Reviewerly/exHarmony huggingface dataset:
Deep learning-based histopathological image analysis for automated detection and staging of melanoma

With recent advances in digital microscopy, quantitative analysis of pathology images has become important in disease diagnosis as well as in understanding the underlying reasons. Digital whole slide images (WSI) are typically obtained from a biopsy slide using a high-resolution slide scanner. The hematoxylin and eosin (H&E) stain is typically used to stain the slides. However, immunospecific stains such as MART-1 (for melanoma) and PD-L1 (for lung cancer) are also sometimes used to validate detection. In advanced stages, cancer cells spread to lymph nodes through the lymph vessels. Therefore, a lymph node biopsy is typically done by doctors to determine if the cancer cells have invaded the lymph node system. High-resolution WSIs typically have very large size, in the order of a billion pixels. Hence, image analysis and diagnosis (quantitative as well as qualitative) is expected to take significant time. In this chapter, we present the state of the art on computer-aided diagnosis and grading of skin cancer/melanoma based on histopathology. The techniques generally include traditional image analysis modules such as preprocessing, segmentation, feature extraction, and classifications. Recently, deep learning techniques such as SegNet and U-Net have been used successfully in histopathological image analysis. The detailed implementation of our works on melanoma detection and proliferation index calculation using deep learning networks is presented and compared with traditional feature-based techniques. Experimental results show superior performance by the deep learning networks.
Arduino based voice controlled wheelchair

Abstract A voice controlled wheelchair prototype was developed using a commercially available manual wheelchair to assist people with both upper and lower limb disabilities. An Arduino microcontroller processes the voice command from the speech recognition module and controls the motor movement of the wheelchair. Bluetooth module was also used to do away with messy wiring and an optional joystick command was also incorporated into the prototype design. The success rate of the wheelchair to recognize the voice commands in English, Chinese and Malay was high. The overall cost of the prototype was kept low to make it affordable.
Heuristic Strategies of Modified Levenberg–Marquardt Algorithm for Fitting Transition Curves

Horizontal curve identification is important both for road safety management and railway maintenance. Parameters of a transition curve are introduced to perform an orthogonal least-squares fitting. During such a fitting process, the Gauss-Newton (GN) method may fail to converge because of an ill-conditioned Hessian matrix. A biobjective fitting model is introduced, and the Levenberg–Marquardt (LM) algorithm is specified to perform the fitting of transition curves. The LM parameter is updated heuristically during iterations according to the specific information explored instead of the standard preset way. Further, another heuristic strategy is proposed to search a path to the optimum instead of the traditional greedy strategy. The heuristic strategies were compared with traditional ones by fitting a transition curve of a railway to the measured points. Monte Carlo simulations were employed to test the robustness and efficiency of the modified LM algorithm, with different initial values, all converging to the same optimum. Results showed that the heuristic strategy for updating the LM parameter has a better robustness than the preset way, and the heuristic strategy for searching a path converges much faster than the traditional one, for which visual interpretations are provided.
Perceptual Texture Similarity Estimation: An Evaluation of Computational Features

Estimation of texture similarity is fundamental to many material recognition tasks. This study uses fine-grained human perceptual similarity ground-truth to provide a comprehensive evaluation of 51 texture feature sets. We conduct two types of evaluation and both show that these features do not estimate similarity well when compared against human agreement rates, but that performances are improved when the features are combined using a Random Forest. Using a simple two-stage statistical model we show that few of the features capture long-range aperiodic relationships. We perform two psychophysical experiments which indicate that long-range interactions do provide humans with important cues for estimating texture similarity. This motivates an extension of the study to include Convolutional Neural Networks (CNNs) as they enable arbitrary features of large spatial extent to be learnt. Our conclusions derived from the use of two pre-trained CNNs are: that the large spatial extent exploited by the networks' top convolutional and first fully-connected layers, together with the use of large numbers of filters, confers significant advantage for estimation of perceptual texture similarity.
Normalization-Based Validity Index of Adaptive K-Means Clustering for Multi-Solution Application

