Reviewerly/exHarmony huggingface dataset:
Visualization and Analysis of Environmental Data

The virtual exploration of place has been employed in a variety of learning environments across many disciplines, creatively expanding upon the experience of place. This chapter explores the value of mapping environmental data as a tool that can enhance students' virtual exploration of place as they investigate local environmental policies and problems within their own urban surroundings. This visualization project engages students in making meaningful connections between the theoretical study of local and global environmental problems and the "observation" and investigation of these data using mapped data. The virtual learning environment is viewed as one that is interactive, exploring how the resources of urban settings can be cultivated in designing virtual methodologies, and where the experience of place can be realized as geographic place.
Decision-making processes in academic libraries: How did academic librarians purchase e-book products?

In this study I use interviews to examine the decision-making processes employed by academic librarians when purchasing e-book products. I introduce Nutt's five-stage framework with some minor revisions and then analyze the different steps of the decision-making processes as described by the interviewees. I put my data into five different models to investigate the variations and similarities across the described decision processes, and then examine the differences between my models and Nutt's models correspondingly.
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams

Continual learning from streaming data sources becomes more and more popular due to the increasing number of online tools and systems. Dealing with dynamic and everlasting problems poses new challenges for which traditional batch-based offline algorithms turn out to be insufficient in terms of computational time and predictive performance. One of the most crucial limitations is that we cannot assume having an access to a finite and complete data set – we always have to be ready for new data that may complement our model. This poses a critical problem of providing labels for potentially unbounded streams. In real world, we are forced to deal with very strict budget limitations, therefore, we will most likely face the scarcity of annotated instances, which are essential in supervised learning. In our work, we emphasize this problem and propose a novel instance exploitation technique. We show that when: (i) data is characterized by temporary non-stationary concepts, and (ii) there are very few labels spanned across a long time horizon, it is actually better to risk overfitting and adapt models more aggressively by exploiting the only labeled instances we have, instead of sticking to a standard learning mode and suffering from severe underfitting. We present different strategies and configurations for our methods, as well as an ensemble algorithm that attempts to maintain a sweet spot between risky and normal adaptation. Finally, we conduct a complex in-depth comparative analysis of our methods, using state-of-the-art streaming algorithms relevant for the given problem.
Comparing Video Activity Classifiers within a Novel Framework

Video activity classification has many applications. It is challenging because of the diverse characteristics of different events. In this paper, we examined different approaches to event classification within a general framework for video activity detection and classification. In our experiments, we focused on event classification in which we explored a deep learning-based approach, a rule-based approach, and a hybrid combination of the previous two approaches. Experimental results using the well-known Video Image Retrieval and Analysis Tool (VIRAT) database showed that the proposed classification approaches within the framework are promising and more research is needed in this area
Confusing and Detecting ML Adversarial Attacks with Injected Attractors

Many machine learning adversarial attacks find adversarial samples of a victim model M by following the gradient of some attack objective functions, either explicitly or implicitly. To confuse and detect such attacks, we take the proactive approach that modifies those functions with the goal of misleading the attacks to some local minima, or to some designated regions that can be easily picked up by an analyzer. To achieve this goal, we propose adding a large number of artifacts, which we called attractors, onto the otherwise smooth function. An attractor is a point in the input space, where samples in its neighborhood have gradient pointing toward it. We observe that decoders of watermarking schemes exhibit properties of attractors and give a generic method that injects attractors from a watermark decoder into the victim model M. This principled approach allows us to leverage on known watermarking schemes for scalability and robustness and provides explainability of the outcomes. Experimental studies show that our method has competitive performance. For instance, for un-targeted attacks on CIFAR-10 dataset, we can reduce the overall attack success rate of DeepFool to 1.9%, whereas known defense LID, FS and MagNet can reduce the rate to 90.8%, 98.5% and 78.5% respectively.
Fundamental results for learning deterministic extended finite state machines from queries

