Reviewerly/exHarmony huggingface dataset:
Perimeter: A Network-Layer Attack on the Anonymity of Cryptocurrencies

Cryptocurrencies are widely used today for anonymous transactions. Such currencies rely on a peer-to-peer network where users can broadcast transactions containing their pseudonyms and ask for approval. Previous research has shown that application-level eavesdroppers, meaning nodes connected to a large portion of the Bitcoin peer-to-peer network, are able to deanonymize multiple users by tracing back the source of transactions. Yet, such attacks are highly visible as the attacker needs to maintain thousands of outbound connections. Moreover, they can be mitigated by purely application-layer countermeasures. This paper presents a stealthier and harder-to-mitigate attack exploiting the interactions between the networking and application layers. Particularly, the adversary combines her access over Internet infrastructure with application-layer information to deanonymize transactions. We show that this attack, namely Perimeter, is practical in today’s Internet, achieves high accuracy in Bitcoin, and generalizes to encrypted cryptocurrencies e.g., Ethereum.
Towards Securing Machine Learning Models Against Membership Inference Attacks

From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress in the research works proposing defence techniques against ML security and privacy attacks. The relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks (MIA) and the related countermeasures. In this paper, we introduce a countermeasure against membership inference attacks (MIA) on Conventional Neural Networks (CNN) based on dropout and L2 regularization. Through experimental analysis, we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the model. Indeed, using CNN model training on two datasets CIFAR-10 and CIFAR-100, we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different classifiers. Moreover, we present a solution to achieve a trade-off between the performance of the model and the mitigation of MIA attack.
Sign Speaks-An IoT Based Smart Gloves for Dumb

Communication is the only means by which we can express our feelings or articulate a message, but for a person with a disability (deaf and dumb) it is hard to connect with a normal individual. As a consequence, a person who loses the capacity to hear and speak is unable to stand with a normal person in a contest. Communication is visual, not auditory, for a person who can't hear and speak. Dumb people are likely to communicate using sign language, but they find it difficult to connect with others who do not understand it. There is also an obstacle to contact between these two communities. The main purpose of this smart gloves paper is to reduce this connection barrier. The key goal of the proposed plan is to design a cost-effective device that can give voice to a voiceless individual with the help of Smart Gloves. This ensures that the use of smart gloves would not drive a wedge for two separate communities. This system must a dream come true for dumb and deaf people to interact normally with people without seeking other's help. In this case, this system must be needed by the disabled people. With the aid of these gloves, people with disabilities will even get a chance to develop in their respective carrier and often helps the nation flourish.
Detecting and Matching Related Objects with One Proposal Multiple Predictions

Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for some sports tracking players is sufficient for game analysis, sports like hockey, tennis and polo may require additional detections, that include the object the player is holding (e.g. racket, stick). The baseline solution for this problem involves detecting these objects as separate classes, and matching them to player detections based on the intersection over union (IoU). This approach, however, leads to poor matching performance in crowded situations, as it does not model the relationship between players and objects. In this paper, we propose a simple yet efficient way to detect and match players and related objects at once without extra cost, by considering an implicit association for prediction of multiple objects through the same proposal box. We evaluate the method on a dataset of broadcast ice hockey videos, and also a new public dataset we introduce called COCO +Torso. On the ice hockey dataset, the proposed method boosts matching performance from 57.1% to 81.4%, while also improving the meanAP of player+stick detections from 68.4% to 88.3%. On the COCO +Torso dataset, we see matching improving from 47.9% to 65.2%. The COCO +Torso dataset, code and pre-trained models will be released at https://github.com/foreverYoungGitHub/detect-and-match-related-objects.
Enhancing Lunar Reconnaissance Orbiter Images via Multi-Frame Super Resolution for Future Robotic Space Missions

This paper presents a novel application of a Multi-frame Super Resolution (MFSR) method for lunar surface imagery called Lunar HighRes-net (L-HRN). In this work, we adapted and used NASA's Lunar Reconnaissance Orbiter (LRO) image database to train the Deep Learning architecture for image super resolution. Additionally, we also gathered an artificial image dataset from our virtual Moon to improve the amount of input data in the neural network training process. The network's architecture follows a standard MFSR algorithm that was enhanced for this specific use case. The proposed MFSR method has been evaluated using the well-known peak signal-to-noise ratio (PSNR) metric against other generic super-resolution methods of the state of the art. This work aims to improve environmental knowledge about the lunar surface to enhance future autonomous robots capabilities on the surface of the Moon.
Attribute-based Encrypted Search for Multi-owner and Multi-user Model

