Reviewerly/exHarmony huggingface dataset:
Hybrid Security Model for Medical Image Protection in Cloud

A cloud computing environment provides a cost-effective way for end-users to remotely store and retrieve private data through an internet connection anytime and anywhere. The security of these data cannot always be guaranteed because they can only be accessed by the end-user through a third-party interface, making them vulnerable to potential breaches of authentication and data integrity. This paper presents a secure hybrid approach for a medical image stored in the cloud that prioritizes data security and integrity. The suggested model employs a combination of Elliptic Curve Cryptography (ECC) and Advanced Encryption Standard (AES) algorithms to ensure authentication and data integrity. The results demonstrate its superiority over existing methods making the proposed model compliant with regulations of sensitive data stored in the cloud, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) that dictate how personal information must be handled, stored, and processed. By contrast with traditional and other hybrid systems, this paper can conclude that this is one of the best methods to guarantee the security of medical images in the cloud.
Skipper: A Federated Siamese Network-Based Group Activity Segregator for IoMT Systems

The social IoMT-based activity-monitoring system comprises several devices with different datasets. It faces challenges like a collection of a global activity dataset which comprises a myriad of activities. In this article, we propose a federated Siamese network-based data-independent group activity segregator—Skipper—which aims to identify anomalies in an activity-monitoring social IoMT system. The novelty of this work is that Skipper does not require any dataset before its deployment, which removes the need for any prior training of the model for activity monitoring. As a proof of concept, we select activities pertaining to school environments to identify low-performing students in a classroom, who would require teachers’ close attention to ensure balanced growth and proper health. Skipper monitors the students independently for their motion signatures through a wearable device that consists of an accelerometer. A federated Siamese network calculates indices that signify the degree of similarity among the students’ activities. Skipper identifies the students who do not perform the same activity. With real-world implementations, we observe that Skipper requires network rates of 10 Kb/s, making it suitable for low bandwidth networks while we achieve just 20% CPU and 10 MB memory utilization on constrained edge devices. Further, with an increasing number of students up to 100, the time delay for final results is limited to 80 s. Hence, Skipper is a fast, easy, and accurate solution for recognizing outliers in IoMT social systems.
From Collective Attribute Association of Groups to Precise Attribute Association of Individuals

Obscured person re-identification (Re-ID) aims to match an obscured image with a complete image of the same person captured by other cameras. As a major challenge in person identification, occlusion severely affects the effectiveness of most traditional person Re-ID methods. To solve this problem, this study proposes a trajectory association method, which, as a pre-processing technique for person Re-ID, can narrow the search range and reduce the problem of degradation caused by mixing. We investigate the method of converting the fuzzy association between sets into the precise association between elements for M video objects and N phone objects (trajectory information) with fuzzy group association relationships at the crime scene. First, we decompose the M-N precise association problem and analyze the similarity of the video objects in the source point and on the trajectories. Then, we define high-similarity points, study their distribution characteristics in different trajectories, and find that there is a significant difference between the distribution of high-similarity points in correct and incorrect matching trajectories. We simplify the full-path association problem into a partial-path high-similarity point distribution difference problem, which effectively reduces the difficulty in accurate association relationship construction. The association experiments in simple and mixed scenarios as well as Re-ID experiments on the PRPW and Market1501 demonstrate the effectiveness of our method.
Semi-Supervised Entity Alignment With Global Alignment and Local Information Aggregation

