Reviewerly/exHarmony huggingface dataset:
A two-stage causality method for time series prediction based on feature selection and momentary conditional independence

Since the actual time series contain a lot of variables and the relations among them are complex. Hence, it is difficult to accurately judge cause and effect by conventional causality methods. Aiming at the problem, a two-stage causal network learning method, the feature selection stage and the conditional independence test stage, is proposed to reveal the causalities between variables and construct an accurate prediction model. In the first stage, there are two steps to perform. Firstly, a feature selection method is utilized to reduce data dimensionality by removing irrelevant and redundant variables. These variables are not only increase computational complexity, but also cover up part of the effective information, which may result in insufficient accuracy of the constructed model. Then, a global redundancy minimization (GRM) scheme is used to further refine the result of the previous step from a global perspective. In the second stage, a momentary conditional independence (MCI) test is performed to test the causalities between variables, which can accurately detect the causal network structure. Finally, an accuracy causal network and subsequent prediction model can be established based on the output of the two-stage model. In this simulations, two benchmark datasets, a coupled Lorenz system and two actual datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method can effectively analyze the causalities between variables and construct an accuracy prediction model.
Analysis and Prediction of Gold Price using CNN and Bi-GRU based Neural Network Model

In recent decades, gold has been one of the most sought-after commodities for long-term and short-term investments, as investors perceive gold as a hedge against unanticipated market occurrences. Gold can be purchased, stored, and is rarely utilized as a payment method. However, it is pretty simple to convert gold into cash in almost any currency. As gold is essential for maintaining value, investment, and national economic stability, it is undoubtedly vital to forecast the price of gold accurately. In this paper, we proposed a hybrid method for forecasting the price of gold based on the combination of 1D Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). Though CNN-GRU, CNN-LSTM, CNN-RNN based hybrid networks or, the individual CNN, Bi-GRU, and GRU provide satisfactory results, our proposed hybrid approach is more reliable since it outperforms other networks and achieves the MAE, MAPE, MedAE, RMSE, MSE, MSLE of 11.88, 0.67%, 8.41, 15.59, 242.90, $76.08\times 10^{-6}$ respectively and $\mathrm{R}^{2}$ Score of 93.56%.
Towards Efficient Segmentation and Classification of White Blood Cell Cancer Using Deep Learning

White Blood cell cancer is a plasma cell cancer that starts in the bone marrow and leads to the formation of abnormal plasma cells. Medical examiners must be exceedingly selective when diagnosing myeloma cells. Moreover, because the final judgment is dependent on human perception and judgment, there is a chance that the conclusion may be incorrect. This study is noteworthy because it creates a software-assisted way for recognizing and identifying myeloma cells in bone marrow scans. MASK-Recurrent Convolutional Neural Network has been utilized for recognition, while Efficient Net B3 has been used for detection. The mean Average Precision (mAP) of MASK-RCNN is 93%, whereas Efficient Net B3 is 95% accurate. According to the findings of this study, the Mask-RCNN model can identify multiple myeloma, and Efficient Net B3 can distinguish between myeloma and non-myeloma cells.
Review of Collaborative Intelligent Tutoring Systems (CITS) 2009-2021

This paper reviews recently published works in the emerging field of Collaborative Intelligent Tutoring Systems (CITS). The paper first provides an overview of the fields of Intelligent Tutoring Systems, Computer-Supported Collaborative Learning, and Collaborative Intelligent Tutoring Systems. We systematically search online bibliographic databases, code their research objectives, qualitatively analyze their methodology, and group papers into 3 categories according to our findings. Then we evaluate the associated systems, highlighting their main features and impacts on student learning. Finally, we identify the gaps for possible future research.
Bayesian multi-task learning using finite-dimensional models: A comparative study

Simultaneous estimation of related tasks has been widely studied in the statistics and machine learning literature, and its effectiveness has been proven in many contexts such as econometrics and bioinformatics. However, state-of-the-art approaches leveraging Gaussian processes are encumbered by high computational costs that hinder their applicability to model-based and adaptive control design. In this paper, we address this issue by approximating non-parametric multi-task models by means of trigonometric basis functions. We estimate the involved parameters in a Bayesian framework using several deterministic and stochastic approaches, and highlight their advantages within an extensive comparative study. Overall, the proposed setup is able to suitably leverage task relatedness to outperform single-task methods, especially when single datasets are small.
Improving sign prediction of network embedding by adding motif features

