Reviewerly/exHarmony huggingface dataset:
Detection of White Blood Cell Cancer using Image Processing

Detection of White Blood Cell (WBC) cancer diseases like Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), and Myeloma is a complex task in medical field because they are sudden in onset. Our proposed method consists of designing and developing an automated system which will assist the medical professionals in correctly diagnosing all the types and sub-types of this disease. In this paper, we have proposed a novel method in which we have taken microscopic blood images as an input image. A dataset of 100 images in which 62 training and 38 testing images is taken. After that we have converted the image to proper format (YCbCr) for segmentation. For segmenting, we have used the combination of Gaussian Distribution, Otsu Adaptive Thresholding and for clustering we have used K-Means method. Using Gray Level Co-occurrence Matrix (GLCM), the features are extracted and were used for classification using Convolutional Neural Network (CNN). The overall accuracy of the system obtained after processing is 97.3%.
Local distinguishability aggrandizing network for human anomaly detection

With the growing demand for an intelligent system to prevent abnormal events, many methods have been proposed to detect and locate anomalous behaviors in surveillance videos. However, most of these methods contain two shortcomings mainly: distraction of the network and insufficient discriminating ability. In this paper, we propose a local distinguishability aggrandizing network (LDA-Net) in a supervised manner, consisting of a human detection module and an anomaly detection module. In the human detection module, we obtain segmented patches of specific human subjects and take them as the input of the latter module to focus the network on learning motion characteristics of each person. In addition, considering that the auxiliary information, such as the specific type of an action, can aggrandize the whole network to extract distinguishable detail features of normal and abnormal behaviors, the proposed anomaly detection module comprises a primary binary classification sub-branch and an auxiliary distinguishability aggrandizing sub-branch, through which we can jointly detect anomalies and recognize actions. To further reduce the misclassification of the extremely imbalanced datasets, we design a novel inhibition loss function and embed it into the auxiliary sub-branch of the anomaly detection module. Experiments on several public benchmark datasets for frame-level and pixel-level anomaly detection show that the proposed supervised LDA-Net achieves state-of-the-art results on UCSD Ped2 and Subway Exit datasets.
Data Management Planning: How Requirements and Solutions are Beginning to Converge

Effective stewardship of data is a critical precursor to making data FAIR. The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions (DMP). We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles, we describe the background, context and historical development, as well as major driving forces, being research initiatives and funders. Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics. Next, we elaborate on emerging common standards for DMPs, especially the topic of machine-actionable DMPs. As sound DMP is not only a precursor of FAIR data stewardship, but also an integral part of it, we discuss its positioning in the emerging FAIR tools ecosystem. Capacity building and training activities are an important ingredient in the whole effort. Although not being the primary goal of this paper, we touch also the topic of research workforce support, as tools can be just as much effective as their users are competent to use them properly. We conclude by discussing the relations of DMP to FAIR principles, as there are other important connections than just being a precursor.
A Novel Gaussian Mixture Model for Classification

Gaussian Mixture Model (GMM) is a probabilistic model for representing normally distributed subpopulations within an overall population. It is usually used for unsupervised learning to learn the subpopulations and the subpopulation assignment automatically. It is also used for supervised learning or classification to learn the boundary of subpopulations. However, the performance of GMM as a classifier is not impressive compared with other conventional classifiers such as k-nearest neighbors (KNN), support vector machine (SVM), decision tree and naive Bayes. In this paper, we attempt to address this problem. We propose a GMM classifier, SC-GMM, based on the separability criterion in order to separate the Gaussian models as much as possible. This classifier finds the optimal number of Gaussian components for each class based on the separability criterion and then determines the parameters of these Gaussian components by using the expectation maximization algorithm. Extensive experiments have been carried out on classification tasks from general data mining to face verification. Results show that SC-GMM significantly outperforms the original GMM classifier. Results also show that SC-GMM is comparable in classification accuracy to three variants of GMM classifier: Akaike Information Criterion based GMM (AIC-GMM), Bayesian Information Criterion based GMM (BIC-GMM) and variational Bayesian gaussian mixture (VBGM). However, SC-GMM is significantly more efficient than both AIC-GMM and BIC-GMM. Furthermore, compared with KNN, SVM, decision tree and naive Bayes, SC-GMM achieves competitive classification performance.
Simultaneous Intensity Bias Estimation and Stripe Noise Removal in Infrared Images Using the Global and Local Sparsity Constraints

Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority.
Exponential stabilization of quantum systems under continuous non-demolition measurements

We present a novel continuous-time control strategy to exponentially stabilize an eigenstate of a quantum measurement operator. In open-loop, the system converges to a random eigenstate of the measurement operator. The role of the feedback is to prepare a prescribed eigenstate with unit probability. To achieve this we introduce the use of Brownian motion to drive the unitary control actions; the feedback loop just adapts the amplitude of this Brownian noise input as a function of the system state. Essentially, it “shakes” the system away from undesired eigenstates by applying strong noise there, while relying on the open-loop dynamics to progressively reach the target. We prove exponential convergence towards the target eigenstate using standard stochastic Lyapunov methods. The feedback scheme and its stability analysis suggest the use of an approximate filter which only tracks the populations of the eigenstates of the measurement operator. Such reduced filters should play an increasing role towards advanced quantum technologies.
GIST: Gesture-free Interaction by the Status of Thumb; an interaction technique for Virtual Environments

User interface has special importance in immersive virtual environments.Interactions based on the simple and conceivable gestures of a hand may enhance immersivity of a Virtual Environment (VE).However, due to the structural issues like small size and complex shape of human hand, recognition of hand gestures are more challenging.This work introduces a novel interaction technique to perform the basic interaction tasks by the simple movement of hand instead of distinct gestures.With an ordinary camera, the fist posture of hand is segmented out from the image stream using the optimal segmentation model.Like pressing a button with a thumb, the status of thumb is traced for the activation or deactivation of the interactions.After the activation of interaction, the trajectory of hand is followed to manipulate a virtual object about an arbitrary axis.Without training and comparison of gestures, the basic interactions required in a VE are performed by the perceptive movement of a hand.By incorporating image processing in the realm of VE, the technique is implemented in a case-study project; FIRST (Feasible Interaction by Recognizing the Status of Thumb).A group of 12 users evaluated the system in a moderate lighting condition.Outcomes of the evaluation revealed that the technique is suitable for Virtual Reality (VR) applications.
Deep semantic cross modal hashing with correlation alignment

Hashing has been extensively applied to cross modal retrieval due to its low storage and high efficiency. Deep hashing which can well extract features of multi-modal data has received increasing research attention recently. However, most of deep hashing for cross modal retrieval methods do not make full use of the semantic label information and do not fully mine correlation of heterogeneous data. In this paper, we propose a Deep Semantic cross modal hashing with Correlation Alignment (DSCA) method. In DSCA, we design two deep neural networks for image and text modality separately, and learn two hash functions. Firstly, we construct a new similarity for the multi-label data, which can well exploit the semantic information and improve the retrieval accuracy. Simultaneously, we preserve the inter-modal similarity of heterogeneous data features, which can exploit semantic correlation. Secondly, the distributions of heterogeneous data are aligned so as to mine the inter-modal correlation well. Thirdly, the semantic label information is embedded in the hash layer of the text network, which can make the learned hash matrix more stable and make the hash codes more discriminative. Experimental results demonstrate that DSCA outperforms the state-of-the-art methods.
Design and Implementation of Artificial Neural Networks to Predict Wind Directions on Controlling Yaw of Wind Turbine Prototype

