Reviewerly/exHarmony huggingface dataset:
Car Detection from Unmanned Aerial Vehicles Based on Deep Learning: A Comparative Study

Aerial Images raise a key challenge for the object detection algorithm, because of the small scale of objects and interference from complex surfaces. To overcome this issue, this paper explores the achievement of these three cutting-edge convolution neural network (CNN) algorithms, namely a two-stage object detector, Mask R-CNN, which is the segmentation algorithm, and one-stage object detector YOLOv3 and YOLOv5. The key goal of this task is to undertake the most exhaustive comparison of these three models. We also conducted some experiments to find out the best model for object detection using different sizes of anchor scales. Various anchor scales are used so that the anchor value might not be the cause for poor performance on detection. For training and testing purposes, we used a large car dataset. The dataset was created using images of UAVs. In this study, YOLOv3 and YOLOv5 outperform Mask R-CNN in precision, and mAP. On the other hand, YOLOv5 outperforms YOLOv3 precision, f1-score, recall and mAP.
A Novel Ensemble Methodology to Validate Fuzzy Clusters of Big Data

The clustering of datasets is a widely used technique in unsupervised machine learning. The cluster quality evaluation is a tricky problem because external validation is usually not possible for clustering. This happens due to the unavailability of external proof. Although there are many methods developed and experimented with to validate the results obtained from clustering, it is always preferred to use more than a few cluster validity measures. The big data usually contain a high percentage of noise in it, making it necessary to use some additional techniques to incorporate while clustering big data. In this work, we have clustered document big data using fuzzy logic-based enhancement of traditional K-Means and K-Medoids. This work has suggested the Ensembling of seven different cluster quality validity measures to determine the best quality of fuzzy clusters. The Reuters standard document dataset is clustered using different cluster numbers, and the proposed ensemble methodology is proven to determine optimal numbers of clusters.
StegoPix2Pix: Image Steganography Method via Pix2Pix Networks

Steganography is the strategy of concealing secret data inside images. With the quick advancement of deep learning-based methods in steganalysis, it turns out to be an extremely challenging to design a secure steganographic method. In this paper, we propose a steganographic technique based on GAN, named StegoPix2Pix. In the proposed method U-Net accepts cover image and secret message information as input to synthesize stego-image. This method can effectively withstand steganalysis equipment. Steganalysis cannot identify our stego-images from other generated images using GAN or similar methods. In the meantime, our technique can generate stego-images of arbitrary size with 0.01 bpp, this is an improvement over other steganographic method those, who only can embed fixed-length message information into cover image. Experimental results show that StegoPix2Pix can accomplish security, reliability, and good visual effect.
Low-Cost Energy Efficient Encryption Algorithm for Portable Device

The number of portable digital devices is escalating day by day with the advancement of technology. As the number of digital devices users is expanding, the security concer for the privacy of their shared information is also thriving. Cyber criminals wait for the opportunities to steal and tamper with the secret information shared by the users. To protect the cyber world from these criminals by ensuring the secured communication channel between users in an affordable cost is now a big challenge for researchers. Considering these concerns, the target of this research is to develop an encryption algorithm for portable devices which will be less costly and energy proficient. For that purpose, a low-cost energy efficient algorithm has been proposed which needs two private keys of sender and receiver and one global public key. A shared symmetric key is generated by mixing those keys in both sender and receiver sides. For different sets of public and private keys, the generated shared symmetric key is dissimilar. This symmetric key can be used for encrypting the plain text and decrypting the cipher text. Lastly, a performance analysis has been performed which shows that the algorithm is resilient against the brute force attack and takes less computational time to execute in comparison with the existing Diffie–Hellman Algorithm.
Quantum-Inspired Neural Network on Handwriting Datasets

With the advancement of machine learning for classifying and categorizing larger sets of data, there is a high need for greater computational power. Quantum computing in machine learning advances to solve this in a lesser time, consuming less energy and power. This paper includes the comparative analysis of Traditional Neural Networks with Quantum Neural Networks using handwritten digits from the simplified version of the MNIST dataset.
Language Identification in Multilingual Text Document Using Machine Learning

Multilingual text documents have been used for decades to overcome language barriers. Multilingual documents in Bangladesh frequently include Bengali, English, and Arabic words. However, Optical Character Recognition (OCR) technologies struggle to correctly identify characters in documents written in multiple languages. Therefore, OCRs require a technique that helps to identify languages from words in the documents. This study proposes a machine learning-based automatic language recognition method and producing OCR masks from multilingual documents. By employing the masks, language-specific OCRs recognize the words in a document to generate machine-encoded text. This research utilizes hand-crafted feature extraction techniques with machine learning algorithms (support vector machine, random forest, decision tree, gradient boosting, and k-nearest neighbors) to identify words from different languages. This research also produces a new dataset for multilingual recognition tasks containing words from Arabic, Bengali, and English. Our suggested approach performs remarkably well at creating language masks for particular OCRs, facilitating multilingual text recognition challenges by simplifying OCR selection.
UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network

