Reviewerly/exHarmony huggingface dataset:
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies

With the increased attention and legislation for data-privacy, collaborative machine learning (ML) algorithms are being developed to ensure the protection of private data used for processing. Federated learning (FL) is the most popular of these methods, which provides privacy preservation by facilitating collaborative training of a shared model without the need to exchange any private data with a centralized server. Rather, an abstraction of the data in the form of a machine learning model update is sent. Recent studies showed that such model updates may still very well leak private information and thus a more structured risk assessment is needed. In this chapter, we analyze existing vulnerabilities of FL and subsequently perform a literature review of the possible attack methods targeting FL privacy protection capabilities. These attack methods are then categorized by a basic taxonomy. Additionally, we provide a literature study of the most recent defensive strategies and algorithms for FL aimed to overcome these attacks. These defensive strategies are categorized by their respective underlying defense principle. The chapter advocates that the application of a single defensive strategy is not enough to provide adequate protection against all available attack methods.
HydRand: Efficient Continuous Distributed Randomness

A reliable source of randomness is not only an essential building block in various cryptographic, security, and distributed systems protocols, but also plays an integral part in the design of many new blockchain proposals. Consequently, the topic of publicly-verifiable, bias-resistant and unpredictable randomness has recently enjoyed increased attention. In particular random beacon protocols, aimed at continuous operation, can be a vital component for current Proof-of-Stake based distributed ledger proposals. We improve upon previous random beacon approaches with HydRand, a novel distributed protocol based on publicly-verifiable secret sharing (PVSS) to ensure unpredictability, bias-resistance, and public-verifiability of a continuous sequence of random beacon values. Furthermore, HydRand provides guaranteed output delivery of randomness at regular and predictable intervals in the presence of adversarial behavior and does not rely on a trusted dealer for the initial setup. Compared to existing PVSS based approaches that strive to achieve similar properties, our solution improves scalability by lowering the communication complexity from $\mathcal{O}\left( {{n^3}} \right)$ to $\mathcal{O}\left( {{n^2}} \right)$ . Furthermore, we are the first to present a detailed comparison of recently described schemes and protocols that can be used for implementing random beacons.
Beyond Vision: A Multimodal Recurrent Attention Convolutional Neural Network for Unified Image Aesthetic Prediction Tasks

Over the past few years, image aesthetic prediction has attracted increasing attention because of its wide applications, such as image retrieval, photo album management and aesthetic-driven image enhancement. However, previous studies in this area only achieve limited success because 1) they primarily depend on visual features and ignore textual information. 2) they tend to focus equally on to each part of images and ignore the selective attention mechanism. This paper overcomes these limitations by proposing a novel multimodal recurrent attention convolutional neural network (MRACNN). More specifically, the MRACNN consists of two streams: the vision stream and the language stream. The former employs the recurrent attention network to tune out irrelevant information and focuses on some key regions to extract visual features. The latter utilizes the Text-CNN to capture the high-level semantics of user comments. Finally, a multimodal factorized bilinear (MFB) pooling approach is used to achieve effective fusion of textual and visual features. Extensive experiments demonstrate that the proposed MRACNN significantly outperforms state-of-the-art methods for unified aesthetic prediction tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction.
Enhancing QUasi-Affine TRansformation Evolution (QUATRE) with adaptation scheme on numerical optimization