Validity evaluation aims to analyze the quality of the clustering algorithm with different measurement criteria.A variety of assessment methods have been introduced in the application of pattern recognition and computer vision.Although it is well known that mining information of massive data is essential, most of the validity indices only provide a single partitioning scheme for clustering validation.Moreover, the conventional evaluation algorithm is susceptible to the density and dimension of the dataset, which leads to assessment failure.In this paper, a normalization-based validity index (NbVI) is proposed for validity evaluation of the adaptive K-means clustering from a multi-solution perspective.According to the concept of high-compact within clusters and high-separation among groups, NbVI attempts to find the maximum relative ratio between normalized inter-distance and normalized intra-distance.The experimental results demonstrate that the proposed NbVI method exhibits excellent performance for the clustering of the density-unbalanced dataset for multi-solution applications.Moreover, the NbVI validation shows high versatility using different clustering algorithms.
Proof of Concept Blockchain-based Voting System

Blockchain is becoming the missing puzzle to solve many digital services problems these days. In this paper, we propose a design and implementation of a Blockchain-based voting system that can be used in national elections. In the paper, we argue that our Blockchain-based voting system is more secure, reliable and it has the ability to protect voter privacy which will help boost the number of voters and their trust in the electoral system as well as reducing considerably the cost of national elections.
Bidirectional Gated Recurrent Units For Human Activity Recognition Using Accelerometer Data

Human activity recognition aims to detect the type of human movement based on sensor data gathered during human activity. Time series classification using deep learning approaches offers opportunities to avoid intensive handcrafted feature extraction techniques where the efficiency and the accuracy are heavily dependent on the quality of variables defined by domain experts. In this paper, we apply recurrent neural networks on data collected from mobile phone accelerometers for the recognition of human activity. More specifically, we use the bidirectional gated recurrent units mechanism. The results show that this technique is promising and provides high quality recognition results.
DeepMoTIon: Learning to Navigate Like Humans

We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as Deep-MoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the components of our network and prove their necessity to imitate humans. Our experiments show that DeepMoTIion outperforms all the benchmarks in terms of human imitation, achieving a 24% reduction in time series-based path deviation over the next best approach. In addition, while many other approaches often failed to reach the target, our method reached the target in 100% of the test cases while complying with social norms and ensuring human safety.
Twitter trolls: a linguistic profile of anti-democratic discourse

This article focuses on anti-democratic discourse and investigates the linguistic profile of Twitter trolls. The troll data consist of some 3.5 million messages in English obtained through Twitter in late 2018. These data originate from potentially state-backed information operations aimed at sowing discord in Western societies. The baseline data, against which the troll data are compared, contain circa 4.4 million messages in English drawn from the Nordic Tweet Stream corpus. A machine learning application that enables us to select genuine personal messages in this corpus is used to prune the data. The empirical part investigates frequency-based characteristics of the two datasets. We utilize a set of automatically-extracted word-list information and the observed frequencies of personal pronouns. Our empirical findings show considerable quantitative differences so that the troll data are shorter, make use of a smaller number of lexical types and tokens, and resemble more formal registers, while the personal messages are more spoken-like. The results could be used to improve automated detection systems whose purpose is to identify troll accounts.
Meshing agile and plan-driven development in safety-critical software: a case study

Organizations developing safety-critical software are increasingly seeking to create better practices by meshing agile and plan-driven development processes. Significant differences between the agile and the plan-driven processes make meshing difficult, and very little empirical evidence on using agile processes for safety-critical software development exists. There are four areas of concern, in particular, for meshing the development of safety-critical software concerning: documentation, requirements, life cycle and testing. We report on a case study of a pharmaceutical organization in which a Scrum process was implemented to support agile software development in a plan-driven safety-critical project. The purpose was to answer the following research question: For safety-critical software, what can a software team do to mesh agile and plan-driven processes effectively? The main contribution of the paper is an elaborated understanding of meshing in the four areas of concern and how the conditions for safety-critical software influence them. We discuss how meshing within the four areas of concern is a contribution to existing research.
Fuzzy-Based Histogram Partitioning for Bi-Histogram Equalisation of Low Contrast Images

