Reviewerly/exHarmony huggingface dataset:
Superradiant switching, quantum hysteresis, and oscillations in a generalized Dicke model

We demonstrate quantum signatures of deterministic nonlinear dynamics in the transition to superradiance of a generalized open Dicke model with different coupling strengths for the co- and counterrotating light-matter interaction terms. A first-order phase transition to coexisting normal and superradiant phases is observed, corresponding with the emergence of switching dynamics between these two phases, driven by quantum fluctuations. We show that this phase coexistence gives rise to a hysteresis loop also for the quantum mechanical system. Additionally, a transition to a superradiant oscillatory phase is observed clearly in quantum simulations.
GAC-GAN: A General Method for Appearance-Controllable Human Video Motion Transfer

Human video motion transfer has a wide range of applications in multimedia, computer vision, and graphics. Recently, due to the rapid development of Generative Adversarial Networks (GANs), there has been significant progress in the field. However, almost all existing GAN-based works are prone to address the mapping from human motions to video scenes, with scene appearances encoded individually in the trained models. Therefore, each trained model can only generate videos with a specific scene appearance, and new models are required to be trained to generate new appearances. Besides, existing works lack the capability of appearance control. For example, users have to provide video records of wearing new clothes or performing in new backgrounds to enable clothes or background changing in their synthetic videos, which greatly limits the application flexibility. In this paper, we propose General Appearance-Controllable GAN (GAC-GAN), a general method for appearance-controllable human video motion transfer. To enable general-purpose appearance synthesis, we propose to include appearance information in the conditioning inputs. Thus, once trained, our model can generate new appearances by altering the input appearance information. To achieve appearance control, we first obtain the appearance-controllable conditioning inputs, and then utilize a two-stage GAC-GAN to generate the corresponding appearance-controllable outputs, where we utilize an Appearance-Consistency GAN (ACGAN) loss, and a shadow extraction module for output foreground, and background appearance control respectively. We further build a solo dance dataset containing a large number of dance videos for training, and evaluation. Experimental results on our solo dance dataset, and iPER dataset show that our proposed GAC-GAN can not only support appearance-controllable human video motion transfer but also achieve higher video quality than state-of-art methods.
Hyperledger frameworks with a special focus on Hyperledger Fabric

Introduction/purpose: The Blockchain market is a developing market, and many industries are increasingly aware of the importance of having and implementing Blockchain due to changing business conditions.After an introductory consideration of the Blockchain concept, the article focuses on the projects under the Linux Hyperledger Initiative, with particular reference to Hyperledger Fabric, notably on the concepts, architecture, organizational focus, private channels and smart contracts, as well as flow of transactions in the Hyperledger Fabric network.In the last, practical part of the paper, we presented a simulation of a business network and created a simple application for money exchange using the Hyperledger Fabric framework.The aim of the paper is to present the Hyperledger frameworks and highlight all the details of Hyperledger Fabric in order to determine whether its application in practice is justified.Methods: Basic methods (analysis, synthesis, deduction, induction, concretization, generalization) as well as general-scientific methods (analytical-deductive and comparative) were used.Results: All relevant features of Hyperledger Fabric were considered, a blockchain business network based on it was built, and an application for cash exchange through the network was created.Conclusion: Hyperledger Fabric is the most comprehensive and the most flexible Hyperledger framework with the largest number of use cases.
Verifying Tight Logic Programs with anthem and vampire

Abstract This paper continues the line of research aimed at investigating the relationship between logic programs and first-order theories. We extend the definition of program completion to programs with input and output in a subset of the input language of the ASP grounder gringo, study the relationship between stable models and completion in this context, and describe preliminary experiments with the use of two software tools, anthem and vampire, for verifying the correctness of programs with input and output. Proofs of theorems are based on a lemma that relates the semantics of programs studied in this paper to stable models of first-order formulas.
Modernization of Education Programs and Formation of Digital Competences of Future Primary School Teachers

Modernization of education programs is carried out through the digital technologies integration into the education system, in particular, into the primary education system. This leads to the integration of courses and disciplines into curricula for the development of digital competences of future primary school teachers. The purpose of the investigation was to study the features of modernization of education programs in order to form digital competences of future primary school teachers. 48 scientific publications have been examined, studied and systematized, where the integration of disciplines for the study of digital competences into the educational process of teaching future teachers is considered; consequently, three dimensions of modernization of curricula have been investigated. The first one concerns the development of general digital competences, the second one - the competence to integrate technology into educational practice, the third one - professional digital competence. It has been revealed that general digital competences are consistent with the context of the education system. Competence to integrate technologies into teaching practice focuses on the context of integration, preparing future teachers for the opportunity to use technology in future teaching of pupils, critically assess the use and teach children, using digital devices in education process. The academic paper proposes the concept of professional digital competence as the ability of a teacher to work in the context of a school education system with a digital form of education, including the process of teaching, managing digital learning environments and professional activities of a teacher. Further research should additionally explore professional digital competence in the framework of primary teacher education.
Regularized matrix data clustering and its application to image analysis