Sign prediction plays an important role in friendship recommendation and trust relationship prediction, so it has attracted a lot of attention from network embedding researchers in the field of machine learning. However, these algorithms may not fully exploit local structural similarity for each pair of nodes in traditional network domain, and few researchers have explored whether network structure features can supplement these embedding methods to boost the performance of sign prediction. In this study, we integrate 3-node and 4-node motifs into four network embedding algorithms, that is, we fuse motif and embedding features for sign prediction in four real undirected signed networks, and the performance can be greatly improved (up to 79.1%). Furthermore, the performance of four popular embedding algorithms are close to each other after fusing motif features, which demonstrates that motif features can compensate for the information of negative edges ignored by unsigned embeddings. Adding motif features to network embedding helps to offset the shortcoming of current embedding algorithms, that is, the inability of uncovering local structure features in classical network domain.
A deep Spatio-temporal network for vision-based sexual harassment detection

Smart surveillance systems can play a significant role in detecting sexual harassment in real-time for law enforcement which can reduce the sexual harassment activities. Real-time detecting of sexual harassment from video is a complex computer vision because of various factors such as clothing or carrying variation, illumination variation, partial occlusion, low resolution, view angle variation etc. Due to the advancement of convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM), human action recognition tasks have achieved great success in recent years. But sexual harassment detection is addressed due to presences of large-scale harassment dataset. In this work, to address this problem, we build a video dataset of sexual harassment, namely Sexual harassment video (SHV) dataset which consists of harassment and non-harassment videos collected from YouTube. Besides, we build a CNN-LSTM network to detect the sexual harassment in which CNN and RNN are employed for extracting spatial features and temporal features, respectively. State-of-the-art pretrained models are also employed as a spatial feature extractor with an LSTM and three dense layer to classify harassment activities. Moreover, to find the robustness of our proposed model, we have conducted several experiments with our proposed method on two other benchmark datasets, such as Hockey Fight dataset and Movie Violence dataset and achieved state-of-the-art accuracy.
Adversarial Attacks on Kinship Verification using Transformer

Visual kinship verification is one of the key research problems in computer vision with significant progress made in the past decade. Meanwhile, the harnessing of visual kinship models may lead to personal privacy leaking and raise people's concerns, especially the heavy social media users. One promising countermeasure is to overlay additional noise on images through the adversarial attack on kinship verification models to protect personal privacy. Motivated by the recent success of Transformer models in visual tasks, we propose a novel Transformer-based adversarial attack method named “Kinship-advTransGAN” towards the attack on kinship verification model. Essentially, Kinship-advTransGAN replaces the well-established CNN structure in conventional advGAN by TransGAN to generate adversarial samples with more sparse noise but comparable successful attacking rate. We verify our proposed method on a few open benchmarks, including FIW datasets and Kaggle Kinship Verification Challenges. Among these challenging tasks, we achieved surprisingly good performance: over 90% successful attacking rate on FIW datasets and 76.86% successful rate of attacks on Kaggle Kinship Verification Challenge, but with less visually perceivable noise on face images.
Cervical Cancer Classification Using Improved Ridge Polynomial Neural Network

Pap smear test is a standard examination for cervical cancer diagnosis. However, this method is very time-consuming and is very subjective in interpreting an image. This paper developed a system to diagnose the cervical cancer phase based on pap smear images. Five classes were investigated, namely: normal, precancerous (CIN1, CIN2, and CIN3), and malignant. The flow of the model is as follows: (i) pre-processes image using amoeba median filter and Gaussian filter (ii) nuclei detection, and segmentation (iii) extracts characteristics image using texture and shape analysis (iv) classify the pap smear image using Ridge Polynomial Neural Network pre-trained by Chaos Optimization. Based on experiments conducted, the proposed method could detect and classify the pap smear images with a sensitivity of 96.8%, specificity of 97.8%, and accuracy of 97%.
Classification of Hateful Memes Using Multimodal Models

Nowadays memes have become popular and are a convenient medium for internet communication. They can spread ideas over the internet and influence people in no time. Most memes are humorous, but some of them may also contain hatred which in some way or the other can be offensive to some people. Hence, an algorithm that identifies offensive memes on the social media platform to avoid spreading hate is required. The classification into hateful or non-hateful memes and analysis of such memes is currently an active domain for researchers. A Meme can be hateful when read along with the image and text combined, which implies that the meme may not be hateful if one pays attention to only the image or text in the meme. Therefore, there is a need for a multimodal approach that understands the relativeness of visual and language information present in the meme. Our work in this paper focuses on the multimodal classification of hate memes using fusion techniques. We have used the dataset provided by Facebook AI for its hateful memes challenge. We use the early fusion technique to combine the image and text modality to build a classifier for this project. For fusion, we have used the baseline models for classification of both image and text that are Inception v3 and BERT, respectively. And we were able to achieve 0.79 as AUC score with 63.3 percent of model accuracy.
Analysis of WBC, RBC, Platelets Using Deep Learning