Optimization problems exists extensively in real life, especially in science and engineering. Over the past decades, various optimization techniques have been developed to solve complex optimization problems in different areas especially that are unable to be solved by traditional methods. QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm is a new novel evolution structure for global optimization, which is a swarm based algorithm and use quasi-affine transformation approach for evolution. Nevertheless, there are still some weaknesses in these QUATRE variants. This paper presents a novel E-QUATRE algorithm in which an automatically generated evolution matrix with self-adaptive mechanism and an adaptive control parameter F are proposed for the enhancement of the QUATRE algorithm. Moreover, this paper also discusses the relationship between QUATRE algorithm, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm, all of which are also famous swarm based Stochastic Algorithms (SAs). Algorithm validation is conducted under CEC2013 test suite on single-objective numerical optimization, and E-QUATRE algorithm is compared with several famous Particle Swarm Optimization (PSO) variants, Differential Evolution (DE) variants and QUATRE variants. The experiment results indicate that the proposed E-QUATRE algorithm has a better performance than these swarm based algorithms with fixed population.
UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.
Patient decision-making personas: An application of a patient-centered cognitive task analysis (P-CTA)

Personas can be used to understand patterns of variation in patients’ performance of cognitive work, particularly self-care decision making. In this study, we used a patient-centered cognitive task analysis (P-CTA) to develop self-care decision-making personas. We collected data from 24 older adults with chronic heart failure and 14 support persons, using critical incident and fictitious scenario interviews. Qualitative analyses produced three personas but revealed that individuals exemplify different personas across situations. The Rule-Following persona seeks clear rules, exercises caution under uncertainty, and grounds actions in confidence in clinician experts. The Researching persona seeks information to gain better understanding, invents strategies, and conducts experiments independently or with clinicians. The Disengaging persona does not actively seek rules or information and does not attempt to reduce uncertainty or conduct experiments. We discuss the situational nature of personas, their use in design, and the benefits of P-CTA for studying patient decision making.
A Comparative Study for Breast Cancer Prediction using Machine Learning and Feature Selection

While there are many factors which could contribute to the occurrence of breast cancer, it is very difficult to attribute the exact environmental and other factors contributing to it, but still it has significance in determining the occurrence of cancer. Using machine learning techniques and regular diagnosis information, we can achieve our goal of assessing the risk of occurrence of breast cancer. Cancer data sets contain many attributes of patient information, but not every feature is relevant in predicting cancer. Feature selection techniques are useful in such scenarios for retaining the relevant feature set. In this paper we are doing a comparative study of the effect of feature selection techniques on the accuracies given by existing machine learning algorithms. For this purpose we have considered the following machine learning algorithms - Logistic Regression, Naive Bayes and Random Forest. The following feature selection techniques have been considered - Sequential Forward Feature Selection, Recursive Feature Elimination, f-test and correlation.The publicly available Breast Cancer Wisconsin (Diagnostic) Data Sets from UCI Repository have been used in this work. The results show that random forest algorithm gives the highest accuracy with feature selection. Furthermore f-test gives better results for the smaller dataset and Sequential Forward Selection for the larger dataset.
Unified Preprocessing and Enhancement Technique for Mammogram Images

In developed countries, breast cancer is one of the foremost reasons for the increase in mortality among women. Microcalcifications in breast tissue are one of the key indications appraised by the radiologist for identification of breast cancer in its early stage. To identifying such microcalcification, masses and architectural distortion in breast preprocessing in mammogram plays a vital role. Additional imaging provides bit more information than an initial screening, and more focus is made to the sceptical masses. For this cause, preprocessing of mammogram images is essential in the process of breast cancer examination as it could reduce the rate of false positive. Prior diagnosis of cancer and other abnormalities in human breast, digital mammogram has appeared as the most accepted screening approach. This manuscript presents contrast enhancement, by using the contrast limited adaptive histogram equalization (CLAHE) and thresholding methods for detecting the breast tumor boundaries from digital mammogram. The proposed techniques were applied in the MIAS database, which contains 322 mammogram images. The breast enhancement and segmentation technique by using thresholding provides promising results. To compare the performance of the studies, contrast improvement index (CII) is used as a performance evaluation parameter.
Image Forgery Detection using Deep Learning: A Survey