Entity alignment is a vital task in knowledge fusion, which aims to align entities from different knowledge graphs and merge them into one single graph. Existing entity alignment models focus on local features and try to minimize the distance between pairs of pre-aligned entities. Despite their success, these models heavily rely on the number of existing pre-aligned entity pairs and the topology information from the rest large set of unaligned entities is still largely unexplored. To overcome the limitation of existing models, we propose a model, termed Global Alignment and Local Information Aggregation, or GALA. GALA constructs global features for the knowledge graphs to be aligned using entity embeddings. It aligns the entities in the graphs by forcing their global features to match with each other and progressively updating the entity embeddings by aggregating local information from the other network. Empirical studies on commonly-used KG alignment data sets confirm the effectiveness of the proposed model.
Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning (CL) studies the problem of learning to accumulate knowledge over time from a stream of data. A crucial challenge is that neural networks suffer from performance degradation on previously seen data, known as catastrophic forgetting, due to allowing parameter sharing. In this work, we consider a more practical online class-incremental CL setting, where the model learns new samples in an online manner and may continuously experience new classes. Moreover, prior knowledge is unavailable during training and evaluation. Existing works usually explore sample usages from a single dimension, which ignores a lot of valuable supervisory information. To better tackle the setting, we propose a novel replay-based CL method, which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge. Specifically, besides the previous raw samples, we store the corresponding logits and features in the memory. Furthermore, to imitate the prediction of the past model, we construct extra constraints by leveraging multi-level information stored in the memory. With the same number of samples for replay, our method can use more past knowledge to prevent interference. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory. We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.
Frame-Recurrent Video Crowd Counting

Since video data contains temporal information, video crowd counting demonstrates more potential than single-frame crowd counting for scenarios requiring high accuracy. However, learning robust relationships among frames efficiently and cheaply is very challenging. Existing methods for video crowd counting lack explicit temporal correlation modeling and robustness, and they are complex. In this paper, we propose the Frame-Recurrent Video Crowd Counting (FRVCC) framework to solve these issues. Specifically, we design a frame-recurrent manner to recursively relate the density maps in the temporal dimension, which efficiently explores long-term inter-frame knowledge and ensures the continuity of feature map responses. FRVCC consists of three plug-in modules: an optical flow estimation module, a single-frame counting module, and a density map fusion module. For the fusion module, we propose the ResTrans network to robustly learn complementary features between visual-based and correlation-based feature maps through residual strategy and vision transformer. To constrain the output distribution to be consistent with the ground truth distribution, we introduce an adversarial loss to rectify the training process. Additionally, we release a large-scale synthetic video crowd-counting dataset, CrowdXV, to evaluate the proposed method and further improve its performance. We have conducted extensive experiments on several video-counting datasets. The results demonstrate that FRVCC achieves state-of-the-art performance and, concurrently, high generalization, high flexibility, and less complexity.
File Encryption-Decryption using Java

Data has been the most valued asset of any industry throughout the course of time. And with the advent of the Internet and virtual platforms, data exchange via text files or media files has been accelerated multiple folds. However, the increase in the amount of data exchange also makes private data more vulnerable to external attacks. Multiple methods have been implemented to ensure safe and reliable pathways of exchanging data like securing the network-line via a VPN or using enhanced communication protocols like FTP(File Transfer Protocol) or SMTP(Secured Mail Transfer Protocol). Even though there have been mulitple methods to secure data transfer ways, the actual data still remains vulnerable and ready to use should these systems get overridden. Instead, a better method might be to actually encrypt the data and ensure that the main data stays independently secure apart from the secure pathways created. To address this specific issue, this paper proposes a system developed purely using java algorithms that can encrypt media files and decrypt them to protect the data they contain. Three different types of algorithms are applied to protect various types of files like mp4, png, jpg, pdf, doc, xml, etc.
Suction Cup Array Working Also as Tactile Sensor to Detect Cups Deformation Using KCF and CNN

We propose a micro-suction cup array device with tactile sensing capabilities. The suction cup array device is equipped with a camera for capturing images of the contact surface. The camera reads the deformation of the suction cups. Image processing estimates the displacement of each suction cup contact surface, its direction, and the contact force distribution. A preliminary experimental device has been fabricated. The master shape of the micro cup was fabricated by 3D laser lithography and UV nanoimprinting processes. A hole in the suction cup was opened by the femtosecond laser beam. Kernelized Correlation Filter was applied to the acquired images and the movement of the contact surface of each suction cup could be estimated. Convolutional Neural Networks (CNN) was used to estimate the pressing force. The correctness (accuracy) of the produced CNN model was 87%, which is a practical accuracy.
Spatiotemporal Interaction Transformer Network for Video-Based Person Reidentification in Internet of Things

