Reviewerly/exHarmony huggingface dataset:
Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.
A new interval differential equation for edge detection and determining breast cancer regions in mammography images

Breast cancer is the most common form of cancer in women. The importance of diagnosing breast cancer is one of the important issues in medical science. Diagnosis of benign or malignant cancer is of great importance in addition to reducing costs in the direction of treatment. A non-destructive test method for early detection of breast cancer is image processing. Image processing has various uncertainties which are generated by different reasons such as sampling to noise, initial digitalization, intensity, and special domain. In this study, a strong image segmentation method based on interval uncertainties is proposed for the breast cancer images diagnosis. The main purpose of this paper is to improve the ordinary Sobel filter based on interval analysis by considering the intensity uncertainties. Simulation results have been implemented on MIAS which is an applicable database for breast cancer detection. The results of the proposed method have been compared with some state of the art methods such as LoG, Prewitt and canny filters. Final results showed that using the proposed method gives better achievement than the others by considering some kinds of uncertainties like Gaussian noise and salt and pepper noise.
SLAKE

To determine the exploitability for a kernel vulnerability, a secu- rity analyst usually has to manipulate slab and thus demonstrate the capability of obtaining the control over a program counter or performing privilege escalation. However, this is a lengthy process because (1) an analyst typically has no clue about what objects and system calls are useful for kernel exploitation and (2) he lacks the knowledge of manipulating a slab and obtaining the desired layout. In the past, researchers have proposed various techniques to facilitate exploit development. Unfortunately, none of them can be easily applied to address these challenges. On the one hand, this is because of the complexity of the Linux kernel. On the other hand, this is due to the dynamics and non-deterministic of slab variations. In this work, we tackle the challenges above from two perspectives. First, we use static and dynamic analysis techniques to explore the kernel objects, and the corresponding system calls useful for exploitation. Second, we model commonly-adopted exploitation methods and develop a technical approach to facilitate the slab layout adjustment. By extending LLVM as well as Syzkaller, we implement our techniques and name their combination after SLAKE. We evaluate SLAKE by using 27 real-world kernel vulnerabilities, demonstrating that it could not only diversify the ways to perform kernel exploitation but also sometimes escalate the exploitability of kernel vulnerabilities.
PRINCE

Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.
Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee

Over-parameterized deep neural networks trained by simple first-order methods are known to be able to fit any labeling of data. Such over-fitting ability hinders generalization when mislabeled training examples are present. On the other hand, simple regularization methods like early-stopping can often achieve highly nontrivial performance on clean test data in these scenarios, a phenomenon not theoretically understood. This paper proposes and analyzes two simple and intuitive regularization methods: (i) regularization by the distance between the network parameters to initialization, and (ii) adding a trainable auxiliary variable to the network output for each training example. Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the clean data distribution despite being trained using noisy labels. Our generalization analysis relies on the connection between wide neural network and neural tangent kernel (NTK). The generalization bound is independent of the network size, and is comparable to the bound one can get when there is no label noise. Experimental results verify the effectiveness of these methods on noisily labeled datasets.
OpenNIR: A Complete Neural Ad-Hoc Ranking Pipeline

With the growing popularity of neural approaches for ad-hoc ranking, there is a need for tools that can effectively reproduce prior results and ease continued research by supporting current state-of-the-art approaches. Although several excellent neural ranking tools exist, none offer an easy end-to-end ad-hoc neural raking pipeline. A complete pipeline is particularly important for ad-hoc ranking because there are numerous parameter settings that have a considerable effect on the ultimate performance yet often are under-reported in current work (e.g., initial ranking settings, re-ranking threshold, training sampling strategy, etc.). In this work, I present a complete ad-hoc neural ranking pipeline which addresses these shortcomings: OpenNIR. The pipeline is easy to use (a single command will download required data, train, and evaluate a model), yet highly configurable, allowing for continued work in areas that are understudied. Aside from the core pipeline, the software also includes several bells and whistles that make use of components of the pipeline, such as performance benchmarking and tuning of unsupervised ranker parameters for fair comparisons against traditional baselines. The pipeline and these capabilities are demonstrated. The code is available, and contributions are welcome.
A virtual-physical collision detection interface for AR-based interactive teaching of robot