The information is shared in form of images through newspapers, magazines, internet, or scientific journals. Due to software like Photoshop, GIMP, and Coral Draw, it becomes very hard to differentiate between original image and tampered image. Traditional methods for image forgery detection mostly use handcrafted features. The problem with the traditional approaches of detection of image tampering is that most of the methods can identify a specific type of tampering by identifying a certain features in image. Nowadays, deep learning methods are used for image tampering detection. These methods reported better accuracy than traditional methods because of their capability of extracting complex features from image. In this paper, we present a detailed survey of deep learning based techniques for image forgery detection, outcomes of survey in form of analysis and findings, and details of publically available image forgery datasets.
Object detection based on SSD-ResNet

Nowadays, with the abundance and diversity of the data sets of the detected objects, detection and recognition technology has achieved excellent performance in learning effect. However, because the target objects are usually very small in many real-world applications while the background environment seldom varies, manually annotating these objects is extremely costly in time and manpower. These problems have challenged the learning effect of standard neural networks. In this paper, we propose a novel method to replace the original network structure and to extend the number of layers for detecting many kinds of dangerous goods among different background environments. Specifically, we employ SSD as the basic network structure and replace the inside VGG16 with a ResNet101 network. The experimental results show that the ResNet network is effective in detecting many kinds of dangerous objects in small data sets. The proposed model outperforms other neural networks in learning efficiency and accuracy.
Sign Language Generation System Based on Indian Sign Language Grammar

Sign Language (SL), also known as gesture-based language, is used by people with hearing loss to convey their messages. SL interpreters are required for people who do not have the knowledge of SL, but interpreters are not readily available. Thus, a machine-based translation system is required to translate the text into SL. In this article, a system is implemented for translating English text into Indian Sign Language (ISL). It acts as a tool for human-computer interaction and eliminates the need for an ISL human interpreter for communicating with people who have hearing loss. The system features a rich corpus of English words and commonly used sentences. It consists of components such as an ISL parser, the Hamburg Notation System, the Signing Gesture Mark-up Language, and 3D avatar animation for generating SL according to ISL grammar. The proposed system has been tested rigorously by SL users. The results proved that the proposed system is highly efficient and achieves an average score of accuracy (i.e., 4.2 for English words and 3.8 for sentences on a scale from 1 to 5). The performance of proposed system has also been evaluated using the BiLingual Evaluation Understudy score, which results in 0.95 accuracy. The proposed system and mobile application together has the potential to bring individuals with hearing loss and their entourage together.
Aleatoric and Epistemic Uncertainty with Random Forests

Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties, referring, respectively, to inherent randomness and a lack of knowledge, with random forests. More specifically, we show how two general approaches for measuring the learner’s aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.
Image compression using HAAR discrete wavelet transform

Image Compression aims at minimal the Storage and for the easy transmission Without affecting pictures quality. In this paper HAAR wavelet based Discrete Wavelet Transform (DWT) is done for the effective and efficient image compression..HAAR DWT provides an easy way of compression as the coefficient are either 1 or -1.The wavelet transforms are used for the time and frequency analysis. In this paper higher compression ratio is obtained after three levels of decompositon. The decomposed can be reconstructed without appreciable loss in the original image.
Synthesizing Facial Photometries and Corresponding Geometries Using Generative Adversarial Networks