Regular language inference, initiated by Angluin, has many developments, including applications in software engineering and testing. However, the capability of finite automata to model the system data is quite limited and, in many cases, extended finite state machine formalisms, that combine the system control with data structures, are used instead. The application of Angluin-style inference algorithms to extended state machines would involve constructing a minimal deterministic extended finite state machine consistent with a deterministic 3-valued deterministic finite automaton. In addition to the usual, accepting and rejecting, states of finite automaton, a 3-valued deterministic finite automaton may have "don't care" states; the sequences of inputs that reach such states may be considered as accepted or rejected, as is convenient. The aforementioned construction reduces to finding a minimal deterministic finite automaton consistent with a 3-valued deterministic finite automaton, that preserves the deterministic nature of the extended model that also handles the data structure associated with it. This paper investigates fundamental properties of extended finite state machines in relation to Angluin's language inference problem and provides an inference algorithm for such models.
Fixed Point Sets of k-Continuous Self-Maps of m-Iterated Digital Wedges

Let Ckn,l be a simple closed k-curves with l elements in Zn and W:=Ckn,l∨⋯∨Ckn,l︷m-times be an m-iterated digital wedges of Ckn,l, and F(Conk(W)) be an alignment of fixed point sets of W. Then, the aim of the paper is devoted to investigating various properties of F(Conk(W)). Furthermore, when proceeding with this work, this paper addresses several unsolved problems. To be specific, we firstly formulate an alignment of fixed point sets of Ckn,l, denoted by F(Conk(Ckn,l)), where l(≥7) is an odd natural number and k≠2n. Secondly, given a digital image (X,k) with X♯=n, we find a certain condition that supports n−1,n−2∈F(Conk(X)). Thirdly, after finding some features of F(Conk(W)), we develop a method of making F(Conk(W)) perfect according to the (even or odd) number l of Ckn,l. Finally, we prove that the perfectness of F(Conk(W)) is equivalent to that of F(Conk(Ckn,l)). This can play an important role in studying fixed point theory and digital curve theory. This paper only deals with k-connected digital images (X,k) such that X♯≥2.
Construction of feedforward neural networks with simple architectures and approximation abilities

The universal approximation property of feedforward neural networks (FNNs) is the basis for all FNNs applications. In almost all existing FNN approximation studies, it is always assumed that the activation function of the network satisfies certain conditions, such as sigmoid or bounded continuity. This paper focuses on building simple architecture FNNs with approximation ability by constructing an activation function. First, for any continuous function defined on [0, 1] and an arbitrary accuracy ϵ &gt; 0 , an FNN is constructed, which has only one neuron and a fixed weight 1 and can approximate the function with an accuracy of ϵ . Thereafter, we study the representation of FNNs for polynomial functions by constructing a proper activation function. It is proved that any algebraic polynomial with the degree n can be represented by an FNN. Further, a piecewise function is constructed as an activation function of an FNN such that the FNN represents the famous Bernstein polynomials.
A Visual Tracker Offering More Solutions

Most trackers focus solely on robustness and accuracy. Visual tracking, however, is a long-term problem with a high time limitation. A tracker that is robust, accurate, with long-term sustainability and real-time processing, is of high research value and practical significance. In this paper, we comprehensively consider these requirements in order to propose a new, state-of-the-art tracker with an excellent performance. EfficientNet-B0 is adopted for the first time via neural architecture search technology as the backbone network for the tracking task. This improves the network feature extraction ability and significantly reduces the number of parameters required for the tracker backbone network. In addition, maximal Distance Intersection-over-Union is set as the target estimation method, enhancing network stability and increasing the offline training convergence rate. Channel and spatial dual attention mechanisms are employed in the target classification module to improve the discrimination of the trackers. Furthermore, the conjugate gradient optimization strategy increases the speed of the online learning target classification module. A two-stage search method combined with a screening module is proposed to enable the tracker to cope with sudden target movement and reappearance following a brief disappearance. Our proposed method has an obvious speed advantage compared with pure global searching and achieves an optimal performance on OTB2015, VOT2016, VOT2018-LT, UAV-123 and LaSOT while running at over 50 FPS.
Estimating the number of usability problems affecting medical devices: modelling the discovery matrix

Abstract Background Usability testing of medical devices are mandatory for market access. The testings’ goal is to identify usability problems that could cause harm to the user or limit the device’s effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today’s margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems. Methods The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model’s parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting. As suggested in the usability literature, we assumed that the probability of detection had a logit-normal distribution. Results We assessed the matrix-based method’s performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing. Conclusions Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.
Action in Hamiltonian Models Constructed by Yang-Baxter Equation: Entanglement and Measures of Correlation