At present, online lead-through and offline programming methods are widely used in programming of industrial robots. However, both methods have some drawbacks for unskilled shopworkers. This paper presents an Augmented Reality (AR)-based interactive robot teaching programming system, which virtually projected the robot onto the physical industrial environment. The unskilled shopworkers can use Handheld Teaching Device (HTD) to move end-effector of virtual robot to follow endpoint of the HTD. In this way, the path of the virtual robot can be planned or tested interactively. In addition, collisions detection between virtual robot and physical environment is key to test the feasibility of robot path. So, a method for detecting virtual-physical collisions is presented in this paper by comparing the depth values of corresponding pixels in depth image acquired by Kinect and computer-generated image in order to get collision-free paths of the virtual robot. The Quadtree model is used to accelerate the collision detection process and get distance between virtual model and physical environment. Using the AR-based interactive robot teaching programming system presented in this paper, all workers even unskilled ones in robot programming, can quickly and effectively get the collision-free robot path.
Learning to Continually Learn

Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work has gone towards preventing the default tendency of machine learning models to catastrophically forget, yet virtually all such work involves manually-designed solutions to the problem. We instead advocate meta-learning a solution to catastrophic forgetting, allowing AI to learn to continually learn. Inspired by neuromodulatory processes in the brain, we propose A Neuromodulated Meta-Learning Algorithm (ANML). It differentiates through a sequential learning process to meta-learn an activation-gating function that enables context-dependent selective activation within a deep neural network. Specifically, a neuromodulatory (NM) neural network gates the forward pass of another (otherwise normal) neural network called the prediction learning network (PLN). The NM network also thus indirectly controls selective plasticity (i.e. the backward pass of) the PLN. ANML enables continual learning without catastrophic forgetting at scale: it produces state-of-the-art continual learning performance, sequentially learning as many as 600 classes (over 9,000 SGD updates).
Online sequential extreme learning machine based adaptive control for wastewater treatment plant

Wastewater Treatment Plant (WWTP) is challenging to regulate for its complex chemical and biological characteristics, and its precise mathematical model is usually not accessible due to the limitation of available measurement. Traditional methods highly rely on human intervention and cannot adapt to the varying environment. Meanwhile, adaptive neural network based control strategies are encountered with dilemmas of local minima, slow convergence, huge time consumption, and low efficiency. To overcome such challenges, in this paper, a novel Online Sequential Extreme Learning Machine (OS-ELM) based adaptive control is discussed. In contrast, no a prior human experience or off-line training phase is required. It also has fast training speed thanks to randomly generated parameters of the hidden layer and Moore–Penrose pseudo-inverse. Moreover, the performance of the system is guaranteed even in the presence of time-varying dynamics and uncertainties through a learning mechanism in an online manner. The stability of the closed-loop system is shown strictly and extensive comparison case studies substantiate the feasibility of the scheme.
A Holistic Cybersecurity Maturity Assessment Framework for Higher Education Institutions in the United Kingdom

As organisations are vulnerable to cyberattacks, their protection becomes a significant issue. Capability Maturity Models can enable organisations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light, web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the United Kingdom. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security regulations, privacy regulations, and best practices that HEIs must be compliant to, and can be used as a self assessment or a cybersecurity audit tool.
Learning a tree-structured Ising model in order to make predictions

