Reviewerly/exHarmony huggingface dataset:
YouTube Comment Analysis Using Lexicon Based Techniques

YouTube is used to watch music videos, comedy shows, how-to guides, recipes, hacks, and more. As of February 2020, more than 500 h of video were uploaded to YouTube every minute. This equates to approximately 30,000 h of newly uploaded content per hour. The amount of content on YouTube has increased dramatically as consumers’ appetites for online video have grown. Indeed, the number of video hours uploaded every 60 s increased by roughly 40% between 2014 and 2020. The direct means of user review for this content is the comment section. To survive this cut-through competition, the content creators should constantly check up on their viewers’ opinions, their reviews, and their sentiments toward the video. Although comments provide a direct means of feedback, the YouTuber cannot actually read all those comments. There may be times when he wants to know the drawbacks of the video. This paper proposes a dashboard that assists the content creators in actually looking at the positivity and negativity they have gained through the video. We opted for lexicon-based techniques over traditional classification to classify the comments into various categories. This project is a boon to the content creator’s ability to increase his viewership.
Value-Added Tax Fraud Detection and Anomaly Feature Selection Using Sectorial Autoencoders

The intentional alteration of a tax return form with the intent to reduce one's tax base is known as tax fraud. Underreporting, which entails filing a tax return with a lower tax base by either raising purchases or lowering sales, is one of the most widespread types of tax fraud. Such an action weakens government spending by reducing government revenues. Since there are not enough resources or auditors to handle the situation, tax authorities must come up with low-cost solutions. Therefore, one of their top priorities should be identifying tax fraud. The vast bulk of research on tax fraud detection is based on supervised machine learning techniques that make use of the findings of tax return audits. Unfortunately, access to audited and labeled tax returns is quite restricted because it is an expensive and time-consuming process. This places severe restrictions on supervised machine learning techniques. The work in this paper focuses on finding solutions to these constraints. We specifically outline our method for finding anomalies in tax returns using stacked autoencoders (SAEs), along with a probability distribution of the suspicious values for each field on the tax return form. By comparing the outcomes of our method with two existing anomaly techniques that have been utilized in the literature, we show how well our model can identify current tax fraud schemes.
Detection SQL Injection Attacks Against Web Application by Using K-Nearest Neighbors with Principal Component Analysis

Web applications are exposed to many attacks, including SQL injection attacks, cross-site scripting, etc. This study will focus on attacks related to SQL-i. SQL-i injection leads to loss of confidentiality, integrity, and availability of data for users or organizations, as a result of which unauthorized persons have to access, update, and delete the user’s database, which leads to many risks at the individual or institutional level. Many methods for detecting SQL injection attacks include static analysis, dynamic analysis, and machine learning techniques. As a result, preventive measures must be implemented to combat the increased risk of SQL injection. This paper proposes using a model to detect these threats by applying machine learning algorithms, precisely the improved K-Nearest Neighbor algorithm, as the primary injection detection mechanism. Experiments show that applying the optimized K-Nearest Neighbor model with principal component analysis produces a dataset with significant advantages that improve model accuracy. After using the proposed model, the results showed good accuracy of 96.75% and time complexity, even with a difference in the number of features in the dataset used.
Some Methods for Digital Image Forgery Detection and Localization

Digital images are a critical wellspring for information. However, a forged image has the potential to spread wrong information and thus may defeat the cause. This resulted in wide research interest in developing image authenticity mechanisms. To be specific, digital image forgery (DIF) detection is an open research area. This paper first provides an overview of (DIF) and its consequences to society at large. Next, image authentication techniques developed by us in the recent past are briefly discussed. In the end, key future research directions are listed with concluding remarks.
A CIF-Based Speech Segmentation Method for Streaming E2E ASR