The uncertain knowledge graph (UKG) generalizes the representation of entity-relation facts with a certain confidence score. Existing methods for UKG embedding view it as a regression problem and model different relation facts independently. We aim to generalize the graph attention network and use it to capture the local structural information. Yet, the uncertainty brings in excessive irrelevant neighbor relations and complicates the modeling of multi-hop relations. In response, we propose UPGAT, an uncertainty-aware graph attention mechanism to capture the probabilistic subgraph features while alleviating the irrelevant neighbor problem; introduce the pseudo-neighbor augmentation to extend the attention range to multi-hop. Experiments show that UPGAT outperforms the existing methods. Specifically, it has more than 50% Weighted Mean Rank improvement over the existing approaches on the NL27K dataset.
Identifying Duplicate Questions Leveraging Recurrent Neural Network

Community Question Answering (CQA) forums are the predominant platform where the users can respond to others’ questions and share acquainted insights. The influx of new questions with linguistic expression variability and ambiguity leads to a haphazard collection of overlapped and unique questions. Hence, the challenge of identifying the equivalent questions emerges so that the users can be redirected to proper references. In this paper, we propose recurrent neural network-based architecture employing word-embedding to assess whether a question-pair is duplicate or not. After a careful pre-processing step, we apply several pre-trained word-embedding models to represent questions semantically in a fixed dimensional real-valued vector. We then apply two different RNN architectures, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) to encode the underlying meaning of question-pairs. Finally, the introduced models predict whether the question-pair is duplicate or not. The experimental results on the benchmark dataset demonstrated that our best models yielded competitive results with an accuracy of 82 and 83% and contribute to the state of the art. In addition, our method is applicable to other textual similarity identification tasks.
You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network

Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data. However, most current models aggregate information from the local neighborhoods of a node. They may fail to explicitly encode global structure distribution patterns or efficiently model long-range dependencies in the graphs; while global information is very helpful for learning better representations. In particular, local information propagation would become less useful when low-degree nodes have limited neighborhoods, or unlabeled nodes are far away from labeled nodes, which cannot propagate label information to them. Therefore, we propose a new framework GSM-GNN to adaptively combine local and global information to enhance the performance of GNNs. Concretely, it automatically learns representative global topology structures from the graph and stores them in the memory cells, which can be plugged into all existing GNN models to help propagate global information and augment representation learning of GNNs. In addition, these topology structures are expected to contain both feature and graph structure information, and they can represent important and different characteristics of graphs. We conduct experiments on 7 real-world datasets, and the results demonstrate the effectiveness of the proposed framework for node classification.
A Real-Time Bangla Local Language Recognition from Voice

Voice is the most natural form of communication and interaction between people. Bangladesh has masses of people who do not know how to read or write but merely speak and also Typing in Bangla can be difficult, especially for those who cannot type fast. Therefore, there is an increasing interest in speech-to-text conversion for speech-oriented human–machine interaction. Google developed its Speech Application Program Interface for speech-related work on mobile and computer applications. The API can recognize up to 120 languages and variants (including Bangla). Therefore, the purpose of this research is to investigate Speech-to-Text (STT) conversion using Google Speech-to-text API for Promito Bangla words. Another purpose of this research is to investigate Speech-to-Text (STT) conversion using Google Speech-to-text API for Local Bangla words, and developing a system that will detect Local Bangla words and will generate the correspondent Promito Bangla words against the Local Bangla word. For our research, we collected 105 Promito Bangla words, 105 local words of Noakhali against the 105 Promito Bangla words, and the local word of Chittagong against the 105 Promito Bangla words. The sum of our collected words was 315. With our proposed system firstly, we tested all Promito Bangla words. After that, we tested all trained Local words of Noakhali and tested all the trained Local words of Chittagong. experimenting with the Promito Bangla Words in Model A, the accuracy rate was amazing 95.23% to be exact. Our analysis shows that increasing the number of training data increases accuracy.
Revisiting Transaction Ledger Robustness in the Miner Extractable Value Era