Nowadays, many data owners choose to outsource their data to public cloud servers while allowing authorized users to retrieve them. To protect data confidentiality from an untrusted cloud, many studies on searchable encryption (SE) are proposed for privacy-preserving search over encrypted data. However, most of the existing SE schemes only focus on the single-owner model. Users need to search one-by-one among data owners to retrieve relevant results even if data are from the same cloud server, which inevitably incurs unnecessary bandwidth and computation cost to users. Thus, how to enable efficient authorized search over multi-owner datasets remains to be fully explored. In this paper, we propose a new privacy-preserving search scheme for the multi-owner and multi-user model. Our proposed scheme has two main advantages: 1) We achieve an attribute-based keyword search for multi-owner model, where users can only search datasets from specific authorized owners. 2) Each data owner can enforce its own fine-grained access policy for users while an authorized user only needs to generate one trapdoor (i.e., encrypted search keyword) to search over multi-owner encrypted data. Through rigorous security analysis and performance evaluation, we demonstrate that our scheme is secure and feasible.
Instance search via instance level segmentation and feature representation

Instance search is an interesting task as well as a challenging issue due to the lack of effective feature representation. In this paper, an instance level feature representation built upon fully convolutional instance-aware segmentation is proposed. The feature is ROI-pooled from the segmented instance region. So that instances in various sizes and layouts are represented by deep features in uniform length. This representation is further enhanced by the use of deformable ResNeXt blocks. Superior performance is observed in terms of its distinctiveness and scalability on a challenging evaluation dataset built by ourselves. In addition, the proposed enhancement on the network structure also shows superior performance on the instance segmentation task.
A Comparative Study of Cybersecurity Awareness on Phishing Among Employees from Different Departments in an Organization

Cybersecurity is an important issue for people who usually use the Internet for their purposes (e.g., ecommerce) in this era of the COVID-19 pandemic. For cyberthreats, phishing, which can be sent via email, can harm information systems in the organization. However, the risks from this kind of threats can be reduced if the employees have cybersecurity awareness. To prove this hypothesis with Thai employees, this paper presents a comparative study of cybersecurity awareness enhancement associated with the employees who work in different departments within the same organization in Bangkok, Thailand. In this study, the first phishing attack simulation was conducted before providing knowledge and training in cybersecurity to the employees and attacking with the second simulation. After result collection and analysis, it has been found that there are significant differences in cybersecurity awareness level between Thai employees from technology-based departments (e.g., IT department) and social-based departments (e.g., HR department) within the same organization. Of course, the technology-based employees are the better. Furthermore, it has been found that the cybersecurity awareness level of Thai employees from the social-based department, which were poor when compared to the other one, was improved obviously after they were involved with the cybersecurity awareness enhancement processes.
A Survey on Various Deep Learning Models for Automatic Image Captioning

Abstract Automatic Image captioning means the generation of a caption for an image by a machine. Image captioning is performed by recognizing objects, attributes and interconnection between them. This task involves computer vision for image understanding, natural language processing for syntax and semantics purpose and machine learning for caption generation. Preferably CNN is used to understand features of an image and RNN is used for sentence generation. Earlier, Machine learning approach was used for this purpose. Input data is used to extract the features in traditional machine learning. Extracting features like handcrafted from large dataset is not so easy and feasible. Later on, Various deep learning-based approaches were proposed. In deep learning, retrieval based and template-based methods were proposed but faced some issues like missing important objects and fixed length caption respectively. Then end to end learning approach based on deep learning network came into existence and image captioning task became more efficient. The objective of this paper is to study and compare various end to end learning-based framework for image captioning using standard evaluation metric and to understand how can these frameworks be used for various research applications. Along with the comparison, futuristic challenges have also been discussed.
A Survey on Deep Reinforcement Learning for Data Processing and Analytics

Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics and general rules from human knowledge and experience to improve their effectiveness. Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments it is interacting with than statically designed algorithms. Motivated by this trend, we provide a comprehensive review of recent works focusing on utilizing deep reinforcement learning to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in deep reinforcement learning. Next, we discuss deep reinforcement learning deployment on database systems, facilitating data processing and analytics in various aspects, including data organization, scheduling, tuning, and indexing. Then, we survey the application of deep reinforcement learning in data processing and analytics, ranging from data preparation, natural language interface to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using deep reinforcement learning in data processing and analytics.
Speckle filtering of ultrasonic images using weighted nuclear norm minimization in wavelet domain