Long utterances segmentation is crucial in end-to-end (E2E) streaming automatic speech recognition (ASR). However, commonly used voice activity detection(VAD)-based and fixed-length segmentation methods may lead to long segments and semantic incompleteness, affecting the user experience and ASR performance. In this paper, we propose a speech segmentation method for streaming E2E ASR to solve the above issues. Both the decoder's dependence on acoustic information and the human average breath frequency are used for judging segment boundaries. Frame-level decoder's dependence information is provided by the Continuous Integrate-and-Fire (CIF) predictor, which optimizes jointly with ASR to guarantee a more suitable segmentation for ASR. Besides, the proposed method does not increase the model parameters and real-time factor (RTF). The experimental results show that our method can accurately detect the pauses in speech, and the segment usually contains relatively complete semantic information. Compared with VAD-based segmentation, 53.5% latency reduction and 3.7% CER reduction relatively are achieved.
Incorporating Co-Visibility Reasoning Into Surface Depth Measurement

Multi-view stereo (MVS) aims to measure the precise surface depth of a scene from observations at multiple photography angles and then densely reconstruct its 3D geometry information. Learning-based MVS approaches have been dominantly popular for their robustness to low texture areas and non-Lambertian surfaces. However, most existing methods focus on estimating depth maps for input images by constructing global cost volumes and designing ingenious yet large variance-based 3D-CNN networks for cost volume regularization. Such approaches ignore the co-visible relation embedded in multiple views, resulting in heavy computation, erroneous cost aggregation from invisible views, and finally inaccurate 3D reconstruction results. In this paper, we propose a Co-visibility Reasoning Multi-View Stereo Network (CR-MVSNet) to explore the co-visible relations hidden in multiple views for reliable multi-view similarity measurement and efficient reconstruction. Precisely, the proposed Co-visibility Reasoning Cost Aggregation (CRCA) module includes the adaptive inter-cost volume aggregation via mining the uncertainty of co-visibility relations in multiple views and the adaptive intra-cost volume aggregation by exploiting spatial contextual information. Moreover, the cost volumes are constructed via the proposed global-to-patch manner to speed up computation. Experimental results show that our approach achieves the best overall performance on the DTU, Tanks and Temples, and ETH3D-test datasets over recent state-of-the-art MVS algorithms. The consistently favorable results on three datasets with completely different depth ranges proved the superiority and generalizability of CR-MVSNet.
Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm

In order to estimate the power system accurately and identify anomaly detection in real time, an identification method of anomaly detection in power system state estimation based on Fuzzy C-means algorithm is proposed. Considering the problems of scale and redundancy of power system measurement data, effective measurement data of the power system is extracted by the principal component analysis method. On this basis, the power system state estimation model established by particle swarm optimization support vector machines is used to judge the operational state of the power system. An anomaly detection identification method based on fuzzy C-means algorithm is proposed to cluster the measured data and identify the anomaly detection of power system. The experimental results show that this method can accurately estimate the state of the power system and has the highest identification accuracy for anomaly detection compared with similar methods. When the equivalent measurement data is affected by noise, the identification delay of this method for anomaly detection in a power system is 1 s, and the real-time performance is high.
PPUP-GAN: A GAN-based privacy-protecting method for aerial photography

With the rapid development of unmanned aerial vehicles (UAVs), aerial photography has also emerged, which brings potential risk of privacy leakage. It is necessary to balance the privacy protection and utility of aerial photography data in UAVs. To address the issue, a GAN-based privacy-protecting and utility-preserving method called PPUP-GAN is proposed. In the PPUP-GAN, the object of interest and background of aerial photography can be defined and processed separately. Then, the balance of privacy-protecting and utility-preserving can be innovatively achieved by maintaining the content of the objects of interest while changing the content of the privacy-related background. Specifically, the image-to-image transformation method is adopted to reduce the risk of privacy leakage of background. In this way, the processed aerial photography can still be employed for subsequent image analysis. We evaluate our method on a real-world UAV image dataset. Experimental results show that our methods can greatly reduce the probability of bystanders being detected in the background. And the security analysis shows that the PPUP-GAN has the ability to resist model inversion attacks.
Traffic Signal Scheduling using Machine Learning