Video-based person reidentification, which is a significant application in the Internet of Things, aims to identify the same person in different video sequences across nonoverlapping cameras. Existing methods usually utilize temporal cues to enhance spatial features. However, these methods learn the temporal and spatial information separately, which breaks the relationship between them and ignores the positive role of temporal information for learning frame-level spatial representation in the process of spatial representation learning. In this article, we propose a novel spatiotemporal interaction transformer network (SITN) to solve this problem. To model the temporal information and the relationship between frames, we introduce a temporal interaction module (TIM) to interact between frame information. Meanwhile, we combine TIM with spatial transformer encoder to explore the positive role of temporal information in the learning procedure of the frame-level spatial feature. Moreover, we propose a transformer local learning scheme by reconstructing the 2-D spatial information of the frame patch sequences and extracting local features in a striped manner to strengthen the discriminative capability of our model. Extensive experiments are conducted on four public benchmarks. The results show that our model is superior compared with state-of-the-art methods.
Bottom-Up Clustering and Merging Strategy for Irregular Curvature Aero-Blade Surface Extraction

Surface extraction plays a significant role in blade reconstruction and measurement, improving manufacturing quality and reducing costs. Boundary extraction and region growth are the most common solutions. However, these methods treat points as complete objects and extract surfaces aggressively. Since the model and geometric constraints of the surfaces are usually blurring, these methods will lead to low accuracy results and dramatically impact the effectiveness of existing methods in practice. This paper presents a novel bottom-up irregular curvature surface extracting method called SuCMIS segmenting blade surface points from an unstructured scanning point cloud. SuCMIS applies adaptive multiple clustering methods based on the local flatness and normal deviation minimum, which improves the robustness to noise and unpredicted systematic errors. Then the curvature-based cluster merging process runs to reconstruct the blade surface. Differ to existing model-based method, SuCMIS converts surface extraction into a bottom-up process with multiply surfaces clustering and merging. It does not rely on evaluating the accuracy of the model and geometric constraints, which overcomes the natural defects of existing methods significantly. Experiments demonstrate SuCMIS’s efficiency and robustness. Compared to baseline methods, SuCMIS improves extracting accuracy with acceptable execution time, especially for complicated and large point clouds, such as blade surfaces.
Automatic Certificate Verification using Computer Vision

The issuance of fake degrees and certificates is one of the biggest concerns in society. This kind of employment through fake certificates will lead to a whole set of workforces who are not properly educated or trained. This can result in poor growth of the organization. The existing method of verifying the certificates involves sending an email of the certificate to the institute, the organization would respond with a reply mail containing all the details of the student along with the verification result. The drawbacks involved in this approach are delays in the verification process and this method becomes complex if the number of certificates to be verified is huge. Our approach to this problem involves detecting the labels in the image and extracting corresponding fields to be verified from the original image as multiple cropped images, the text in these cropped images is recognized using the EasyOCR module available in python. The institute database is searched for a match with the extracted details and the results after querying are sent back. This entire verification process is implemented as client-server architecture for easy access.
Two Stage Job Title Identification System for Online Job Advertisements

Data science techniques are powerful tools for extracting knowledge from large datasets. Analyzing the job market by classifying online job advertisements (ads) has recently received much attention. Various approaches for multi-label classification (e.g., self-supervised learning and clustering) have been developed to identify the occupation from a job advertisement and have achieved a satisfying performance. However, these approaches require labeled datasets with hundreds of thousands of examples and focus on specific databases such as the Occupational Information Network (O*NET) that are more adapted to the US job market. In this paper, we present a two-stage job title identification methodology to address the case of small datasets. We use Bidirectional Encoder Representations from Transformers (BERT) to first classify the job ads according to their corresponding sector (e.g., Information Technology, Agriculture). Then, we use unsupervised machine learning algorithms and some similarity measures to find the closest matching job title from the list of occupations within the predicted sector. We also propose a novel document embedding strategy to address the issues of processing and classifying job ads. Our experimental results show that the proposed two-stage approach improves the job title identification accuracy by 14% to achieve more than 85% in some sectors. Moreover, we found that incorporating document embedding-based approaches such as weighting strategies and noise removal improves the classification accuracy by 23.5% compared to approaches based on the Bag of words model. Further evaluations verify that the proposed methodology either outperforms or performs at least as well as the state-of-the-art methods. Applying the proposed methodology to Moroccan job market data has helped identify emerging and high-demand occupations in Morocco.
Voting Classifier on Ensemble Algorithms for Breast Cancer Prediction