In public transaction ledgers such as Bitcoin and Ethereum, it is generally assumed that miners do not have any preference on the contents of the transactions they include, such that miners eventually include all transactions they receive. However, Daian et al. S &P’20 showed that in practice this is not the case, and the so called miner extractable value can dramatically increase miners’ profit by re-ordering, delaying or even suppressing transactions. Consequently an “unpopular” transaction might never be included in the ledger if miners decide to suppress it, making, e.g., the standard liveness property of transaction ledgers (Garay et al. Eurocrypt’15) impossible to be guaranteed in this setting. In this work, we formally define the setting where miners of a transaction ledger are dictatorial, i.e., their transaction selection and ordering process is driven by their individual preferences on the transaction’s contents. To this end, we integrate dictatorial miners into the transaction ledger model of Garay et al. by replacing honest miners with dictatorial ones. Next, we introduce a new property for a transaction ledger protocol that we call content preference robustness (CPR). This property ensures rational liveness, which guarantees inclusion of transactions even when miners are dictatorial, and it provides rational transaction order preservation which ensures that no dictatorial miner can improve its utility by altering the order of received candidate transactions. We show that a transaction ledger protocol can achieve CPR if miners cannot obtain a-priori knowledge of the content of the transactions. Finally, we provide a generic compiler based on time-lock puzzles that transforms any robust transaction ledger protocol into a CPR ledger.
Sentiment Analysis of Restaurant Reviews Using Machine Learning

Nowadays, everything is based on the Internet. People generally love to express their opinions. Now people use different kinds of online platforms such as Google, Facebook, and Twitter to express their opinions about different kinds of things such as foods, products, services, and restaurants. Customer or user opinions are very important information for both customers and the management to know customer sentiment and whether the customers like the service or not. However, evaluating individual customer opinions (unstructured texts) is so difficult, when the amount of reviews is large. As a result, we ought to include a technique to automatically evaluate customer opinions or reviews and deliver the essential findings in a definite way. In this paper, we have created a dataset of 1000 reviews, applied different machine learning classification algorithms to those reviews, and observed their accuracy. We got the highest accuracy of 97.6% for Support Vector Classifier (SVC) to determine the reviews as positive, negative, and neutral.
Healthcare Professionals Credential Verification Model Using Blockchain-Based Self-sovereign Identity

In terms of healthcare, fundamental trust and reliability depend on health professionals. But there are several records of doctors practicing with forged licenses worldwide which derogate the impression, reduce trust, and put patients’ lives at stake. Professionals need to verify their identity to ensure that they are licensed to do medical practices. Also, they have to share their personal information credulously to verify their certificates to the hospital they are intended to do medical practices, or to the medical college to pursue higher degrees which are not secure enough as there lies the biggest threat of misusing their personal information. To overcome these two major problems, a system is required that will authenticate a healthcare professional’s digital credentials with minimal identity reveal. In this research work, we propose a privacy-preserving Self-Sovereign Identity (SSI) model leveraging a decentralized Blockchain which verifies healthcare professionals’ credentials to impede them from practicing with fraudulent licenses, disclosing minimal information, and providing data secrecy which proves an attribute from a credential without disclosing the actual value and provides a secured end-to-end communication channel for data transmission.
A New Approach to Garbled Circuits

A garbling scheme is a fundamental cryptographic building block with a long list of applications. The study of different techniques for garbling a function, towards optimizing computation and communication complexity, has been an area of active research. Most common garbling techniques work by representing each gate in the circuit as a set of ciphertexts that encrypt its truth table row-by-row. In this work we present a new garbling scheme in the random oracle (RO) model that garbles circuits in the gate-by-gate paradigm by capturing the gate functionality ( $$\textsf{AND}, \textsf{XOR}$$ ) as a whole rather than as a set of ciphertexts. The final gate garbling requires $$4\kappa $$ bits of communication in expectation, 4 RO calls for garbling and 1 RO call for evaluation. We prove that the scheme satisfies privacy in the non-programmable random oracle model and against PPT adversaries. We also show how this scheme can be extended to support free-XOR and garble any gate functionality over binary inputs.
Argument Mining on Clinical Trial Abstracts on Lung Cancer Patients

This study presents an architecture to detect all the argument components and argument relations of the abstract from any paper available on PubMed describing a randomized clinical trial on lung cancer patients. The selected abstracts portray the background, intervention, and outcome of only immunotherapy drugs on lung cancer patients. The pipeline requires two classification models—sequence classification model and relation classification model for detecting argument components and argument relations, respectively. Next, a graph is built to represent all the abstract sentences by nodes and the argument relations by edges. The argument components are represented by node colors. The sequence classification model gives satisfactory performance scores for detecting all classes and individual classes. Although the relation classification model gives satisfactory scores for detecting all classes, it does not show promising results for detecting only support relationships or only attack relationships. This requires adding a more profound dataset consisting of more relationships and further modification of the model in the future studies.
An Efficient and Secure Data Deduplication Scheme for Cloud Assisted Storage Systems with Access Control