Human blood composition is mainly described into three components which are White Blood Cell (WBCs), Red Blood Cell (RBCs) and platelets. The Complete Blood Cell (CBC)count is used to diagnose the health of a particular person. Proper identification of blood components is the major factor for various uncertainties and health issues in the human body. This paper deals with the analysis of different blood cells using the You Only Look Once (YOLO) framework and has been trained with a dataset of blood smear images taken from BCCD (Blood Cell Count and Detection). Diseases such as dengue, bone marrow disorder, thyroid condition, iron deficiency require blood cell count for the diagnosis. Ordinary methods used in the hospital laboratories require counting of blood cells manually using devices. This led to imprecise outcomes which were strenuous, slow and laborious. The proposed method focuses on obtaining better accuracy with YOLOv5 as compared to previous versions of YOLO models which is based on automatic detection, segmentation and count of each blood cell from blood smear images. Also, Real time implementation can take place and immediately results can be sent for further diagnosis of patient. The main objective of this paper is to identify three major categories of blood cells and improved accuracy is achieved for detection and segmentation of blood cells. The outcome of the experiment on YOLO v5s concludes that highest mAP was observed for 8 batches,75 epochs with mAP value as 93%.
Active Learning for Human-in-the-Loop Customs Inspection

We study the human-in-the-loop inspection scenario at custom offices, where an AI-assisted algorithm supports officers by recommending a set of imported goods to be inspected.If the inspected items are fraudulent, the officers can levy extra duties.The updated decisions are used as additional training data for successive iterations.Inspecting only the likely fraudulent items may lead to an immediate gain in revenue, yet it does not bring new insights for learning dynamic trade patterns.In contrast, including uncertain items in the inspection helps gradually acquire new knowledge that will be used as supplementary training resources to update the system.Based on real multiyear logs obtained from three countries, we demonstrate that some degree of exploration is necessary to cope with domain shifts in trade data.Our work demonstrates that a hybrid strategy of jointly selecting likely fraudulent and uncertain items will eventually outperform an exploitation-only strategy.
Function Approximation for Adaptive Learning of Label Distributions

Rather than traditional pattern and signal analysis problems, label distribution learning aims to predicate the probabilistic labels of appended instances. In this work, a novel approach to LDL is proposed for adaptive learning of label distributions of instances, which adopts function approximation to label predication. As a consequence, the predication of probabilistic labels of instances are able to be accomplished with self-taught optimization of approximate factorizations, and stable calculation efficiency can be achieved. Experiments on diverse artificial data sets demonstrate the proposed method is able to give stable complexities on different data sets while comparable performance is obtained, and insensitive to sizes of data sets.
Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ASC) is an interesting and challenging research task to identify the sentiment polarities of aspect words in sentences. Previous attention-based methods rarely consider the position information of aspect and contextual words. For an aspect word in a sentence, its adjacent words should be given more attention than the long distant words. Based on this consideration, this article designs a position influence vector to represent the position information between an aspect word and the context. By combining the position influence vector, multi-head self-attention mechanism and bidirectional gated recurrent unit (BiGRU), a position-enhanced multi-head self-attention network based BiGRU (PMHSAT-BiGRU) model is proposed. To verify the effectiveness of the proposed model, this article makes a large number of experiments on SemEval2014 restaurant, SemEval2014 laptop, SemEval2015 restaurant, and SemEval2016 restaurant data sets. The experiment results show that the performance of the proposed PMHSAT-BiGRU model is obviously better than the baselines. Specially, compared with the original LSTM model, the Accuracy values of the proposed PMHSAT-BiGRU model on the four data sets are improved by 5.72, 6.06, 4.52, and 3.15%, respectively.
Performance analysis of DIBR-based view synthesis with kinect azure

DIBR (Depth Image Based Rendering) can synthesize Free Navigation virtual views with sparse multiview texture images and corresponding depth maps. There are two ways to obtain depth maps: through software or depth sensors, which is a trade-off between precision versus speed (computational cost and processing time). This article compares the performance of depth maps estimated by MPEG-I’s Depth Estimation Reference Software with that acquired by Kinect Azure. We use IV-PSNR to evaluate their depth maps-based virtual views for the objective comparison. The quality metric with Kinect Azure regularly stay around 32 dB, and its active depth maps yields view synthesis results with better subjective performance in low-textured areas than DERS. Hence, we observe a worthy trade-off in depth performance between Kinect Azure and DERS, but with an advantage of negligible computational cost from the former. We recommend the Kinect Azure for real-time DIBR applications.
Clustering students’ writing behaviors using keystroke logging: a learning analytic approach in EFL writing