Abstract We propose a novel regularized mixture model for clustering matrix‐valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (eg, low rankness, spatial sparsity) for the mean signal of each cluster. We formulate the problem as a finite mixture model of matrix‐normal distributions with regularization terms, and then develop an expectation maximization type of algorithm for efficient computation. In theory, we show that the proposed estimators are strongly consistent for various choices of penalty functions. Simulation and two applications on brain signal studies confirm the excellent performance of the proposed method including a better prediction accuracy than the competitors and the scientific interpretability of the solution.
Visual Analytics for Temporal Hypergraph Model Exploration

Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.
Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Robot will be used in the grinding industry widely to liberate human beings from harsh environments. In the grinding process, optimal trajectory planning will not only improve the processing quality but also improve the machining efficiency. The aims of this study are to propose a new algorithm and verify its efficiency in achieving the optimal trajectory planning of the grinding robot. An objective function has been defined terms of both time and jerk. Improved whale optimization algorithm (IWOA) is proposed based on whale optimization algorithm (WOA) and differential evolution algorithm (DE). Mutation operation and selection operation of DE are imitated in the part of initialization to process the population initialized by WOA, and then, the search tasks of WOA are performed. Motion with a constant velocity of the end-effector is considered during fine grinding. The continuity of acceleration and velocity will be achieved by minimizing jerk, and at the same time, smooth robot movement can be obtained. Cubic spline interpolation is implemented. A six-axis industrial robot is used for this research. Results show that optimal trajectory planning based on IWOA is more efficient than others. This method presented in this paper may have some indirect significance in robot business.
Machine Learning of Concepts Hard Even for Humans: The Case of Online Depression Forums

Social scientists of mixed-methods research have traditionally used human annotators to classify texts according to some predefined knowledge. The “big data” revolution, the fast growth of digitized texts in recent years brings new opportunities but also new challenges. In our research project, we aim to examine the potential for natural language processing (NLP) techniques to understand the individual framing of depression in online forums. In this paper, we introduce a part of this project experimenting with NLP classification (supervised machine learning) method, which is capable of classifying large digital corpora according to various discourses on depression. Our question was whether an automated method can be applied to sociological problems outside the scope of hermeneutically more trivial business applications. The present article introduces our learning path from the difficulties of human annotation to the hermeneutic limitations of algorithmic NLP methods. We faced our first failure when we experienced significant inter-annotator disagreement. In response to the failure, we moved to the strategy of intersubjective hermeneutics (interpretation through consensus). The second failure arose because we expected the machine to effectively learn from the human-annotated sample despite its hermeneutic limitations. The machine learning seemed to work appropriately in predicting bio-medical and psychological framing, but it failed in case of sociological framing. These results show that the sociological discourse about depression is not as well founded as the biomedical and the psychological discourses—a conclusion which requires further empirical study in the future. An increasing part of machine learning solution is based on human annotation of semantic interpretation tasks, and such human-machine interactions will probably define many more applications in the future. Our paper shows the hermeneutic limitations of “big data” text analytics in the social sciences, and highlights the need for a better understanding of the use of annotated textual data and the annotation process itself.
Conceptual Analysis on Higher Education Strategies for Various Tech-Generations

Higher education is finding importance throughout the globe due to two reasons that include challenges to increase Gross Enrolment Ratio (GER) towards 100% and the possibility of further HE system innovations due to drastic changes in technology and the creativity of new Tech-generations. In this paper, we made a detailed analysis of the effect of technology on industry and the creation of new tech generations in society. The predictive analysis methodology is used for discussing the effect of technologies on industries and its effects on the creation of new tech generations. Finally, the possible higher education strategies to fulfil the anticipated desires of the tech-generations in society are analysed. Based on the analysis a set of postulates are suggested to integrate technology with the higher education system to develop industry acceptable qualified professionals to serve satisfactorily in so-called Tech-society.
Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Abstract A job seeker’s resume contains several sections, including educational qualifications. Educational qualifications capture the knowledge and skills relevant to the job. Machine processing of the education sections of resumes has been a difficult task. In this paper, we attempt to identify educational institutions’ names and degrees from a resume’s education section. Usually, a significant amount of annotated data is required for neural network-based named entity recognition techniques. A semi-supervised approach is used to overcome the lack of large annotated data. We trained a deep neural network model on an initial (seed) set of resume education sections. This model is used to predict entities of unlabeled education sections and is rectified using a correction module. The education sections containing the rectified entities are augmented to the seed set. The updated seed set is used for retraining, leading to better accuracy than the previously trained model. This way, it can provide a high overall accuracy without the need of large annotated data. Our model has achieved an accuracy of 92.06% on the named entity recognition task.
Determination and Measurement of Color Dissimilarity