We study the problem of learning a tree Ising model from samples such that subsequent predictions made using the model are accurate.The prediction task considered in this paper is that of predicting the values of a subset of variables given values of some other subset of variables.Virtually all previous work on graphical model learning has focused on recovering the true underlying graph.We define a distance ("small set TV" or ssTV) between distributions P and Q by taking the maximum, over all subsets S of a given size, of the total variation between the marginals of P and Q on S; this distance captures the accuracy of the prediction task of interest.We derive nonasymptotic bounds on the number of samples needed to get a distribution (from the same class) with small ssTV relative to the one generating the samples.One of the main messages of this paper is that far fewer samples are needed than for recovering the underlying tree, which means that accurate predictions are possible using the wrong tree.
Articulating Experience

Third wave HCI initiated a slow transformation in the methods of UX research: from widely used quantitative approaches to more recently employed qualitative techniques. Articulating the nuances, complexity, and diversity of a user's experience beyond surface descriptions remains a challenge within design. One qualitative method — micro-phenomenology — has been used in HCI/Design research since 2001. Yet, no systematic understanding of micro-phenomenology has been presented, particularly from the perspective of HCI/Design researchers who actively use it in design contexts. We interviewed 5 HCI/Design experts who utilize micro-phenomenology and present their experiences with the method. We illustrate how this method has been applied by the selected experts through developing a practice, and present conditions under which the descriptions of the experience unfold, and the values that this method can provide to HCI/Design field. Our contribution highlights the value of micro-phenomenology in articulating the experience of designers and participants, developing vocabulary for multi-sensory experiences, and unfolding embodied tacit knowledge.
The Histological Diagnosis of Colonic Adenocarcinoma by Applying Partial Self Supervised Learning

Abstract Background The cancer of colon is one of the important cause of morbidity and mortality in adults. For the management of colonic carcinoma, the definitive diagnosis depends on the histological examination of biopsy specimens. With the development of whole slide imaging, the convolutional neural networks are being applied to diagnose colonic carcinoma by digital image analysis. Aim The main aim of the current study is to assess the application of deep learning for the histopathological diagnosis of colonic adenocarcinoma by analysing the digitized pathology images. Materials &amp; Methods The images of colonic adenocarcinoma and non neoplastic colonic tissue have been acquired from the two datasets. The first dataset contains ten thousand images which were used to train and validate the convolutional neural network (CNN) architecture. From the second dataset (Colorectal Adenocarcinoma Gland (CRAG) Dataset) 40% of the images were used as a train set while 60% of the images were used as test dataset. Two histopathologists also evaluated these images. In this study, three variants of CNN (ResNet-18, ResNet-34 and ResNet-50) have been employed to evaluate the images. Results In the present study, three CNN architectures(ResNet-18, ResNet-30, and ResNet-50) were applied for the classification of digitized images of colonic tissue. The accuracy (93.91%) of ResNet-50 was the highest which is followed by ResNet-30 and ResNet-18 with the accuracy of 93.04% each. Conclusion Based on the findings of the present study and analysis of previously reported series, the development of computer aided technology to evaluate the surgical specimens for the diagnosis of malignant tumors could provide a significant assistance to pathologists.
RGB-T Saliency Detection via Low-Rank Tensor Learning and Unified Collaborative Ranking

Saliency detection is a significant research topic in the field of image processing and computer vision. Currently, most saliency detection methods are applied to RGB images, so that they may encounter adverse scenarios characterized by complex background, inclement weather, and low illumination. Fusing complementary advantages of RGB and thermal infrared (RGB-T) images can effectively boost saliency detection performance. Therefore, we propose a novel RGB-T saliency detection method in this letter. To this end, we first regard superpixels as graph nodes and calculate the affinity matrix for each feature. Then, we propose a low-rank tensor learning model for the graph affinity, which can suppress redundant information and improve the relevance of similar image regions. Finally, a novel ranking algorithm is proposed to jointly obtain the optimal affinity matrix and saliency values under a unified structure. Test results on two RGB-T datasets illustrate the proposed method performs well when against the state-of-the-art algorithms.
Predictions of Subjective Ratings and Spoofing Assessments of Voice Conversion Challenge 2020 Submissions