The conventional histogram equalisation (CHE), though being simple and widely used technique for contrast enhancement, but fails to preserve the mean brightness and natural appearance of images. Most of the improved histogram equalisation (HE) methods give better performance in terms of one or two metrics and sacrifice their performance in terms of other metrics. In this paper, a novel fuzzy based bi-HE method is proposed which equalises low contrast images optimally in terms of all considered metrics. The novelty of the proposed method lies in selection of fuzzy threshold value using level-snip technique which is then used to partition the histogram into segments. The segmented sub-histograms, like other bi-HE methods, are equalised independently and are combined together. Simulation results show that for wide-range of test images, the proposed method improves the contrast while preserving other characteristics and provides good trade-off among all the considered performance metrics.
Cautious Adaptation of Defiant Components

Systems-of-systems are formed by the composition of independently created software components. These components are designed to satisfy their individual requirements, rather than the global requirements of the systems-of-systems. We refer to components that cannot be adapted to meet both individual and global requirements as components. In this paper, we propose a cautious adaptation approach which supports changing the behaviour of such defiant components under exceptional conditions to satisfy global requirements, while continuing to guarantee the satisfaction of the components' individual requirements. The approach represents both normal and exceptional conditions as scenarios; models the behaviour of exceptional conditions as wrappers implemented using an aspect-oriented technique; and deals with both single and multiple instances of defiant components with different precedence order at runtime. We evaluated an implementation of the approach using drones and boats for an organ delivery application conceived by our industrial partners, in which we assess how the proposed approach help achieve the system-of-systems' global requirements while accommodating increased complexity of hybrid aspects such as multiplicity, precedence ordering, openness, and heterogeneity.
“The Best of Both Worlds!”

Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences “better.” Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users’ interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of interest or AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach that integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users’ interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground-truth segmentation compared to its individual components.
Blockchain-Based Coordination: Assessing the Expressive Power of Smart Contracts

A common use case for blockchain smart contracts (SC) is that of governing interaction amongst mutually untrusted parties, by automatically enforcing rules for interaction. However, while many contributions in the literature assess SC computational expressiveness, an evaluation of their power in terms of coordination (i.e., governing interaction) is still missing. This is why in this paper we test mainstream SC implementations by evaluating their expressive power in coordinating both inter-users and inter-SC activities. To do so, we exploit the archetypal Linda coordination model as a benchmark—a common practice in the field of coordination models and languages—by discussing to what extent mainstream blockchain technologies support its implementation. As they reveal some notable limitations (affecting, in particular, coordination between SC) we then show how Tenderfone, a custom blockchain implementation providing for a more expressive notion of SC, addresses the aforementioned limitations.
Research on Key Technologies of Personalized Education Resource Recommendation System Based on Big Data Environment

Abstract This paper focuses on the construction of personalized education resource recommendation system and the research of key technologies in the big data environment, designs a personalized recommendation system architecture that can handle PB education resources, and implements an educational resource personalized recommendation system based on this architecture. A hybrid recommendation algorithm based on content and collaborative filtering is designed to meet the recommendation novelty requirements and solve the cold start problem of the recommendation system. Research a flexible, reliable, high-performance personalized recommendation system architecture that can store and process PB-level data and can be recommended in real time, research personalized recommendation engine, data pre-processing and data mining model construction, and implement the recommendation system. It is important to determine the availability of the system by performing related functions and performance verification.
Analyzing Uncertainty in Complex Socio-Ecological Networks

Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty. The aim of this paper is to analyze the impact of the Bayesian network structure on the uncertainty of the model, expressed as the Shannon entropy. In particular, three strategies for model structure have been followed: naive Bayes (NB), tree augmented network (TAN) and network with unrestricted structure (GSS). Using these network structures, two experiments are carried out: (1) the impact of the Bayesian network structure on the entropy of the model is assessed and (2) the entropy of the posterior distribution of the class variable obtained from the different structures is compared. The results show that GSS constantly outperforms both NB and TAN when it comes to evaluating the uncertainty of the entire model. On the other hand, NB and TAN yielded lower entropy values of the posterior distribution of the class variable, which makes them preferable when the goal is to carry out predictions.
Hybrid-loss supervision for deep neural network