Within the past few years the number of vehicles increased drastically and therefore the traffic of vehicles became a major issue in urban as well as in rural areas. Major traffic is happening in the area where many roads do intersect with each other. Our existing traffic signal is not real-time and it is run according to how it is programmed earlier irrespective of traffic. To avoid traffic, traffic signals should give the priority to the road that has the maximum density of vehicles. By doing this we can pass the maximum number of vehicles in a certain period of time. This type of signal acts according to the real-time situation, and take a decision smartly. Hence this system is also called a smart traffic light system. The purpose of this study is to get the traffic situation on the roads in real-time and acts accordingly. Using a web camera that should be mounted on the signals, we can get real-time footage of the roads and by using image processing methods, we can determine the densities of vehicles on each road. Signals which are programmed priorly or wrong signal scheduling was found to play the greatest role in causing vehicle traffic. This smart traffic signal scheduling system is definitely a better option in comparison with existing traffic signal scheduling as it is taking the decision according to the traffic situations.
A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

Abstract The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation.
Fuzzy-EPO Optimization Technique for Optimised Resource Allocation and Minimum Energy Consumption with the Brownout Algorithm

Cloud Computing is an eminent and reputable agenda which relies on large-scale distributed processing to provide access to their resources and services. In the cloud environment a rigorous management system is mandatory to collect all information regarding task processing levels and proving impartial resource provisioning through the levels of Quality of Service (QoS). These concerns can be settled by employing a meta-heuristic optimization-based resource management. Subsequently, this paper presents a Fuzzy Emperor Penguin Optimization (Fuzzy-EPO) algorithm-based resource provisioning framework for heterogeneous cloud environment. To deploy the optimal set of virtual machines (VM) to physical machines the VM allocation model is employed. The proposed Fuzzy-EPO algorithm does the VM consolidation mainly to reallocate overloaded VM to under-loaded PM to minimize the migration time and the brownout mechanism is adopted to reduce the rate of energy consumption. CloudSim simulation platform is used to implement the proposed system. The simulation results expose that the proposed Fuzzy-EPO based system is effective in restraining the proportion of service level agreement violation and increasing QoS requirements for providing proficient cloud service.
Individual dynamics for effective implementation of mainstream English curriculum at a school for the deaf

Promoting dynamics of curriculum implementation that help all teachers and learners meet their needs and understand who they are is critical. Such is equally needful in special schools which use the mainstream curriculum to teach learners with disabilities. This study sought to explore the dynamics that enhance implementing a mainstream English curriculum at a school for the deaf in Eswatini. This was aimed to assist teachers in reflecting on and critiquing their practices and experiences to improve their classroom actions; and to raise awareness of the natural identity of both teachers and learners at the school with the hope of meeting individual needs and, ultimately, the teaching and learning goals. Utilizing purposive sampling, this qualitative study selected six teachers of English Language at the school. The pragmatic paradigm, action research design, and natural identity theory framed the study. Semi-structured interviews, Zoom focus-group discussions, document review, and reflective activities were used as data-generation methods. Findings revealed that implementing the mainstream English curriculum at the school seemed to overlook the importance of individual dynamics, which are vital in meeting unique individual needs. Personal dynamics combine the strengths of top-down and bottom-up dynamics Thus, relieving the remarkable tension between the dominant top-down and bottom-up dynamics. Consequently, this study recommends a balance between the top-down, bottom-up, and individual dynamics to successfully implement the mainstream English curriculum at the school.
Teleportation of Masked Information

In this contribution, we investigated the possibility of teleporting classical/quantum masked information, which may be coded either in a single qubit or qutrit. For this purpose, different systems are used as quantum channels; two-qubit, three-qubit, two qutrit systems, and different protocols are applied. All the teleported masked information are retrieved as masked states at the receiver station. The number of operations that may be performed by the receiver are limited. It is shown that, one can teleport masked classical information with maximum fidelity, while for quantum information the maximization depends on the weight parameter of the teleported state and the used joint measurements. Teleporting the total masked state is better than teleporting its marginals, where the fidelity of total masked state is maximum. However, the fidelity of teleporting masking quantum information via three qubit systems may be maximized by controlling the weight of the initial masked state and the polarization of the mediator. In some cases, the receiver need to diagonalize the final teleported state to maximize its fidelity, and consequently reduces the required local operations.
Pruning feature maps for efficient convolutional neural networks