By using the quantum Yang-Baxterization approach, we investigate the dynamics of quantum entanglement under the actions of different Hamiltonians on the different two-qubit input states and analyze the effects of the Yang-Baxter operations on it. During any quantum process that takes place in a noisy environment, quantum correlations display behavior that does not increase. We point out that for two-qubit systems subject to actions of different Yang-Baxter operations the loss of correlations can be mitigated by the appropriate choice of the initial states and the Yang-Baxterization process. We show that in a noisy environment it possible to create the optimal conditions for performing any quantum information task.
Machine colour constancy: a work in progress

Abstract The term “colour constancy” denotes the human capability to recognise an object as the same entity even if its colours change due to illumination. Implementing such a human capability is highly desirable in computer vision to retrieve, detect, recognise and track objects regardless of the illumination. Many efforts have been made in this direction, leading to several “machine colour constancy” (MCC) algorithms, that is, routines inspired by human colour constancy (HCC) that are aiming to achieve an image representation independent of the light and thus invariant against light changes. Different to HCC, which is subject to illusions and only partially removes illuminant effects, MCC pursues the implementation of a perfect, total removal of light effects from images. Such an implementation represents a major challenge and is therefore a focus of ongoing research. In fact, the currently available MCC algorithms only work under constrained domains. Therefore, a priori knowledge about the image content is needed to choose the most appropriate MCC procedure and to properly set its parameters. Here, we present the main common assumptions underlying most of MCC algorithms. Our work shows that two main issues should be addressed in the future to ensure MCC is more efficacious and useable: first, the lack of a reliable MCC algorithm operating without supervision in multiple scenarios; and second, the difficulty which any user encounters when choosing the MCC procedure most appropriate for a particular application.
Blockchain-Autonomous Driving Systems

Blockchain is the upcoming new information technology that could have quite a lot of significant future applications. In this chapter, the communication network for the reliable environment of intelligent vehicle systems is considered along with how the blockchain technology generates trust network among intelligent vehicles. It also discusses different factors that are effecting or motivating automotive industry, data-driven intelligent transportation system (D2ITS), structure of VANET, framework of intelligent vehicle data sharing based on blockchain used for intelligent vehicle communication and decentralized autonomous vehicles (DAV) network. It also talks about the different ways the autonomous vehicles use blockchain. Block-VN distributed architecture is discussed in detail. The different challenges of research and privacy and security of vehicular network are discussed.
Technology and scaling up: Evidence from an NGO for adolescents with intellectual disabilities in China

This paper takes the innovative practices of a Chinese NGO serving adolescents with intellectual disabilities (ID) as a case to analyze whether technology can effectively promote the impact scaling of NGOs providing services for intellectually disabled adolescents. It was found that the use of technology can have positive effects on expanding service areas and population range in terms of improving professional skills and knowledge, reducing scaling costs, increasing flexibility, and enhancing government and social trust. Challenges faced by the program included user retainment, digital literacy of target users, difficulty teaching practical skills virtually, and the loss of specificity through standardization. Based on the case study, this paper has summarized a “COATE influencing factor model,” composed of a series of considerations that NGOs serving adolescents with ID should make prior to deciding which technological means to implement in their service model.
Information Security in Nursing

The concept of information security in nursing can improve the basic understanding of security issues in nursing. This study aimed to report on information security in nursing that considers the technical, physical, and administrative aspects of information. We used a hybrid model to analyze the concept and conducted on-site interviews. The analysis revealed 7 attributes: physical aspect: environmental control, facility stability; technical aspect: information accessibility, taking advantage of features; and administrative aspect: systematicity of work, execution of education, professional responsibility. Our model could help identify the physical, technical, and administrative attributes and definitions of information security in nursing.
Multi‐feature fusion of deep networks for mitosis segmentation in histological images