Abstract Feedback is an essential component of learning environments. However, providing feedback in populated classes can be challenging for teachers. On the one hand, it is unlikely that a single kind of feedback works for all students considering the heterogeneous nature of their needs. On the other hand, delivering personalized feedback is infeasible and time-consuming. Available automated feedback systems have helped solve the problem to some extent. However, they can provide personalized feedback only after a draft is submitted. To help struggling students during the writing process, we can use machine learning to cluster students who benefit the same from feedback using keystroke logs. We can apply the results in automated feedback systems that provide process feedback. In this study, we aim to find homogeneous student profiles based on their writing process indicators. We use fourteen process indicators to find clusters in the data set. We used these measures in a four-stage analysis, including (a) data preprocessing, (b) dimensionality reduction, (c) clustering, and (d) the analysis of the writing quality. Clustering techniques identified five different profiles: Strategic planners, Rapid writers, Emerging planners, Average writers, and Low-performing writers. We further validated the emerged profiles by comparing them concerning students' writing quality. The present work broadens our knowledge of how students interact with writing tasks and addresses how variations in writing behaviors lead to qualitatively different products. We discuss the theoretical underpinnings and potentials of finding profiles of students during writing in higher education.
Design of Chinese named entity recognition algorithm based on BiLSTM-CRF model

This paper implements a Chinese named entity recognition algorithm based on bidirectional LSTM (BiLSTM) and CRF model. Named entity recognition is an important part in the field of natural language processing. It is not only a typical time-series data processing problem, but also a typical sequence annotation problem. Due to the complexity of Chinese semantic ambiguity and polysemy, the task of Chinese named entity recognition is more difficult. BiLSTM uses two reverse LSTM networks to provide additional context information for the algorithm model. CRF can effectively control the conversion relationship between output sequences and further improve the recognition accuracy. In order to prevent over fitting, Dropout mechanism is also adopted in the network. The algorithm is implemented based on tensorflow platform, and the recognition rate is significantly improved compared with using single LSTM model. The experiments also verified the influence of Embedding dimension, parameter optimizer and Dropout rate on recognition accuracy.
FPGA Based Hardware Accelerator for Sorting Data

Sorting data is one of the most important processes in data processing. Fast processing is urgently needed for real time data access. Therefore, hardware accelerator is used to fasten the data processing. In this paper, we present FPGA based hardware accelerators for data sorting using bubble, selection, insertion and merge sorting algorithms. Further, we provide a fair comparison between them in terms of execution time, and area. Our implementations result in that for small data set, merge sort is the best sorting algorithm in terms of execution time. Therefore, it can be used as a parallel cooperative system with CPU for high speed data processing.
Complementary set encryption for privacy-preserving data consolidation

Privacy-preserving data consolidation is one of the prominent research topics in the big data era, mostly because of the need of consolidating data from various database communities, which are highly sensitive or competitive in nature. This leads to a new emerging problem namely how to accomplish data consolidation among different organization data sets or databases appropriately without revealing any competitive or sensitive information to non-owners. To address this issue, we first introduce a new encryption notion called “complementary set encryption” (CSE) to apply to privacy-preserving data consolidation. In this notion, to provide privacy protection, the data in one database will be encrypted under a set W prior to being sent to the other database, and a decryption key will be generated with another set Q. The data will be decrypted and consolidated into the other database, if and only if both sets W and Q are complementary. Here, the “complementary” means that the intersection of set W and set Q is empty and meanwhile the union of both is to be completed into a universal set exactly. We then describe two constructions of CSE under the public-key setting. Our first construction is designed under a weaker security notion – “payload hiding”, which only preserves the data privacy but achieves a higher performance. The second construction is a stronger security notion, which we refer to as an “attribute hiding”, which preserves both the privacy of the data and its associated set W. Finally, we provide a formal analysis to prove the security of our two constructions, followed by a theoretical performance comparison and an experimental evaluation. In particular, the first construction is of an independent interest in the context of cryptography since it was able to stand out for its efficiency as a predicate encryption scheme in the sense that it features both low communication overhead and computational costs simultaneously, and only requires a private key size of O(1), a ciphertext size of O(1), and only O(1) pairing computations.