In recent years, convolutional neural networks have become increasingly important in the field of machine learning, especially for computer vision. However, deep network models are difficult to deploy on hardware-constrained devices because of huge number of parameters, storage requirements and computational cost. This paper proposes a pruning feature maps method to delete redundant feature information in the deep network. Thus, it can simplify network structure at the same time reduce the computational complexity and speed up the operation. We first define a small chi-square supervised set. The feature maps of this set and the training set are extracted. Then, two variance matrices are constructed. The differences between the variance matrices are then used to establish chi-square distances. Through continuous experiments, the optimal position threshold is set, and the feature maps corresponding to the channels below the position threshold are pruned. Experiment performances show that the method can reduce network redundancy, storage space and network complexity totally up to 70%. This is accomplished without appreciably diminishing the network's accuracy. In the worst case, the accuracy difference for images classification by using simplified network and network before simplification was less than 0.4%. The use of a small pre-trained network also speeds up the network training. Together, these improvements constitute an important step towards the effective implementation of CNNs on constrained devices.
Partitioning-Based Data Sharing Approach for Data Integrity Verification in Distributed Fog Computing

With the increasing popularity of the internet of things (IoT), fog computing has emerged as a unique cutting-edge approach along with cloud computing. This study proposes an approach for data integrity verification in fog computing that does not require metadata stored on the user side and can handle big data efficiently. In the proposed work, fuzzy clustering is used to cluster IoT data; dynamic keys are used to encrypt the clusters; and dynamic permutation is used to distribute encrypted clusters among fog nodes. During the process of data retrieval, fuzzy clustering and message authentication code (MAC) are used to verify the data integrity. Fuzzy clustering and dynamic primitives make the proposed approach more secure. The security analysis indicates that the proposed approach is resilient to various security attacks. Moreover, the performance analysis shows that the computation time of the proposed work is 50 times better than the existing tag regeneration scheme.
A Feedback-Enhanced Two-Stage Framework for judicial machine reading comprehension

Machine Reading Comprehension (MRC) is the task of teaching machines to understand and answer questions based on a given text. In the judicial domain, MRC has garnered attention as a novel strategy for addressing a variety of legal issues. Judicial MRC generally requires interpretability, meaning that the model should not only answer the question correctly but also provide supporting evidence sentences. A straightforward approach is to handle the two tasks separately, which ignores the relation between the two tasks and leads to the loss of annotated information. To make better use of the limited judicial MRC annotation data, we simulate the strategies used by humans in solving MRC problems and propose the Feedback-Enhanced Two-Stage Framework for Machine Reading Comprehension (FETSF-MRC). This framework consists of two cascaded modules: (a) a scanning module that identifies evidence sentences and (b) a detailed reading module that focuses on the evidence to identify candidate answers and provides feedback to the scanning module. Experiments on two Chinese judicial reading comprehension datasets (CJRC, CAIL2020) and an open-domain English dataset HotpotQA show that FETSF-MRC achieves superior performance compared to several baselines. FETSF-MRC outperforms the best baseline by 1.12% and 1.49% in joint F1 score on the CJRC and CAIL2020 datasets, respectively, and achieves 73.74% joint F1 score on the HotpotQA.
3D Point Cloud Classification with ACGAN-3D and VACWGAN-GP

Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors.In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (ACGAN-3D) and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (VACWGAN-GP).Unlike other classifiers, we are able to enlarge the limited data set with the data produced by generative models.We consequently aim to increase the success of the model by training it with more data.As suggested by the conventional ACGAN models, in addition to the real dataset, we train the Discriminator with synthetic data generated by the Generator using the class label.By doing so, we ensure that Discriminator can discriminate between the real data and the synthetic data.Thus, as the training evolves, the Generator is trained to produce more realistic synthetic data, which in turn forces Discriminator to classify or discriminate better.Defined originally on 2D images, our ACGAN-3D modifies this conventional ACGAN model in order to classify 3D point clouds by updating the neural network layers.Our second model VACWGAN-GP, on the other hand, demonstrates similar abilities with more stable training by replacing its Discriminator with Critic and by modifying its loss function.In this model, we managed to merge Wasserstein GAN-GP with conditional GAN in order to improve the classifier's performance.The proposed models ACGAN-3D and VACWGAN-GP were tested extensively on 3D datasets and comparisons with the other state-of-the-art studies have revealed our clear advantages on various aspects.While ACGAN-3D can be preferred with its compact design, our second method VACWGAN-GP stands out for higher performance.