Wind energy as one of the new renewable energies has an important role in replacing fossil energy sources in Indonesia. In order to make the wind turbine's performance more efficient in extracting energy from the wind, it is necessary to control the actuation movements pitch and yaw of the wind turbine horizontal . Controlling the actuator yaw can increase the absorption efficiency of the power to the rotor face toward the direction of the wind. The purpose of this thesis is to be able to predict the direction of the coming wind, then move the turbine rotor in the predicted direction. In this final project a wind turbine prototype is used with a precision of 5.3%, then for the data acquisition section, a wind direction sensor is built to change the amount of wind direction to a quantity that can be measured in units of degrees, and anemometer to measure wind speed. In making the wind direction prediction algorithm, artificial neural network (ANN) method is used with input parameters such as wind speed, temperature, humidity, pressure, and altitude. Data acquisition is done at one minute intervals with long data collection for one day, 1072 data are obtained, the data is then fed to the ANN model that has been prepared. Based on the results of tests that have been done, it is found that the Mean Absolute Error in the model is 0.4%.
Social-Communication Web Technologies in the Higher Education as Means of Knowledge Transfer

The presented research deals with an expert scientific and pedagogical assessment of the factors of the transition from information and communication technology Web 2.0 to Web 3.0, which is decisive for the author's stated more global task of scientific research, namely, the computerization effect of the knowledge essence in terms of scientific and pedagogical conditions, their distribution, generation and effective assimilation by participants. In this context, the pedagogical trend is clearly followed - the development of “machine pedagogy” as the theory and practice of knowledge acquisition by computers and computer-based network entities. As modern and common platforms where one can observe the emergence of new trends, the author highlights wikis, blogs and forums as existing tools for network sharing of knowledge and types of professional competence with the maximum possible involvement of target groups of professional-oriented higher education institution, which is a prerequisite for reforming and development of new standards of higher education in Ukraine.
Securing IoT-Driven Remote Healthcare Data Through Blockchain

Blockchain is the latest technology which is used in cryptocurrencies such as bitcoin and ether. Blockchain is the decentralized distributed ledger, which is based on the peer-to-peer network method. As the blockchain is mainly developed for implementation in the virtual cryptocurrency such as bitcoin, so its main purpose is clear that is security. Even though the healthcare industry is leading in majority of fields whether it is technology, equipment, researches, medicines, etc. We have even reached to remote locations through IoT devices and but one major thing is still lacking that is security of the data. Huge amount of data is being generated everyday from patient’s medical checkups, treatments, symptoms, etc., which is to be dealt with care as they are very crucial and can be tempered by hackers which can lead to serious problems. Therefore, such critical data must need to be secured with blockchain as it makes it very difficult to tamper with data. This paper deals with the implementation of the blockchain to solve the above-mentioned problems.
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection in Task Oriented Dialog

The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. Our findings are three-fold:First, we curate and release ROSTD (Real Out-of-Domain Sentences From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, our examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset.Second, we explore likelihood ratio based approaches as an alternative to currently prevalent paradigms. Specifically, we reformulate and apply these approaches to natural language inputs. We find that they match or outperform the latter on all datasets, with larger improvements on non-artificial OOD benchmarks such as our dataset. Our ablations validate that specifically using likelihood ratios rather than plain likelihood is necessary to discriminate well between OOD and in-domain data.Third, we propose learning a generative classifier and computing a marginal likelihood (ratio) for OOD detection. This allows us to use a principled likelihood while at the same time exploiting training-time labels. We find that this approach outperforms both simple likelihood (ratio) based and other prior approaches. We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.
A Character-Centric Neural Model for Automated Story Generation

Automated story generation is a challenging task which aims to automatically generate convincing stories composed of successive plots correlated with consistent characters. Most recent generation models are built upon advanced neural networks, e.g., variational autoencoder, generative adversarial network, convolutional sequence to sequence model. Although these models have achieved prompting results on learning linguistic patterns, very few methods consider the attributes and prior knowledge of the story genre, especially from the perspectives of explainability and consistency. To fill this gap, we propose a character-centric neural storytelling model, where a story is created encircling the given character, i.e., each part of a story is conditioned on a given character and corresponded context environment. In this way, we explicitly capture the character information and the relations between plots and characters to improve explainability and consistency. Experimental results on open dataset indicate that our model yields meaningful improvements over several strong baselines on both human and automatic evaluations.
The Effect of Experiential Learning and Adversity Quotient on Problem Solving Ability