Multi-loss-joint-optimization has been proven to be valid in computer vision literature. However, the learned deep sub-features usually fit their disjoint constraints, which yield confrontation and spatial inconsistency among the sub-features with nonshared FC layers. In this paper, we propose a Hybrid-loss supervision (HLS) framework in order to obtain smoother and more spatially consistent features with shared FC layers. First, we analyze the shortcomings of the monitoring with single-loss in the existing framework theoretically. Then, we selected two notable loss functions (e.g., Center loss and Weighted loss) to instantiate the HLS framework by linear combination. By instantiating the framework with two standard loss functions, the network has learned more compact intra-class deep features and uniform inter-class deep features. The HLS framework can significantly boost the efficiency of existing convolution networks for both image classification task and object detection task without increasing network parameters and computational complexity. Extensive experimental results on different vision tasks demonstrate consistent improvement can be achieved across a variety of datasets (e.g., CIFAR-10/100, ImageNet-2012, PASCAL VOC and MS-COCO) and different convolutional neural network architectures.
A Survey of Exploitation Techniques and Defenses for Program Data Attacks

In recent years, program data attacks (PDA) have become a popular topic in the field of network security. PDAs are memory corruption vulnerability exploitation techniques that target the control plane and data plane in the target program without injecting any code. We divide PDAs into return-oriented programming (ROP) attacks and data-oriented programming (DOP) attacks, and pioneering research has shown that PDAs are Turing-complete. However, although PDAs have been known for a long time, their threats have not been adequately addressed. In this survey, we followed a systematic research method to introduce a series of statistical analyses of a corpus of publications; then, we systematized the current knowledge on PDA exploits and effective defence mechanisms. Methods have been classified according to the approach that they follow and we have identified three protection approaches: L1 defence, L2 defence and L3 defence. This systematization suggests that stronger policies are needed, especially protection methods against DOP attacks. Furthermore, the summarization of current techniques shows that performance and compatibility problems are the main barriers to widespread adoption. We remind researchers in the security community to recognize the significance of these properties in the real world and hope that this systematization of knowledge will help them find new solutions to make progress in this area.
SSL Malicious Traffic Detection Based On Multi-view Features

In recent years, as more and more softwares use SSL encryption protocol to improve the security and integrity of communications, the encrypted traffic is growing, which brings new challenges to cyber attack detection. Since most of the SSL traffic is unreadable ciphertext, traditional pattern recognition and deep packet inspection are not applicable. In addition, the current machine learning methods are not fully applicable to encrypted traffic detection. The detection of encrypted malicious traffic is still an open problem. In this paper, we propose an SSL malicious traffic detection method based on multi-view features. Our method comprehensively extracts features from multiple views, including flow statistics, SSL handshake field, and certificate to retain key original information. We test four machine learning models, i.e., SVM, Decision Tree, Random Forest, and XGBoost on the CTU Malware dataset. The results show that XGBoost performs best reaching an accuracy of 97.71%, which is better than other studies on the CTU dataset.
Depth-guided view synthesis for light field reconstruction from a single image

Light field imaging has recently become a promising technology for 3D rendering and displaying. However, capturing real-world light field images still faces many challenges in both the quantity and quality. In this paper, we develop a learning based technique to reconstruct light field from a single 2D RGB image. It includes three steps: unsupervised monocular depth estimation, view synthesis and depth-guided view inpainting. We first propose a novel monocular depth estimation network to predict disparity maps of each sub-aperture views from the central view of light field. Then we synthesize the initial sub-aperture views by using the warping scheme. Considering that occlusion makes synthesis ambiguous for pixels invisible in the central view, we present a simple but effective fully convolutional network (FCN) for view inpainting. Note that the proposed network architecture is a general framework for light field reconstruction, which can be extended to take a sparse set of views as input without changing any structure or parameters of the network. Comparison experiments demonstrate that our method outperforms the state-of-the-art light field reconstruction methods with single-view input, and achieves comparable results with the multi-input methods.
Cauchy noise removal using group-based low-rank prior

Although the extensive research on Gaussian noise removal, few works consider the Cauchy noise removal problem. In this paper, we propose a novel group-based low-rank method for Cauchy noise removal. By exploiting the nonlocal self-similarity of natural images, we consider a group of similar patches as an approximate low-rank matrix, and formulate the denoising of each group as a low-rank matrix recovery problem. Meanwhile, we develop the alternating direction method of multipliers algorithm to solve the proposed nonconvex model with guaranteed convergence. Experiments illustrate that our method has superior performance over the state-of-the-art methods in terms of both visual and quantitative measures.