Medical images such as ultrasound images suffer from multiplicative speckle noise which reduces the contrast of ultrasound images and adversely affects the diagnosis process. Discrete Wavelet transform (DWT) is widely accepted in the area of image processing due to its time localization, multi-resolution and sparseness properties. DWT decomposes the noisy image into approximation coefficients and detail coefficients. The detail wavelet coefficients of clean image have sparsity property, whereas in the case of noisy image, sparsity of detail coefficients is reduced due to the presence of speckle noise. Thus, in the proposed method, low rank based Weighted Nuclear Norm Minimization (WNNM) is applied on detail coefficients to reveal the sparsity property of DWT. WNNM is applied on the group matrix of non-local similar patches of detail subbands of DWT to approximate the low-rank denoised version of the subbands. Moreover, less amount of edge and structure information is present in the approximation coefficients of DWT. Thus, in the proposed method, Non-local Means (NLM) filter with Square-Chord distance is also used to denoise speckle noise from approximation coefficients of DWT. Exhaustive experiments are conducted on various images such as real US images, simulated kidney image and synthetic image. It is observed that the mean improvement in terms of PSNR and CNR values of the proposed hybrid method are 1.50% and 3.81% respectively over WNNM based Despeckling using Low-Rank Approximation (DLRA) method. Qualitative and quantitative analyses show that the proposed hybrid method performed better than existing speckle reduction methods in terms of both speckle reduction and edge preservation.
COP-E-CAT

In order to ensure that analyses of complex electronic healthcare record (EHR) data are reproducible and generalizable, it is crucial for researchers to use comparable preprocessing, filtering, and imputation strategies. We introduce COP-E-CAT: Cleaning and Organization Pipeline for EHR Computational and Analytic Tasks, an open-source processing and analysis software for MIMIC-IV, a ubiquitous benchmark EHR dataset. COP-E-CAT allows users to select filtering characteristics and preprocess covariates to generate data structures for use in downstream analysis tasks. This user-friendly approach shows promise in facilitating reproducibility and comparability among studies that leverage the MIMIC-IV data, and enhances EHR accessibility to a wider spectrum of researchers than current data processing methods. We demonstrate the versatility of our workflow by describing three use cases: ensemble prediction, reinforcement learning, and dimension reduction. The software is available at: https://github.com/eyeshoe/cop-e-cat.
Complete weight enumerators for several classes of two-weight and three-weight linear codes

In this paper, for an odd prime p, by extending Li et al.'s construction [17], several classes of two-weight and three-weight linear codes over the finite field Fp are constructed from a defining set, and then their complete weight enumerators are determined by using Weil sums. Furthermore, we show that some examples of these codes are optimal or almost optimal with respect to the Griesmer bound. Our results generalize the corresponding results in [15], [17].
A longitudinal study of the impact of refactoring in android applications

Mobile applications have to continuously evolve in order to meet new user requirements and technological changes. Addressing these constraints may lead to poor implementation and design choices, known code smells. Code refactoring is a key practice that is employed to ensure that the intent of a code change is properly achieved without compromising internal software quality. While previous studies have investigated the impact of refactoring on traditional code smells in desktop applications, little attention has been paid to the impact of refactoring activities in mobile application development. We aim to develop a broader understanding of the impact of refactoring activities on Android and traditional code smells in Android apps. We conduct a longitudinal empirical study by analyzing the evolution history of five open-source Android apps comprising 652 releases and exhibiting a total of 9,600 refactoring operations. We consider 15 common Android smell types and 10 common traditional Object-Oriented (OO) code smell types to provide a broad overview of the relationship between refactoring and code smells. We find that code smells are widespread across Android applications, but smelly classes are not particularly targeted by refactoring activities and, when they are, it is rare for refactoring to actually remove a smell. These somewhat surprising results indicate that it is critical to understand better the real quality issues that Android developers face, and to develop a model of code smells and refactoring that can better address their needs in practice.
Optimized Local Weber and Gradient Pattern-based medical image retrieval and optimized Convolutional Neural Network-based classification