Cloud storage helps clients reduce the burden of data management and storage, attracting more data owners to keep their private information on the cloud. Among the massive amounts of confidential information that are being created rapidly nowadays, storage efficiency has become a primary concern for reliable data deduplication. Numerous deduplication algorithms have been developed to minimize storage requirements and network traffic, but those schemes are not efficient in real life. Besides, they also lack access control as cloud service providers manage most of the operations. We propose a deduplication architecture that combines access control with convergent encryption to ensure the confidentiality of data uploaded to the remote server along with the encryption keys. We use the SHA-256 hash function with Proof of Ownership (PoW) to achieve data integrity and authenticate users for secure deduplication.
Research on Attack Signal Feature Extraction Method of Multipath TCP Transmission System Based on Wavelet Energy Entropy

Multipath transmission technology provides a strong theoretical support for the realization of parallel multi-channel data transmission for multi-host mobile terminal devices, and is an ideal scheme for high performance and high quality data transmission in wireless network environment. MPTCP is one of the representative achievements, and its security and robustness have aroused wide concern and discussion among researchers at home and abroad. In view of the characteristics of non-stationary signals transmitted by MPTCP, this paper combines wavelet transform analysis method and information entropy theory, and uses wavelet energy entropy to achieve the method of feature extraction of attack signals. Finally, the simulation analysis is carried out and good experimental results are obtained. The following conclusions are drawn: according to the law of wavelet energy entropy changing with wavelet decomposition scale, the attack signal is different from the normal signal. Based on this feature, attack signals in MPTCP transmission system can be well detected, which provides a new thinking and new means for anomaly detection and online monitoring of MPTCP transmission system.
A Dual-Stream Input Faster-CNN Model for Image Forgery Detection

With the development of multimedia technology, the difficulty of image tampering has been reduced in recent years. Propagation of tampered images brings many adverse effects so that the technology of image tamper detection needs to be urgently developed. A faster-rcnn based image tamper localization recognition method with dual-flow Discrete Cosine Transform (DCT) high-frequency and low-frequency input is presented. For capturing subtle transform edges not visible in RGB domain, we extract high-frequency features from the image as an additional data stream embedding model. Our network model uses low-frequency images as the subject data to detect object consistency in different regions, further complements high-rate streams to strengthen image region consistency detection, and complements duplicate stream object tampering detection. Extensive experiments are performed on the CASIA V2.0 image dataset. These results demonstrate that faster-rcnn-w outperforms existing mainstream image tampering detection methods in different evaluation indicators.
Docker-Based Full Life-Cycle Experimental Cloud Platform Design and Implementation

The university laboratory that adopts the virtual machine method has problems such as the large maintenance workload of the experimental environment, complex configuration, fixed experimental time, and the inability to save the process. We propose a lightweight experimental environment based on Docker. Monitoring service, high availability session management, and resource protection mechanism of Docker runtime are designed. The full life cycle service runs after the Docker container is created and provides a management interface for the upper cloud platform. The prototype system of the cloud experimental platform is realized, which simplifies the construction and management of the cloud experimental platform in colleges and universities.
A Survey of Multipath Transport Mechanism in Data Center Networks

With the profound revolution in the data center industry and the increasing size of data center networks (DCNs), researchers have been focusing on achieving efficient and fast transport of traffic within the data center. In addition, the number of devices equipped with multiple interfaces is increasing, which allows multiple paths to be used simultaneously. Multipath transmission mechanisms can significantly improve the performance of data transmission in DCNs. However, some issues remain when MPTCP is deployed in DCNs. We have investigated multipath transport mechanisms in data center networks. First, the Multipath TCP (MPTCP) protocol is briefly described. Then, a study of routing algorithms, congestion control, and energy-saving techniques for applying MPTCP in DCNs are summarized separately. Finally, we propose the future direction of the multipath transmission mechanism in DCNs.
DGA Detection Using Similarity-Preserving Bloom Encodings

The sanitization of concise data samples can be challenging, as they do not provide a clear distinction between sensitive and non-sensitive parts within individual samples. In this context, traditional sanitization and anonymization measures are not applicable. We consider the detection of algorithmically generated domains through machine learning as an example of such a case, where the benign samples may leak sensitive information. Within this scenario, we evaluate the use of a similarity-preserving Bloom encoding technique to obscure the training samples.