with many labelled code smells. The proposed dataset has been used to train and predict code smell using machine learning algorithms. Based on the high confidential F1-score in evaluation, the ml-Codesmell dataset demonstrates a strong correlation between features and labels. Regarding these advantages, the ml-Codesmell dataset is expected to be helpful for studies on detecting code smell using machine learning approaches in software development.
Method for Evaluating Quality of Automatically Generated Product Descriptions

In recent years, deep learning accuracy has improved. Widely diverse content has been generated automatically. Along with studies of automatic content generation, difficulties associated with evaluation methods for the generated content have become important. The evaluation methods differ among the generated content types. As described herein, we propose evaluation methods aimed at motivating readers to purchase products through automatically generated product descriptions. Specifically, after extracting and modifying the 17 evaluation items from related research, we categorized evaluation items according to three axes signifying grammar, content, and attributes. Furthermore, to demonstrate the benefits of the proposed axes, we conduct user experiments of two types using the results obtained from deep learning of three types. Experiment 1 targets subjects who have not decided what to buy. Experiment 2 targets subjects with an image of a product that they want to buy. As results, we identified the benefits of the proposed method in both cases.
Improving Khmer-Vietnamese Machine Translation with Data Augmentation methods

Machine translation has achieved significant improvements with the development of neural models. However, such approaches require large-scale parallel data, which are hard to collect for low-resource language pairs. This paper solves this problem by applying a pretrained multilingual model and fine-tuning it with a low-resource bilingual dataset. In addition, we propose two data-augmentation strategies to receive new training data, including: (i) back-translating with the dataset from the source language; (ii) translating sentences from the source language to the target one through a pivot language. The proposed approach is applied to the Khmer-Vietnamese machine translation. Experimental results show that our proposed approach gains 4.426% BLEU score higher than the Google translator model using a test set of 2000 Khmer-Vietnamese sentence pairs.
Random Generative Adversarial Networks

Generative Adversarial Networks have surprisingly shown great ability in synthesizing high-fidelity and diverse images while resolving the problem of so-called mode collapse still remains a challenge to all researches in this field. In this paper, we propose a new GANs method called Random Generative Adversarial Networks (RandomGANs), a combination of Random Forest algorithm and Generative Adversarial Networks, in which utilizes multiple discriminators where each discriminator is considered as a decision tree and each of them will be separately trained on a separated dataset that sampled without replacement from the original dataset. We empirically demonstrate (1) the quality of generated images in RandomGANs and their strong similarity to real images, (2) show how different knowledge each discriminator learns by comparing with others and overcoming the mode-collapse problem by encouraging the Discriminator to discover a wide range of different modes in the input data, and (3) indicate how our model betters in stabilizing the training process between generator and discriminator part.
Automated Journalism Based on Sports Analysis

Despite increasing amounts of data and ever improving natural language techniques, work on automated journalism is still relatively scarce. Reporting news requires significant amount of time and effort of the journalists and they need more domain knowledge as well. If this editorial process is automated, more working time of journalists can be reduced considerably while they can spend more on in depth reporting. Overall, this recent development is sure to grow, as it provides the industry with more accurate, up-to-date, and effortless reporting. Automated journalism is rising in the news world, especially in sports coverage where structured data is abundant and readily available. Algorithms churn out full length articles in masses and practically instantaneously, using big data, and this bloat the sheer volume of articles available to news readers. Noting the importance of perceived credibility and readability in sports news articles in particular, as well as the pertinence of text objectivity and by lines in automated journalism, an experiment was developed to understand how these factors interact. Automatically generated text can also be easily customized to reflect preferences that are particular to players’ style and level of experience of the viewers.
Classification of EEG Signals Based on GA-ELM Optimization Algorithm

There are many unpredictable problems in motion visualization and observation in BCI system, such as interference from external noise and visual fatigue of subjects. These problems seriously affect the performance of the whole BCI system. To solve this problem, this paper designed the experimental paradigm of imagination and observation, and built the eeg acquisition platform by combining UNITY and MATLAB. Ten healthy subjects participated in the experiment, which was divided into two stages: in the first stage, each subject was required to perform five experiments at the same time. In the second stage, after an interval of more than one month, the eeg signals of the 10 subjects were collected again (the same experimental paradigm). In pattern recognition and Hilbert huang transform time and frequency domain characteristics of extreme learning machine recognition classification based on genetic algorithm, and using the basic method of SVM algorithm and ELM comparison between the results and draw HHT and optimization algorithm of single collection of experiment acquisition signal has a significant effect, high classification rate can reach 85.3%.
A Mask Image Recognition Attention Network Supervised by Eye Movement