This paper presents a novel image retrieval and classification model for medical images so-called as Content-Based Medical Image Retrieval. For the image retrieval, the main contribution of this paper is to develop a pattern descriptor termed as Optimized Local Weber and Gradient Pattern. A hybrid algorithm with the integration of two well-known meta-heuristic algorithms like Barnacles Mating Optimization and Jaya Algorithm, namely Jaya-based Barnacle Mating Optimization, is used for improving the Optimized Local Weber and Gradient Pattern-based image retrieval. Once the Optimized Local Weber and Gradient Pattern features are determined for training and testing images, the retrieval of images is performed by the logarithmic similarity computation. Moreover, an improved deep learning model called Optimized Convolutional Neural Network is employed for performing the image classification. As an improvement, the activation function of Convolutional Neural Network with sigmoid, tanh, Relu, and RRelu and the maximum epoch are optimized by the same Jaya-based Barnacle Mating Optimization. Finally, extensive experiments are carried out over publicly available datasets, demonstrating that the proposed retrieval and classification models are better than the existing models.
Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning

The emergence of smart technology has equipped humans with wearables and sensors that collect relevant data related to individuals and their surroundings. In healthcare, the collected data can monitor user emotion, behavior, and activity, which leads to the development of personalized decisions and improves lifestyle. In this paper, we analyzed the existing health fog framework and identified its limitations in terms of security, performance, and accuracy. Based on these limitations, we propose a secure health fog (SHF) framework that collects data from different Internet of Things (IoT) devices and maintains a personalized repository for adaptive model tuning. The adaptive model improves periodically based on user feedback and generates a personalized recommendation. Moreover, the existing IoT devices mostly rely on low-cost and low-power Zigbee technology, which is vulnerable to different attacks, such as device control, eavesdropping, fake device injection, malicious insider, man-in-the-middle, masquerading, message tampering, privacy leakage, and replay attack. Therefore, we propose a Zigbee Secure Health Fog (ZigbeeSHF) protocol, which uses symmetric and public-key cryptography to prevent these attacks. For data migration security, we concatenate the encrypted data with the encrypted digital signature to provide data authenticity, integrity, and confidentiality. To support our claims, we use the automated formal verification tools Scyther and AVISPA (Automated Validation of Internet Security Protocols and Applications), which evaluate the protocols based on the threat model and exploits the vulnerabilities in different attacking environments. The results of both tools ensure prevention against the mentioned attacks. As a proof of concept, we also evaluate the accuracy and performance of our proposed framework in a smart studio apartment. The result shows that the adaptive model tuned for an individual user is very effective, and the average accuracy of 32.5% is improved after one month. Furthermore, the proposed ZigbeeSHF protocol requires 7.93% and 25.35% more computation time than Zigbee with and without installation code, respectively.
Distributed Recommendation Inference on FPGA Clusters

Deep neural networks are widely used in personalized recommendation systems. Such models involve two major components: the memory-bound embedding layer and the computation-bound fully-connected layers. Existing solutions are either slow on both stages or only optimize one of them. To implement recommendation inference efficiently in the context of a real deployment, we design and implement an FPGA cluster optimizing the performance of both stages. To remove the memory bottleneck, we take advantage of the High-Bandwidth Memory (HBM) available on the latest FPGAs for highly concurrent embedding table lookups. To match the required DNN computation throughput, we partition the workload across multiple FPGAs interconnected via a 100 Gbps TCP/IP network. Compared to an optimized CPU baseline (16 vCPU, AVX2-enabled) and a one-node FPGA implementation, our system (four-node version) achieves 28.95× and 7.68× speedup in terms of throughput respectively. The proposed system also guarantees a latency of tens of microseconds per single inference, significantly better than CPU and GPU-based systems which take at least milliseconds.
Generative Adversarial Network (GAN): a general review on different variants of GAN and applications

Deep learning plays a very important role in the research area in the field of Artificial Intelligence (AI) and Machine Learning (ML) and many models have been developed based on GAN applications. Generative Adversarial Networks (GAN) is an unsupervised learning method which is based on a very popular logic known as zero-sum game theory for two players. The main objective of Generative Adversarial Networks is to calculate the distribution of original samples using discriminator and the work of Generator is to generate new samples from the real data samples. In current years, Generative Adversarial Networks (GAN) has made big development essentially withinside the area of computer vision, image classification, speech and language processing and so on. The paper explains about the basic introduction of GAN and different types of GAN were introduced with its applications. Original GAN version and its changed classical variations has been surveyed. The problems and limitations of GAN and the future work of GAN models were discussed.