The Voice Conversion Challenge 2020 is the third edition under its flagship that promotes intra-lingual semiparallel and cross-lingual voice conversion (VC). While the primary evaluation of the challenge submissions was done through crowd-sourced listening tests, we also performed an objective assessment of the submitted systems. The aim of the objective assessment is to provide complementary performance analysis that may be more beneficial than the time-consuming listening tests. In this study, we examined five types of objective assessments using automatic speaker verification (ASV), neural speaker embeddings, spoofing countermeasures, predicted mean opinion scores (MOS), and automatic speech recognition (ASR). Each of these objective measures assesses the VC output along different aspects. We observed that the correlations of these objective assessments with the subjective results were high for ASV, neural speaker embedding, and ASR, which makes them more influential for predicting subjective test results. In addition, we performed spoofing assessments on the submitted systems and identified some of the VC methods showing a potentially high security risk.
Detection of Face Recognition Adversarial Attacks

Deep Learning methods have become state-of-the-art for solving tasks such as Face Recognition (FR). Unfortunately, despite their success, it has been pointed out that these learning models are exposed to adversarial inputs – images to which an imperceptible amount of noise for humans is added to maliciously fool a neural network – thus limiting their adoption in sensitive real-world applications. While it is true that an enormous effort has been spent to train robust models against this type of threat, adversarial detection techniques have recently started to draw attention within the scientific community. The advantage of using a detection approach is that it does not require to re-train any model; thus, it can be added to any system. In this context, we present our work on adversarial detection in forensics mainly focused on detecting attacks against FR systems in which the learning model is typically used only as features extractor. Thus, training a more robust classifier might not be enough to counteract the adversarial threats. In this frame, the contribution of our work is four-fold: (i) we test our proposed adversarial detection approach against classification attacks, i.e., adversarial samples crafted to fool an FR neural network acting as a classifier; (ii) using a k-Nearest Neighbor (k-NN) algorithm as a guide, we generate deep features attacks against an FR system based on a neural network acting as features extractor, followed by a similarity-based procedure which returns the query identity; (iii) we use the deep features attacks to fool an FR system on the 1:1 face verification task, and we show their superior effectiveness with respect to classification attacks in evading such type of system; (iv) we use the detectors trained on the classification attacks to detect the deep features attacks, thus showing that such approach is generalizable to different classes of offensives.
An investigation of cross-project learning in online just-in-time software defect prediction

Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting whether software changes are defect-inducing or clean based on machine learning classifiers. Building such classifiers requires a sufficient amount of training data that is not available at the beginning of a software project. Cross-Project (CP) JIT-SDP can overcome this issue by using data from other projects to build the classifier, achieving similar (not better) predictive performance to classifiers trained on Within-Project (WP) data. However, such approaches have never been investigated in realistic online learning scenarios, where WP software changes arrive continuously over time and can be used to update the classifiers. It is unknown to what extent CP data can be helpful in such situation. In particular, it is unknown whether CP data are only useful during the very initial phase of the project when there is little WP data, or whether they could be helpful for extended periods of time. This work thus provides the first investigation of when and to what extent CP data are useful for JIT-SDP in a realistic online learning scenario. For that, we develop three different CP JIT-SDP approaches that can operate in online mode and be updated with both incoming CP and WP training examples over time. We also collect 2048 commits from three software repositories being developed by a software company over the course of 9 to 10 months, and use 19,8468 commits from 10 active open source GitHub projects being developed over the course of 6 to 14 years. The study shows that training classifiers with incoming CP+WP data can lead to improvements in G-mean of up to 53.90% compared to classifiers using only WP data at the initial stage of the projects. For the open source projects, which have been running for longer periods of time, using CP data to supplement WP data also helped the classifiers to reduce or prevent large drops in predictive performance that may occur over time, leading to up to around 40% better G-Mean during such periods. Such use of CP data was shown to be beneficial even after a large number of WP data were received, leading to overall G-means up to 18.5% better than those of WP classifiers.