Mask image modeling has become a hot topic in the field of computer vision, which is widely used in the Image recognition of semantic breakage. The interference in the long-distance observation leads to the low ISAR (Inverse Synthetic Aperture Radar) imaging quality for aerospace vehicles. Mask image can simulate and close to the real ISAR image, and adequate simulation mask images can improve the robustness of the classifier. However, the classification performance of DNN for ISAR images is insufficient, especially for the mask images with high interference and high masking rate. To improve the machine recognition ability with the help of human brain recognition process, we propose Gaze Attention Supervise Network, which the fixation information is applied to assist in generating the attention of the network feature layer by attention supervised module. We apply eye-tracking label in mask image dataset as the supervision of network training. The proposed network fully learns the eye gaze region information to generate attention view. The results showed that classification performance improved 10.62% ~ 15.22%, especially in the small training dataset with high masking rate.
Multi-source Domain Adaptation Based on Data Selector with Soft Actor-Critic

Multi-source domain adaptation (MDA) aims to transfer the knowledge learned from multiple-sources domains to the target domain. Although the source domains are related to the target domain, the difference of data distribution between source and target domains may lead to negative transfer. Therefore, selecting the high-quality source data is conducive to mitigate the problem. However, the existing methods select the data with uniform criteria, neglecting the variety of multiple source domains. In this paper, we propose a reinforced learning Data Selector with the Soft Actor-Critic (DSAC) algorithm for MDA. Specifically, the Soft Actor-Critic (SAC) algorithm has two Q-value Critic networks, it can better judge the performance of the data. Select the data in multi-source domains to migrate with our target domain, and use the difference in loss both before and after the model to determine the quality of the data and whether it is retained. Extensive experiments on the representative benchmark demonstrate that our method performs favorably against the state-of-the-art approaches.
An Axiomatic Analysis for Object Detection and Recognition Using Deep Learning

In the modern era, object detection and analysis is one of the crucial tasks. Numerous challenges have to be faced by the researchers to analyze the objects in images and real-time videos. Such analysis is required in multiple domains such as monitoring health, self-driving including detection of an anomaly, and many more. With the advanced usage of deep learning (DL) models and GPUs, the efficacy of object detection has been improved very well. Therefore, in this paper, we have provided a detailed study of both cases (images and videos) to detect specific objects using DL models. The existing approaches have been compared using on global wheat-head detection dataset to check the performance for object detection and recognition at different stages. From this analysis, we can say that DL is a backbone of computer vision to detect and recognize the various objects in a real-time scenario.
Towards enabling learnware to handle heterogeneous feature spaces

The learnware paradigm was recently proposed by Zhou (2016) with the wish of developing the learnware market to help users build models more efficiently by reusing existing well-performed models rather than starting from scratch. Specifically, a learnware in the learnware market is a well-performed pre-trained model with a specification describing its specialty and utility, and the market identifies helpful learnware(s) for the user's task based on the specification. Recent studies have attempted to realize a homogeneous prototype learnware market initially through Reduced Kernel Mean Embedding (RKME) specification, which requires all models in the market to share the same feature space. However, this limits the application scope of the learnware paradigm because various pre-trained models are often obtained from different feature spaces in real-world scenarios. In this paper, we make the first attempt to enable the learnware to handle heterogeneous feature spaces. We propose a more powerful specification to manage heterogeneous learnwares by integrating subspace learning in the specification design, along with a practical approach for identifying and reusing helpful learnwares for the user's task. Empirical studies on both synthetic data and real-world tasks validate the efficacy of our approach.
Deconstructive Human Confront Acknowledgment Utilizing Profound Neural Arrange

Face reconstruction or face optimization as a biometric, has a several advantages in forensic application. The size and structure of face are static of an individual. For reliable human face image reconstruction, the implementation system requires huge facial image datasets. Further the assessment of the system should be done by employing a testing procedure. This paper deals with the analysis and rectification of human face images for reconstruction and optimization of human face images. The advantage of using input human face image for reconstruction in forensic application and automatic face recognition system is that they are free from wide variety of poses, expression, illumination gestures and face occlusion. The whole research is divided into two phases; in the first phase reconstruction of destructed part of human face image is being done with template matching. Second phase deals with deep neural network applications to match the image carried out in phase one. The proposed algorithm is used to reconstruct the image and at the same time, reconstructed image is used as test image for biometrical face recognition. After reconstruction of image, it is examined with various well-known algorithms (SVMs, LDA, ICA, PCA & DNNs) of face recognition system for the evaluating the performance of speed, memory usage and metrics of accuracy.
Adaptive Blockchain Solution to Fight Against Financial Corruption in an Organization