This study examined the effect of experiential learning and the adversity quotient in the problem-solving ability of accounting vocational college students.The subjects were 120 participants selected and assigned to control and experimental groups.The experimental group was given experiential learning with four steps.Learners participated in a concrete experience (do), reflected on that experience and other information (reflect), developed theories based on experiences and knowledge (think), and formulated a conclusion or solved a problem (apply).The control group was given direct instruction to learning.The data were collected through two instruments, a questionnaire of Student Adversity Quotient Profile (SAQP) and rubrics of problem-solving by Polya.The data were analyzed by using two ways ANOVA, the results were the problem-solving ability of students taught by experiential learning was different from the ones taught by direct instruction, the students' problem-solving ability differed between high and low adversity quotients, and there were interactions between experiential learning and adversity quotients that improved students' problem-solving ability.The steps in experiential learning are a holistic learning process.
Contextual Parameter Generation for Knowledge Graph Link Prediction

We consider the task of knowledge graph link prediction. Given a question consisting of a source entity and a relation (e.g., Shakespeare and BornIn), the objective is to predict the most likely answer entity (e.g., England). Recent approaches tackle this problem by learning entity and relation embeddings. However, they often constrain the relationship between these embeddings to be additive (i.e., the embeddings are concatenated and then processed by a sequence of linear functions and element-wise non-linearities). We show that this type of interaction significantly limits representational power. For example, such models cannot handle cases where a different projection of the source entity is used for each relation. We propose to use contextual parameter generation to address this limitation. More specifically, we treat relations as the context in which source entities are processed to produce predictions, by using relation embeddings to generate the parameters of a model operating over source entity embeddings. This allows models to represent more complex interactions between entities and relations. We apply our method on two existing link prediction methods, including the current state-of-the-art, resulting in significant performance gains and establishing a new state-of-the-art for this task. These gains are achieved while also reducing convergence time by up to 28 times.
SubSpace Capsule Network

Convolutional neural networks (CNNs) have become a key asset to most of fields in AI. Despite their successful performance, CNNs suffer from a major drawback. They fail to capture the hierarchy of spatial relation among different parts of an entity. As a remedy to this problem, the idea of capsules was proposed by Hinton. In this paper, we propose the SubSpace Capsule Network (SCN) that exploits the idea of capsule networks to model possible variations in the appearance or implicitly-defined properties of an entity through a group of capsule subspaces instead of simply grouping neurons to create capsules. A capsule is created by projecting an input feature vector from a lower layer onto the capsule subspace using a learnable transformation. This transformation finds the degree of alignment of the input with the properties modeled by the capsule subspace.We show that SCN is a general capsule network that can successfully be applied to both discriminative and generative models without incurring computational overhead compared to CNN during test time. Effectiveness of SCN is evaluated through a comprehensive set of experiments on supervised image classification, semi-supervised image classification and high-resolution image generation tasks using the generative adversarial network (GAN) framework. SCN significantly improves the performance of the baseline models in all 3 tasks.
Super Resolution Reconstruction of Images Based on Interpolation and Full Convolutional Neural Network and Application in Medical Fields

The traditional image to enlarge algorithms include nearest neighbor interpolation, bilinear interpolation and high-order interpolation. In order to achieve super-resolution reconstruction of images, a new algorithm combining traditional algorithms and deep learning is proposed. The framework is divided into two parts. Firstly, the deep reconstruction of the low-resolution data is performed by the ability of deep learning to extract features automatically. Then, combining with the traditional interpolation reconstruction results, the deep learning algorithm is used again for training and learning, and finally the high-resolution reconstructed data is obtained. The algorithm is validated using an online public test data set. The results show that the algorithm has a significant effect on the MSE (mean squared error) and PSNR (Peak Signal to Noise Ratio). Compared with the traditional interpolation algorithm and the single deep learning algorithm, the proposed algorithm has higher performance. Moreover, the proposed algorithm is perfect for the reconstruction of the details, the outline is clear, and the high-quality image is obtained.