Artificial data synthesis is currently a well-studied topic with useful applications in data science, computer vision, graphics, and many other fields. Generating realistic data is especially challenging, since human perception is highly sensitive to non-realistic appearance. In recent times, new levels of realism have been achieved by advances in GAN training procedures and architectures. These successful models, however, are tuned mostly for use with regularly sampled data such as images, audio, and video. Despite the successful application of the architecture on these types of media, applying the same tools to geometric data poses a far greater challenge. The study of geometric deep learning is still a debated issue within the academic community, as the lack of intrinsic parametrization inherent to geometric objects prohibits the direct use of convolutional filters, a main building block of today’s machine learning systems. In this article, we propose a new method for generating realistic human facial geometries coupled with overlayed textures. We circumvent the parametrization issue by utilizing a specialized non-rigid alignment procedure, and imposing a global mapping from our data to the unit rectangle. This mapping enables the representation of our geometric data as regularly sampled 2D images. We further discuss how to design such a mapping to control the distortion and conserve area within the target image. By representing geometric textures and geometries as images, we are able to use advanced GAN methodologies to generate new plausible textures and geometries. We address the often-neglected topic of relationship between texture and geometry and propose different methods for fitting generated geometries to generated textures. In addition, we widen the scope of our discussion and offer a new method for training GAN models on partially corrupted data. Finally, we provide empirical evidence demonstrating our generative model’s ability to produce examples of new facial identities, independent from the training data, while maintaining a high level of realism—two traits that are often at odds.
A robust line-tracking photogrammetry method for uplift measurements of railway catenary systems in noisy backgrounds

For electrical railways, contact wires are used to ensure stable and continuous power supply to trains operating on the infrastructure. One crucial design parameter used to control the catenary design is the vertical displacement. The traditional uplift measuring equipment of catenary systems usually requires that targets are installed on the wire, and/or that measurement devices are mounted on the support structure of catenary systems or temporary poles. These methods require track access and substantial time and manpower for installation and measurement. Thus, a portable vision-based tracking system is proposed to enable remote, non-contact and non-target uplift measurements of catenary systems. The challenge in measuring the catenary displacement is to detect a linear rigid body in a noisy background environment with a high sampling frequency. To detect the displacement, a vision-based system with a novel line-tracking technique is used to achieve robust and accurate measurement. The proposed technique is based on a coarse subset and line search and subpixel detection. The line-tracking method is successfully employed for the identification of the important vertical displacements of a contact wire in front of a noisy background. The accuracy, robustness and applicability of the proposed tracking method are demonstrated through numerical experiments, the field uplift measurement of the railway catenary system and the identification of the contact wire in a diverse city environment.
Applications of shapelet transform to time series classification of earthquake, wind and wave data

Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named “Shapelet transform”, which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly “white-box” machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.
Access Control in Fog Computing: Challenges and Research Agenda

Fog computing is an intermediate computing layer that has emerged to address the latency issues of cloud-based Internet of things (IoT) environments. As a result, new forms of security and privacy threats are emerging. These threats are mainly due to the huge number of sensors, as well as the enormous amount of data generated in IoT environments that needs to be processed in real time. These sensors send data to the cloud through the fog computing layer, creating an additional layer of vulnerabilities. In addition, the cloud by nature is vulnerable because cloud services can be located in different geographical locations and provided by multiple service providers. Moreover, cloud services can be hybrid and public, which exposes them to risks due to their infinite number of anonymous users. Access control (AC) is one of the essential prevention measures to protect data and services in computing environments. Many AC models have been implemented by researchers from academia and industry to address the problems associated with data breaches in pervasive computing environments. However, the question of which AC model(s) should be used to prevent unauthorized access to data remains. The selection of AC models for cloud-based IoT environments is highly dependent on the application requirements and how the AC models can impact the computation overhead. In this paper, we survey the features and challenges of AC models in the fog computing environment. We also discuss the diversity of different AC models. This survey provides the reader with state-of-the-art practices in the field of fog computing AC and helps to identify the existing gaps within the field.
Binomial-Polyadic Binary Data Encoding by Quantity of Series of Ones

The method of developing an effective syntactic representation of binary data arrays based on binomial-polyadic encoding is described. The created coding is based on the structural representation of arrays of binary data on the number of series of ones in binary polyadic space on the basis of vertical and horizontal schemes of coefficients weight calculation. This coding can be used in big data technologies to reduce time delays and improve the reliability of data transmission from any source of information in telecommunications systems, to improve the security of information resources based on the development of post-quantum cryptographic protection technologies. The evaluation of the maximum number of bits that can be assigned to represent the code numbers is performed.