One of the leading causes of death in humans is cancer, with Breast cancer accounting for the majority of deaths among women. Both men and women are developing more Breast cancer cases every day. Breast cancer has become a huge health concerning issue. If Breast cancer is detected in early stages like stages 0 and 1, it can be cured without too much health risk which will reduce the death rate. Computer-Aided Detection or CAD systems are available to detect whether a woman or man has Breast cancer or not. The purpose of our research paper is to find a technique or algorithm that can help in the early detection of Breast cancer to save lives and reduce casualties. Here, different Machine Learning (ML) algorithms like decision tree, Support vector machine, ensemble algorithm of random forest with bagging and Ada boosting, and Voting Classifier are used. Breast cancer prediction with a Voting Classifier on ensemble algorithms is built. The performance of applied models is analyzed in terms of Accuracy, Precision, Recall, F1-score, and Support. The Voting Classifier gives the best results with 0.96 Accuracy because it uses the mean of ensemble algorithms to improve Accuracy value.
Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language Processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection.
Novel Empirical Block Chain Ecosystem with Deep Neural Key Exchange Technique

One of the key challenges faced by public networks is the establishment of a secure communication channel. Generally, authentication is accomplished by sharing a secret key for encrypting and decrypting the data by the sender and receiver. However, if an attacker has the ability to determine the secret key, then the data is no longer safe. Thus, effective techniques are required to improve the security of data transmitted in the Internet of Things (IoT) environment. This paper devises a Blockchain (BC) based Neural Key Exchange (NKE) for providing data protection while transferring information. The proposed system consists of various entities, like user, owner, server, BC, and Trusted Authority (TA). The introduced authentication approach is realized using different steps, such as initialization, registration, login, public key generation, authentication, data protection, validation, and data sharing. Further, the Deep Neural Network(DNN) is employed for generating the key to provide the data protection. The devised BC_DNN-SecKeyAuth technique is verified for its effectiveness considering different performance measures, like memory usage and computational cost, and the experimentation of the devised scheme reveals the superiority of the developed BC_DNN-SecKeyAuth scheme by attaining minimal values of memory usage and minimal computational cost at 125MB and 3.263sec.
Image Security Utilizing Hybrid Model of Steganography and Asymmetric Cryptography Methods

With the recent technologies, digital data is mostly used in every communication and data transferring process. Moreover, digital images are highly preferred by people nowadays. In order to establish a secure transferring or exchange of data, the digital data needs to be secured. There are different techniques and algorithms that can used to provide security to the digital data. In this research work, a hybrid digital image security technique is proposed by combining two different types of methods and algorithms, which are used to protect the digital image. The proposed combination of steganography and asymmetric cryptography techniques are utilized to secure the digital images.
Dynamic Clustering Algorithm for Video Summarization on VSUMM Dataset

The extent of videos being produced per day across the world is enormous, and in the past few years it has increased to an unprecedented level. Information extraction from a video, however, is more difficult than information extraction from an image. A viewer must see the entire video in order to understand its context. Aside from context awareness, it is nearly impossible to make a universally applicable summary video because each person has a different preferred keyframe. A number of approaches came into existence for tackling this problem which include supervised and unsupervised learning techniques, and some associated with Deep Learning techniques. However, it would require a significant amount of individualized data labelling if we attempted to approach problem video summarizing via a supervised learning method. In this paper, we developed an algorithm based on Dynamic Clustering of projected frame histograms approach to address the challenge of video summarization using unsupervised learning. We have tested the performance of the approach on the VSUMM, a benchmark dataset and showcased that using dynamic clustering algorithm has been proven to perform competitively with some existing approaches.