This Blockchain technology is one of the most promising new technologies, having the potential to digitally transform a wide range of systems for better security, autonomy, transparency, auditability, speed, cost savings, and efficiency. These capabilities make blockchain the optimal solution to combat corruption, the issue plaguing society from within. The paper proposes a blockchain model capable of handling finances in any organization by adapting to its financial structure. It has the ability that it can be tailored by the organization on-demand as the organization’s structure modifies from the addition of employees or projects under it. Rather than storing entire data on-chain, the blockchain model stores most of the data, that are constantly being updated and created, off-chain and feed them to the blockchain using Chainlink External API. This helps in making the implemented solution scalable and efficient in use for real-life applications.
Performance Analysis of Reversible Full Adders in Noisy Intermediate Scale Quantum (NISQ) Devices

Quantum computation harnesses the power of quantum physics to create new and disruptive capabilities. A high level of quantum readiness to explore the novel application areas of the quantum paradigm is essential for a significant impact. A more resilient foundation of quantum cryptography-based emerging computation starts with investigating the performance analysis of reversible full adder. In this work, reversible equivalents of reversible full adder are designed and simulated by utilising elementary quantum gates like NOT, CNOT, and Toffoli gate. The simulation outcomes are then compared with the real backend results obtained from the IBM quantum experience, and the superior design with maximum percentage outcomes is reported. Both the ideal and noisy conditions are considered for experimental setups. The mitigation results of measurement error are also examined for the designs of reversible adders, and the improvements are summarised.
Strong dispersion property for the quantum walk on the hypercube

We show that the discrete time quantum walk on the Boolean hypercube of dimension $n$ has a strong dispersion property: if the walk is started in one vertex, then the probability of the walker being at any particular vertex after $O(n)$ steps is of an order $O(1.4818^{-n})$. This improves over the known mixing results for this quantum walk which show that the probability distribution after $O(n)$ steps is close to uniform but do not show that the probability is small for every vertex. A rigorous proof of this result involves an intricate argument about analytic properties of Bessel functions.
Crowd Management System Based on Hybrid Combination of LSTM and CNN

Automatic recognition of violence and nonviolence activities in the crowd management system is a broad area of interest in today’s scenario. In this paper, we propose a hybrid combination of the Convolution Neural Networks (CNNs), and Long Short-Term Memory (LSTM) model to recognize violence/nonviolence activities in a crowded area. In the proposed approach a stream of video is applied to a pretrained Darknet-19 network, then a CNN with LSTM network is used to extract spatial and temporal features from the video. In the end, these spatial features are applied to a fully connected layer to identify the violence/nonviolence condition. The experimental results show that 98.1% accuracy was achieved in the case of video, and 97.8% accuracy was achieved in the case of the image frame by our proposed violence/nonviolence detection model.
Machine Learning and Web Integrated Chatting Forum Which Detected Mental Health of the User

Nowadays in this 21st century, youth is mainly leaning on chatting forums instead of real-life conversations trying to convey their emotions to their close friends with the help of texts, emojis, etc. On the contrary, the receiver may find it difficult to understand the emotions due to mistyped texts, misunderstandings, or any other issues. Henceforth, this bafflement may lead to more depression, and anxiety and may lead to serious threats to life like suicides. Among all the reasons, almost one-half of the people attempt suicide due to depression, anxiety is the main reason as other people cannot understand them. To overcome this problem of the understanding mood of the people and motivating them, the proposed model can be one of the best applications which can help the depressed user to overcome his/her stress, anxiety, etc. with the help of motivational quotes. The proposed model is a chat-based forum that is designed to monitor the mental health of the user the model consists of two parts: (i) Web Development; (ii) Machine Learning. This application is built with a Machine Learning algorithm and integrated with web development. Web scraping and creation of datasets have been done with the help of ‘Tweet’ and ‘Pandas’. ML model is trained using ‘ScikitLearn’. Further, the Machine Learning model is integrated with the chat-based Forum web application with the help of ‘Flask’.