Abstract Mitotic cell detection in pathological images is significant for predicting the malignancy of tumors and the intelligent segmentation of these cells. Overcoming human error generated by pathologists in reading the images while enabling fast detection through high computing power remains a very challenging task. In this study, we proposed a method that fuses handcrafted features and deep features to segment mitotic cells in whole‐slide images. The handcrafted feature extraction strategy was based on four measure indices of the Gray Level Co‐occurrence Matrix. The deep feature extraction strategy was based on natural image knowledge transfer. Finally, the two strategies were fused to classify and distinguish the image pixels for the segmentation of mitotic cells. We used the AMIDA13 dataset and the pathological images collected by the Department of Pathology of Anhui No. 2 Provincial People's Hospital as the experimental dataset. We compared the Areas Under Curve (AUC) of Receiver Operating Characteristic obtained through the handcrafted feature model, the improved deep feature model with knowledge transfer, the classic U‐NET model, and the proposed multi‐feature fusion model. The results showed that the AUC values of our proposed method had 0.07 and 0.05 improved to classic U‐NET model on test dataset and validation dataset respectively, while achieved the best segmentation performance and detected most of true‐positive cells, representing a breakthrough for clinical application. The experiments also indicated that the staining uniformity of pathological tissue impacted the model performance.
Integration of Machine Learning on Distributed Acoustic Sensing Surveys

&amp;lt;p&amp;gt;Distributed Acoustic Sensing (DAS) is an innovative technique which has been recently employed for near-surface geophysics purposes. It involves the use of fibre-optic cable as a sensor. The fibre is analysed by sending a laser pulse from an interrogator unit. The phase of the backscattered signal contains the information on the strain on the cable, enabling the detection of a passing acoustic wave with enough energy for the cable excitation. Allowing the interrogation of long profiles and the generation of a dense spatial sampling, uneasy to obtain with classic geophysical techniques, DAS instrumentation then proved its relevance for seismic applications but also for infrastructure monitoring.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;During DAS acquisition, and more precisely when closely looking at infrastructures integrity, it is necessary to clearly identify the source of the acoustic vibrations at the structure neighbourhood. Indeed, in the context of pipeline monitoring for example, it appears important to be able to classify events which generate seismic signals recorded by DAS systems and which can be related to a potential threat for the structure. In order to launch an alarm if necessary, the source identification must be fast, accurate and robust. Moreover, because DAS acquisition can generate traces every few meters along fibres of tens of kilometres, the used machine-learning algorithm must demonstrate its ability to handle a big amount of data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we analyse the efficiency of the Random Forests (RF) machine-learning algorithm applied to data acquired with DAS system for the discrimination of event sources. RF algorithm has been selected because of its ability to handle large numbers of attributes related to signal characteristics and to enable a good reliability for the discrimination of sources. This algorithm has already proved its efficiency for automated classification of seismic waveforms (e.g. earthquakes, volcanic tremors, rock falls, avalanches, etc.).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We focus our study on tests lead along a gas pipeline instrumented with fibre-optic cable. Different third-party works have been conducted: excavation, saw sections, drill, jackhammer, etc. We work on the discrimination of six classes of seismic source. After running a detection phase based on a threshold on signal energy, we obtain several hundred of exploitable seismic traces to inject to the RF algorithm. We demonstrate the efficiency of the application of machine learning on DAS data to discriminate seismic waveforms from the correct class, with an overall precision on our test set of 99%.&amp;lt;/p&amp;gt;
Group-based local adaptive deep multiple kernel learning with lp norm

The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning. However, the existing DMKL methods are hard to find suitable global model parameters to improve classification accuracy in numerous datasets and do not take into account inter-class correlation and intra-class diversity. In this paper, we present a group-based local adaptive deep multiple kernel learning (GLDMKL) method with lp norm. Our GLDMKL method can divide samples into multiple groups according to the multiple kernel k-means clustering algorithm. The learning process in each well-grouped local space is exactly adaptive deep multiple kernel learning. And our structure is adaptive, so there is no fixed number of layers. The learning model in each group is trained independently, so the number of layers of the learning model maybe different. In each local space, adapting the model by optimizing the SVM model parameter α and the local kernel weight β in turn and changing the proportion of the base kernel of the combined kernel in each layer by the local kernel weight, and the local kernel weight is constrained by the lp norm to avoid the sparsity of basic kernel. The hyperparameters of the kernel are optimized by the grid search method. Experiments on UCI and Caltech 256 datasets demonstrate that the proposed method is more accurate in classification accuracy than other deep multiple kernel learning methods, especially for datasets with relatively complex data.