There are millions of different colors that exist in this nature. There are colors that can easily be distinguished from other colors, but many are also difficult to distinguish. The ability to distinguish colors is important in the recognition of an object, especially objects in an image. Color can be represented in a three-dimensional RGB color space and each color will have an RGB value with a range of values ??from 0-255. Conceptually, colors with different RGB values ??are different colors, but the human eye may not necessarily be able to distinguish them. This study aims to determine and measure the color dissimilarity that can be distinguished by humans. With an experiment comparing a color with a color made with a variation of RGB values, this study resulted that two colors that have a Delta E (?E) value of 8 can be distinguished by the eye. This result is obtained if both colors have a value of ?R, ?G or ?B of at least 8, or a value of ?RG, ?RB or ?GB of at least 6, or a value of ?RGB of at least 5.
Object-level Attention for Aesthetic Rating Distribution Prediction

We study the problem of image aesthetic assessment (IAA) and aim to automatically predict the image aesthetic quality in the form of discrete distribution, which is particularly important in IAA due to its nature of having possibly higher diversification of agreement for aesthetics. Previous works show the effectiveness of utilizing object-agnostic attention mechanisms to selectively concentrate on more contributive regions for IAA, e.g., attention is learned to weight pixels of input images when inferring aesthetic values. However, as suggested by some neuropsychology studies, the basic units of human attention are visual objects, i.e., the trace of human attention follows a series of objects. This inspires us to predict contributions of different regions at object level for better aesthetics evaluation. With our framework, region-of-interests (RoIs) are proposed by an object detector, and each RoI is associated with a regional feature vector. Then the contribution of each regional feature to the aesthetics prediction is adaptively determined. To the best of our knowledge, this is the first work modeling object-level attention for IAA and experimental results confirm the superiority of our framework over previous relevant methods.
Distortion-Aware Monocular Depth Estimation for Omnidirectional Images

A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth estimation network to address this challenge on indoor panoramas with two steps. First, we introduce a distortion-aware module to extract calibrated semantic features from omnidirectional images. Specifically, we exploit deformable convolution to adjust its sampling grids to geometric variations of distorted objects on panoramas and then utilize a strip pooling module to sample against horizontal distortion introduced by inverse gnomonic projection. Second, we further introduce a plug-and-play spherical-aware weight matrix for our objective function to handle the uneven distribution of areas projected from a sphere. Experiments on the 360D dataset show that the proposed method can effectively extract semantic features from distorted panoramas and alleviate the supervision bias caused by distortion. It achieves state-of-the-art performance on the 360D dataset with high efficiency.
NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators

We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadratic programme which, when considering obstacles, joint limits, and manipulability on a 7 degree-of-freedom robot, is generally solved in a few ms. While NEO is not intended to replace state-of-the-art motion planners, our experiments show that it is a viable alternative for scenes with moderate complexity while also being capable of reactive control. For more complex scenes, NEO is better suited as a reactive local controller, in conjunction with a global motion planner. We compare NEO to motion planners on a standard benchmark in simulation and additionally illustrate and verify its operation on a physical robot in a dynamic environment. We provide an open-source library which implements our controller.
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is critical for clinical tasks. However, existing explainability techniques lack an important component for trustworthy and reliable decision support, namely a notion of uncertainty. In this paper, we address this lack of uncertainty by proposing a deep ensemble approach where a collection of DNNs are trained independently. A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable. The class activation mapping method is used to assign a relevance score for each time step in the time series. Results demonstrate that the proposed ensemble is more accurate in locating relevant time steps and is more consistent across random initializations, thus making the model more trustworthy. The proposed methodology paves the way for constructing trustworthy and dependable support systems for processing clinical time series for healthcare related tasks.
A multi-objective privacy preservation model for cloud security using hybrid Jaya-based shark smell optimization

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.